Peter Fisk

“The future of music is access, not ownership” says Daniel Ek, founder and CEO of Spotify. “We no longer compete for shelf space, we compete for attention” adds Lucian Grainge, the CEO of Universal Music Group.

TikTok has become the new radio. It’s the primary platform for launching new music. Or YouTube, or Fortnite, or QQ. Artists now have to think like startups. It’s no longer just about the song, but the story, the brand, the community.

The music industry has undergone one of the most radical transformations of any creative sector in the past two decades.

From vinyl and CDs to downloads and now streaming, it has shifted not just in how music is distributed and consumed, but in how value is created, shared, and monetised. At the heart of this reinvention lies ecosystem thinking—a strategic mindset where companies co-create value by building interconnected platforms, partnerships, and services rather than operating as isolated entities.

Ecosystem thinking

Ecosystem thinking transformed the music industry from a linear value chain—where record labels controlled production, distribution, and promotion—into a dynamic, digital-first network of platforms, creators, tech companies, rights holders, fans, and brand partners. This reinvention enabled new business models, global scale, personalised experiences, and powerful feedback loops of data and innovation.

Before the digital era, the music industry operated under a vertically integrated model. Artists signed to labels who controlled recording, marketing, manufacturing, and distribution. Revenues flowed primarily through physical sales. This model was lucrative but rigid, and power was concentrated in the hands of a few major players.

The rise of file-sharing platforms like Napster in the late 1990s exposed the vulnerability of this model. While illegal, peer-to-peer sharing revealed the latent consumer demand for digital access, personalization, and convenience. The industry’s initial response was defensive—lawsuits and DRM restrictions—rather than innovative.

This fragmentation of control marked the beginning of a new phase: reinvention through ecosystems.

Phase 1: The rise of platform ecosystems

The true shift began with Apple’s iTunes in 2001, which offered a legal alternative to piracy by unbundling albums into single tracks, priced affordably. iTunes created a platform ecosystem in which Apple aggregated content from labels and delivered it through proprietary devices like the iPod. The key shift was toward access over ownership—users didn’t need CDs, they just needed a device and a store.

Apple’s model integrated hardware, software, and content. The success of iTunes proved that digital music could be monetized at scale—but the model still emphasised downloads, a one-time transactional economy.

Phase 2: Streaming and the subscription ecosystem

The next leap came with Spotify (founded in 2006, launched in 2008), which championed streaming as a service. Instead of buying individual tracks, users could subscribe and gain access to an entire music library. This was not just a new revenue model—it was an entirely new ecosystem logic:

• Platform-centric: Spotify didn’t own the content but created a platform where listeners, artists, labels, curators, advertisers, and developers could interact.

• Data-driven: Personalization engines like “Discover Weekly” used listening behavior to recommend new music, creating a virtuous cycle of engagement.

• Global reach: Spotify scaled rapidly by partnering with mobile operators and telecoms in emerging markets.

• Multi-sided revenue: Free users brought in ad revenue; premium users brought subscription income; artists and labels got new promotional and monetization tools.

This shift made continuous access, algorithmic discovery, and social sharing central features of music consumption.

“Streaming didn’t just change the format; it changed the business model, the marketing, and the global flow of culture” says Rob Stringer, Chairman, Sony Music Group

Ecosystem value creation

Spotify and competitors like Apple Music, YouTube Music, Amazon Music, and TikTok now function as platform orchestrators—connecting and enabling a vast range of ecosystem participants:

• Artists: Self-publishing tools (e.g. Spotify for Artists, SoundCloud) allow artists to release and promote music directly, monitor analytics, and monetize streams—democratizing entry.

• Fans: Personalized playlists, AI-generated recommendations, and social features deepen emotional connections and increase engagement.

• Labels & Rights Holders: Gain access to real-time data on performance, regional preferences, and virality—transforming strategy.

• Advertisers & Brands: Can target specific audiences through audio ads, branded playlists, and partnerships with artists.

• Third-party Developers: Build integrations via APIs for DJ tools, fitness apps (e.g., Peloton), or AI-music analysis platforms.

In this model, value is co-created through the interplay of multiple participants. The platform becomes more than a distributor; it is a marketplace, a promoter, a data provider, and a collaboration space.

Ecosystem reinvention beyond streaming

The reinvention of the music industry didn’t stop at streaming. It extended into a broader creative and commercial ecosystem, with music integrated into:

• Social media platforms like TikTok, where music clips go viral, sparking new hits and reviving old ones.

• Gaming environments such as Fortnite or Roblox, where artists hold virtual concerts, selling digital merchandise and building new fan experiences.

• Brand partnerships, where companies use music and artists to tell stories, build cultural relevance, and reach new audiences.

• Fitness and wellness, through collaborations with apps like Calm, Strava, or Apple Fitness+.

Artists now think in ecosystems too—launching podcasts, virtual experiences, NFTs, fashion collaborations, and exclusive fan clubs (like Patreon or Discord communities).

Monetization and new value pools

Through ecosystem thinking, the music industry has found new monetization streams:

• Subscriptions (Spotify, Apple Music)

• Ad revenues (YouTube, free-tier platforms)

• Live-streamed events and virtual concerts

• Digital merchandise, NFTs, and metaverse performances

• Brand partnerships and sync deals

• Fan subscriptions and exclusives

In some cases, artists make more from social integrations or brand deals than from streams alone. The rise of “middle class” musicians—who don’t top charts but thrive within niche ecosystems—is enabled by direct fan relationships and alternative monetization.

Ecosystem enablers: data, AI, and open APIs

Data is the nervous system of the modern music ecosystem. Spotify’s discovery algorithms, YouTube’s content ID, and TikTok’s trend monitoring all use AI to connect artists to fans more efficiently than ever before. APIs allow innovation at the edges, enabling third parties to build remix apps, lyric tools, visualizations, and fan engagement features.

Moreover, open data helps drive artist-centric tools like Songkick for tours, Chartmetric for insights, and LANDR for mastering and promotion—extending the value chain horizontally.

Winning in an ecosystem world

Reinvention through ecosystems has required a new kind of leadership in music:

• Orchestrators like Spotify or TikTok balance the needs of creators, users, brands, and regulators.

• Labels now operate more like venture capitalists—investing in artist development, building brands, and managing rights across platforms.

• Artists act as entrepreneurs—developing personal brands, multi-platform presence, and diverse revenue streams.

Ecosystem thinking is strategic, collaborative, and adaptive. Success is no longer about controlling assets, but enabling others to create value with them.

Despite its many successes, the music ecosystem still faces challenges, and need for further business model reinvention:

• Artist compensation remains a hot topic—many argue that streaming revenues are too low.

• Market concentration is a risk, with a few platforms controlling discovery and monetization.

• Algorithmic influence shapes not just consumption, but what gets created—potentially narrowing diversity.

• Copyright management across platforms and countries remains complex.

Nonetheless, the industry continues to innovate, exploring AI-generated music, blockchain-based rights tracking, immersive virtual experiences, and deeper fan personalisation.

Music as a living ecosystem

The reinvention of the music industry through ecosystem thinking has redefined how music is made, discovered, shared, and monetized. It has turned passive listeners into active participants, centralized players into enablers, and static catalogues into living, evolving digital experiences.

More than a technological shift, this transformation reflects a deeper change in business philosophy: from ownership to access, from control to coordination, and from isolated value chains to dynamic, networked ecosystems.

As platforms, creators, fans, and brands continue to co-evolve, the music industry stands as a powerful example of how ecosystem thinking can unlock growth, resilience, and creativity in a digital world.

Here are profiles of the leading digital music platforms and how each has strategically positioned itself within the new music ecosystem:

1. Spotify

Founded: 2006 (Sweden)
Business Model: Freemium streaming (ad-supported + subscription)
Strategy: Ecosystem Orchestrator + Data Innovator

Spotify is the world’s largest music streaming platform by users, with over 600 million monthly active users and around 236 million premium subscribers (as of 2025). Its core strength lies in data-driven personalization, with features like Discover Weekly, Release Radar, and Wrapped. Spotify positions itself as a neutral platform between creators and listeners, offering artist tools (Spotify for Artists), podcasts, and an expanding AI stack (e.g. AI DJs, real-time lyric translation). It has made strategic acquisitions in podcasting (e.g. Anchor, Gimlet) and is moving into audiobooks, aiming to become the “audio home” across formats.

2. Apple Music

Founded: 2015 (USA)
Business Model: Subscription-only streaming
Strategy: Premium Experience + Vertical Integration

Apple Music leverages the Apple ecosystem (iOS, AirPods, Apple Watch) to deliver a high-quality, seamless user experience. It emphasizes exclusive content, artist-led shows (e.g., Elton John’s Rocket Hour), and higher-quality audio (lossless, Dolby Atmos). Unlike Spotify, Apple focuses less on social discovery and more on curation and integrationwith user lifestyles, including fitness, spatial audio, and live radio (e.g. Apple Music 1). Apple uses music as a value-added feature to retain subscribers within its larger services bundle.

3. YouTube Music

Founded: 2015 (USA)
Business Model: Freemium streaming + integrated with YouTube Premium
Strategy: Video-Music Integration + Global Reach

Owned by Google, YouTube Music benefits from deep integration with YouTube, the world’s most-used platform for music videos. It excels in global accessibility and virality, especially in emerging markets and among Gen Z. Its ecosystem strength lies in combining video, audio, user-generated content, and fan engagement. Many music trends now begin on YouTube Shorts. Google’s AI also helps power personalized recommendations and smart playlists, while its advertising infrastructure supports monetization for both majors and independents.

4. Amazon Music

Founded: 2007 (as Amazon MP3), rebranded in 2016
Business Model: Bundled with Prime + stand-alone subscriptions
Strategy: Ecosystem Bundle + Smart Devices

Amazon Music leverages its Prime ecosystem and Alexa-enabled smart devices to build frictionless music experiences. Its strategy emphasizes access through voice, integration with shopping and home automation, and bundling music with Prime subscriptions. While it has less cultural influence than Spotify or YouTube, it is strong in households and among passive users. It also offers high-definition and spatial audio options to compete on quality.

5. TikTok (ByteDance)

Founded: 2016 (China)
Business Model: Ad-based + e-commerce + music licensing
Strategy: Viral Discovery + Creator-Driven Ecosystem

TikTok has emerged as the most powerful music discovery platform for younger audiences. Songs often go viral on TikTok before reaching traditional charts. The platform’s short-form video format and algorithmic feed prioritize engagement and shareability. TikTok is building a deeper music ecosystem through SoundOn (artist distribution platform), licensing deals with major labels, and partnerships with streaming services. It doesn’t replace music platforms but acts as a catalyst for discovery and culture, influencing everything from Spotify playlists to brand campaigns.

6. SoundCloud

Founded: 2007 (Germany)
Business Model: Freemium streaming + creator subscriptions
Strategy: Independent Artist Hub + Creator Monetization

SoundCloud pioneered open music sharing and remains a go-to platform for independent and experimental artists. Its strategic focus is on creator empowerment—offering tools for publishing, monetizing, and analyzing tracks. It allows artists to control rights and monetize directly through SoundCloud Premier, Repost, and fan-powered royalties. The platform is a testing ground for trends, subcultures, and new genres, often ahead of mainstream platforms.

7. Bandcamp

Founded: 2008 (USA)
Business Model: Direct artist-to-fan sales
Strategy: Ethical Commerce + Artist Control

Bandcamp offers an alternative model centered around ownership and direct support. Fans can buy digital downloads, vinyl, merch, and more, with a majority of revenues going directly to artists. Bandcamp Fridays (fee-free sales days) have become a cultural event. It fosters niche and indie communities by emphasizing transparency and artist-first ethics. While small in scale, it has loyal users and is often used by creators as a primary income source.

8. Tencent Music Entertainment (TME)

Founded: 2016 (China)
Business Model: Freemium streaming + virtual gifts + karaoke + social
Strategy: Super App Ecosystem + Monetization Variety

TME operates QQ Music, Kugou, and Kuwo, dominating China’s streaming space. Its strategy blends music, social interaction, gaming, and virtual gifts, making music part of a broader entertainment super-app. Revenue comes not just from ads or subscriptions, but also from microtransactions, digital merchandise, and fan-driven economies. TME is a case study in ecosystem monetization diversity, with an emphasis on engagement and community.

9. Deezer

Founded: 2007 (France)
Business Model: Freemium streaming
Strategy: Open Partnerships + Local Curation

Deezer positions itself through localization, openness, and integration. It has partnered with telcos, hardware makers, and brands to expand globally. It also advocates for user-centric payment models, aiming to create fairer revenue shares. Deezer emphasizes editorial curation and regional content, particularly in Europe and Latin America.

10. Audiomack

Founded: 2012 (USA)
Business Model: Freemium streaming
Strategy: Emerging Market Focus + Hip-Hop & Afrobeats Culture

Audiomack is a fast-growing player in Africa, the Caribbean, and the U.S. urban music scene. Its strategy centers on youth culture, mobile-first consumption, and direct artist uploading. It builds local ecosystems by investing in emerging artists and providing tools for real-time metrics and monetization.

Ecosystems Inc … examples of how to reinvent every industry

The reinvention of the music industry offers powerful lessons for other sectors. Once dominated by physical sales and industry gatekeepers, music has evolved into a dynamic, digital-first ecosystem led by platforms like Spotify, YouTube, and TikTok. These platforms don’t just distribute content—they connect creators, fans, advertisers, developers, and data in ways that continually generate value and innovation.

Other industries—from healthcare to fashion, education to finance—can learn from music’s transformation by embracing ecosystem thinking. This means shifting from linear, siloed value chains to multi-sided platforms where different actors co-create value. Key characteristics include:

• Connectivity: Linking diverse stakeholders through digital platforms.

• Personalization: Using data to tailor experiences in real-time.

• Continuous value creation: Delivering ongoing services rather than one-off transactions.

• Network effects: Gaining value as more users, creators, and partners join.

• Open architecture: Allowing integration, innovation, and adaptation over time.

The result is not just a better product, but a more responsive, scalable, and future-ready business model. Just as music moved from ownership to access, so can many other industries—from selling to streaming, from control to collaboration.

1. Healthcare … from treatments to health ecosystems

Old Model: Siloed providers offering one-off services (e.g. hospitals, insurers, pharmacies).

Ecosystem Model: Platforms like CVS Health, Ping An Good Doctor, or Babylon Health connect care, diagnostics, insurance, wearables, and virtual consultations into integrated health ecosystems.

• Personalization: Just like Spotify recommends music, health ecosystems can use AI to personalize treatment plans or preventive care.

• Platform Strategy: Connect patients, doctors, insurers, pharmacies, and digital health startups.

• Value Creation: Continuity of care, lower costs, and better outcomes through real-time data sharing.

Ping An Good Doctor, launched by Ping An Insurance, is China’s largest digital health platform. With over 400 million registered users, it offers online consultations, diagnostics, health checkups, medicine delivery, and AI-powered triage — all in one app.

• Platform Orchestration: Connects patients, doctors (both in-house and external), hospitals, pharmacies, and insurers.

• AI-Driven Personalization: Uses AI to provide initial diagnoses, route patients to the right doctor, and suggest health plans.

• Vertical Integration: Builds offline “One-Minute Clinics” (smart booths) across Chinese cities to bridge physical-digital care.

