Nearly thirty years ago, the great Harvard Business School Professor Clayton Christensen described a challenge every large, successful company must navigate: the ‘innovator’s dilemma.' It stems from the recognition that long-term performance requires a difficult balancing act: sustaining today’s businesses while pursuing new opportunities to fuel future growth. When these pursuits compete for resources, the legacy business typically wins, on the logic that it’s better to invest in today’s profitable products, customers, and business models than in something with uncharted market potential. This logic is compelling; thus the dilemma.

This challenge becomes existential in times of great change and disruption - and we are in such a time now. While major technology breakthroughs have always fueled this phenomenon, the current AI wave is like nothing that has been seen before. This technological marvel is not merely a new competitor on the horizon; it is reshaping the very ground on which industries compete by enabling disruption across every aspect of a modern organization - pressing leaders to act with an urgency that cannot be overstated.

In this article, we share our view on what it will take for organizations to succeed in this new era for enterprise innovation: the Intelligence Era. To frame the problem to be solved, we briefly recap the role innovation plays in large enterprises and the root causes underlying the innovator’s dilemma - which have existed long before the advent of AI and are still a challenge today. We then look at how the mechanisms of innovation have evolved in response, from centralized R&D labs to incubators to corporate VC groups. 

Finally, we discuss how AI is positioned to transform enterprise innovation and what this means for leaders. 

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Why Companies Innovate and Why It’s Hard

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Innovation is solving problems in new and valuable ways. This general definition encompasses a myriad of narrower ones, corresponding to different types of problems that need to be solved. For example, a company might want to improve its products to stay competitive (product innovation) while reaching its customers in new ways (marketing innovation) and finding more efficient ways to work (efficiency innovation). 

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At the level of a business as a whole, there are three big problems to solve with innovation
(Figure 1):

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Any one of these, on its own, is difficult; harder still is doing all three at the same time - and this is precisely the challenge the innovator’s dilemma refers to. The reasons for this are many, but here we’ll highlight four of the most fundamental:

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‍Strategy is short-term and focused on the core.
Because the core business is usually responsible for the bulk of near-term financial commitments and easier to predict, strategy for it often takes the form of an annual budgeting exercise with only a nod to the next year or two. However, transformative and new growth force leaders to place strategic bets on how their industry, customers, and competitors will evolve over the mid to long term - which is difficult when the typical strategy process is primarily backward-looking, driven by historical performance rather than future possibilities. 

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‍Long-term investment commitment isn't there.
Innovation requires sustained investment over time, yet most organizations prioritize resources based on near-term financial performance. This is especially problematic for new growth initiatives, which rarely yield immediate returns. Innovation budgets may be treated as discretionary, leading to cycles where projects start with enthusiasm but are deprioritized or defunded when business conditions tighten. The challenge is further compounded by the fact that traditional financial metrics are ill-suited to measuring uncertain, long-term innovation bets.

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‍Leaders are pulled towards the core.
Many executives recognize the need for transformation and new growth, but day-to-day responsibilities and performance metrics are tied to the performance of the core business. They may be reluctant to back initiatives that, in the short term, compete for resources or appear to disrupt core business priorities. Even when leaders are committed to change, they must navigate organizational inertia and cultural resistance, which can slow progress. 

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‍Capacity to execute is insufficient.
Organizations need distinct capabilities, processes, and talent for different types of innovation. Core business excellence requires optimization and efficiency, while new growth demands experimentation and risk-taking. Few organizations successfully build and maintain both sets of capabilities simultaneously, especially when they require different cultures, incentives, and ways of working. Without the necessary execution muscle, they can end up spinning their wheels—launching initiatives that struggle to scale, or worse, getting stuck in cycles of “innovation theater” without real impact. 

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A Brief History of Enterprise Innovation

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While the three goals for innovation described above have remained constant over the past century, the mechanisms to support them have evolved considerably. This evolution can be understood as taking place in six broad eras:

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1920s–1940s: 

The Birth of Industrial Innovation

The early 20th century saw the rise of centralized R&D, as companies like GE, Dupont, AT&T, and IBM pioneered systematic approaches to technological innovation. These efforts enabled long-term investments in foundational science and engineering, laying the groundwork for industrial and consumer breakthroughs that fueled rapid economic growth. However, they had their limitations: innovation was often slow and expensive, collaboration across organizations was limited, and it was often difficult to translate pure research into market opportunities – a challenge that persists today.

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1950s–1960s: 

Post-War Expansion  & Cold War Innovation

After World War II, economic growth and geopolitical pressures drove innovation to new heights. Skunkworks teams tackled high-risk, high-reward projects, particularly in defense and aerospace. Companies like IBM formalized R&D management practices, pioneering new ways to manage and measure innovation. This period also saw significant investment in higher education and research institutions, fostering the talent pipelines and foundational technologies that would define the modern era. 

