A fascinating tension defines today's business landscape: AI technology races forward while organisational structures crawl behind. Let's explore how forward-thinking organisations are bridging the divide between technological possibility and institutional reality.
1. Introduction: The AI Implementation Gap
A financial analyst completes a two-week reporting process in just three hours using AI.
Yet deploying this same tool required six months of committee reviews, policy revisions, and executive approvals.
This stark contrast captures the central tension of our AI moment: technology that advances at breathtaking speed colliding with institutional structures moving at a comparatively glacial pace.
While this pattern isn't entirely new—we saw similar gaps with electricity and the internet—the acceleration differential with AI appears unprecedented. We're witnessing a powerful "flywheel effect" in AI development where each advancement directly enables faster future advances: AI systems help create better AI systems, improved models attract more investment, wider adoption generates more valuable data, and the cycle intensifies.
This technological momentum is challenging individual companies and testing our entire institutional ecosystem. Legacy approval processes, regulatory frameworks, workforce skills, and cultural norms are all under pressure to evolve at a pace they weren't designed for. The friction between what's technically possible and what's institutionally implementable creates extraordinary opportunities and significant barriers.
The organisations that thrive will not necessarily be those with the most advanced technology, but those that most effectively bridge this implementation gap. The question isn't whether AI will transform our economy but how quickly our supporting systems can adapt to enable that transformation while addressing legitimate concerns.
2. Historical Context: The Pattern of Progress 🔄
Looking back at the grand arc of innovation, we discover something fascinating: progress doesn't just happen—it accelerates. Human advancement has followed a super-exponential growth curve from stone tools that evolved painfully slowly over millennia to digital technologies that transform overnight.
This isn't random chance. Each breakthrough creates the foundation for faster subsequent innovations. Think of it as compound interest for technology—the more we build, the quicker we can make the next thing.
The Threshing Machine Story: AI's Revealing Ancestor 🌾
Let's take a practical example that perfectly mirrors our current AI moment: the threshing machine.
This agricultural innovation seems straightforward—a mechanical device to separate grain from stalks. The concept first appeared in documents as early as 1636, yet widespread adoption didn't happen until nearly 200 years later in the 1800s.
Why such a delay? The bottleneck wasn't the core technology but the supporting ecosystem:
- Manufacturing infrastructure couldn't yet produce precision components
- Distribution channels were insufficient for widespread delivery
- Financial systems lacked mechanisms for funding new ventures
- Skilled talent was scarce and concentrated in limited areas
- Cultural resistance came from workers fearing job displacement
Do you think this sounds familiar to you? It should. We're experiencing the same pattern with AI, just at a dramatically compressed timescale.
The Innovation-Adoption Gap: A Business Constant ⏱️
This pattern—where technological capability races ahead of institutional readiness—isn't unique to AI:
✅ Electricity transformed factories only after businesses completely redesigned workflows and organisational structures
✅ Computers sat underutilised until companies developed entirely new processes
✅ Internet potential remained partially untapped until business models evolved
Case Study: In the 1980s, economist Robert Solow famously observed, "You can see the computer age everywhere but in the productivity statistics." It took nearly a decade before organisational adaptations allowed computing's impact to emerge in economic data.
What makes our current moment unique isn't the pattern but the pace. While the threshing machine's adoption cycle spanned centuries and electricity's spanned decades, AI's institutional adaptation is compressed to years, though still visibly lagging behind the technology itself.
The Strategic Advantage of Historical Awareness 🔍
For forward-thinking leaders, this historical perspective offers a competitive edge. Understanding that institutional adaptation always follows technological capability, but never immediately, allows for strategic planning:
- Where are your organisational structures creating AI implementation friction?
- Which adoption barriers from previous technological revolutions appear in your AI initiatives?
- How can you accelerate your institutional adaptation while competitors remain stuck?
The businesses that thrive in the AI economy won't necessarily be those with the most advanced technology, but those that most effectively bridge the gap between technological capability and institutional readiness.
Remember: History doesn't just repeat itself—it offers a playbook for navigating the present. The innovation-adoption gap is inevitable, but your response to it is entirely within your control.
3. The AI Acceleration Flywheel: Why This Time Is Different
AI technology isn't just advancing—it's accelerating in a way that fundamentally differs from previous technological revolutions. At the heart of this phenomenon is a self-reinforcing cycle that powers unprecedented growth and creates new strategic imperatives for businesses.