Ping An’s ecosystem model has reduced friction, increased access to care, and allowed rapid scaling. Like Spotify’s hybrid model of human + algorithmic curation, it blends AI with professional expertise.

2. Education … from institutions to learning ecosystems

Old Model: Universities and schools as gatekeepers of learning.

Ecosystem Model: Platforms like Coursera, Khan Academy, and Duolingo offer modular, lifelong learning through content, tools, and community.

• Access over Ownership: Just as music moved from CDs to streaming, learning is shifting from degrees to continuous, on-demand content.

• Ecosystem Design: Involve content creators (e.g. professors), learners, employers, edtech tools, and credentialing bodies.

• Feedback Loops: Learning paths are continuously updated based on learner performance and job market needs.

Coursera, founded in 2012 by Stanford professors, is a global online learning platform with over 140 million users and partnerships with 300+ top universities and companies.

• Multi-Sided Platform: Connects learners, educators, institutions, and employers — creating a rich, interconnected education ecosystem.

• Modular Learning: Offers flexible, stackable credentials (courses, certificates, degrees), much like how Spotify unbundled albums into playlists and songs.

• Personalization & AI: Suggests learning paths based on skills, career goals, and usage patterns. Uses AI to tailor content recommendations, mirroring music algorithms.

• Corporate & Government Partnerships: Offers Coursera for Business, Government, and Campus — embedding education into workforce systems.

Coursera has become a leading force in democratizing education globally, moving from a content library to a full ecosystem of skills, credentials, and career pathways. Like Spotify, it redefined access and empowered both creators (educators) and consumers (learners).

3. Automotive … from selling cars to mobility-as-a-service

Old Model: Buy or lease a vehicle from a dealership.

Ecosystem Model: Platforms like Tesla, Uber, and MaaS apps like Bolt connect cars, ride-hailing, energy, insurance, and smart city infrastructure.

• Platform Logic: Tesla connects software, EV charging, insurance, and over-the-air updates.

• Network Effects: Uber builds an ecosystem of drivers, riders, restaurants, and logistics.

• Data as Asset: Predictive maintenance, usage-based insurance, route optimization—all fed by real-time data.

Tesla is not just a car company — it’s an integrated mobility, energy, and software platform. Its success lies in treating the car as a node in a broader ecosystem, not just a product.

• Vertical Integration: Controls everything from battery production to software, energy services (Powerwall, Solar Roof), and even insurance.

• Continuous Upgrades: Like Spotify’s streaming updates, Tesla delivers over-the-air software updates to improve vehicle performance and add features post-purchase.

• Network Effects: Connects Tesla vehicles into shared systems — from autonomous driving data learning to Supercharger networks.

• AI + Data Loop: Uses real-time driving data for autopilot training and fleet optimization, akin to music platforms using listening data for personalization.

Tesla’s integrated model offers a seamless user experience while continuously expanding into adjacent spaces (robotaxis, energy storage, grid services). This mirrors how platforms like Apple Music or YouTube built adjacent services around core content.

4. Fashion … from product-driven to creator-driven ecosystems

Old Model: Seasonal collections pushed by fashion houses to retailers.

Ecosystem Model: Platforms like Instagram, Depop, and StockX empower creators, resellers, and consumers to co-create and trade fashion in real time.

• Creators as Brands: Influencers and micro-brands gain traction without traditional backing.

• Circular Economy: Secondhand marketplaces and upcycling apps form part of the fashion loop.

• Digital Goods: Virtual fashion in gaming and the metaverse (e.g. Balenciaga in Fortnite).

StockX launched in 2016 as a “stock market of things,” enabling users to buy and sell sneakers, streetwear, and luxury items with price transparency, authentication, and real-time demand signals. It now processes billions in GMV annually and has become a cultural hub for sneakerheads and collectors.

• Two-Sided Marketplace: Connects sellers (resellers, retailers, individuals) and buyers (enthusiasts, investors) in a transparent pricing environment, like Spotify connects artists and fans.

• Data as Currency: Provides real-time pricing charts, historical trends, and volume data — turning fashion into a speculative, dynamic asset class.

• Trust Infrastructure: Builds authentication, condition grading, and transaction security — key to ecosystem health and stickiness.

• Cultural Integration: Acts as a hub for sneaker culture, drops, and community-driven demand, leveraging scarcity and social influence like viral hits in music.

StockX transformed secondhand goods into financial assets. Much like how Spotify allowed obscure tracks to gain global traction, StockX gave niche fashion products global visibility and liquidity. The company monetizes not just transactions but the data, culture, and community around them.

5. Finance … from products to financial wellness ecosystems

Old Model: Banks offering siloed services—checking, loans, investing.

Ecosystem Model: Platforms like Revolut, Alipay, and Plaid connect personal finance, investing, insurance, crypto, and rewards.

• API-driven Ecosystems: Fintechs connect to banks, credit bureaus, payroll providers, and e-commerce platforms.

• User Control: Like artists using Spotify for Artists, customers can manage their financial data and insights in one place.

• Embedded Finance: Financial services appear within non-financial platforms (e.g. BNPL in retail).

Revolut started in 2015 as a travel-focused money app and evolved into a financial super app, offering banking, crypto, stock trading, budgeting, and more to 40+ million users worldwide.

• All-in-One Platform: Combines checking, saving, FX, trading, lending, and insurance — turning financial services into a continuous engagement experience, like Spotify’s audio ecosystem.

• Personalized Insights: AI-driven notifications and budgeting tools help users manage money better, mirroring personalized recommendations in music apps.

• APIs + Open Banking: Connects with third-party fintechs, creating a modular system where users can plug in services.

• Gamified UX: Rewards, challenges, and community engagement drive usage, similar to TikTok’s engagement mechanics.

Revolut is redefining consumer expectations in banking — focusing on UX, real-time updates, and financial control. Like Spotify gave users control over what and how they consume music, Revolut puts customers in control of their financial lives.

6. Media and Entertainment … from channels to content ecosystems

Old Model: Studios create, control, and distribute content through owned channels.

Ecosystem Model: Platforms like Netflix, YouTube, and Twitch enable content creation, distribution, monetization, and fan engagement across communities.

• Creator Economy: Anyone can be a content creator, and monetization is built into the ecosystem (ads, subscriptions, donations).

• Personalization: AI-driven recommendations akin to Spotify’s playlists.

• Community-Driven Discovery: Fans co-create culture, memes, remixes—similar to how TikTok shapes music.

Netflix, founded in 1997 as a DVD rental company, became the first major streaming service in 2007. Today it’s a global media powerhouse, with 270+ million subscribers in 190+ countries, and a leader in both content production and delivery.

• Vertical Control of Value Chain: Produces, distributes, and curates content end-to-end — like Apple in music or Tesla in mobility.

• Data-Driven Personalization: Uses viewer data to drive algorithmic recommendations, content commissioning, and global rollouts (e.g. “House of Cards” was greenlit based on viewer preferences).

• Global Localism: Builds region-specific content ecosystems (e.g. K-dramas, Spanish thrillers) that scale globally, turning local hits into global phenomena (à la “Squid Game”).

• Multi-Stakeholder Platform: Connects creators, audiences, advertisers (in ad-tier), and now game developers — extending the platform into new media.

Netflix reshaped how content is consumed, produced, and monetized. Like Spotify, it removed traditional industry gatekeepers and used algorithms and audience feedback to shape creative direction. It turned entertainment into a dynamic, participatory, data-led experience.

Explore more

• Next Generation Business Models: Redefining value, ownership, scale, and trust by Peter Fisk
• Reinventing business ecosystems for more profitable growth by Peter Fisk
• Building a Butterfly Brand: How “branded ecosystems”​ achieve more in a world of relentless change by Peter Fisk
• Has Apple become more or less innovative in recent years? by Peter Fisk
• Cross Industry Innovation for Future Business Ecosystems by Z Punkt
• Ecosystem Strategy Map by Julian Kawohl
• How do you Design a Business Ecosystem by BCG Henderson

Over the past two decades, Asia has transformed from being the world’s factory to becoming its innovation engine. From super apps and AI platforms to smart logistics and consumer electronics, Asian companies are now at the forefront of technological disruption and business reinvention. They are reshaping industries, influencing global standards, and outpacing many Western counterparts—not just in scale but in the speed and imagination of innovation.

How did this transformation happen? And why are companies like Bytedance, DJI, Grab, Jio, Haier, Ping An, Sea and Tencent now regarded as among the world’s most innovative?

1. Massive, Demanding, Digital-First Markets

Asia’s biggest asset is its population: more than 4.7 billion people, including the fastest-growing middle class in the world. These markets are mobile-first, young, and digitally native—forcing companies to innovate quickly and at scale.

Take Bytedance, the parent company of TikTok. Founded in China in 2012, it leveraged AI-driven personalization to reimagine content consumption. Unlike platforms that rely on social graphs, TikTok’s content is algorithmically curated, creating an addictive user experience that has since been copied globally. The Chinese market’s diversity and size gave Bytedance an ideal sandbox to refine this model before going global.

Similarly, Grab, founded in Malaysia and now headquartered in Singapore, has evolved from a ride-hailing app into Southeast Asia’s leading super app. It integrates transportation, payments, food delivery, insurance, and more—meeting the needs of consumers in fragmented, infrastructure-light environments. This model was born out of necessity in a region where digital infrastructure had to leapfrog traditional systems.

2. Innovating for Inclusion and Scale

Asian innovators often solve problems unique to their regions—lack of physical infrastructure, informal economies, or underserved populations. These constraints inspire inclusive innovation that often proves globally relevant.

Jio, the Indian telecom and digital services company launched by Reliance Industries in 2016, disrupted India’s telecom market with free calls and ultra-low data costs. By building its network from scratch using 4G and investing heavily in digital content, cloud, and services, Jio connected over 450 million Indians online in just a few years. This was not just innovation in pricing—it was a digital nation-building strategy. Now, Jio is extending into healthcare, education, fintech, and AI with homegrown ambition.

Ping An, one of the world’s largest insurance and financial services companies, turned itself into a technology-first enterprise. It uses AI, big data, and blockchain to serve over 220 million customers and 500 million internet users. Its Good Doctor app became China’s leading health tech platform, with AI-driven diagnostics and 24/7 telemedicine. Ping An’s transformation reflects how Asian firms are embedding tech at the core of traditional sectors like insurance, banking, and healthcare.

3. Reimagining Business Models

Many Asian companies are not just adopting Western models—they are inventing new ones. The concept of the super app, for instance, did not originate in Silicon Valley but in China and Southeast Asia.

Tencent’s WeChat is a prime example. Starting as a messaging app, it became a platform for payments, news, games, shopping, and government services. By enabling third-party mini-programs within the app, Tencent created a mobile-first operating system for everyday life. This level of integration, with seamless UX across services, is rare outside Asia and has influenced global tech giants like Meta and PayPal.

Similarly, Sea Group, based in Singapore and best known for its gaming arm Garena and e-commerce platform Shopee, has created regionally tailored services that blend social engagement with transaction. Shopee’s gamified shopping experience and localized promotions turned it into a leading e-commerce player across Southeast Asia and Latin America.

4. Flexible Ownership and Ambitious Leadership

Ownership structures in Asia are often more flexible and founder-led than in the West. This allows for bold decisions, long-term thinking, and alignment between innovation and execution.

Consider Haier, the Chinese home appliance giant. It reinvented itself into a modular, platform-driven organization using a management philosophy called “Rendanheyi”—empowering small, autonomous teams to act like startups. Haier’s shift from manufacturing to an ecosystem-based innovation model allowed it to acquire GE Appliances in the US and become a leader in smart home solutions globally.

Asian founders and leaders often exhibit a unique blend of nationalism and entrepreneurial drive. Masayoshi Son of SoftBank exemplifies this with his long-term, global investment thesis built around AI and connectivity. Similarly, Mukesh Ambani’s Jio strategy was as much about transforming India as it was about dominating markets.

5. Supportive Governments and Innovation Ecosystems

Governments across Asia have played a crucial role in enabling innovation through infrastructure, policy, and funding.

In China, the government’s “Made in China 2025” strategy incentivized R&D in AI, robotics, and semiconductors. The creation of special economic zones and digital infrastructure hubs enabled companies like DJI (the global leader in drones) and SenseTime (a major AI player) to scale rapidly.

Singapore and South Korea have long invested in education, IP protection, and startup incubation. Singapore’s EDB (Economic Development Board) and Temasek have backed ventures in fintech, biotech, and green energy. South Korea’s Samsung and Hyundai ecosystems are supported by government-led research and supplier networks, allowing them to stay globally competitive in semiconductors, mobility, and clean tech.

Even in India, digital public goods like Aadhaar (digital identity) and UPI (real-time payments infrastructure) have given startups a massive platform to innovate and scale. IndiaStack is now being exported to other developing economies.

6. Bold Visions, Global Playbooks

Asian innovators are increasingly building for the world. Ant Group, for example, pioneered mobile payments with Alipay and now drives global fintech innovation. Tokopedia and Gojek merged to create GoTo Group, aiming to be Southeast Asia’s leading digital ecosystem.

Japanese firms like Rakuten are combining e-commerce, fintech, and mobile telecoms under one brand, while NTT is pushing the boundaries of smart cities and digital twins. In South Korea, Kakao has replicated the super app model with a creative edge in content, entertainment, and NFTs.

Even in frontier tech, Asia is advancing rapidly. SK Hynix and TSMC are global semiconductor leaders. Baidu and Naver are pioneering autonomous vehicles and language AI. In Japan, Preferred Networks is pushing the edge in deep learning for industrial automation and robotics.

7. A Future Shaped in Asia

The next decade of innovation will be increasingly shaped in Asia—not just because of its population or capital, but because of its mindset.

Asian companies are not afraid to leapfrog. They build for complexity, scale, and inclusion. They merge physical and digital worlds. They rewire traditional industries with AI, data, and platform logic. And they move fast.

As the world grapples with climate change, aging populations, urban congestion, and economic inequality, Asia’s innovation playbook—problem-solving at scale, embedding tech everywhere, and integrating services around user needs—may become the global blueprint.

Companies like Bytedance, Grab, Jio, Haier, Ping An, and Tencent are just the beginning. The next wave—from Indonesia to India, Japan to Korea, China to Vietnam—is coming. And they’re not just competing with Silicon Valley. They’re defining what innovation looks like in the 21st century.

Predicting which companies will see the most significant stock market growth by 2030 involves identifying those best positioned at the intersection of exponential technologies, structural global shifts, and strong execution. These are companies driving or benefiting from megatrends such as AI, climate tech, biotech, digital transformation, and changing demographics.

Here is a curated selection of companies across sectors and geographies that are well-positioned for outsized growth — along with the reasons why. (Remember these are not investment recommendations, simply an analysis of markets and companies, and illustration of their value-based growth potential!).

For each company we consider the strategic significance of the company—”why” it is well-positioned to grow significantly. We also identify the “2030 growth catalyst”, meaning the specific events likely to supercharge the company’s growth by 2030, and the broader “growth drivers”, ie the structural, longer-term factors that will sustain and support the company’s growth over time – technologies, capabilities, business models, partnerships, or geographic expansion.