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1970s–1980s:

Globalization & Efficiency Driven Innovation

The 1970s and 1980s were marked by globalization and deregulation, reshaping industries and increasing competition. Companies began leveraging strategic partnerships, collaborative R&D consortia, and early forms of open innovation to access external expertise and share risks. Japanese manufacturing philosophies like kaizen, lean production, and just-in-time systems influenced global firms, driving incremental efficiency and quality improvements. Mergers and acquisitions became prominent tools for scaling, diversifying, and integrating new capabilities. However, while these practices broadened the ways innovation was sourced, many companies still struggled to respond to disruptive changes, highlighting gaps in organizational agility.

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1990s: 

The Digital Revolution

The 1990s ushered in the internet age, fundamentally transforming how companies innovated. Corporate venture capital surged as a key tool for accessing emerging technologies, particularly in the tech and biotech sectors. Early platform innovation began to reshape traditional business models, with companies like Amazon, eBay, and Microsoft building ecosystems that leveraged network effects. Many organizations adopted horizon teams, guided by frameworks like the “Three Horizons,” to balance short-term, incremental innovation with longer-term, transformative investments. While these helped accelerate the pace of innovation, many organizations still struggled to be agile in experimentation and scaling.

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2000s: 

The Age of Disruption

The early 2000s saw an explosion of mechanisms designed to adapt to fast-moving markets and to harness the disruptive potential of technology startups. Innovation labs, corporate accelerators, and internal incubators became popular tools to foster agility and experimentation. Many corporations appointed Chief Innovation Officers to align innovation efforts with strategy and ensure accountability. Despite these advancements, companies often struggled to commercialize ideas generated in labs and incubators, leaving them isolated from core business operations – revealing a persistent gap between experimentation and scaling. 

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2010s:

The Ecosystem Era

The 2010s marked the rise of platforms and ecosystems as dominant innovation models. Companies like Apple, Amazon, and Salesforce demonstrated how platforms could generate exponential growth by leveraging network effects. Ecosystems extended innovation beyond company walls, fostering co-creation with partners, developers, and even competitors. Sandbox environments, particularly in fintech and software, became popular for experimentation, allowing for rapid iteration and testing in controlled settings. The proliferation of APIs and developer tools further fueled the ecosystem economy, enabling seamless integration and collaboration. 

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Today, most companies employ a mix of the above approaches, tailored to their needs.  Looking across this history, we see four patterns that will continue to influence how enterprise innovation evolves in the future:

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• Evolution of the role humans play.
  The role of humans in driving innovation has continually evolved—from hands-on inventors and scientists to managers of structured innovation processes to orchestrators of external ecosystems. Increasingly, human creativity is augmented by AI and automation, shifting the emphasis from execution to direction-setting. 

• Compression of innovation cycles.
  Each era has seen a significant acceleration in the time required to move from an idea to a market-ready innovation. What once took decades in centralized R&D labs now happens in months or even weeks, driven by digital tools, cloud-based development, and real-time customer feedback loops. This compression is not just about speed—it changes the nature of innovation itself, favoring rapid iteration over long-term bets and requiring companies to build systems that can sustain continuous adaptation.

• Democratization of innovation capabilities.
  Innovation was once the domain of a few well-funded R&D labs, but over time, it has become more distributed. First, through global research partnerships and consortia, then through open innovation models, and now through ecosystems that allow even startups and individuals to contribute meaningfully to breakthrough innovations.

• Shift from resources as an advantage to speed.
  Historically, innovation was fueled by access to capital, talent, and proprietary knowledge—advantages that large enterprises benefited from.. While these are still important, over time partnerships, ecosystems, and digital platforms have leveled the playing field. Increasingly, the primary determinant of success is not how much a company owns but how fast and flexibly it can respond to new opportunities. 

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The Next Era of Innovation: The Intelligence Era

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Today, we are at the onset of a new period in the history of enterprise innovation: The Intelligence Era. This era is defined by the transformative impact of artificial intelligence (AI), which is changing not only the pace of innovation but its very nature in four key ways:

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AI will become the ‘Innovation Oracle.’ (evolution of the role humans play). 

In the future, AI will act as an “oracle,” providing businesses with clear, validated answers to central innovation questions such as ‘What emerging trends will reshape markets?’, ‘Which unmet customer needs offer the greatest potential? and, critically, ‘What should we build?’ By analyzing vast, complex datasets - from consumer behavior to market dynamics - AI can reveal opportunities that were previously invisible. This shifts the role of innovators from one of discovery to one of validation and execution, dramatically reducing the time required to pinpoint promising ideas.

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AI will turbocharge the innovation process. (compression of innovation cycles). 

Beyond identifying opportunities, AI can accelerate every stage of the innovation lifecycle, including:

• Trend Spotting: AI can monitor and interpret global patterns in real time, providing actionable insights into emerging technologies, cultural shifts, and competitive threats.
• Ideation: Generative AI tools enhance brainstorming and expand creative horizons by not only producing thousands of potential ideas in minutes – but full business plans, strategies, financial models, and testing plans.
• Prototyping: AI-driven design and simulation tools enable rapid iteration, reducing costs and speeding up development cycles.
• Testing and Iteration: AI optimizes product testing by predicting outcomes, identifying flaws, and recommending improvements with unparalleled accuracy. 

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AI will be the great equalizer. (democratization of innovation capabilities).