Knowledge Amplification: AI Building Better AI
The most remarkable aspect of modern AI is its ability to accelerate its development. Unlike steam engines that couldn't directly improve themselves, today's AI systems actively contribute to creating the next generation of AI.
Researchers at leading AI labs now routinely use AI assistants to help write and debug complex code, analyse vast scientific literature for insights, and optimise model architectures. Microsoft reports that developers using AI coding assistants complete tasks over 50% faster than those working without them, directly accelerating how quickly new capabilities reach the market.
This creates a feedback loop where AI advances enable faster AI research, producing more capable systems at an accelerating pace. The process that once took years now happens in months or even weeks.
Infrastructure Growth: The Foundation That Enables Everything
Behind every AI breakthrough is specialised infrastructure evolving at a remarkable speed. The computational power available for AI training has doubled approximately every six months since 2012—a pace that makes Moore's Law look glacial by comparison.
This infrastructure evolution isn't just about raw computing power. Specialised chips designed specifically for AI workloads have improved training efficiency several times over in just a few years. Meanwhile, cloud platforms have democratized access to these capabilities, allowing organizations of all sizes to leverage advanced AI without massive capital investments.
The result is a continuous expansion of what's possible. Applications that would have been prohibitively expensive or technically infeasible just three years ago are now commercially viable, opening new frontiers for innovation and adoption.
Market Expansion: The Virtuous Investment Cycle
As AI capabilities grow, so do commercial applications across industries, creating a powerful economic feedback loop. Each new capability unlocks dozens of business use cases, from customer service automation to complex decision support systems. These successful implementations drive further investment, attracting more talent and resources.
Consider how a regional bank implemented AI for fraud detection, reducing false positives by 68% while increasing actual fraud identification. This success prompted them to expand their AI initiatives across lending, customer service, and operations, committing resources to fund even more advanced capabilities.
This virtuous cycle operates across the entire economy. Venture capital investment in AI has grown more than tenfold in the past six years, providing the financial fuel for increasingly ambitious research and commercial applications.
Data Network Effects: The Ultimate Competitive Advantage
The virtuous data cycle may be the most powerful accelerant in the AI flywheel. Every AI interaction generates valuable feedback that can be used to improve future performance. More diverse data creates more capable and generalisable models, which drive wider adoption across more varied applications.
Organisations that deploy AI solutions today aren't just solving current problems—they're building data advantages that compound over time. A customer service AI that processes millions of interactions becomes increasingly adept at handling edge cases and complex scenarios, creating performance gaps that competitors may find challenging to overcome.
This compounding data advantage represents a fundamental shift in competitive dynamics. In previous technological revolutions, followers could quickly catch up to leaders by implementing similar technologies. With AI, early movers may establish self-reinforcing advantages that grow over time rather than diminish.
Strategic Implications for Business Leaders
The AI flywheel creates strategic imperatives unlike any previous technology. The compounding nature of AI advantages means gaps between leaders and followers may grow exponentially rather than linearly. Organizations that delay implementation aren't just falling behind—they may be ceding insurmountable advantages to competitors.
This doesn't mean companies should rush into AI implementation without strategy. Rather, it suggests the need for thoughtful but decisive action. Even modest implementations that generate valuable data and organizational learning can begin building momentum that accelerates over time.
Forward-thinking organizations are pursuing platform approaches rather than isolated applications—building AI capabilities that can be leveraged across multiple business processes. They're recognizing that data strategy and AI strategy are fundamentally intertwined, with today's data practices determining tomorrow's AI capabilities.
While your organization deliberates implementation timelines, competitors who have already deployed AI solutions are experiencing compounding benefits from their flywheel effects. In the emerging AI economy, the advantage of early momentum may prove decisive.
4. Institutional Friction Points: Where AI Meets Reality
Even as AI technology races forward at breathtaking speed, organisations are discovering that implementation challenges have less to do with the technology itself and more to do with the human systems it must integrate with. Let's explore these friction points that are slowing down the AI revolution.
OrganiSational Processes: The Bureaucratic Bottleneck
Most established companies have built decision-making processes designed for a world where change happened quarterly, not daily. These legacy workflows are creating significant bottlenecks for AI adoption.