1. Nvidia … powering the AI revolution

Founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, NVIDIA began as a graphics chip company for gaming. Under CEO Jensen Huang, it has evolved into a dominant force in AI computing, data centers, autonomous vehicles, and more. It grew exponentially with the rise of GPU computing and now leads the AI revolution with its full-stack AI platform, including CUDA software, GPUs, and supercomputers like DGX.

• 2025 Market Cap: $3 trillion
• 2030 Potential Market Cap: $6 trillion
• Why: The foundational infrastructure provider for AI, from GPUs to full-stack AI computing platforms.
• 2030 Growth Catalyst: Enterprise adoption of generative AI, AI-as-a-platform services, expansion into robotics and autonomous systems.
• Growth Drivers:
  • World’s leading supplier of GPUs, central to AI, machine learning, and edge computing.
  • Expanding into AI software, networking, and data centre platforms.
  • Strong ecosystem dominance, high margins, and strategic leadership in generative AI.

2. ASML … the machines that make semiconductors

Founded in 1984 as a joint venture between Philips and ASM International, ASML is headquartered in Veldhoven, Netherlands. Under CEO Peter Wennink, it has become the global monopoly in extreme ultraviolet (EUV) lithography. ASML plays a pivotal role in the semiconductor value chain, enabling the production of the smallest and most advanced chips. Its technology is critical to Moore’s Law and the future of AI and high-performance computing.

• 2025 Market Cap: $500 billion
• 2030 Potential Market Cap: $1.2 trillion
• Why: Sole supplier of EUV lithography, essential for advanced semiconductor manufacturing.
• 2030 Growth Catalyst: Surge in demand for next-gen chips across AI, 5G, and quantum.
• Growth Drivers:
  • High-margin, high-barrier tech monopoly
  • Long-term supplier contracts with TSMC, Intel, Samsung
  • Expanding capacity to meet global reshoring demand

3. Tesla … accelerating the transition to clean energy

• 2025 Market Cap: $750 billion
• 2030 Potential Market Cap: $2.5 trillion
• Why: Beyond EVs: energy, AI robotics, and autonomy leader.
• 2030 Growth Catalyst: Commercialisation of autonomous vehicles and Tesla Energy at scale.
• Growth Drivers:
  • Energy storage and solar business
  • Robotaxi network
  • Dojo AI training platform and humanoid robots

4. BYD … dreams driving the future of mobility

BYD (Build Your Dreams) was founded in 1995 by Wang Chuanfu as a rechargeable battery company. It entered the auto industry in 2003 and became one of the world’s largest EV makers. Known for vertical integration, BYD manufactures its own batteries, chips, and EV components. Backed by investors like Warren Buffett, it leads China’s EV market and is rapidly expanding into global markets.

• 2025 Market Cap: $100 billion
• 2030 Potential Market Cap: $450 billion
• Why: Vertically integrated EV manufacturer dominating China and expanding globally.
• 2030 Growth Catalyst: Global mass-market EV adoption, battery exports.
• Growth Drivers:
  • In-house batteries and chips
  • Affordable EV models for emerging markets
  • Global expansion (ASEAN, LATAM, EU)

5. Reliance Jio … becoming India’s leading lifestyle brand

• 2025 Market Cap: $250 billion
• 2030 Potential Market Cap: $800 billion
• Why: India’s most diversified conglomerate, pivoting from oil to digital, retail, and clean energy.
• 2030 Growth Catalyst: Jio as India’s dominant digital ecosystem across telecom, finance, and commerce.
• Growth Drivers:
  • Jio Financial, JioMart, JioCinema, and 5G services
  • Green hydrogen and solar investments
  • 1.4B person digital consumer base

6. MercadoLibre … the digital backbone of Latin America

Founded in 1999 by Marcos Galperin in Argentina, MercadoLibre is Latin America’s leading e-commerce and fintech platform. It operates in 18 countries, offering online marketplaces, digital payments, logistics, and credit services. With its MercadoPago and MercadoEnvios platforms, it serves both consumers and merchants. Under Galperin’s leadership, it continues to scale digital services in a region with growing internet penetration and fintech demand.

• 2025 Market Cap: $80 billion
• 2030 Potential Market Cap: $350 billion
• Why: Amazon + PayPal of Latin America with strong moat in logistics and fintech.
• 2030 Growth Catalyst: Latin America’s digital finance revolution and rising e-commerce penetration.
• Growth Drivers:
  • MercadoPago’s banking and credit services
  • Logistics and fulfillment infrastructure
  • Cross-border commerce expansion

7. Eli Lilly … biotech powerhouse, AI-powered drugs

• 2025 Market Cap: $850 billion
• 2030 Potential Market Cap: $2.2 trillion
• Why: Dominates obesity and diabetes treatment with Mounjaro; strong biotech pipeline.
• 2030 Growth Catalyst: Mainstream use of GLP-1 drugs for metabolic disease and longevity.
• Growth Drivers:
  • FDA approvals for Alzheimer’s and cancer drugs
  • Global rollout of Mounjaro/Zepbound
  • Expansion in emerging health markets

8. TSMC … the world’s largest chip maker

Founded in 1987 by Morris Chang, TSMC (Taiwan Semiconductor Manufacturing Company) pioneered the pure-play foundry model. Headquartered in Hsinchu, Taiwan, and led by C.C. Wei, it is the world’s largest contract chip manufacturer. TSMC produces the most advanced chips for top tech companies globally and is central to innovation in AI, smartphones, and high-performance computing.

• 2025 Market Cap: $600 billion
• 2030 Potential Market Cap: $1.2 trillion
• Why: World’s most advanced semiconductor foundry powering Apple, Nvidia, AMD, etc.
• 2030 Growth Catalyst: AI, automotive, and IoT chips becoming core infrastructure.
• Growth Drivers:
  • Geographical diversification (US, Japan, EU fabs)
  • Leadership in 3nm and advanced packaging
  • Supply chain resilience and premium pricing

9. NextEra Energy … America’s leading clean energy business

Founded in 1984 and headquartered in Florida, NextEra Energy is the parent of Florida Power & Light and NextEra Energy Resources. Under CEO John Ketchum, it leads in wind and solar energy generation in North America. The company is at the forefront of the clean energy transition, investing in grid modernization and storage, and leveraging policy support like the Inflation Reduction Act.

• 2025 Market Cap: $150 billion
• 2030 Potential Market Cap: $300 billion
• Why: Largest renewable energy generator in the US.
• 2030 Growth Catalyst: Scaling solar, wind, and grid-scale battery storage.
• Growth Drivers:
  • IRA subsidies and regulatory tailwinds
  • Digital grid infrastructure
  • Distributed energy services for businesses and consumers

10. ARM … designing next generation chips

ARM was founded in 1990 as a joint venture between Acorn Computers, Apple, and VLSI Technology. Based in Cambridge, UK, it designs energy-efficient chip architectures widely used in mobile and embedded systems. Now led by CEO Rene Haas, ARM has expanded into data centers and AI edge computing. Its flexible licensing model powers over 250 billion chips globally, making it foundational to modern electronics.

• 2025 Market Cap: $120 billion
• 2030 Potential Market Cap: $400 billion
• Why: Dominant CPU architecture for mobile, AI edge, IoT, and increasingly cloud.
• 2030 Growth Catalyst: AI at the edge, IoT proliferation, and high-margin licensing.
• Growth Drivers:
  • Embedded AI chips for smart devices
  • Expanding royalty streams from data centers
  • Strategic shift toward value-based licensing

In summary

In addition here are some “bonus picks” … smaller, high-potential, higher-risk companies that could see significant stock market growth by 2030:

1. UiPath … smart robotics from Romania

Founded in 2005 in Romania by Daniel Dines and Marius Tîrcă, UiPath is a global leader in robotic process automation (RPA). Headquartered in New York, the company helps enterprises automate repetitive tasks across systems using AI-powered bots. Led by co-founder and CEO Daniel Dines, UiPath has grown rapidly with a strong customer base in finance, healthcare, and logistics. Its automation-first platform is expanding into intelligent automation and process mining.

• 2025 Market Cap: $10 billion
• 2030 Potential Market Cap: $100 billion
• Why: UiPath is a leader in robotic process automation (RPA), offering AI-powered tools that automate repetitive tasks in enterprise workflows. With the convergence of AI and automation, demand for intelligent systems to boost productivity is surging.
• 2030 Growth Catalyst: Enterprise AI adoption, mass automation of back-office functions, AI copilots for every worker.
• Growth Drivers:
  • Expansion into end-to-end AI-powered automation platforms
  • Deeper integrations with cloud platforms (Microsoft, AWS, Google)
  • Growing demand in finance, healthcare, government sectors
  • Upselling AI-based orchestration and analytics services

2. Palantir … making sense of complex data

Founded in 2003 by Peter Thiel, Alex Karp, and others, Palantir started as a data analytics company for defense and intelligence. Under CEO Alex Karp, it expanded into commercial sectors with platforms like Foundry and Gotham that help organizations integrate and make sense of complex data. With increasing adoption in healthcare, finance, and manufacturing, Palantir is gaining traction as AI-driven decision-making becomes mission-critical.

• 2025 Market Cap: $55 billion
• 2030 Potential Market Cap: $300 billion
• Why: Palantir specializes in AI-driven big data analytics, serving defense, intelligence, and commercial clients. It’s gaining traction for its Foundry and AIP platforms that offer mission-critical decision-making tools powered by LLMs.
• 2030 Growth Catalyst: AI-infused enterprise decision platforms; scaled use across commercial sectors and public infrastructure.
• Growth Drivers:
  • Accelerating shift from bespoke to scalable AI platforms
  • Strong US and NATO government contracts (defense, healthcare, intelligence)
  • Commercial expansion in pharma, manufacturing, energy
  • Thought leadership in AI ethics, security, and national resilience

3. Sea Group … Singapore retail meets entertainment

• 2025 Market Cap: $30 billion
• 2030 Potential Market Cap: $120 billion
• Why: Sea operates Southeast Asia’s dominant e-commerce (Shopee), digital financial services (SeaMoney), and gaming (Garena). With Southeast Asia’s growing middle class and digital infrastructure, Sea is well positioned as a regional tech super-app.
• 2030 Growth Catalyst: Regional super app combining shopping, fintech, and entertainment in one platform.
• Growth Drivers:
  • Shopee scaling e-commerce in Indonesia, Vietnam, Philippines, Brazil
  • SeaMoney expansion into payments, insurance, lending
  • Return of gaming growth via Garena’s new titles or cross-border IP
  • Digital infrastructure and logistics play in fast-growing economies

4. Illumina … leading the genomics revolution

Founded in 1998 and based in San Diego, Illumina is a pioneer in DNA sequencing technologies and genomics solutions. The company, now led by CEO Jacob Thaysen, enables researchers, hospitals, and pharmaceutical companies to sequence genomes quickly and affordably. Despite recent strategic challenges, it remains critical to the advancement of precision medicine, population genomics, and biotech R&D.

• 2025 Market Cap: $30 billion
• 2030 Potential Market Cap: $120 billion
• Why: Illumina is the global leader in DNA sequencing technology. Its platforms power genomics research, precision medicine, and diagnostics, all central to the healthcare revolution.
• 2030 Growth Catalyst: The “genomics-as-infrastructure” moment: sequencing becomes routine for population health, cancer, rare diseases, and AI-informed medicine.
• Growth Drivers:
  • Rapid adoption of genomic testing by health systems
  • Falling cost of sequencing (towards $100/genome)
  • New clinical diagnostics partnerships and AI applications
  • Strategic spinouts or divestments (e.g. Grail) to focus on core

5. CleanSpark … sustainable bitcoin mining

Founded in 1987 and headquartered in Nevada, CleanSpark is a Bitcoin mining and energy technology company that focuses on sustainable mining powered by renewables. Led by CEO Zachary Bradford, CleanSpark distinguishes itself by leveraging energy-efficient operations and vertically integrated power sourcing. The company is also investing in microgrids and grid-interactive energy systems, making it a unique clean-tech and crypto hybrid growth story.

• 2025 Market Cap: $2 billion
• 2030 Potential Market Cap: $12 billion
• Why: CleanSpark provides sustainable Bitcoin mining and microgrid energy solutions. It bridges crypto infrastructure with renewable energy tech — offering a scalable model for energy-intensive industries.
• 2030 Growth Catalyst: Convergence of clean energy + crypto infrastructure + grid balancing services.
• Growth Drivers:
  • Renewable-powered mining leadership (low carbon footprint)
  • Expansion into smart microgrid tech and virtual power plants
  • Leverage of carbon credits and green finance
  • Positioned well in US-based digital asset ecosystem with policy tailwinds

6. KlimaDAO … blockchain for climate change

Launched in 2021, KlimaDAO is a decentralized autonomous organization (DAO) aiming to accelerate climate action through blockchain and carbon markets. Built on the Polygon network, it tokenizes carbon credits into the KLIMA token, incentivizing demand for verified carbon offsets. KlimaDAO operates without a central corporate leadership but is stewarded by a community of developers, climate activists, and governance token holders. It seeks to create a transparent, liquid, and scalable market for environmental assets, and is part of a broader trend toward regenerative finance (ReFi).

• 2025 Market Cap: $100 million (token-based market cap)
• 2030 Potential Market Cap: $10 billion
• Why: KlimaDAO is a decentralized autonomous organization creating a programmable carbon market via blockchain. It tokenizes carbon credits to create transparency and liquidity in the voluntary carbon market.
• 2030 Growth Catalyst: Institutional and government adoption of on-chain carbon markets and climate finance infrastructure.
• Growth Drivers:
  • Rise of nature-based carbon markets and climate tokens
  • Integration with ESG reporting platforms and supply chains
  • Partnerships with climate registries and offset projects
  • Regulation and demand for transparent carbon accounting

In today’s world of accelerating change, disruption is no longer the exception, it is the norm.

Technology, climate imperatives, shifting customer expectations, and geopolitical volatility are reshaping the business landscape. In this environment, incremental improvement is no longer sufficient. True business transformation—a fundamental reinvention of how a company creates and captures value – is necessary for survival and long-term success.

But what does transformation really mean? At its core, transformation is about reimagining the future of the business: what it stands for, how it competes, how it works, and how it wins. It’s a step beyond operational change or digital upgrades. It is a whole-system shift—combining purpose, strategy, operating model, culture, capabilities, and performance.

What is business transformation?

Business transformation involves a step change in performance driven by a shift in core business logic—how value is created and delivered. This could mean moving from products to platforms, from fossil fuels to renewables, from a local to a global model, or from analogue to digital. In some cases all of these.

True transformation usually touches four interconnected domains:

• Reimagining purpose and vision – defining the ‘why’ of the organisation; its role in a rapidly changing world, its aspirations, and what it uniquely offer, supported by a vision of what better can look like.

• Redefining strategy and priorities – refocusing ‘where and how’ to play – reshaping the strategy, business model, customer propositions, innovating products and services, and future business portfolio.

• Reinventing culture and organisation – creating the internal conditions—leadership, mindset, capabilities, behaviours, structures, and systems—that enable change and innovation.

• Rewiring operations and performance – shifting the core activities, capabilities, and technology infrastructure to support new ways of working, and delivering performance short and long-term, for sustained value creation.

At its best, business transformation results in significant growth, market leadership, and renewed relevance. It can mean, over time, a fundamental shift in the core business. It can transform performance, creating significant new profit streams, and doubling or tripling the market value over 4-5 years. At its worst, it is superficial, disconnected, and fails to deliver impact. All the more reason to get it right.

