From Print to Web to AI: Creating Sustainable Value in the AI Era

In the rapidly evolving landscape of artificial intelligence, we’re witnessing a fundamental transformation in how value flows through information ecosystems. AI answer engines like Claude, ChatGPT, Google AI Overviews, and Perplexity are creating revolutionary new ways for users to discover and synthesise information. For organisations with proprietary data — whether news content, financial insights, scientific research, or market intelligence — this shift presents an unprecedented opportunity to reimagine value creation and exchange.

Recent developments highlight the urgency of establishing sustainable models. Cloudflare has begun implementing AI bot management to help content creators establish proper value exchange, while news organisations, including The New York Times, are negotiating new frameworks for content usage. These actions underscore a growing realisation: when AI models process and synthesise proprietary information, we need innovative approaches to ensure all stakeholders — creators, platforms, and users — benefit from the value created.

This evolution feels familiar. In the mid-1990s, as I spearheaded the UK’s first online regional newspaper for the Leicester Mercury and later for Regional Independent Media and its one-hundred or so newspaper titles, we enthusiastically embraced the web’s potential to expand our reach and create new value streams. Today, the opportunity is even greater. AI isn’t just an intermediary; it’s a powerful enabler embedded in workflows, accelerating decisions and creating insights at unprecedented scale.

The strategic question for Boards and executives is clear: How can organisations leverage AI’s transformative capabilities while establishing sustainable value exchange models that benefit all participants? Drawing from my firsthand experience of the print-to-web transition, this article explores parallels with today’s AI era, showcases innovation leaders who’ve created successful models, and provides actionable recommendations for maximising value creation. The lesson? We have powerful tools and frameworks, now let’s use them to build something extraordinary.

Learning from the Digital Transformation Journey

In the mid 1990s, when the internet opened boundless opportunities for traditional media. I was at the forefront, leading the digital charge in the UK media and publishing industry. At the time, it felt revolutionary. We digitised newspaper archives and content, posted live stories, and made everything freely available, believing the web would expand our reach and create new revenue opportunities.

This was the era of experimentation and discovery between content creators and platforms. Publishers provided content to drive traffic, while search engines and early aggregators like Google and Yahoo! indexed our content, sending users our way in exchange for ad impressions. The model showed promise, but as the ecosystem evolved, we learned valuable lessons about value exchange and sustainable business models.

Looking back through the lens of the AI Stages of Adoption (AISA), we moved quickly from passive observation to Experimenting and Adopting. What we learned was the critical importance of building capabilities across what evolved into the Five Pillars — particularly in Governance & Accountability and Value Realisation. These lessons have informed today’s more sophisticated approaches to digital transformation.

The transition taught the industry important lessons about adaptation and evolution. Local newsrooms faced challenges; in the UK alone, over 300 titles closed between 2009 and 2019, according to the Press Gazette. However, this disruption also sparked innovation, with successful organisations finding new models for sustainable journalism through subscriptions, memberships, and diversified revenue streams.

My role in that transition taught me valuable lessons about strategic transformation. We learned that success requires balancing openness with sustainability, ensuring that innovation benefits all stakeholders. Boards at the time, including my own, gained crucial insights about the importance of governance frameworks for new digital ecosystems. The result? A generation of media organisations that learned to build direct relationships with audiences and create sustainable value exchange models.

This wasn’t just a media evolution, it was a blueprint for any data-rich industry navigating technological transformation. Fast-forward to today, and we can apply these lessons to harness AI’s incredible potential for value creation.

Today’s Opportunity: AI as a Value Multiplier

AI answer engines represent the next evolution of digital innovation, offering unprecedented capabilities for value creation. Where search engines connected users to sources, tools like ChatGPT and Perplexity synthesise and enhance information, creating new insights and value. This transformation opens entirely new possibilities: users get enhanced experiences while data owners can explore innovative monetisation and partnership models. And we’re just at the beginning — agentic AI systems that can autonomously take actions on behalf of users will create even more profound value exchange opportunities, where proprietary data becomes the foundation for AI agents that don’t just inform but actively solve problems, execute tasks, and create outcomes.

The scale of opportunity is staggering. AI models trained on vast datasets can generate insights impossible through traditional analysis. This isn’t confined to news publishing; it enhances every sector with valuable data. Consider financial market data: AI can identify patterns and generate analyses that multiply the value of raw information. Forward-thinking firms are already leveraging these capabilities to create premium services.

