A reflective essay in support of the next digital workforce, cultural renewal and social good

Core thesis: every technological revolution creates new wealth, new institutions and new social stresses. The leaders of the Agentic AI age can become bastions of humanity if they convert capability into public good: education, culture, access, safety, civic trust and opportunity for the younger workforce.

Introduction: every revolution creates a moral question

Every major socio-economic revolution begins with machinery, capital and ambition; but it is judged, finally, by whether it enlarges the human condition. The Industrial Revolution gave Britain and then the world the factory, the railway, the steel mill, the telegraph, the chemical works and the mass-produced book. It also gave society urban overcrowding, dangerous work, child labour, social dislocation and a painful rebalancing between capital and labour. The digital and Agentic AI revolution is different in its tools but similar in its moral challenge. It places reasoning systems, autonomous agents, synthetic content, robotics and data-driven decision-making at the centre of the economy. It promises productivity, discovery and new forms of abundance, but it also threatens to fracture opportunity unless we build a social architecture around it.

The question, therefore, is not simply whether AI will be powerful. It clearly will be. The question is whether the builders, owners and governors of this new capability can become stewards of humanity as well as stewards of enterprise value. History suggests that revolutionary wealth can be converted into public good when it is channelled into institutions: libraries, galleries, universities, museums, model villages, hospitals, scholarships and cultural foundations. The task now is to translate that older covenant into the digital philanthropy age: not as public relations, not as indulgence, but as a structural commitment to arts, culture, education, young people, ethical skills and human agency.

The Industrial Revolution and the rise of institutional philanthropy

The first Industrial Revolution did not arrive as a neat story of progress. It was a complex equation of invention, capital, migration, hardship, enterprise and social reform. New industrial fortunes were created at remarkable speed, often in places where the civic infrastructure had not yet caught up with the scale of change. In the textile towns, the ports, the coalfields and the steel cities, private wealth and public need stood side by side. The railway could shrink distance; the factory could increase output; but neither could automatically create dignity, literacy, culture or shared prosperity.

This is where the great industrial philanthropists matter. Andrew Carnegie’s story is perhaps the clearest example. Having made immense wealth in steel, Carnegie argued in The Gospel of Wealth that the central problem of the age was the administration of wealth in a way that preserved social harmony. His most famous institutional expression of that principle was the library. Between 1886 and 1919, Carnegie’s donations funded 1,679 new public library buildings in the United States alone, according to the U.S. National Park Service. Those buildings were not merely book repositories. They were civic engines of self-education. They helped working people, immigrants and young people gain access to knowledge that had previously been the preserve of the privileged.

The pattern repeated in different forms in Britain. Sir Henry Tate converted part of a sugar fortune into a national cultural legacy by gifting his collection of contemporary paintings to the nation, forming the nucleus of what became Tate Britain. William Hesketh Lever, whose wealth came from soap and consumer products, is remembered not only for industrial success but also for Port Sunlight, the Lady Lever Art Gallery and the Leverhulme Trust’s continuing support for research and education. George Cadbury and the Cadbury family built Bournville around the idea that industrial employment should be connected to housing, education, green space and a better quality of life for workers and their families. Henry Wellcome’s pharmaceutical fortune was ultimately converted into one of the world’s most significant health-research endowments, with the Wellcome Trust established after his death in 1936 to improve health through research.

These examples were not perfect. Industrial philanthropy must be seen with honesty, including the power imbalance between employer and worker, the paternalistic assumptions of the era, and the fact that charitable giving could not, by itself, correct all structural inequalities. Yet the lasting lesson is profound. When industrial wealth funded durable public institutions, it extended the benefits of a revolution beyond the immediate owners of capital. It gave society ladders: ladders of literacy, culture, health, education and aspiration.

Arts and culture as democratic infrastructure

One of the most important lessons from the industrial age is that philanthropy at its best did not treat arts and culture as decorative extras. It treated them as public infrastructure. A gallery, a library, a theatre, a museum, a park or a music hall gave working people a form of participation in civilisation that was not defined solely by labour. Culture became a counterweight to the machine. It reminded society that a person is not only a unit of productivity; a person is a citizen, a creator, a reader, a performer, a parent, a dreamer and a contributor.