Transformational companies around the world

Some companies have stood out in recent years for their ability to successfully reinvent themselves, responding to massive shifts with agility and vision.

DSM … science-led reinvention

Originally a Dutch coal mining company, DSM has continuously reinvented itself for over a century. Most recently, it divested from chemicals and refocused on biosciences, nutrition, and health. The merger with Firmenich accelerated this pivot, creating a purpose-driven leader in sustainable ingredients. DSM’s transformation illustrates how companies can shed legacy businesses to focus on high-growth, impact-led markets.

Schneider Electric … the world’s most sustainable company

Once a provider of electrical components, Schneider Electric transformed into a global leader in energy management and automation. Its purpose—“to be the digital partner for sustainability and efficiency”—has guided a decade-long pivot towards software, smart systems, and decarbonisation. The company reorganised around digital platforms and embedded sustainability into every part of its strategy and culture. It now helps thousands of companies monitor and reduce their carbon footprint while achieving sustained double-digit growth.

Fujifilm … from camera film to healthcare 

When digital photography disrupted its core market, Fujifilm—unlike Kodak—chose to radically reinvent itself. It leveraged its deep expertise in chemistry and materials to pivot into healthcare, cosmetics, and advanced materials. Its diversification into medical imaging, regenerative medicine, and pharmaceuticals allowed it not only to survive the death of film but to thrive in new sectors. Fujifilm’s transformation is a masterclass in strategic foresight, reinvention of capabilities, and disciplined execution.

Ping An … from insurer to multi-sector ecosystem

Ping An, one of world’s largest insurers, has undergone a radical transformation into a technology-driven ecosystem for financial and healthcare services. Recognising the disruptive potential of digital platforms, Ping An invested in AI, blockchain, and cloud, creating industry-leading apps like Good Doctor (healthcare) and Lufax (finance). Its strategy involved turning traditional financial services into tech-powered, data-rich customer experiences—while also developing internal AI tools to increase operational efficiency. Ping An’s transformation exemplifies how traditional players can redefine their core by embedding technology at scale.

Microsoft … ready to win in an age of AI

Under CEO Satya Nadella, Microsoft reimagined itself from a PC-centric, legacy software firm into a global cloud powerhouse. Nadella reinvigorated Microsoft’s purpose “to empower every person and every organization on the planet to achieve more,” steering it towards cloud infrastructure (Azure), remote collaboration (Teams), and generative AI (Copilot). The transformation was cultural as well as strategic—moving from internal rivalry to openness and innovation. The result: a trillion-dollar leap in market value and renewed relevance.

IKEA … from flatpacks to circularity 

IKEA has evolved from a low-cost furniture retailer to a purpose-led sustainability leader. It has committed to becoming climate positive by 2030, embraced circular product design, and explored new service models including buy-back, rentals, and urban formats. The transformation includes not only rethinking its supply chain and materials, but also transforming its culture—empowering employees to innovate and enabling faster experimentation.

On … scaling a start-up into lifestyle leader

Swiss sportswear brand On Running combines elite performance with sustainability. Its transformation is unique—scaling rapidly while maintaining its core design ethos and environmental focus. The CFO played a pivotal role, helping develop a circular subscription model for recyclable shoes and using data to optimise global expansion. It exemplifies how newer companies can build transformation into their growth journey from the start.

Relentless reinvention … once you’ve transformed, transform again

Business transformation, of course, is no longer a one-off project, it’s a relentless, continuous process. As markets evolve due to technological disruption, shifting customer expectations, environmental challenges, and geopolitical uncertainty, companies must constantly reinvent themselves to stay competitive. Transformation is not just about reacting to change; it’s about building the capabilities to anticipate, shape, and lead it.

This requires becoming an ambidextrous organisation—able to simultaneously optimize today’s core business while exploring and scaling tomorrow’s opportunities. It means balancing operational efficiency with strategic innovation, and fostering cultures where adaptability and experimentation thrive alongside discipline and performance.

Amazon exemplifies this duality, continually reinventing its model—from retail to cloud services, logistics, and AI—while maintaining tight operational excellence. Schneider Electric has pivoted from hardware manufacturing to smart energy solutions, combining legacy strengths with digital innovation. DBS Bank, once a slow-moving institution, has transformed into a technology-driven, customer-centric bank by embedding agile practices and a startup mindset across the organization.

Agility is central to this model—organizational structures must be flexible, decision-making decentralized, and leadership aligned on a shared purpose. Spotify’s “squad” model, for instance, empowers cross-functional teams to experiment quickly and bring new value to market fast.

Many organisations seek to build a dual approach to transformation, most simply illustrated by the double portfolio of “exploit and explore” – developing a portfolio of initiatives to enhance today’s business, while also initiatives to create the future. You need to work on both, and gradually explore becomes exploit. Some companies do this structurally like Ping An, with co-CEOs, one exploiting its core insurance business, the other developing new businesses in healthcare, mobility and real estate. Or think about Alphabet’s X company, working on exploring the future beyond search. Investors, of course, seek both.

Ultimately, transformation is no longer about fixing broken parts; it’s about continuously reimagining the whole—products, models, ecosystems, and ways of working. Businesses that embed this capacity to evolve—who see reinvention as a muscle, not a moment—will be best placed to thrive in an age where change is the only constant.

3 of the biggest challenges for transformation

Even the most visionary leaders face deep challenges in delivering transformation at scale. Three of the most critical and complex leadership challenges are balancing a portfolio of innovations, navigating dual transformation (external and internal), and aligning the board and executive team.

1. Balancing today and tomorrow

Leaders must manage a dynamic portfolio of innovations that span immediate improvements and longer-term reinvention. This means simultaneously optimising the core business (Horizon 1), incubating adjacent innovations (Horizon 2), and exploring radical new ventures (Horizon 3). The challenge is resource allocation: how to invest in transformative opportunities without compromising current performance. Leaders must create space for experimentation while maintaining operational excellence. This tension between “running the business” and “changing the business” often stretches leadership focus and organisational attention. This demands leaders who are performer-transformers, and a dual transformation approach.

2. Combining strategic and organisational change

Transformation must occur both externally—through a new strategic vision, value proposition, and business model—and internally, through organisational structure, culture, processes, and capabilities. It’s not enough to define a new direction; leaders must also mobilise the company to deliver it. This requires orchestration across functions, integration of digital and sustainability agendas, and active engagement with customers, partners, and ecosystems. Internally, leaders must rewire the organisation for agility, collaboration, and learning. That means letting go of legacy hierarchies, empowering cross-functional teams, and developing new talent and mindsets. Strategic ambition must be matched by organisational readiness.

3. Aligning the board and executive team

Perhaps the most overlooked but critical challenge is aligning the leadership at the top. True transformation starts with a shared dream—a compelling vision that stretches beyond today’s constraints. Yet many boards are anchored in risk management and short-term financials, while transformation requires bold bets and long-term thinking. Leaders must bridge this gap, building trust, framing transformation in terms of future value creation, and managing performance across multiple time horizons. Within the executive team, cohesion is vital: alignment of goals, clarity of roles, and a shared commitment to lead the change. Without unified leadership, even the best strategies falter.

In short, transformation is as much about leadership as it is about strategy. It demands courage, coherence, and collaboration across the leadership system to shape the future and deliver it with discipline.

Accelerating the transformation journey

While every company’s journey is unique, successful transformations tend to follow several common principles and practices:

1. Start with purpose

Transformation is more likely to succeed when anchored in a compelling, future-facing purpose. Purpose inspires stakeholders, provides clarity during uncertainty, and guides strategic choices. Companies like Unilever and Danone have demonstrated how a strong sense of purpose can align business transformation with broader societal needs.

2. Envision a bold future

Companies need to articulate a bold vision—not just improve the present. A compelling narrative about the future energises teams and creates direction. Disney, for instance, didn’t just go digital—it reimagined itself as a streaming-first, direct-to-consumer company.

3. Redesign the business model

Transformation involves rethinking how the business creates and captures value. This may involve shifting from selling products to services, building platforms, or monetising data. Adobe transitioned from packaged software to cloud subscriptions. Ørsted moved from fossil fuels to renewable energy production. These moves require strategic courage and long-term investment.

4. Make culture a strategic asset

Culture often makes or breaks transformation. Companies need to foster learning, psychological safety, accountability, and openness to change. Microsoft’s cultural reset—from “know-it-alls” to “learn-it-alls”—was a critical enabler of its business transformation.

5. Invest in capability building

Transformation demands new skills—in AI, customer experience, sustainability, or agile working. Leading companies build these capabilities through training, hiring, and partnerships. DBS Bank, for example, turned itself into a “technology company in banking,” with internal accelerators and digital talent programs.

6. Structure for process and cultural agility

Legacy hierarchies often slow down transformation. High-performing companies redesign around networks, cross-functional teams, and empowered local units. Amazon’s “two-pizza teams” and Spotify’s squad model are iconic examples of agile structures supporting innovation.

7. Operationalise with systems and metrics

Transformation should be operationalised—translated into targets, incentives, and accountability systems. Schneider Electric embedded sustainability KPIs across the organisation, ensuring that purpose translated into performance.

8. Deliver quick wins while shaping the long term

Transformation is a marathon, but quick wins build momentum. Successful leaders sequence transformation to deliver near-term impact—such as new product lines, cost savings, or customer wins—while building the foundation for longer-term shifts.

Transformation as a leadership imperative

Great business transformations start with great leadership. In times of radical change, leaders play an essential role—not just as decision-makers, but as visionaries, mobilisers, architects, and role models. They must be bold enough to imagine a future beyond today’s business model, and disciplined enough to bring it to life across the entire organisation.

Here’s a detailed breakdown of what leaders must do to drive successful transformation—starting with vision, and following through with strategy, organisation, and execution—along with powerful real-world examples.

1. Have the courage to imagine a different future

The first and most critical responsibility of a transformative leader is to define a new vision that breaks from legacy logic. This demands courage—because it often involves letting go of what made the company successful in the past.

• Challenge the status quo. Leaders must ask: What business are we really in? What future could we shape—not just survive? They must be willing to explore and articulate a future that may not yet exist in their industry.
• Inspire ambition. A bold vision isn’t just a stretch target—it’s a compelling narrative about how the organisation can solve bigger problems, serve new needs, or change how value is created.

Satya Nadella became CEO in 2014 when Microsoft was in decline. He reframed its purpose as “empowering every person and every organization on the planet to achieve more,” moving beyond Windows to cloud, AI, and collaboration tools. His vision reinvigorated the company—and set the direction for all future strategy and culture.

Masahiko Uotani redefined Shiseido’s identity in the face of stagnation —from a Japanese cosmetics brand to a global beauty innovator focused on skin science and wellness. This allowed the company to pivot strategically into health, tech, and sustainability.

2. Translate vision into strategy and bold priorities

Vision without strategy is just a dream. Leaders must translate vision into clear, actionable priorities that redefine how the company competes.

• Redefine the business model. This might involve shifting from products to services, analog to digital, or linear to circular models. Leaders must make deliberate choices about where and how to play.
• Create new value propositions. What does the customer of the future need? Leaders must reimagine how they deliver value—and sometimes who they serve.
• Make hard trade-offs. Strategy is about focus. Leaders must say no to legacy businesses or behaviours that don’t support the transformation.

Henrik Poulsen led Ørsted’s exit from fossil fuels, reorienting the business around offshore wind. The strategy was clear: become a global leader in renewable energy. This meant divesting legacy assets and doubling down on future growth, even when it meant short-term pain.

Peter Ma led Ping An’s transformation from a traditional insurer into a tech-powered ecosystem. He strategically invested in AI, blockchain, and platforms like Good Doctor and Lufax—turning disruption into opportunity.

3. Mobilise the organisation and lead cultural change

No transformation succeeds without people. Leaders must create the conditions—mindsets, structures, and behaviours—for the organisation to embrace the future.

• Model the new mindset. Leaders must embody the values and behaviours they expect. If they want curiosity, agility, and collaboration, they must live them daily.
• Flatten hierarchy and empower teams. Organisational agility is critical. Leaders should enable cross-functional teams, decentralised decision-making, and rapid learning.
• Create psychological safety. Transformation involves risk and failure. Leaders must build trust, reward experimentation, and make it safe to speak up.

Shigetaka Komori led a deep cultural shift at Fujifilm, from a traditional, hierarchical firm to an entrepreneurial, innovation-led company. He protected R&D budgets during crisis and empowered teams to explore healthcare and materials science as new business areas.

Piyush Gupta transformed DBS Bank into “a tech company in banking.” He broke silos, invested in digital skills, and created an innovation culture that permeated all levels. DBS now regularly ranks among the world’s most innovative banks.

4. Align Systems, capabilities, and resources

Once strategy is defined and culture is shifting, leaders must align the business system to support the new model. This means rewiring the organisation for execution.

• Build future-ready capabilities. Leaders need to invest in the skills, technologies, and partnerships/ecosystems that the new strategy requires—whether it’s AI, sustainability, digital, or data.
• Redesign metrics and incentives. People do what they’re measured and rewarded for. Leaders must update KPIs, dashboards, and incentives to reflect transformation goals.
• Fund the future. Capital allocation is critical. Leaders must direct investment toward the bold bets—while responsibly managing legacy operations.

Jean-Pascal Tricoire embedded sustainability into the Schneider Electric’s performance metrics, linking executive pay to ESG outcomes. He built capabilities in digital energy and IoT, aligning strategy, talent, and structure to make sustainability a core growth engine.

Shantanu Narayen led Adobe’s shift from boxed software to cloud subscriptions. This meant new pricing, capabilities, and go-to-market models. The company retrained teams, revamped platforms, and restructured to deliver recurring customer value.

5. Sustain momentum and communicate relentlessly

Transformation isn’t a single initiative—it’s a multi-year journey. Leaders must maintain belief, energy, and accountability through ambiguity and change.

• Celebrate progress. Leaders must share quick wins, milestones, and success stories to maintain momentum, the belief and desire for change, and deliver progress.
• Communicate consistently. A compelling transformation story should be repeated often—internally and externally—so everyone understands the vision, their role, and the journey ahead.
• Evolve as the future unfolds. Leaders must stay adaptive, revisiting assumptions and updating direction as conditions change. Transformation never really ends.

Paul Polman made sustainability the centre of Unilever’s strategy and communicated it constantly—with investors, employees, and customers. He built long-term credibility and trust by showing how purpose and profit could reinforce each other.

John Iossifidis sustained momentum at Al Ghurair by driving transformation progressively at all levels, built around a strong sense of shared, meaningful purpose and vision, then empowering business units and managers to innovate and transform.

Relentless reinvention, the engine of value creation

Here are some compelling examples of the performance impact of business transformation, showing how companies that reimagined their purpose, strategy, and organisation achieved substantial profit growth, market value gains, and long-term resilience—often from entirely new business areas.