Consider what happens when a financial services firm wants to implement an AI-powered risk assessment tool. Their standard technology approval process—designed for stable, well-understood systems—involves sequential IT security, compliance, legal, privacy, and executive committees reviews. What worked perfectly well for implementing a database update becomes painfully inadequate for an AI solution that evolves weekly.
A healthcare executive recently shared: "By the time we completed our six-month approval process for an AI diagnostic tool, the technology had already undergone three major capability upgrades. We essentially approved yesterday's technology."
This misalignment creates a competitive disadvantage that grows more severe each month. Forward-thinking organisations are responding by creating parallel approval tracks for AI initiatives, maintaining necessary oversight while acknowledging the unique characteristics of rapidly evolving technology.
Regulatory Landscapes: Navigating Uncertain Waters
The regulatory environment for AI remains in flux, creating significant uncertainty for organizations. From the EU's comprehensive AI Act to industry-specific guidelines and state-level regulations in the US, companies face a complex patchwork of requirements.
This uncertainty often leads to one of two problematic responses: excessive caution that restricts AI to narrow, low-value use cases, or a "wait and see" approach that delays implementation entirely. Both strategies sacrifice competitive advantage in the name of regulatory clarity that may not arrive for years.
The most successful organizations are adopting principled approaches—developing clear internal guidelines based on emerging best practices and ethical considerations, while maintaining the flexibility to adapt as regulatory frameworks evolve. This balanced approach enables continued innovation within responsible boundaries.
Workforce Adaptation: The Human Element
Perhaps the most significant friction point is the human element of AI integration. Organizations are discovering that successful implementation isn't primarily a technical challenge but a workforce transformation challenge.
The skills gap is real and growing. While technical AI expertise remains critically important, "bridge roles" are equally valuable, which can translate business problems into AI solutions and integrate AI outputs into business processes. Combining domain knowledge with AI literacy, these hybrid skills are desperately scarce.
Traditional educational institutions are struggling to keep pace with AI's rapid evolution. The technology landscape has often shifted significantly when a curriculum is developed, approved, and taught, burdening organisations with building their training programs and continuous learning environments.
A retail executive summarised this challenge: "We can implement the technology in weeks, but developing the human capabilities to leverage it effectively takes months or even years. That's our real bottleneck."
Cultural Resistance: The Hidden Implementation Barrier
Beneath the surface of process and policy lies a more fundamental friction point: cultural resistance to AI adoption. This resistance stems from multiple sources and manifests in ways that can be difficult to address directly.
Legitimate concerns about job displacement create understandable anxiety. Even when AI implementations aim to augment rather than replace workers, uncertainty about long-term impacts creates passive resistance to adoption. This isn't just about front-line workers—executives and middle managers often harbor the same concerns about their own roles.
Status and identity issues also create friction, particularly among knowledge workers. When professionals have spent decades developing expertise that suddenly appears replicable by AI, the resistance isn't just about job security—it's about a fundamental challenge to professional identity and sense of value.
A consulting firm partner noted: "The most surprising resistance came from our senior analysts—people who pride themselves on insights that AI systems can now generate in seconds. Their concerns weren't about compensation but about purpose: 'If a machine can do this, what am I bringing to the table?'"
Bridging the Gap: The Adaptation Imperative
These friction points represent the central challenge of our AI moment. The technology is racing ahead while our human systems—our organizations, regulations, skills, and cultures—struggle to keep pace.
Leading organizations are taking holistic approaches to addressing these friction points:
- Creating adaptive governance frameworks that maintain appropriate oversight while acknowledging AI's evolutionary nature
- Developing clear principles for responsible AI use that can guide decisions while regulatory frameworks evolve
- Investing heavily in workforce development with emphasis on the uniquely human skills that complement AI capabilities
- Addressing cultural concerns through transparent communication about how AI will transform roles rather than simply eliminate them
The technology may be advancing exponentially, but human systems change at a more measured pace. Bridging this gap isn't just a technical challenge—it's perhaps the most important strategic imperative businesses face in the coming decade.
As one CEO put it, "We initially thought our AI transformation would be limited by technology. We've learned it's actually limited by our ability to transform ourselves."
5. Case Studies: Friction in Action
The difference between AI's potential and its real-world implementation becomes clearest when we examine actual organizations navigating this landscape. These composite examples, drawn from common patterns observed across industries, illustrate key implementation challenges.