This is directly relevant to Agentic AI. The risk of the AI age is that productivity becomes the only measurement. If all that is counted is process speed, automation rate, cost reduction and margin improvement, then the revolution will be economically impressive but socially thin. The arts and culture must therefore sit at the centre of digital philanthropy. AI can help preserve endangered languages, open archives, support community theatre, widen access to music and visual arts, make museums more interactive, and allow young creators to produce work that once required expensive equipment or privileged networks. But it must be done in a way that respects copyright, provenance, attribution and human creativity.

The next digital patronage should not merely fund elite cultural institutions. It should fund creative access at community level: youth studios, AI-enabled local archives, digital apprenticeships in theatre and media, public-interest datasets for culture, regional creative labs, and new forms of collaboration between artists and technologists. The industrialists who funded libraries understood that access to knowledge was a leveller. The AI giants must understand that access to creative tools can be a leveller too, provided that the human artist remains visible, respected and fairly rewarded.

Young people and the next workforce covenant

The most urgent social question of the Agentic AI age is the future of the younger workforce. Industrial Britain often absorbed young people into physical work before it had built a proper architecture of education and protection. Historians have shown the severe reality of child labour in the late eighteenth and early nineteenth centuries. We should not make an equivalent mistake in the digital age by allowing young people to become casual passengers in systems they do not understand, cannot govern and cannot economically influence.

The next workforce covenant must begin with capability. Young people need AI literacy, but not in the superficial sense of knowing how to write prompts. They need to understand data, ethics, verification, model limitations, cyber risk, intellectual property, human-centred design, critical thinking, collaboration and the difference between automation and judgement. They need to learn how to work with agents, supervise agents, challenge agents and build agentic workflows that are safe, explainable and productive. They also need the confidence to ask moral questions: Who benefits? Who is excluded? What is being measured? What is being hidden? What happens when the system is wrong?

This is why philanthropy in the AI age should not only fund scholarships after the fact. It should create living bridges between education and enterprise. It should support apprenticeships, fellowships, local AI academies, civic innovation studios, arts-and-technology residencies, teacher training, open courseware and safe sandboxes where young people can practise with real tools on real problems. In previous blogs I have argued that the next digital workforce will not be created by software alone. It will be created by the deliberate combination of enterprise strategy, upskilling, governance, cultural confidence and human imagination. Agentic AI makes that argument more urgent, not less.

The AI giants and the digital philanthropy age

The current AI revolution is being shaped by a small number of extraordinary organisations: model builders, cloud providers, semiconductor companies, frontier AI laboratories, platform companies and the foundations connected to them. They have the ability to influence education, labour markets, culture, research, healthcare, security and public administration at a scale that earlier industrialists could barely imagine. That power brings a direct responsibility.

Encouragingly, there are signs that this responsibility is being recognised. In May 2026, Reuters reported that the OpenAI Foundation committed $250 million to help workers and economies navigate AI disruption, including research on labour-market impact and support for communities affected by automation. The same month, Anthropic and the Gates Foundation announced a $200 million partnership to support AI-related public goods in areas including health and education. Google has also announced a $1 billion initiative to support AI training and tools for U.S. higher education institutions and nonprofits. Microsoft has made large commitments to AI and cloud education, including programmes aimed at equipping millions of people with AI skills. These are significant signals, not because they solve the problem, but because they indicate the shape of a possible new covenant.

However, digital philanthropy must go beyond donations, credits and announcements. The lesson from Carnegie is not simply that he gave money; it is that he helped create institutions which survived him. The lesson from Wellcome is not simply that wealth was endowed; it is that an independent mission was built around research for human health. The lesson from Bournville and Port Sunlight is that the social setting of work matters. The lesson from Tate is that cultural access can be a national asset.

The AI giants can therefore become bastions of humanity if they adopt five practical commitments. First, they should build permanent public-interest institutions, not only short-term grant programmes. Secondly, they should support independent evaluation of AI’s social and labour-market effects, including uncomfortable findings. Thirdly, they should fund the cultural and creative commons with respect for artists, writers, performers and local communities. Fourthly, they should place young people at the centre of AI transition, especially those outside elite educational pathways. Fifthly, they should treat AI governance, safety, transparency and inclusion as philanthropic duties as well as regulatory obligations.

Agentic AI for social good

Agentic AI is particularly important because it moves AI from a passive tool to an active collaborator. Properly designed, AI agents can help a charity write funding bids, help a community group map local needs, help a small theatre produce accessible materials, help a young apprentice learn a technical skill, help a local authority identify road defects, help a doctor triage information, help a teacher personalise support, or help a social enterprise manage complex workflows. The social good potential is not abstract. It is operational.