• Fujifilm‘s operating profit from new business segments grew by over 400% from 2010 to 2023. Market cap rose from around ¥1.5 trillion in 2010 to almost ¥6.5 trillion in 2024, a 4x growth in value creation.
• Microsoft‘s cloud revenues grew from $6.3B in 2015 to over $117B in 2024 (Azure, Office 365, Dynamics). Market cap rose from $340B in 2014 to over $3 trillion by 2024, which is a 780% increase.
• Schneider Electric‘s doftware and services now contribute around 50% of revenues and growing. Share price increased from €60 in 2015 to over €220 in 2024, reflecting nearly 4x market cap growth.
• PingAn’s tech platform revenue grew to 15% of total group revenue, from almost zero in 2013. Group market cap rose from $60B in 2010 to over $200B by 2021. Return on equity consistently above 18–20%.
• DSM‘s EBITDA margins improved by ~400 basis points over 10 years. Share price rose from €40 in 2010 to €160+ by 2022 representing 4x growth in market value.
• Unilever‘s “Sustainable Living Brands” grew 69% faster than the rest of the portfolio and delivered 75% of growth (2019). Market cap increased from £70B in 2010 to over £110B+ by 2024, despite market volatility.

Transformation is not a one-off change, but a journey over many years of many change projects, and ultimately becomes an ongoing capability. In an era of rapid disruption and rising stakeholder expectations, companies must continuously reinvent themselves to stay relevant and create value.

The leaders who succeed have their heads up not heads down – not just managing their companies, but shaping their futures.

The most innovative businesses today are reimagining what it means to create, deliver, and capture value.

Moving far beyond traditional “new” models – like auctions and exchanges, licensing and subscriptions – a next generation of business models is emerging — powered by AI, new technologies like blockchain and robotics, ecosystems, unlocking intangible assets, and catalysed by urgent environmental and social challenges.

Across every sector, these radical business models are reshaping how companies grow and compete. What defines them is not just their use of technology — but their redefinition of value itself. They:

• Shift from ownership to access, or from access to participation

• Replace centralised control with decentralised collaboration

• Convert data, trust, and relationships into monetisable assets

• Build value systems where profit and purpose reinforce each other

The future of business isn’t just digital. It’s distributed, decentralised, and deeply human in its impact.

These models are the blueprint for the next generation of business — intelligent, inclusive, and built around the intangible assets that now drive competitive advantage. In a world where code, creativity, and climate matter more than capital, these pioneers are not just changing business. They’re reinventing it.

Here’s a global look at some of the most radical business models reshaping industries today.

1. Brand-led ecosystems and cultural capital models

Examples: Patagonia (US), On Running (Switzerland), Glossier (US)

Certain brands today are building entire ecosystems around cultural identity, shared purpose, and community contribution. Patagonia’s transformation into a non-profit trust reshaped its business into an engine for climate action. Glossier used its loyal fanbase to co-create products and fuel word-of-mouth growth. On Running leverages its elite athlete endorsements and sustainability credentials to grow a purpose-led premium community.

Here, the brand itself becomes the platform — an intangible asset that compounds over time through trust, activism, and user collaboration.

• These businesses monetise cultural alignment, not just products. Brands capture ideas and propositions that are much more than technology. Their model grows stronger the more people believe in it.

2. Data-native platforms and relationship-driven ecosystems

Examples: Tesla (US), Bytedance (China), Ping An (China)

Some companies have made data their central business model. Tesla doesn’t just sell cars; it continuously collects driving data to improve its autonomous systems and energy products. Ping An, a Chinese financial services group, builds interconnected services — from health to banking to insurance — around deep customer data and long-term trust.

In these models, relationships — often invisible — become the key strategic asset. The more a business knows about its users, the more predictive and sticky its offerings become.

• Data becomes the flywheel. The more it’s used, the more value it creates — forming a self-reinforcing advantage that’s hard to replicate.

3. Regenerative and circular value models

Examples: Notpla (UK), Bext360 (Africa/US), TerraCycle/Loop (US)

These models embed sustainability and circularity into the business logic itself. Notpla, a London startup, makes biodegradable packaging from seaweed. Bext360 uses blockchain and AI to trace every coffee bean back to its origin, rewarding sustainable farming practices. Loop, developed by TerraCycle, builds reusable packaging systems for brands like Nestlé and Unilever.

Here, transparency, traceability, and customer trust become key intangible assets. The brand value is inseparable from environmental impact and social equity.

• Consumers increasingly demand climate-positive action — not just neutrality. These businesses are turning sustainability into a competitive advantage, not a compliance burden.

4. AI-native autonomous enterprises

Examples: Lindy.ai (US), Cognosys (Global), Hypertype (EU)

These businesses don’t just use AI — they are built around it. Entire workflows, decision-making structures, and customer interactions are being run by AI agents and autonomous systems. Lindy.ai, for instance, creates a personalised “chief of staff” AI that handles scheduling, communication, and task prioritisation. As these systems mature, companies may no longer need large administrative layers.

These models rely heavily on intangible assets — particularly proprietary data, user behaviour insights, and relationship history — to continuously learn and personalise services at scale.

• These businesses radically lower operational costs and redefine scale. A team of five people with the right AI stack can now rival the output of a 100-person company

5. DAOs: decentralized autonomous organisations

Examples: KlimaDAO (Global), CabinDAO (US), BitDAO (Singapore)

DAOs replace traditional corporate hierarchies with decentralized governance, where decisions are made by token-holders and enforced by smart contracts. KlimaDAO, for example, is building a blockchain-based economy around carbon credits. CabinDAO is creating a global co-living network governed by its digital community.

The value lies in community trust, governance transparency, and open-source code — intangible assets that replace boardrooms with consensus mechanisms.

• DAOs challenge the fundamental idea of top-down control, instead enabling collective ownership and direction — a model better suited to borderless, digital-native organizations

6. Tokenized ownership and micro-incentives

Examples: Rally.io (US), RealT (US), Sweat Economy (UK)

Tokenization enables companies to convert physical or digital value into fractional, tradeable ownership. RealT tokenizes real estate properties, allowing individuals to buy shares and receive rental income in crypto. Sweat Economy rewards people for physical activity with tokens that can be traded or spent. Rally.io enables creators to launch their own branded tokens, giving fans both access and investment-like upside.

Here, brands and communities are transformed into economic ecosystems where participation equals ownership — and engagement drives value.

• These models offer new ways to align incentives and unlock value that used to be passive or inaccessible.

7. Emotionally Intelligent AI Companions

Examples: Character.ai (US), Replika (US), Soul Machines (NZ)

A new category of business is emerging at the intersection of AI, emotion, and digital identity. Character.ai lets users build AI personalities that can engage in realistic conversations. Soul Machines develops digital humans for customer service, healthcare, and education — capable of reading and responding to emotions.

The core value is in relationships and trust — ephemeral yet deeply powerful assets. Users don’t just use these services; they form bonds with them.

• These models shift business from transactional to relational. You’re not just selling a service — you’re co-creating an emotional experience.

8. Embedded climate accounting and ethical nudging

Examples: Doconomy (Sweden), Earthchain (UK), Klarna’s carbon insights (Sweden)

These businesses integrate carbon tracking and impact measurement into daily spending. Doconomy’s credit card tracks users’ carbon footprints and can even block purchases beyond a set CO2 limit. Earthchain works with retailers to embed product-level climate impact at the point of sale.

Such models rely on consumer consciousness and ethical engagement — new forms of value that turn impact awareness into competitive edge.

• These models turn climate literacy into action, and offer companies a new lever for differentiation beyond price and product.

Reinventing the concept of business

These radical new business models are reshaping how companies grow and compete. However organisations have been reinventing their organisations for quite some time – a concept that became very popular with the advent of online platforms like Airbnb or Uber. Everyone wanted to reinvent their business as a platform, an exchange, or subscription.

So while many of these forms of business models are not new, its worth taking a few steps back to remind ourselves about what is a business model, what are the diverse options available, and how to we build them and transition from old to new.

The evolution of business models

Historically, businesses were built on simple models:

• Product sales (e.g., Ford selling cars),

• Service models (e.g., legal firms billing by the hour), or

• Retail and distribution (e.g., department stores buying wholesale and selling at a markup).

These models were linear and predictable — until they weren’t.

The digital revolution triggered a seismic shift. The rise of the internet in the 1990s enabled new intermediaries, such as eBay’s auction marketplace, and Amazon’s online retail model. In the 2000s, platforms and apps introduced further models based on ecosystems and data. Then came mobile, social media, and cloud computing — which reduced the costs of reaching customers and operating at scale.

Today, companies no longer compete only on products or services, but on the architecture of their business model. A superior product with an inferior model may lose. A simple idea with a breakthrough model can win big.

The expanding universe of business models

Over time, a wide range of business models has emerged. Some are classic, others born of digital transformation. Many combine multiple components. Here’s a snapshot of key types:

1. Product Sales

• The most traditional model: make something, sell it for more than it cost.

• Example: Toyota, Zara — selling cars or fashion with volume and margin.

2. Licensing

• Charging others to use intellectual property.

• Example: ARM Holdings licenses its chip designs to tech manufacturers around the world.

3. Razor and Blades

• Sell one item cheap (the razor), make money on the consumables (the blades).

• Example: HP printers and ink, or Nespresso and coffee pods.

4. Subscription

• Recurring revenue in exchange for ongoing access.

• Example: Netflix, Spotify, Adobe Creative Cloud.

5. Freemium

• Offer a free basic version; charge for premium features.

• Example: Dropbox, Zoom, and Notion — converting a small % of free users to paid ones.

6. Marketplace or Platform

• Connect buyers and sellers, take a cut.

• Example: Uber, Airbnb, Alibaba. These rely on building trust, liquidity, and network effects.

7. Franchise

• License the brand and model to independent operators.

• Example: McDonald’s, Anytime Fitness, or Marriott Hotels.

8. Auction and Exchange

• Dynamic pricing based on demand.

• Example: eBay, or StockX in the sneaker and fashion resale market.

9. Data Monetization

• Offer free services, and monetize user data or insights.

• Example: Google and Facebook generate vast revenues through targeted advertising.

10. Usage-Based or Pay-As-You-Go

• Charge only for what’s used — ideal for cloud computing or utilities.

• Example: AWS (Amazon Web Services), Twilio, Zipcar.

11. Crowdsourcing and Crowdfunding

• Leverage communities to build or fund products.

• Example: Kickstarter, GoFundMe, or Threadless for design.

12. Direct-to-Consumer (D2C)

• Bypass traditional retail to own customer relationships and data.

• Example: Warby Parker, Allbirds, or India’s boAt electronics.

13. Circular and Sharing Economy

• Based on reuse or shared access instead of ownership.

• Example: ThredUp, Fat Llama, or Lime scooters.

14. Ecosystem-Based

• Creating a constellation of products, services, and partners that reinforce each other.

• Example: Apple’s walled garden, or Tencent’s super app WeChat in China.

Examples of global business model innovation

• Haier evolved from a traditional appliance manufacturer into a platform of micro-enterprises. Each business unit within Haier operates like a startup — incentivized to serve customers directly, with shared services in the background. The result: agility, innovation, and resilience.
• Unilever in emerging markets uses a reverse logistics model for its “Shakti” initiative in India — empowering rural women as micro-entrepreneurs who sell hygiene products locally and collect packaging for reuse.
• Tesla’s model integrates software, hardware, and energy services — combining direct-to-consumer sales, over-the-air updates, energy storage, and AI-based autonomous driving. Its value is no longer just in vehicles but in data, brand, and energy platforms.
• Shopify enabled a new kind of distributed entrepreneurship — with tools, templates, and infrastructure that allowed anyone to become an online retailer. Its model grows by helping others grow.

How to build a new business model

Creating a new business model starts with rethinking the assumptions of your current one. Key questions include:

• Who is our customer — and what job are they hiring us to do?

• What unique value can we offer — and how can we deliver it differently?

• What relationships, data, or brand assets can we leverage?

• How can technology reshape the cost or experience of delivery?

• Where is the money coming from — and is that the only way?

One powerful tool is the Business Model Canvas — a simple framework that breaks a business into nine components: customer segments, value propositions, channels, customer relationships, revenue streams, key activities, key resources, key partners, and cost structure.

To innovate, test different combinations:

• Could we shift from selling to subscription?

• Could we serve new customer segments through platforms?

• Could we turn data into a new product or insight engine?

• Could we create new value by collaborating with unlikely partners?

Transitioning from old to new models

Transitioning to a new business model is one of the hardest — and most critical — leadership challenges. It means letting go of legacy assumptions, revenue streams, and ways of working. The shift must often be staged and carefully managed.

Here are key steps:

1. Start small: Pilot the new model in one segment, geography, or customer base.

2. Learn fast: Measure what works, adapt quickly, and scale only what proves value.

3. Align incentives: Ensure internal teams are rewarded for embracing new models, not protecting old ones.

4. Communicate clearly: Customers, investors, and employees need to understand why the change matters.

5. Keep both engines running: During transition, companies may need to balance the legacy model (“Engine 1”) and the new model (“Engine 2”) — as explained in O’Reilly and Tushman’s ambidextrous organization framework.

Jump to the future

We now see even more radical models emerge — powered by AI, blockchain, sustainability pressures, and new social expectations. Business models will become more fluid, adaptive, and ecosystem-based. Value will increasingly flow from intangible assets like trust, brand, relationships, and data.

The ability to design, test, and evolve business models will be the most important skill of future-ready leaders. Because in a world of change, your business model — not just your product — will decide whether you win or vanish.

Business model innovation is no longer a luxury. It’s a necessity. Whether you’re a startup or a century-old corporation, your future depends on your ability to imagine new ways to create and capture value — and to let go of the past to embrace what comes next.

Technology is no longer a background enabler of business strategy; it is core to the strategy. Each year, the World Economic Forum identifies the most promising breakthroughs set to reshape markets, industries, and society. The Top 10 Emerging Technologies of 2025 is more than a list of inventions—it’s a roadmap to where value will be created, disrupted, and redefined.

For business leaders, the implications are immediate. These technologies—from AI that reasons like a human to sustainable materials that unlock circular economies—are not distant possibilities. They are investment priorities, talent magnets, and innovation accelerators already moving from lab to market. They will change customer expectations, supply chains, and competitive dynamics.

Understanding these shifts is essential for building resilience and seizing opportunity. The leaders who engage early—experimenting, partnering, and scaling—will not only navigate disruption but set the pace of progress in their industries.

Here are 10 emerging technologies that have the potential to reshape industries, redefine healthcare, and accelerate the transition to a more sustainable and intelligent world.

• Structural Battery Composites – Batteries integrated into the structure of vehicles or aircraft.
• Osmotic Power (Blue Energy) – Harvesting clean energy from the salt gradient between seawater and freshwater.
• Advanced Nuclear Technologies – Safer, simpler, and more cost-effective reactors.
• Engineered Living Therapeutics – Microbes programmed to produce drugs inside the body.
• GLP-1s for Neurodegenerative Disease – Repurposing weight loss drugs to tackle Alzheimer’s and Parkinson’s.
• Autonomous Biochemical Sensors – Real-time monitoring of health markers.
• Green Nitrogen Fixation – Sustainable ammonia production for fertilizers.
• Nanozymes – Synthetic nanomaterials mimicking enzymes for healthcare and industry.
• Collaborative Sensing – Networks of distributed sensors powered by AI.
• Generative Watermarking – Tracing the origins of AI content for trust and transparency.