One of the most profound impacts of AI is its democratizing effect. Advanced innovation capabilities that were once the exclusive domain of industry leaders will become accessible to companies of all sizes. As AI tools become cheaper, easier to use, and more widely available, organizations will no longer need vast R&D budgets or specialized expertise to compete. In this new landscape, the playing field is more level than ever, and competitive advantage shifts from those who can surface and develop promising ideas to those that can execute on them. This means…

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Execution will be everything. (shift from resources as advantage to speed).

In a world where the AI Innovation Oracle provides all the ‘answers’ - and everyone has equal access to its pronouncements - the defining factor for success will not be what companies innovate but how well they execute. Winning organizations will be those that act quickly on AI-driven insights, outpacing competitors in bringing ideas to market.

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Winning in the Intelligence Era

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How can companies solve the (age-old) problems of navigating disruption and the innovator’s dilemma in this (brand new) era we are entering? We believe there are five required elements for success. The first three have always been essential (though there are new, AI-related nuances to them), while the last two are distinct for the Intelligence Era:

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1. Strategic Clarity ‍

Winning companies will have strategies that clearly link innovation to overarching business goals. This requires them to clearly differentiate between core sustaining, core transformative, and new growth innovation, set strategic and financial goals for each type, and clarify how they contribute to success based on explicit assumptions about the future.
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In the Intelligence Era, strategic clarity takes on new dimensions, with AI both a required subject of strategy and a critical tool for developing it. Companies will need to develop goals that reflect the transformative potential of AI, while using its unique superpowers to identify patterns and opportunities that would remain invisible to human strategists alone.

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‍2. Leadership Conviction‍

Leaders create the conditions for innovation to thrive. Winning organizations will ensure leadership teams are fully aligned on a shared vision for innovation and the priorities that flow from it, and that these priorities are operationally linked to and drive all innovation activities at all levels.

‍Achieving this conviction in the Intelligence Era will require leadership teams to complement their domain expertise with a firsthand understanding of AI’s capabilities and limitations - and to have a shared mental model and language for AI that enables them to discuss and make decisions on it as a team.

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3. Investment Commitment‍

Companies will need to augment their investment and governance processes to ensure sufficient resources are committed to nurture - and scale - transformative and new growth innovations, even when results are uncertain or take time to materialize.‍

The Intelligence Era changes both what to invest in and how to govern those investments. The speed of AI advancement means traditional annual budgeting cycles and stage-gate processes will be increasingly insufficient for innovation needs. Leading companies will reimagine investment approaches that include rolling investment models (moving from static to adaptive funding) and additional metrics beyond ROI (e.g., learning velocity).

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4. Human-AI Collaboration‍

The role of AI in innovation is not just efficiency - it’s about co-creation. AI can enhance and amplify human creativity - and even create on its own. AI has evolved from a back-office tool to a front-line collaborator, capable of identifying unmet needs, generating ideas, and even refining execution strategies. AI can also surface insights that humans might overlook, revealing opportunities that aren’t obvious.

However, technology alone is not enough - success depends on how organizations combine AI with human creativity and evolve the way people approach innovation. Successful companies will train teams to work alongside AI and adapt workflows to unlock the best of both parties. Innovation teams won’t just be product managers and designers; they’ll be AI + human hybrids, constantly iterating in collaboration with intelligent systems.

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5. Execution Capacity. 

With AI providing the ‘answers,’ execution becomes the ultimate differentiator. When everyone has access to similar AI capabilities and insights, competitive advantage shifts dramatically toward those who can act on those insights with speed, precision, and scale.

This represents perhaps the most profound shift in the Intelligence Era. In previous eras, advantage often came from proprietary data, unique insights, or secret methodologies. Now, as AI democratizes access to insights and ideas, the ability to rapidly test, refine, and scale becomes the critical success factor.

Superior execution capacity requires:
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• Continuous Experimentation Engines: Whether they build it in-house or partner with leading firms, companies will need dedicated mechanisms to rapidly discover, prototype, test, and iterate new ideas (e.g., ventures studios, incubators)
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• Scaling Expertise: To avoid the ‘scaling valley of death,’ companies will need specialized teams skilled in transitioning concepts from proof-of-concept to enterprise scale, with expertise in change management, integration, and operational excellence
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• Friction Removal: Winning companies will systematically identify and seek to reduce organizational barriers to rapid execution, examining everything from procurement processes to compliance reviews to technical debt - with AI-driven solutions playing a prominent role
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‍In the Intelligence Era, the question shifts from "What should we do?" to "How quickly and effectively can we do it?" Organizations that build superior execution muscles will outpace competitors even when everyone has access to the same AI-generated insights.

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A New Imperative for Leaders

The Intelligence Era marks a turning point in the history of innovation, where AI provides unprecedented insights, but execution will separate leaders from laggards. Success will depend not just on adopting advanced tools but on reimagining how organizations innovate - aligning strategy, leadership, investment, and talent to act with speed and purpose. The companies that thrive will be those that harness AI as a partner, build systems that fuel rapid experimentation, and foster a culture of consistent execution. The future of innovation has arrived, and the race is on - not only to discover what to do, but to deliver it first and best.

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