But agentic systems also carry risk. If they are poorly governed, they can make decisions too quickly, reproduce bias, create plausible falsehoods, obscure accountability or displace human judgement. The answer is not to stop progress; the answer is to civilise progress. That means building AI Canvas methods, readiness assessments, governance councils, audit trails, human-in-the-loop controls, ethical procurement models and clear responsibility structures. In the same way that industrial society eventually developed safety standards, labour protections and public education, the AI age must develop the civic protocols of intelligent automation.

The opportunity is to use Agentic AI as an amplifier of social imagination. It can help philanthropists identify gaps, measure outcomes, connect donors with projects, reduce administrative waste, and support smaller organisations that lack professional grant-writing capacity. It can also democratise expertise. A young person in Liverpool, Birmingham, Nairobi or Kuala Lumpur should be able to access tools that help them learn, create, test, build and contribute. That is the real promise: not an AI revolution that merely concentrates capability, but one that distributes agency.

A new model: from charitable giving to capability giving

The digital philanthropy age should move from the idea of charitable giving to the deeper idea of capability giving. Money matters, but capability is more durable. Capability giving means giving communities access to tools, training, data, compute, mentorship, governance frameworks, cultural platforms and routes into employment. It means building the conditions in which people can solve their own problems, tell their own stories and shape their own futures.

This requires partnership. Philanthropy cannot operate in a vacuum. The strongest historical examples often involved cooperation between benefactors, civic authorities, educators, architects, librarians, artists, doctors and reformers. The same will be true now. AI philanthropy should connect model companies with universities, schools, local authorities, cultural institutions, unions, charities, enterprise bodies and young people themselves. It should respect place. The needs of a post-industrial town, a rural school, a creative cluster, a maritime city, a developing economy and a global health network are not the same.

For the C-suite, this is not merely a moral argument. It is a strategic argument. Organisations that invest in the next workforce, responsible AI and social legitimacy will be more resilient. They will understand risk earlier. They will attract better talent. They will be trusted partners to government and society. They will avoid the fragile arrogance that sometimes accompanies technological dominance. Above all, they will understand that in a complex economy, trust is an asset.

Conclusion: hope in the future

In my opinion there is a great deal of hope in the future. History does not tell us that every revolution becomes humane by accident. It tells us the opposite. It tells us that progress becomes humane when capital, conscience, governance and imagination are deliberately joined together. The Industrial Revolution created wealth and disruption; philanthropy at its best converted some of that wealth into libraries, galleries, universities, parks, villages, health research and opportunity. The Agentic AI revolution now asks for its own version of that settlement.

The giants of AI can become bastions of humanity, but only if they understand the path to success within the complex equation. The equation includes productivity, yes, but also dignity. It includes innovation, but also culture. It includes automation, but also human agency. It includes shareholder value, but also social value. It includes safety, transparency, education, creativity and the young workforce that will inherit the systems we are now building.

The challenge is not to be nostalgic about Carnegie, Cadbury, Tate, Leverhulme or Wellcome. The challenge is to learn from the architecture they left behind: durable institutions, public access, civic ambition and the belief that wealth created by a revolution carries obligations beyond the balance sheet. If the AI age can absorb that lesson, then digital philanthropy can become one of the great civilising forces of the twenty-first century. That is the hopeful path: to ensure that the most powerful technology of our time is not merely intelligent, but wise enough to serve humanity.

Selected references and source notes

1. Andrew Carnegie, The Gospel of Wealth, originally published 1889; Carnegie Corporation of New York historical text.

2. U.S. National Park Service, Carnegie Libraries: The Future Made Bright, noting 1,679 Carnegie-funded public library buildings in the United States between 1886 and 1919.

3. Tate & Lyle, Henry Tate biography, noting his gift of contemporary paintings to the nation forming the nucleus of Tate Gallery.

4. Bournville Village Trust, Bournville’s Story and Foundation of Bournville Village Trust, noting the Cadbury family’s model village and trust legacy.

5. Leverhulme Trust, History of the Trust, noting William Hesketh Lever’s industrial fortune and the Trust’s education and research mission.

6. Wellcome, History of Wellcome, noting the Wellcome Trust’s foundation after Henry Wellcome’s death in 1936 and its mission to improve health through research.