Deep dives into each tech

Structural Battery Composites (SBCs)

What they are. Structural battery composites integrate the functions of a rigid, load-bearing material and a rechargeable battery in the same component. Instead of packing heavy cells inside a passive casing, the “case” itself becomes the battery—e.g., portions of an EV’s body, a drone’s wing spar, or even building panels doubling as energy storage.

Why it matters. Weight and volume are the enemies of electrification. SBCs promise large system-level gains: lighter vehicles (longer range with the same pack or same range with smaller packs), simplified architectures, fewer parts, and potentially lower costs and lifecycle emissions. In aerospace, where every kilogram counts, embedding storage into primary structures could unlock longer endurance for e-aviation and UAVs. In the built environment, SBCs hint at walls and facades that store and smooth on-site renewable electricity.

How it works. SBCs typically combine carbon-fiber reinforcement (mechanical strength) with solid-state or polymer electrolytes and engineered interfaces that can carry both ionic and structural loads. The technical challenge is balancing electrochemical performance (capacity, cycle life, safety) with mechanical properties (stiffness, toughness, crash behavior) in a single laminate or sandwich structure.

Trajectory and hurdles. Early prototypes exist, but scaling means qualifying entirely new multifunctional materials for safety-critical use. Key issues include damage tolerance and repairability, thermal management, fire safety, end-of-life recycling, and the need for new standards and test protocols (you’re crash-testing the “battery” as part of the chassis). Supply chains must adapt for co-manufacturing energy and structural layers at high yield. Still, the Forum frames SBCs as an inflection point for materials-energy convergence with cross-sector impact over the coming decade.

Bottom line. If validated at scale, SBCs could rewrite design rules for EVs, aircraft, marine craft, and buildings—collapsing separate subsystems into lightweight, power-storing structures and pushing electrification into domains where today’s batteries struggle.

Collaborative Sensing

What it is. Collaborative sensing links many disparate sensors—cameras, LiDAR, smartphones, vehicles, traffic lights, weather stations—into a network that fuses data in real time. AI sits atop this mesh to extract context and coordinate actions across domains such as mobility, emergency response, and environmental monitoring.

Why it matters. A single sensor has limited field-of-view and reliability; thousands working together create resilience, coverage, and richer situational awareness. Think traffic lights that dynamically retime based on live road, air-quality, and transit feeds; cars that “see” around corners using other vehicles’ sensors; mine sites and ports that coordinate machinery more safely; cities that detect floods or wildfire smoke early and route resources accordingly.

Enablers and challenges. Advances in edge computing, low-latency networks, and multimodal AI enable on-the-fly sensor fusion. The hard parts are governance and trust: privacy, security, data ownership, consent, and bias in models that make operational decisions. Powering massive sensor fleets, creating interoperable standards, and ensuring cyber-resilience are also essential. The WEF emphasizes that trust and safety frameworks must grow alongside deployments.

Outlook. As infrastructure digitizes, collaborative sensing becomes a backbone for smart cities and autonomy. Expect early value in controlled environments (industrial sites, campuses) and progressive rollouts in transport and public safety—where benefits can be measured in minutes saved and lives protected.

Green Nitrogen Fixation

What it is. Ammonia is indispensable for fertilizer, underpinning roughly half of global food production. Today it’s made mainly via Haber-Bosch, a high-temperature, high-pressure process fueled by fossil hydrogen—responsible for about 2% of global energy use and significant CO₂ emissions. “Green” nitrogen fixation swaps in low-carbon pathways: electrolytic or photochemical routes, bio-engineered microbes and enzymes, or hybrid systems that leverage renewable electricity.

Why it matters. Decarbonizing ammonia tackles a major industrial emitter while making food systems less exposed to gas price shocks. It also enables on-site or distributed production near farms, potentially reducing transport emissions and improving access in emerging markets.

State of play. The report notes momentum from lab to pilot scale across electrochemical nitrogen reduction, plasma catalysis, photocatalysis, and biocatalytic approaches, though achieving energy efficiency, selectivity (suppressing hydrogen evolution), and durability at scale remains tough. Systems engineering—coupling intermittent renewables to continuous production, storage, and safety—will determine competitiveness against incumbent Haber-Bosch.

Risks and policy. Certification of “green ammonia,” clear carbon accounting, and incentives (e.g., for green H₂) can accelerate adoption. Bio-based routes raise biosafety and containment questions; any distributed production must meet stringent safety standards given ammonia’s hazards.

Outlook. With fertilizer demand steady and shipping eyeing ammonia as a zero-carbon fuel, green fixation could influence both agriculture and maritime decarbonization if cost and performance targets are met.

Nanozymes

What they are. Nanozymes are engineered nanomaterials that mimic natural enzymes’ catalytic activity while often being more stable, cheaper, and easier to manufacture at scale. They can catalyze redox reactions, peroxidase-like activity, and more—opening use in biosensing, diagnostics, environmental remediation, and targeted therapies.

Why it matters. Natural enzymes are powerful but fragile (temperature, pH, proteolysis) and costly to produce. Nanozymes tolerate harsher conditions, can be tailored for activity and targeting, and integrate with electronics for readouts. Potential wins: faster, rugged point-of-care tests; catalytic filters that break down pollutants; oncology or neurodegeneration interventions where nanozymes activate drugs or scavenge reactive species.

Evidence and momentum. The WEF synthesis highlights accelerating research and early commercialization, including companies pursuing nanozyme-enabled diagnostics and therapeutics. Market forecasts anticipate rapid growth as materials and selectivity improve, though clinical use will demand rigorous safety and biodistribution data, plus regulatory clarity for nanomedicine.

Caveats. Key challenges are specificity (avoiding off-target reactions), biocompatibility and clearance, and standardized assays to compare catalytic performance with native enzymes. For environmental uses, lifecycle and ecotoxicity assessments are essential.

Outlook. By combining catalysis, materials science, and device integration, nanozymes could become a general-purpose tool for robust sensing and intervention in messy, real-world settings where proteins struggle.

Engineered Living Therapeutics

What they are. “Living medicines” use engineered microbes or cells that reside in the body and manufacture therapeutic molecules on demand—think programmable probiotics that sense inflammation and release anti-inflammatory factors, or bacteria that convert toxic metabolites into harmless products.

Why it matters. Conventional drugs are static; living systems can adapt, persist, and target niches (gut, skin, tumor microenvironments). That raises the prospect of long-acting, responsive therapies for chronic disease, metabolic disorders, infections, and even cancer, potentially with lower costs and improved adherence.

State of play. Synthetic biology, chassis optimization, and AI-assisted design are converging to improve safety circuits (kill switches, auxotrophy), delivery, and manufacturability. Early trials and regulated probiotics offer learnings, but regulatory pathways must evolve to assess genetically modified organisms living inside patients. Post-market monitoring, horizontal gene transfer risks, and environmental containment for excreted organisms are active considerations.

Outlook. The Forum frames engineered living therapeutics as a meaningful step toward adaptive medicine—if safety engineering and governance keep pace with ingenuity. Expect initial approvals in tightly controlled indications and delivery sites, then expansion as toolkits mature.

GLP-1s for Neurodegenerative Disease

What this means. GLP-1 receptor agonists (GLP-1 RAs)—best known for type 2 diabetes and obesity—are being investigated for Alzheimer’s and Parkinson’s. Mechanistically, they cross the blood-brain barrier and may reduce neuroinflammation, improve insulin signaling, enhance cellular energy management, and support clearance of toxic proteins.

Why it matters. Neurodegenerative diseases impose huge human and economic burdens, with limited disease-modifying options. If GLP-1s slow progression, even modest effects could extend independent living and reduce care costs. The report notes mixed early clinical results but enough signals to justify larger, rigorous trials now under way. Affordability and supply constraints remain salient.

Risks and realities. Translating metabolic benefits to neuroprotection is non-trivial; dosing, duration, and patient selection matter. Frailty and unintended weight loss in older patients require careful oversight. Policymakers may need new reimbursement models if upfront drug costs offset long-term care savings.

Outlook. Repurposing a mature drug class accelerates timelines. If efficacy is proven, GLP-1s could shift neurology from symptom management to disease modification and spur combination regimens with other agents (e.g., GIP co-agonists). W

Autonomous Biochemical Sensing

What it is. Analytical devices that continuously detect and quantify biochemical markers with minimal human intervention—wired for real-time data capture and often self-powered. Beyond glucose monitors, think wearables for inflammatory cytokines, continuous hormone tracking, microbial “whole-cell” biosensors that report pollutants or pathogens, and environmental nodes that surveil soil and water chemistry.

Why it matters. Continuous data can move systems from reactive to preventive: early infection warnings, real-time food safety validation, leak/spill detection before public exposure, and adaptive agriculture. During crises, a distributed biochemical “nervous system” can inform faster, targeted responses.

Hurdles. Sensors must balance sensitivity with longevity in messy environments; fouling and biofilms degrade performance. Engineered microbial sensors raise biosafety and permitting issues. Then there’s data governance: privacy for health signals, secure telemetry, and analytics that avoid false alarms.

Outlook. Expect early traction in industrial and regulated settings where ROI is clear (pharma, food processing, water utilities), followed by consumer health niches. As materials and wireless power advance, biochemical sensing will become a standard layer of digital infrastructure.

Osmotic Power Systems

What they are. Osmotic (salinity-gradient) power harvests energy when fresh and salty water mix, typically across a selective membrane that drives ion flow and electricity generation. Advances in membranes and system design have revived a previously niche concept, with pilot plants exploring coastal and estuarine deployments.

Why it matters. It’s clean, steady (not intermittent like wind/solar), and colocates with abundant coastal water flows. Integrating osmotic systems with desalination or wastewater treatment may share infrastructure and enable resource recovery (e.g., lithium from brines), improving project economics. The report cites research momentum and early investment signals.

Challenges. Membrane cost, durability (fouling, scaling), and energy density are central. Environmental permitting must address impacts on estuarine ecosystems and salinity plumes. Grid integration and hybridization with other renewables can improve capacity factors but add complexity.

Outlook. If new membranes and modular designs hit cost targets, osmotic power could complement baseload-seeking grids and provide resilient coastal microgrids—especially valuable as electrification and water stress rise.

Advanced Nuclear Technologies

What they are. A new wave of nuclear systems—such as small modular reactors (SMRs), alternative coolants, advanced fuels, and passive safety designs—aims to deliver firm, low-carbon power with improved safety, cost, and siting flexibility. These can serve grids, industrial heat, or hydrogen production.

Why it matters. Electrification (EVs, heat pumps, AI data centers) is driving demand for reliable clean power. Advanced nuclear offers high capacity factors with small land footprints and potential load-following. Modularization and factory fabrication promise better cost control than bespoke gigawatt plants.

Issues to solve. Licensing pathways, financing models, spent-fuel strategies, and public acceptance remain pivotal. Supply chains for advanced fuels (e.g., HALEU) and skilled labor must scale. Integration with district heat, desalination, or industrial processes can boost value but requires tailored regulation.

Outlook. The Forum highlights advanced nuclear as a repurposing of established tech for new demands—poised to help decarbonize grids if projects demonstrate schedule and budget discipline. Early deployments at industrial sites and remote locations are likely beachheads.

Generative Watermarking

What it is. Invisible (or machine-readable) markers embedded in AI-generated content—images, audio, video, and potentially text—to signal provenance and enable authentication at scale. Watermarks may be cryptographic, signal-processing-based, or standardized metadata, designed to persist through common transformations.

Why it matters. As synthetic media proliferates, societies need infrastructure to discern origin and maintain trust—especially during elections, disasters, or markets-moving events. Watermarking complements other provenance approaches (e.g., content credentials) and can help platforms, newsrooms, and courts assess authenticity. The Forum elevates this as a key trust-and-safety technology for the networked world.

Limits and governance. Determined adversaries may attempt removal or spoofing; standards and broad adoption are critical. Watermarks raise questions around privacy, interoperability, open vs. proprietary schemes, and how to treat non-watermarked content. The WEF flags the need for shared norms and technical collaboration to prevent fragmentation and evasion.

Outlook. Expect rapid maturation as major model providers and platforms adopt watermarking and provenance stacks, paired with detection and disclosure policies. It’s not a silver bullet against misinformation, but as part of a layered authenticity ecosystem, it’s a pragmatic step toward healthier information markets.

Themes that stand out

• Convergence of biology and technology: From programmable microbes to wearable health sensors, biotech is becoming deeply integrated into our daily lives.
• Energy reinvention: Clean, efficient power—from blue energy to structural batteries and next-gen nuclear—is reshaping the global energy mix.
• Healthcare reimagined: Therapies are becoming more personalized, real-time, and even self-generating inside the body.
• AI and accountability: As generative AI scales, watermarking and collaborative sensing show that transparency and ethics are rising priorities.
• Sustainability through innovation: Green nitrogen and osmotic energy represent a broader shift toward technologies that work with nature, not against it.

These breakthroughs remind us that technology is not just about speed or scale, it’s about direction. And the direction we choose matters.

We live in a culture obsessed with youth. Silicon Valley entrepreneurs in their twenties are hailed as the ultimate visionaries, Olympic athletes in their thirties are considered veterans, and companies often prize “fresh thinking” as if it is exclusively the province of the young. The unspoken assumption is that mental sharpness, creativity, and problem-solving peak early in life and then decline in an inevitable slide.

But what if that assumption is wrong? What if, in fact, the human mind is designed to reach its peak not in the frantic energy of youth but in the reflective depth of maturity? New research from Dr. Gilles Gignac at the University of Western Australia has quantified what many have sensed: that overall brain performance — when you blend knowledge, judgment, perspective, and emotional balance — does not peak until around 60 years old.

This finding challenges more than just stereotypes. It suggests that the very arc of human development is different than we thought. And it invites a powerful question: if our brain is capable of reaching new heights later in life, how do we train ourselves — mind and body — to ensure we actually get there? And what does it mean for how we live, work, and organise society?

The cult of youth, the virtue of age

Walk into any gym and you’ll find posters of athletes in their twenties, physiques at their physical prime. Open the business pages and you’ll see stories of young disruptors breaking rules and changing industries. The message is everywhere: the best years are early years.

It is true that certain mental functions — particularly those linked to raw processing speed, memory recall, or reaction times — are sharper in youth. Psychologists call this fluid intelligence, and like sprinting ability, it tends to peak earlier.

But the human brain is not built only for speed. Over decades, it accumulates knowledge, builds patterns of recognition, refines judgment, and develops what researchers call crystallized intelligence. This is the wisdom that lets an experienced investor spot market shifts, a seasoned doctor diagnose complex conditions, or a grandparent know just when to step in with the right advice. It’s why Warren Buffett made the bulk of his fortune after fifty. It’s why Nelson Mandela, released from prison at 71, became one of history’s most revered leaders.

I’m not saying all this because I’m 60 (well, I am in my 50s!), but society assumes that it’s all downhill from your 40s. Wrong, there is so much more to give.

Why the brain peaks at 60

So why does peak performance arrive so late? Neuroscience and psychology give us several clues:

• Accumulated Knowledge
  Decades of learning, working, reading, and engaging with the world build a vast library of information. At 60, the brain is a well-stocked archive.
• Pattern Recognition
  Experience wires the brain to recognize patterns more quickly. A chess master in their sixties may not move as fast as a teenager, but their intuition about which move to make is far more accurate.
• Emotional Regulation
  Studies show older adults are generally better at managing stress, emotions, and interpersonal dynamics. They bring calm and perspective where younger people may react impulsively.
• Judgment and Wisdom
  Decisions aren’t made only on facts — they’re shaped by values, trade-offs, and long-term consequences. Older adults draw on decades of judgment.
• Neuroplasticity Continues
  Once thought to stop in youth, we now know the brain continues to rewire itself throughout life. Learning new skills, practicing mindfulness, or exercising can stimulate fresh neural connections even at 70 or beyond.