What we’re witnessing is multi-speed adoption across industries — a core principle I’ve observed in AI transformation. Organisations are at different stages: some are still observing the opportunities, others Experimenting with AI tools, while leaders have reached Transforming or Scaling stages where they’ve fundamentally restructured to maximise AI-enabled value creation. This varied maturity creates competitive advantages for early movers.

Scientific research outputs demonstrate the potential beautifully. Journals and institutions can use AI to enhance peer-reviewed studies, creating new products like AI-powered research assistants or automated literature reviews that add value beyond traditional publishing. Engineering firms are using AI to enhance their IP, creating simulation tools and design assistants that multiply the value of their core datasets. Customer service data becomes a goldmine for creating AI assistants that improve customer experience while reducing costs.

The opportunity is greater now because AI is faster, smarter, and more capable. Training datasets grow exponentially; OpenAI’s models, for instance, have evolved from millions to trillions of parameters, enabling increasingly sophisticated value creation. Unlike the web era’s simple distribution, AI creates entirely new products and services. Boards must recognise this isn’t just an evolution, it’s a transformation opportunity affecting innovation potential, revenue streams, and competitive advantages across sectors.

Innovation Leaders: Bloomberg and the Financial Times

Not all organisations simply adapted to digital disruption — some used it as a catalyst for innovation. Two standout examples are Bloomberg and the Financial Times, which illustrate how strategic approaches to AI and digital transformation can create sustainable competitive advantages while demonstrating excellence across the Well-Advised strategic priorities.

Bloomberg, a powerhouse in financial data, transformed its proprietary datasets into an AI-powered competitive advantage. It developed BloombergGPT, a 50-billion-parameter large language model trained exclusively on its financial datasets. Launched in 2023, this AI enhances Bloomberg Terminal capabilities, outperforming general-purpose models on tasks like sentiment analysis, news classification, and financial Q&A, according to research on arXiv.

This approach exemplifies mastery across Well-Advised: Innovation, New Products and Services (BloombergGPT itself), Customer Value and Growth (enhanced terminal capabilities that delight users), and Revenue, Margin, and Profit (premium services commanding higher fees). Bloomberg has essentially reached the Transforming stage of AI adoption, where AI fundamentally enhances their value proposition while creating new revenue streams. Looking ahead, Bloomberg’s model points toward a future where agentic AI systems will leverage proprietary financial data not just to analyse but to execute trades, manage portfolios, and make real-time decisions — all while maintaining the value exchange that sustains innovation.

The success stems from Bloomberg’s integrated ecosystem approach: data collection, AI training, and delivery work seamlessly together. Terminals remain essential for traders, generating billions in subscription revenue — now enhanced by AI capabilities that multiply value. By developing proprietary AI solutions, Bloomberg creates premium experiences that benefit both the company and its customers.

The Financial Times offers another innovation model, one rooted in strategic foresight. It introduced subscription services in 2002 and a metered paywall in 2007, pioneering sustainable digital journalism. This strategy delivered impressive results: by 2019, it reached one million paying readers, 75% digital. Fast-forward to 2023, and the FT boasts 1.295 million digital subscribers, generating over £500 million in annual revenue, per Press Gazette data.

Two-thirds of revenue now comes from digital subscriptions and advertising. The FT leveraged analytics to understand reader behaviour, building direct relationships that create value for both readers and the publication. They’ve demonstrated strong capabilities across all Five Pillars, particularly in Value Realisation & Lifecycle Management through sophisticated subscription analytics that continuously improve the reader experience.

These successes highlight key innovation principles: proactive value creation, direct customer relationships, and ecosystem thinking. Bloomberg’s AI integration shows how proprietary data can fuel innovation that benefits all stakeholders, while the FT demonstrates the power of sustainable subscription models. Both transformed potential disruption into competitive advantage.

Building Sustainable AI Ecosystems

The call for universal data openness — often framed as essential for innovation — contains important truths. Open access can accelerate innovation and democratise knowledge. But sustainable ecosystems require nuanced approaches that recognise different data types serve different purposes. Public-good datasets, like government-funded climate models or open-source code repositories, thrive on openness, maximising societal benefit while supporting creators through public funding.