Large Enterprise Example: The Financial Services Implementation Gap
Consider the journey of a leading financial institution that embarked on implementing an AI-powered risk assessment system in 2023. The technology promised to reduce fraud detection time from days to minutes while significantly improving accuracy rates.
Despite having substantial resources—a dedicated innovation team, significant budget, and executive support—the implementation timeline stretched from the projected 3 months to over a year. Why? The organisation's governance framework required sequential approvals from risk, compliance, legal, IT security, and executive committees, each with their own meeting cadences and information requirements.
"We built our governance processes for technologies that change annually, not weekly," explained their Chief Digital Officer. "Each committee asked thoughtful questions, but their approval processes weren't designed for iterative technologies that continue evolving after deployment."
Meanwhile, more agile competitors had deployed similar solutions and were already capturing the market benefits of faster risk assessment and improved customer experience.
Public Sector Example: Healthcare's Regulatory Navigation Challenge
In healthcare, regulatory uncertainty creates implementation barriers even when the technology itself is mature. Several major hospital systems have explored AI-powered diagnostic assistants, which have demonstrated impressive results in clinical trials.
However, implementation has moved slowly as hospitals navigate complex questions about medical liability, patient privacy, and how AI recommendations should be documented in medical records. Legal departments often default to the most conservative interpretations without clear regulatory guidance.
A healthcare innovation director summarised the challenge: "We're trying to implement 2025 technology within a regulatory framework designed for 2005 technology. The questions regulators ask are valid, but the approval pathways weren't built for adaptive systems."
The result? Many healthcare organisations have limited their AI implementations to administrative functions with minimal regulatory complexity, leaving substantial clinical value unrealised.
Small Business Example: Bridging the Expertise Gap
For smaller organisations, implementation challenges often revolve around access to expertise rather than bureaucratic processes. A mid-sized professional services firm recognized that AI could transform its client reporting and analysis capabilities, potentially placing it on a competitive footing with much larger competitors.
Unlike enterprise organisations with specialised data science teams, this firm faced a fundamental knowledge gap. Its IT support was outsourced to a managed service provider with minimal AI experience. Cloud-based AI solutions offered accessibility, but the firm still needed internal expertise to implement and manage these tools effectively.
Their solution? Creating a "hybrid" role—upskilling an analytically minded team member with AI implementation training. This approach required patience as this individual developed the necessary expertise through practical application, but ultimately proved more effective than trying to hire scarce (and expensive) specialised talent.
International Comparison: Cultural and Regulatory Influences
Implementation timeframes vary dramatically across countries and regions, highlighting how institutional environments impact adoption rates. Organisations operating in the multiple areas report consistent patterns in their ability to implement identical AI solutions:
Nordic and Baltic countries consistently show faster implementation cycles, with organisations reporting 30-50% shorter timelines than similar implementations in other European markets. These regions combine more adaptive regulatory approaches with business cultures that emphasise experimentation and flatter organisational hierarchies.
In contrast, organisations in regions with more hierarchical business cultures and rule-based (rather than principle-based) regulatory frameworks experience longer implementation cycles, even when technical capabilities are identical.
Learning From Implementation Patterns
These examples highlight a crucial insight: the rate-limiting factor in AI adoption isn't typically technological readiness but institutional adaptation capacity. Organisations successfully navigating these challenges share several approaches:
- They've developed governance frameworks designed explicitly for AI that balance oversight with implementation speed
- They focus on building internal AI literacy broadly across the organisation rather than concentrating expertise in specialised teams
- They approach regulatory questions proactively, developing principled approaches to responsible implementation rather than waiting for perfect regulatory clarity
- They recognise that implementation is an ongoing process rather than a one-time event, creating feedback loops that continuously improve both the technology and the organisational processes surrounding it
The organisations making the most progress aren't necessarily those with the most significant AI budgets or most advanced technologies—they're the ones most effectively addressing the institutional friction points that slow implementation.
6. Emerging Solutions: Bridging the AI Implementation Gap
Forward-thinking organisations aren't just identifying AI implementation challenges—they're actively developing solutions that help bridge the gap between technological possibility and institutional reality. These emerging approaches offer valuable lessons for any organisation navigating the AI transformation journey.
New Governance Models: Reimagining Oversight for Speed and Safety
Traditional governance frameworks simply weren't built for technologies that evolve weekly rather than annually. Leading organisations are developing new models that maintain necessary oversight while enabling the agility AI implementation requires.