In short, youth gives us raw horsepower, but age gives us the steering wheel. At 60, the two meet in a unique balance.

Training your brain for its peak

If our best mental years can arrive later, the question becomes: how do we make sure we actually reach them? Just as an athlete trains their body, we can train our brain — and the two, in fact, are inseparable. Here’s how:

1.  Keep Learning, Always

The brain is like a muscle: it grows with use. People who continue to learn languages, study history, explore new technologies, or take up musical instruments maintain cognitive agility far longer. It is no coincidence that some of the most innovative entrepreneurs, like Reid Hoffman (LinkedIn) or Arianna Huffington, are perpetual learners who reinvent themselves midlife.

2.  Exercise the Body to Exercise the Brain

Physical activity is one of the most powerful brain enhancers. Aerobic exercise boosts blood flow, delivers oxygen, and stimulates growth factors that promote neuroplasticity. Walking, running, swimming, or yoga can all keep neural pathways sharp.

3.  Sleep, Rest, Recover

Sleep isn’t downtime — it’s brain training time. Memory consolidates, synapses reset, and creative insights emerge in dreams. Consistent, quality rest is an underrated tool for long-term mental performance.

4.  Mindfulness and Meditation

Mindfulness practice thickens areas of the brain linked to attention and reduces stress hormones that impair memory. Leaders from Steve Jobs to Ray Dalio have credited meditation with sharper judgment.

5.  Social Connection

Loneliness accelerates cognitive decline, while strong relationships stimulate emotional and intellectual engagement. Conversations challenge the brain in ways puzzles cannot. In fact, research shows that regular social interaction is as critical to longevity as not smoking.

6.  Purpose and Curiosity

The most important driver may be purpose. People who believe they still have meaningful work, relationships, or contributions to make continue to stretch their abilities. Think of Jane Goodall, still traveling and advocating for the planet at nearly 90. Purpose keeps the brain alive.

Examples of Genius at 60

History is full of people who demonstrate the late flowering of human potential.

• Leonardo da Vinci – Painted Mona Lisa in his early 60s.

• Claude Monet – Painted the Water Lilies series in his 60s.

• Mark Twain – Wrote The Adventures of Huckleberry Finn in his 60s.

• Laura Ingalls Wilder – Published Little House on the Prairie series in her 60s.

• Frank McCourt – Published Angela’s Ashes at 66.

• Charles Darwin – Published On the Origin of Species at 50, continued groundbreaking work into his 60s.

• Dmitri Mendeleev – Refined the periodic table in his later 50s–60s.

• Benjamin Franklin – Invented bifocals and engaged in diplomacy in his 60s.

• Peter Higgs – Developed the Higgs boson theory in his 60s, recognized with Nobel Prize later.

• Ray Kroc – Built McDonald’s into a global empire in his 50s–60s.

• Colonel Sanders – Franchised KFC at 65.

• Vera Wang – Entered fashion design at 40, but global influence peaked in her 60s.

• Warren Buffett – While active for decades, his most transformational acquisitions (Berkshire Hathaway conglomerate expansion, Coca-Cola investment) occurred in his 60s–70s.

• Charlie Munger – Partnered with Buffett, peak influence in investment strategy well into 60s and 70s.

• George Soros – Quantum Fund major bets and philanthropic strategy peaked in his 60s.

• Walt Disney – Launched Disneyland at 60 (opened 1955 when he was 53, continued innovating into 60s with new projects).

• Amancio Ortega (Zara/Inditex) – Major global expansion of Inditex occurred in his late 50s and 60s.

• Henry Ford – Continued to innovate (Model T, production methods, Ford Foundation) into his 60s.

• John Pemberton (Coca-Cola) – Commercialized Coke, with major business growth post-50s.

• David Rockefeller – Built Rockefeller family banking and philanthropic influence well into his 60s and 70s.

• Bernard Marcus & Arthur Blank (Home Depot) – Launched Home Depot in their 50s; expansion continued aggressively into their 60s.

• Peter Thiel – While an outlier in youth entrepreneurship, his major strategic influence at PayPal, Palantir, and Founders Fund peaked in his 50s–60s.

Closer to home, think of the colleague who becomes the “go-to” problem solver not because they are the fastest, but because they’ve “seen it before” and know what matters. Or the mentor whose one sentence reframes a dilemma you’ve wrestled with for weeks. That is the power of brains that have ripened, not just raced.

What it means for business

If people truly peak later than we thought, businesses need to rethink how they value, deploy, and develop talent.

1.  Rethinking Retirement

Mandatory retirement at 60 or 65 may cut people off just as they reach their prime. Imagine if Warren Buffett had been forced out at 60. Companies may need new models that blend senior talent with emerging leaders.

2.  Multi-Generational Teams

The best organizations will harness both youthful speed and mature wisdom. Young staff may drive experimentation; older colleagues provide context and judgment. Together they are stronger.

3.  Training and Reskilling Across Life

Education shouldn’t stop in our twenties. Businesses that invest in mid-career and late-career learning will unleash new levels of innovation.

4.  Leadership Development

Boards should reconsider the bias toward younger CEOs. Some of the most effective leaders — Satya Nadella at Microsoft, Christine Lagarde at the IMF, Tim Cook at Apple — took the helm later in life.

5.  Designing Work for Cognitive Longevity

Flexible schedules, remote work, and roles that emphasize judgment over speed can help older employees thrive. These aren’t perks; they are performance multipliers.

What it means for Society

Beyond business, the findings have profound social implications.

• Ageism Needs to End
  The idea that older people are less valuable is not only unfair — it’s scientifically wrong. Societies that sideline elders waste their richest human capital.
• Healthcare Priorities
  Investing in cognitive health — from fitness programs to lifelong education — should be a public health priority, not a luxury.
• Politics and Policy
  If peak judgment arrives at 60, perhaps we should not worry that leaders are “too old.” The problem is not age itself but whether they maintain curiosity, energy, and purpose.
• Redefining Success in Life
  The obsession with “achieving everything early” creates unnecessary pressure. If the best is yet to come, then life is a marathon, not a sprint.

Unlocking your brain power

The science is clear: the brain does not burn out early; it matures into brilliance. But like an athlete training for the Olympics, reaching your peak at 60 requires preparation. The choices you make at 30, 40, and 50 determine how strong, resilient, and creative your mind will be when it blossoms later.

The formula is simple but profound: move your body, feed your mind, rest deeply, connect widely, and live with purpose.

Imagine a society where 60 is not seen as the beginning of decline but as the arrival of mastery. Where businesses design careers that crescendo, not taper. Where individuals know that each decade is not a fading echo of youth but a step toward the fullest expression of who they can be.

Brilliant at 60

If you are 25 and anxious about “running out of time,” relax — your best years are still ahead. If you are 45 and wondering if you’ve peaked, remember: you are only halfway to your summit. And if you are 60, welcome — you are entering the golden age of your mind.

The late bloom of brilliance is not an accident of nature; it is the very design of human life. Train your brain, move your body, stay curious, live with purpose — and the best of you will still be to come.

Dove’s Campaign for Real Beauty launched in 2004, challenging beauty stereotypes by featuring “real women” of different shapes, sizes, ages, and ethnicities instead of traditional models. Sparked by research showing only 2% of women considered themselves beautiful, the campaign sought to redefine beauty standards and boost self-esteem. Memorable initiatives included the “Evolution” video (2006), exposing the manipulation of beauty through digital editing, and the “Real Beauty Sketches” (2013), highlighting women’s self-perception versus how others saw them. Over two decades, it has evolved into a movement, sparking global dialogue on inclusivity, authenticity, and body confidence.

20 years of real beauty

To mark 20 years of advocating for Real Beauty, Dove launched a campaign addressing a new threat to authentic representation: the influence of AI-generated imagery. Recognizing that standard AI prompts often produce idealised, unrealistic beauty norms, Dove saw an opportunity to shift how beauty is reflected in digital environments.

Their proprietary prompts, grounded in their longstanding Real Beauty philosophy, led to more inclusive visual outputs—proving the brand’s enduring cultural relevance. Supported by a global study that revealed one in three women feel pressured to change their appearance due to online beauty standards, Dove committed to redefining beauty once again—this time within the algorithms that shape our digital world.

To counter the narrow definitions embedded in AI’s outputs, Dove partnered with Pinterest on an initiative that empowered women to define beauty on their own terms. Users were invited to select characteristics that reflected their vision of beauty. In turn, these preferences populated their Pinterest feeds with diverse, authentic imagery.

This custom experience launched with a high-impact homepage takeover and was reinforced by a strategic paid campaign and robust full-channel rollout. The campaign didn’t just challenge AI’s defaults—it transformed them, ensuring Dove’s Real Beauty message re-emerged at the top of the algorithm. The initiative primarily targeted millennial women aged 25 to 54, strengthening their connection with Dove’s Real Beauty values.

The campaign delivered an impressive 787 million impressions and exceeded all key benchmarks. It achieved a 2.9 percentage point increase in brand association and drove engagement levels that were 21.4% higher than Pinterest’s norm for women. Ultimately, the personalized Pinterest feeds became visual proof of Dove’s continued leadership in shaping how beauty is represented—and celebrated—online.

There’s Nothing Like This: The Strategic Genius of Taylor Swift

Kevin Evers book is a compelling exploration of how one of the world’s biggest pop stars became one of its savviest business leaders. More than a biography, the book offers a strategic playbook—showing how Taylor Swift built, evolved, and protected a global brand with what Evers argues is the foresight and discipline of a top CEO.

He tells the story chronologically, tracing Swift’s evolution from teenage songwriter to cultural icon. He begins with her decision to sign with a small record label over a major one, allowing her greater creative control—a rare move at just 14 years old. This set the tone for a career defined by calculated risks, long-term thinking, and a deep understanding of underserved markets. Her early music catered directly to teenage girls—an overlooked demographic in country music at the time—positioning her as an immediate disruptor.

At the heart of Swift’s strategy is her ability to build and maintain deep, emotional connections with her audience. The book highlights how she transformed the fan-artist relationship through handwritten notes, personal invitations to listening parties, and pioneering use of social media. She wasn’t just promoting songs—she was building community. Evers calls this a model of “fan obsession,” where loyalty and engagement are not byproducts of fame, but drivers of it.

What’s most striking in the book is how Swift consistently turned obstacles into opportunities. Whether it was the fallout from the Kanye West VMAs incident, the backlash around her political silence, or the loss of ownership over her master recordings, she responded not with retreat but with reinvention. Her decision to re-record her entire back catalog to regain ownership wasn’t just a business move—it was a cultural moment, reshaping how artists think about intellectual property and control.

Swift’s artistic reinventions—from country to pop, indie-folk to electro—are framed as strategic expansions into new markets, not just aesthetic choices. And her command of digital platforms, from Tumblr to TikTok, shows her fluency in how attention works in the modern age.

• Teenage country star: At 14, Swift turned down a deal from RCA to sign with Scott Borchetta’s nascent Big Machine Records, advocating to write her own song, targeting an untapped teen-girl demographic. Her debut album spent 24 weeks at number 1, affirming the “Blue Ocean” strategy of serving underserved audiences.

• Fan obsession and connection: Swift would personally hand-write notes, invite fans to intimate listening sessions, and cultivate deep relationships via early social media. This “fan-obsession” helped turn followers into lifelong advocates.

• Resilience and anti-fragility: Post-2009 MTV VMA incident, she doubled down on songwriting authenticity in Speak Now, showcasing her ability to turn setbacks into strengths. The book argues she doesn’t just recover; she grows stronger from adversity.

• Genre and platform reinvention: Strategic shifts: from country to pop (1989), then onto indie-folk (Folklore/Evermore), leveraging disruption as opportunity. Harnessed TikTok and streaming to drive speculation and algorithm-friendly campaigns.

• Ownership and monetisation: Re-recorded her masters—an unscripted, high-stakes strategy that gained her rights control and royalties. Expanded product ecosystem: vinyls, direct deals with AMC, and merch—leveraging her “conglomerate” status.

• Economic powerhouse: the 151-date Eras global tour grossed nearly $2 billion. Each album-era integrated into live experiences, delivering hours of content and underlining brand maturity.

The book devotes significant attention to her financial ecosystem, culminating in the global success of that Eras Tour. Evers portrays the tour not just as a performance spectacle but as a masterclass in product bundling, scarcity, and immersive branding—with impacts stretching into the billions of dollars in economic activity.

Ultimately, There’s Nothing Like This argues that Swift is a blueprint for the modern brand: adaptable, emotionally resonant, fan-powered, and strategically self-aware. Her career is not just an artistic journey but a case study in how to lead, evolve, and endure in a volatile world.

Why do I like this book? This is not just a book for Swifties, it’s essential reading for anyone interested in strategy, branding, or leadership in the 21st century.

Another Way: Building Companies That Last… and Last… and Last

Dave Whorton and Bo Burlingham present a bold alternative to the high-growth, venture-backed startup model that dominates Silicon Valley. Instead of building companies designed to “get big fast” and exit quickly, the authors advocate for the Evergreen approach: building purpose-driven, profitable, and enduring companies that prioritize people over hype, and values over valuations.

At the heart of the book is the belief that business can be both principled and prosperous—and that long-term thinking leads to deeper impact and more resilient success. Drawing on the experiences of dozens of founders who rejected the startup treadmill, Whorton (a former VC himself) and Burlingham (author of Small Giants) identify seven core principles that define Evergreen companies, known as the Seven Ps:

• Purpose – A compelling reason for being that goes beyond making money.

• Perseverance – The grit to stay focused on the long game, not just quarterly results.

• People First – Prioritizing employees, customers, and communities over short-term gains.

• Private – Remaining privately held to avoid the pressures of public markets or outside investors.

• Profit – Using profitability as a discipline and fuel for growth, not an end in itself.

• Paced Growth – Choosing steady, manageable growth over rapid scaling.

• Pragmatic Innovation – Embracing change and technology when it serves the company’s mission and culture—not for its own sake.

Through compelling case studies—ranging from small family-run businesses to mid-sized industry leaders—the book demonstrates how Evergreen companies often outperform flashier competitors in the long run. These businesses tend to have higher employee retention, stronger cultures, deeper customer loyalty, and greater resilience in economic downturns.

The book also tracks Whorton’s own journey from working at Kleiner Perkins to founding the Tugboat Institute, which champions Evergreen businesses. His story reflects the broader theme: that meaningful, values-driven work is not only possible in business—it may be the best way to build something truly lasting.

In an era obsessed with unicorns and IPOs, Another Way is a powerful reminder that enduring value comes from integrity, intention, and investment in people—not from chasing the next big thing. It offers both inspiration and a practical framework for entrepreneurs, leaders, and investors who want to build businesses that endure for generations.

Why do I like this book? Because reinvention, or rather relentless reinvention, is now the big challenge and opportunity for every business in a world of continuous change.