However, commercially-funded proprietary datasets — those requiring significant investment in curation, verification, and maintenance — need sustainable value exchange models. Without proper frameworks, we risk creating an imbalanced ecosystem where value creation becomes unsustainable. The key is ensuring that those who invest in creating high-quality data can continue to do so while enabling AI’s transformative potential.

Statistics demonstrate the importance of sustainability: thriving digital ecosystems emerge where value exchange benefits all participants. Successful models show that balanced approaches work best.

Innovative value exchange models are emerging everywhere. Creative Commons licences enable sharing with attribution while preserving creator recognition. API-based licensing, like Bloomberg’s model, enables controlled access that benefits both data owners and users. Metered paywalls, as the FT employs, offer generous free access while ensuring sustainability through subscriptions.

The key principle? Build ecosystems where everyone wins. Boards should champion hybrid models: share non-core data freely to foster innovation and goodwill, while creating premium services around proprietary assets. This isn’t about restricting access, it’s about ensuring sustainable value creation. True innovation empowers both creators and consumers.

Board-Level Recommendations for Value Creation

For Boards embracing this AI-driven transformation, strategic action creates competitive advantage. These recommendations align with the Five Pillars framework for AI maturity. Just as organisations progressing through the AI Stages of Adoption must build capabilities systematically, creating sustainable value requires excellence across governance, infrastructure, operations, value realisation, and cultural dimensions.

Governance & Accountability: Start with opportunity mapping: identify proprietary datasets and assess their potential for AI enhancement. Implement frameworks that enable innovation while ensuring responsible AI use. Establish clear data governance protocols that facilitate value creation. Assign your AI Centre of Excellence (AICoE) to oversee data strategy, ensuring alignment with business objectives and ESG goals.

Technical Infrastructure: Build assets proactively. Develop dynamic APIs that enable controlled access and value exchange. Implement technical capabilities like versioning and access tiers. Create premium AI interfaces that deliver enhanced insights, following Bloomberg’s successful model.

Operational Excellence: Measure value creation continuously: track new revenue streams, customer satisfaction improvements, and innovation metrics. Establish monitoring systems that optimise value delivery and create feedback loops for continuous improvement.

Value Realisation & Lifecycle Management: Innovation is key. Bloomberg’s model shows how proprietary AI can create premium value; adapt this approach for your datasets, whether financial, scientific, or operational. Develop tiered service models that serve different customer needs while maximising value creation.

People, Culture & Adoption: Partner strategically: engage with AI platforms to create win-win relationships. Early collaboration yields better outcomes than adversarial approaches. Build internal capability to understand and leverage AI opportunities while fostering a culture of innovation. Prepare your workforce for the agentic AI era by developing skills in AI oversight, agent orchestration, and value chain management — ensuring your organisation can harness autonomous AI systems effectively.

These steps transform potential into advantage, moving your organisation toward the Transforming and Scaling stages where AI becomes a value multiplier rather than just a tool.

A Vision for the Future

Unlike the web era, we enter the AI age with powerful frameworks and proven models. Boards must act now to maximise value creation and build sustainable ecosystems. The opportunity — from today’s AI answer engines to tomorrow’s agentic systems — is too significant to approach tentatively. The organisations that establish robust value exchange models today will be best positioned to thrive when AI agents become the primary interface between businesses and their customers.

As I reflect on my journey from print to AI, I’m energised by the possibilities ahead: AI offers us the chance to create ecosystems where innovation thrives, value multiplies, and all stakeholders benefit. If you own valuable data, you hold the keys to extraordinary value creation — use them wisely to build the future.

About the Author

Mario Thomas is a transformational business leader with nearly three decades of experience driving operational excellence and revenue growth across global enterprises. As Head of Global Training and Press Spokesperson at Amazon Web Services (AWS), he leads worldwide enablement delivery and operations for one of technology's largest sales forces during a pivotal era of AI innovation. A Chartered Director and Fellow of the Institute of Directors, and an alumnus of the London School of Economics, Mario partners with Boards and C-suite leaders to deliver measurable business outcomes through strategic transformation. His frameworks and methodologies have generated over two-billion dollars in enterprise value through the effective adoption of AI, data, and cloud technologies.