A multinational financial services company transformed their approach after realising its standard approval process was causing it to miss critical market opportunities. They developed a tiered governance system that classifies AI projects based on data sensitivity, decision-making authority, and potential impact. Low-risk applications now follow an expedited pathway with appropriate guardrails, reducing implementation timelines from months to weeks while maintaining rigorous oversight for high-risk applications.
This "right-sized governance" approach acknowledges that not all AI implementations carry the same risk profile. By tailoring oversight to the specific characteristics of each use case, organisations can accelerate innovation while maintaining appropriate protections.
Education and Training: Building OrganiSation-Wide AI Fluency
The skills gap for effective AI implementation extends far beyond technical specialists. Organisations leading in AI adoption are investing in broader capability development across their workforce.
A manufacturing company initially struggling with AI project prioritisation developed a two-day intensive workshop called "AI Opportunities." This program helps business teams understand AI capabilities, recognise high-value use cases, and anticipate implementation requirements. The result? Better project proposals, more realistic expectations, and smoother implementation processes.
What makes these programs particularly effective is their practical focus. Rather than abstract discussions of how AI works, they emphasise concrete applications within specific business contexts. The most successful programs combine conceptual understanding with hands-on experience, allowing teams to develop capabilities through guided practice.
Regulatory Innovation: Finding Balance in Uncertain Terrain
The regulatory landscape for AI remains in flux, but promising approaches are emerging that balance innovation with necessary protections.
Healthcare organisations facing regulatory uncertainty have succeeded with controlled implementation approaches that generate evidence while limiting risk. Rather than waiting for complete regulatory clarity, they implement AI solutions with careful monitoring and transparent evaluation in limited settings.
This "learn by doing" approach acknowledges that regulatory frameworks will inevitably evolve as we gain more experience with AI systems in practice. By engaging constructively with regulatory questions rather than avoiding them, organisations can influence the development of appropriate frameworks while continuing to move forward with implementation.
Cultural Adaptation: Building Confidence Through Involvement
Perhaps the most critical innovations address the cultural barriers to AI adoption, which often prove more challenging than technical or regulatory hurdles.
A professional services firm recognised that its AI document analysis tool was meeting resistance despite clear efficiency benefits. Their solution? A collaborative implementation approach that directly involved the associates who would be using the system. These teams participated in defining how the AI would be integrated into workflows, what success metrics would be used, and how human judgment would remain central to client service.
This collaborative approach transformed the narrative from "AI replacing our expertise" to "AI enhancing our capabilities." By creating genuine ownership among affected teams, the firm achieved adoption rates far exceeding those of previous technology implementations.
Case Study: Transformation at Regional Bank
A mid-sized regional bank illustrates how these emerging solutions work together in practice. When implementing an AI-powered risk assessment system, they faced governance challenges, skills gaps, regulatory uncertainty, and cultural resistance—the full spectrum of institutional friction points.
Their integrated approach included:
- A streamlined governance model specific to AI projects with clear thresholds for different levels of review
- An AI literacy program for lending teams and risk analysts, focused on practical applications rather than technical details
- A phased implementation beginning with lower-risk use cases while engaging regulators on more complex applications
- A collaborative approach that involved lending officers in defining how AI recommendations would be presented and incorporated into decision processes
The results were remarkable. Not only did they achieve their implementation goals ahead of schedule, but they also found that lending teams became advocates for broader AI adoption after experiencing the benefits firsthand.
The Transformation Imperative
These emerging solutions reveal a critical insight: the institutions that will thrive in the AI economy aren't just those adopting the technology—they're those transforming themselves to leverage it effectively.
Organizations that develop appropriate governance models, invest in building broad AI literacy, engage constructively with regulatory questions, and address cultural concerns proactively are creating capabilities that extend beyond any single AI implementation. They're building institutional adaptability that will serve them through multiple waves of technological advancement.
In a real sense, institutional adaptation is becoming a core competitive advantage—perhaps even more important than technological adoption itself.
7. The Path Forward: Navigating the AI Transformation
The gap between AI's technological capabilities and our institutional readiness creates both challenges and opportunities. Let's explore what lies ahead and how organisations can position themselves for success.