AI First: The Playbook for a Future‑Proof Business and Brand

Adam Brotman and Andy Sack have created a clear, action-oriented guide for business leaders navigating the transformative power of artificial intelligence. Rather than treating AI as just another tool, the authors argue that businesses must reimagine themselves from the ground up—placing AI at the very heart of how they operate, innovate, and grow. Becoming “AI-first” is not about chasing hype; it’s about building a new kind of organization that can thrive in a radically different era.

Drawing on their experience at companies like Starbucks and Forum3, Brotman and Sack lay out a practical framework to help leaders understand what it truly means to be AI-first. The shift begins with mindset: AI isn’t a department or a plug-in—it’s a strategic capability that must be championed from the top. The book emphasizes that CEOs and executive teams must lead this transformation by fostering a culture that’s data-fluent, experimentation-friendly, and deeply aligned around AI’s role in shaping the customer experience and brand identity.

The book advocates for starting small—using pilot projects to demonstrate value quickly and generate internal momentum. But they also stress that this is not just a technology shift; it’s a business and cultural one. Companies must redesign jobs, retrain teams, and rethink how humans and machines work together. AI should not replace people, but elevate their contributions—freeing them to focus on creativity, strategy, and empathy.

One of the strongest messages is about brand: in a world where AI makes everything more personalized and efficient, your brand becomes your greatest differentiator. How you use AI—transparently, ethically, and intelligently—will shape customer trust and loyalty more than ever.

• 95% of marketing to be AI-driven within five years, says Sam Altman

• AI-first leadership mindset is pivotal—begin with executive awareness and advocacy

• Talent and job design must evolve, redefine roles and nurture AI fluency across teams

• Early pilot wins demonstrate practical ROI, critical to gaining buy-in

• Brand differentiation as AI becomes ubiquitous, strategic integration is ke

Ultimately, AI First offers both inspiration and a practical roadmap. It’s for leaders who understand that the future won’t wait—and that preparing for it means rethinking the fundamentals of how their business works, delivers value, and earns attention in an increasingly intelligent world.

Why do I like this book? Because too many books have become tech obsessed about AI. The real insights come from how to apply it to business, in radical, creative and profitable ways.

Building a StoryBrand: Clarify Your Message So Customers Will Listen

Donald Miller offers a powerful marketing guide built around a deceptively simple truth: if customers don’t understand what you offer within the first few seconds, they’ll tune out. To solve this, Miller introduces the StoryBrand Framework, a seven-part storytelling formula that helps businesses clarify their message and make it irresistibly compelling.

At the heart of the book is the idea that the customer is the hero of the story—not the brand. Too many companies position themselves as the star, talking about their history, features, and accomplishments. But great marketing, Miller argues, casts the brand as the guide—the wise mentor who helps the customer overcome challenges and achieve success.

Using the structure of classic storytelling, Miller breaks the framework into seven key elements:

• A character (your customer)

• Has a problem (external, internal, and philosophical)

• And meets a guide (your brand)

• Who gives them a plan (clear steps or solutions)

• And calls them to action (buy now, sign up, etc.)

• That helps them avoid failure (what’s at stake)

• And ends in success (what transformation they’ll achieve)

The book is full of practical examples, website critiques, and brand scripts that make it easy to apply the framework across all types of messaging—websites, sales pitches, ads, social media, and presentations. Whether you’re a startup founder or a marketing director, Miller shows you how to cut through the noise and tell a story your customers will actually care about.

The result is clearer communication, stronger customer engagement, and a more consistent, confident brand voice. Building a StoryBrand isn’t just a marketing book—it’s a strategic toolkit for anyone trying to grow their business by making their message matter.

Why do I like this book? Brands are my first love – I started out 35 years ago as a brand manager – but have become trivialised and marginalised by many business leaders. Yet in a world of intangible assets, they matter more than ever.

Superagency: What Could Possibly Go Right With Our AI Future

Silicon Valley legend and Linkedin founder Reid Hoffman has written a hopeful, deeply reasoned exploration of how artificial intelligence can enhance—not replace—human potential. Rather than focusing on fear or speculation, Hoffman (co-founder of LinkedIn and a long-time AI investor) builds a pragmatic, optimistic case for how AI, if developed and deployed thoughtfully, can amplify human agency and make us more capable of solving the world’s biggest challenges.

The central idea of the book is that AI should not be seen as a threat to humanity, but as a partner—a “thinking companion” that augments our abilities, expands our choices, and helps us navigate complexity. Hoffman calls this enhanced capacity “superagency,” and argues that it represents a new era of empowerment, much like past revolutions in language, printing, and computing.

Hoffman organizes the book around a series of key ideas:

• AI as Amplifier: Like fire, electricity, or the internet, AI doesn’t have values—it takes on the intent of the people using it. That’s why shaping its future starts with us.

• Informational GPS: AI can serve as a guide, helping individuals and institutions make better decisions faster, by mapping possibilities and clarifying consequences.

• Deploy, Then Reflect: Hoffman champions an iterative approach—build AI applications, deploy them responsibly, observe impacts, and refine. Waiting for perfection is too slow; improvement comes through use.

• Human-Centered Design: AI should be built to support human goals—enhancing learning, creativity, entrepreneurship, and empathy. He gives examples like AI tutors, AI coaches, and tools for scientific discovery.

• Guardrails and Governance: While optimistic, Hoffman is clear-eyed about the risks—from misinformation to bias to power concentration. He argues for collaborative governance involving companies, governments, and civil society.

• Private Commons: A call for shared access to powerful models and tools to ensure AI benefits are widespread—not monopolized by the few.

Throughout, Hoffman shares insights from his experience at the frontlines of tech innovation and policy, and makes the case that ethical ambition, not fear, should guide our approach to AI. He warns against paralysis through pessimism, urging readers to engage actively, thoughtfully, and with purpose in shaping the AI era.

In short, Superagency is a manifesto for building a future where human and artificial intelligence work together, not in competition. It’s a refreshing, empowering vision for leaders, technologists, and citizens who want to shape AI not just to do more—but to be more.

The Thinking Machine

This is a compelling biography of Jensen Huang by Stephen Witt, the visionary co-founder and CEO of Nvidia, and a deep dive into how he transformed a struggling graphics chip company into a dominant force powering the modern AI revolution.

Witt chronicles Huang’s journey from his early days as a Taiwanese immigrant with a passion for technology to his breakthrough leadership at Nvidia. The book highlights Huang’s remarkable ability to anticipate and shape future technology trends, especially his foresight in recognizing the potential of graphics processing units (GPUs) beyond gaming—pioneering their use for artificial intelligence and deep learning.

Central to the story is Huang’s relentless focus on innovation, risk-taking, and building a strong company culture that thrives on boldness and agility. Under his leadership, Nvidia not only revolutionized computer graphics but became the backbone of AI infrastructure, powering everything from data centers to self-driving cars.

Witt also explores the technical breakthroughs Nvidia made, such as the development of CUDA, a parallel computing platform that allowed GPUs to be used for general-purpose processing, unlocking new possibilities in AI research and applications.

Beyond the technology, The Thinking Machine portrays Huang as a strategic thinker and charismatic leader who blends engineering expertise with business acumen, inspiring his teams to push boundaries and redefine industries.

In essence, the book is both a portrait of a singular leader and a case study in how vision, innovation, and perseverance can create a company that not only adapts to change but shapes the future itself.

The Optimist

This is a detailed and intimate biography of Sam Altman, one of the most influential figures in the tech world today. Keach Hagey’s book traces Altman’s journey from a precocious young entrepreneur in St. Louis to becoming the CEO of OpenAI, a leading organization at the forefront of artificial intelligence development.

Hagey paints a portrait of Altman as a visionary leader driven by a combination of relentless ambition, intellectual curiosity, and a deep sense of responsibility toward the future of technology and humanity. The book explores Altman’s unique leadership style, characterized by bold decision-making, a willingness to embrace risk, and a rare ability to navigate complex ethical and business challenges.

A central theme is Altman’s optimistic belief in technology’s potential to solve some of the world’s most pressing problems, balanced by his awareness of AI’s risks and the need for careful governance. The biography covers major milestones, including Altman’s time at Y Combinator, his efforts to steer OpenAI toward commercial success while maintaining its mission, and his role in popularizing transformative AI technologies like ChatGPT.

Hagey also delves into the personal side of Altman’s life—his relationships, doubts, and the pressures of leading in an era of rapid technological change. The book provides insights into the challenges of managing a cutting-edge tech company amid public scrutiny, ethical dilemmas, and intense competition.

Ultimately, The Optimist presents Sam Altman as a complex figure whose optimism about technology’s future is tempered by realism, making him a compelling example of modern leadership in a disruptive age.

 

AI is reshaping the way we live, work, and create. Algorithms now draft marketing copy, suggest medical treatments, trade stocks, and design products. In the rush to harness these tools, a fear lingers: what remains for humans to contribute? Yet, instead of competing head-to-head with machines, the most forward-thinking companies are learning to blend human and artificial intelligence — creating what many call augmented intelligence.

In this partnership, AI provides speed, scale, and precision. But what truly unlocks value is the “human umami” … the unique, hard-to-replicate contribution that people bring. Like the “fifth taste” in cooking, human umami is subtle yet essential. It’s what gives augmented intelligence depth, richness, and resonance.

Umami is often called the “fifth taste.” It’s subtle, savory, and hard to define—less obvious than sweet, salty, sour, or bitter, but it makes food richer, deeper, and more satisfying. Without umami, a dish might still be edible, but it lacks depth, resonance, and completeness.

When applied to people, “human umami” means the secret ingredient humans bring that artificial intelligence cannot replicate. AI can be faster, more accurate, more scalable—but it lacks the deep flavour of humanity.

So, what is this “human umami”, and how are organisations using it to build advantage?

Meaning-making … asking “why”

Machines can analyze billions of data points in seconds, but they don’t care what any of it means. Humans, by contrast, are wired for meaning. We seek context, purpose, and narrative.

In healthcare, for instance, AI systems can flag anomalies in scans with remarkable accuracy. But it is the doctor who explains the diagnosis, who situates it in the patient’s life, and who helps them understand why the treatment matters. The human role is not just technical — it’s about translating data into stories people can act on.

Some companies are explicitly recognizing this. Philips, which has repositioned itself around health technology, frames its AI solutions not just as diagnostic tools but as decision support for clinicians. The machine sees, but the human interprets.

Moral and ethical judgment … the compass

AI is indifferent to ethics. It optimizes for the objectives it is given. If those objectives are flawed, biased, or incomplete, the results can be harmful. Humans are the ones who bring a moral compass to augmented intelligence.

Consider the financial sector. Algorithmic trading systems are capable of executing thousands of trades per second. But left unchecked, they can destabilize markets or exploit vulnerabilities. That’s why regulators and firms alike rely on human oversight, embedding ethical considerations into system design.

Microsoft has tried to institutionalize this through its Office of Responsible AI, ensuring every deployment is reviewed not just for functionality but for fairness, transparency, and safety. Here, human umami means setting the rules of the game and taking responsibility for consequences.

Creative leaps and intuition … the sparks

AI is powerful at remixing what already exists. But true breakthroughs — the leap across domains, the imaginative spark — remain stubbornly human.

Take product design. When Nike designs a new sneaker, it may use AI to simulate materials or predict customer demand. Yet the spark of originality — combining fashion, sport, culture, and emotion — comes from human designers who sense what might resonate next. AI can assist, but it cannot originate in the way humans do.

Some firms are now using this deliberately. Adobe’s “Firefly” AI tools are marketed not as replacements for designers but as amplifiers of creativity. The human sets direction, experiments with wild ideas, and makes judgment calls; the machine speeds up iterations. It’s a dance, not a substitution.

Emotional resonance … feeling and belonging

Perhaps the sharpest line between human and artificial intelligence is emotional depth. Machines can simulate empathy — they can detect sentiment, adjust tone, and mimic warmth. But only humans can truly feel and share in another’s joy, grief, or awe.

In customer service, many companies now use AI chatbots to handle basic queries. Yet when emotions run high — a lost shipment, a medical emergency, a bereavement — customers crave human connection. Airlines like Delta still emphasize the human touch in customer care, especially during disruptions, while using AI in the background to give staff faster insights.

Leaders, too, rely on emotional resonance. Satya Nadella at Microsoft or Mary Barra at GM are not simply steering organizations strategically; they are inspiring belief, building trust, and rallying teams. This is something no machine can replicate.

Contextual wisdom … navigating grey zones

AI thrives on patterns and rules. But real life is full of nuance, contradiction, and ambiguity. Humans excel in grey zones, where judgment matters more than calculation.

Law firms, for instance, are increasingly adopting AI to sift through case law or draft routine contracts. But when cases hinge on cultural nuance, precedent interpretation, or the delicate reading of intent, human lawyers still lead. The “wisdom” to know when rules should bend — or when a precedent should be challenged — remains human territory.

In retail, too, contextual wisdom matters. AI can recommend products, but only a human can grasp the subtleties of cultural trends or ethical backlash. Consider Patagonia: its decision to discourage over-consumption, or to donate profits to environmental causes, wasn’t the product of a data model. It was a judgment rooted in values and context.

Agency and vision … choosing the future

The most profound element of human umami is agency. Machines don’t define goals; they pursue them. Humans, by contrast, imagine futures, set ambitions, and decide what matters.

AI can simulate thousands of scenarios for a city’s energy grid. But it is human leaders who choose whether the priority is lowest cost, lowest emissions, or most resilient supply. That choice is not computational — it is political, ethical, and visionary.

Tesla, for example, uses immense AI capabilities in self-driving technology. But the larger vision — to accelerate the world’s transition to sustainable energy — is distinctly human. Elon Musk’s bold (sometimes polarizing) ambition frames the work; the AI merely enables it.

Companies harnessing “Human Umami”

Forward-looking organizations are beginning to formalize the role of human umami in augmented intelligence:

• IDEO, the design firm, blends machine learning into its design process but insists that the most valuable ideas come from empathic design research — understanding people’s unarticulated needs through observation and conversation.

• Unilever uses AI to optimize supply chains but balances it with human oversight on sustainability trade-offs, ensuring the pursuit of efficiency does not erode ethical commitments.

• DBS Bank in Singapore has introduced AI for fraud detection and personalized finance, but it also trains its employees in “human skills” — storytelling, ethical reasoning, empathy — to complement the technology.

• L’Oréal uses AI to help customers test beauty products virtually, but the brand’s resonance rests on human creativity in campaigns and cultural relevance in messaging.

These companies recognize that technology alone is not enough. What differentiates them is their ability to blend — to combine machine efficiency with human judgment, imagination, and care.

The future of AI

As AI grows ever more capable, the temptation will be to let machines take over more tasks. But the organizations that thrive will be those that nurture the human umami — the meaning, ethics, creativity, empathy, wisdom, and agency that only people can bring.

In practice, this means redesigning work. Rather than asking “What can AI replace?” the better question is “How can AI amplify what humans do best?” It means investing in human skills — not just technical upskilling, but deepening our capacities for imagination, ethics, and emotional intelligence. And it means leaders articulating bold visions that machines can never supply.

Human umami is not a relic to be protected against automation. It is the essential ingredient that makes augmented intelligence flavorful, meaningful, and humane. Without it, AI is efficient but hollow. With it, we create a partnership that is not only more productive but also more purposeful.