The Adaptation Timeline
Not all institutional elements will evolve at the same pace, creating a predictable pattern of change:
Internal Processes & Governance (6-18 months)
Organisations have direct control over approval workflows and decision structures, making this the fastest area of adaptation. Companies are already implementing tiered governance models that maintain oversight while enabling faster implementation.
Workforce Capabilities (1-3 years)
Building organisation-wide AI literacy takes more time but remains within organisational control. Forward-thinking companies are creating practical training programs focused on application rather than technical details, though scaling these efforts requires sustained investment.
Regulatory Frameworks (3-5+ years)
Comprehensive regulatory clarity will take the longest to emerge, with significant regional variations. While sandboxes and experimental frameworks offer promising approaches, organisations should prepare for a multi-year period of regulatory evolution.
Your AI Transformation Playbook
The most successful organisations are taking a practical, phased approach to institutional adaptation:
First, implement a tiered strategy that begins with high-value, lower-risk applications where institutional barriers are minimal. A mid-sized financial services firm started with AI-powered document processing for internal operations before tackling more complex customer-facing applications.
Next, invest in hybrid talent development. The heroes of successful AI implementation aren't purely technical experts—they're translators who bridge domains. Identify analytically minded team members with strong business knowledge and upskill them with practical AI implementation training.
Finally, engage proactively with regulatory questions rather than viewing uncertainty as a reason to delay. Develop clear internal principles, participate in industry associations, and document your evaluation frameworks to demonstrate responsible implementation.
The Bigger Picture
The implications extend far beyond individual companies. AI could add trillions to global GDP only if organisations successfully implement it. The gap between leaders and laggards may create unprecedented performance disparities.
One manufacturing CEO observed: "We thought we were undertaking a technology project, but we were undertaking an organisational transformation. The technology was easy – building an organisation that could effectively implement it was the real challenge and competitive advantage."
The future belongs to organisations that can evolve structures, develop people, engage constructively with regulation, and build cultures that embrace augmentation rather than fear automation. The most valuable asset in the AI economy isn't any particular technology—it's your organization's ability to adapt continuously to technological change.
The AI revolution is well underway. The question isn't whether your organisation will participate – it's whether you'll lead or follow the transformation.
8. Conclusion: When Flywheels Converge 🚀
Throughout our exploration of AI's institutional challenges, we've witnessed a fascinating dynamic: the flywheel of technological advancement spinning rapidly while organisational structures struggle to keep pace. But there's a profound shift happening that savvy business leaders are already leveraging.
The technological flywheel of AI continues to accelerate, with each breakthrough enabling faster innovations in a self-reinforcing cycle. New models improve research capabilities, better infrastructure enables more ambitious applications, and broader adoption generates valuable data that powers the next generation of solutions.
But there's another flywheel now gaining momentum: institutional adaptation.
Just as technological progress compounds, organisational learning follows a similar pattern. Each company that successfully reimagines its governance approach creates blueprints others can follow. Every effective training program builds knowledge that spreads through professional networks. Regulatory experiments in healthcare informatics approaches in financial services, creating cross-industry learning.
These institutional adaptations aren't just reactions to change—they're essential components of progress itself. The most powerful AI models are worthless without organisations capable of implementing them effectively.
The emerging convergence between these two flywheels makes our current moment so transformative. As AI capabilities advance, they create urgent demand for institutional innovation. As organisations evolve, they enable more effective AI implementation, which drives demand for even more advanced capabilities.
The real winners in this new landscape won't be defined by who has access to the most advanced technology (which is increasingly democratised through cloud services), but by who builds the most adaptive organisation. The competitive edge belongs to those who can transform governance structures, workforce capabilities, and organisational culture at a pace that matches technological evolution.
A manufacturing executive recently captured this perfectly: "We spent six months evaluating AI solutions, but once we found the right technology, it took us eighteen months to transform our organisation to use it effectively. If I could do it again, I'd start the organisational transformation first."
As we move forward, expect the gap between technological possibility and organisational reality to narrow, not because technology will slow down, but because our institutions will speed up. The organisations that thrive will be those that embrace this dual transformation, recognising that their ability to adapt may ultimately be more valuable than any specific technology they adopt.
The AI revolution isn't just changing what we can do—it's fundamentally transforming how we organize ourselves to do it. That organisational transformation may ultimately prove to be AI's most profound and lasting impact.
The future belongs not just to the technologically advanced, but to the institutionally adaptable. Is your organisation ready?