7. Jane Humphries, Childhood and Child Labour in the British Industrial Revolution, Economic History Review, 2012.

8. Reuters, OpenAI Foundation commits $250 million to help workers, economies navigate AI disruption, 27 May 2026.

9. Reuters, Anthropic and Gates Foundation launch $200 million partnership for AI in health and education, 14 May 2026.

10. Reuters, Google commits $1 billion for AI training at U.S. universities, 6 August 2025.

11. Microsoft AI and digital skills commitments as reported in 2025 public coverage of its AI education and nonprofit programmes.

Note: This essay uses historical examples as interpretive parallels rather than direct equivalences. It recognises both the achievements and limitations of industrial philanthropy and applies those lessons to the emerging digital philanthropy age.

Excutive thesis

The next digital workforce is no longer a metaphor for software adoption. It is becoming an operating reality in which human colleagues, copilots, autonomous agents, workflow orchestration tools and governed data platforms increasingly work side by side. For the C-suite, the implication is clear: the future workforce cannot be built only by reskilling today’s employees or buying agentic AI platforms. It must also be grown deliberately by developing a new generation of workers who know how to think, decide, collaborate and govern in an AI-rich workplace.

This is not simply a social-responsibility argument. It is a survival argument. Stanford’s 2025 AI Index reports that 78% of organisations were already using AI in 2024, up from 55% the year before, while private generative AI investment continued to grow strongly [1]. McKinsey’s 2025 workplace research finds that nearly all companies are investing in AI, but only 1% describe themselves as mature in deployment; the barrier is not employee readiness alone, but leadership’s ability to rewire the business around AI-enabled work [2]. Companies that fail to build the young talent pipeline for this new form of work risk creating elegant AI strategies without enough people capable of supervising, challenging, improving and responsibly scaling them.

What young people must practise and conquer

1. AI fluency, not superficial prompt literacy

Young people need to move beyond treating AI as a faster search engine or essay generator. They must understand the practical grammar of AI systems: how models are trained, where hallucination appears, how context windows, retrieval, tools and agents differ, and why outputs must be tested against evidence. Prompting remains useful, but the stronger capability is AI task design: defining the job to be done, selecting the right tool, sequencing human and machine steps, and knowing when not to automate.

2. Critical judgement at the jagged frontier

The most dangerous future employee is not the one who cannot use AI. It is the one who trusts it too much. Dell’Acqua and colleagues, working with Boston Consulting Group, showed that AI can improve performance on tasks inside its capability frontier but worsen outcomes on tasks just outside it. In their experiment, consultants using GPT-4 completed 12.2% more tasks and 25.1% faster on certain knowledge tasks, yet were 19% less likely to reach correct answers on a complex task outside the frontier [3]. Young workers therefore need disciplined scepticism: source checking, assumption testing, adversarial questioning, statistical intuition and the confidence to say, “the machine may be fluent, but it is not yet right.”

3. Agent management and digital delegation

Agentic AI changes the shape of junior work. Instead of only doing tasks, early-career employees will increasingly brief, monitor and coordinate task-performing systems. This demands a new craft: decomposing work into sub-tasks, setting success criteria, designing guardrails, auditing intermediate outputs, managing exceptions and escalating risk. Microsoft’s 2025 Work Trend Index describes the emergence of “Frontier Firms” where digital labour is embedded into strategy and workflows, and where leaders are actively considering both workforce upskilling and expanded capacity through digital labour [4]. Young people should therefore practise being “agent supervisors” before they become line managers.

4. Data, evidence and workflow literacy

Agentic AI is only as useful as the workflows and data into which it is embedded. Young people need practical literacy in data quality, provenance, bias, privacy, permissions, process mapping and measurement. They do not all need to become data scientists, but they do need to know why poor metadata, weak controls, broken integrations and unowned datasets turn promising AI into operational risk. OECD analysis makes a related point: most AI-exposed workers will not need specialist machine-learning skills, but AI will change the tasks they perform and increase the importance of management, business, digital, cognitive and emotional skills in exposed occupations [5].

5. Human advantage: communication, empathy and organisational intelligence

As AI absorbs more routine information processing, the premium on human interaction rises. Stanford’s Future of Work with AI Agents project finds that many tasks are likely to require human-agent collaboration, and that workers often prefer higher levels of human agency than technologists assume [6]. This matters. The young worker who can translate between engineers, customers, regulators, finance, operations and frontline staff will be more valuable than the young worker who merely produces technically plausible outputs. Communication, negotiation, ethical sensitivity, active listening and stakeholder management become core productivity skills, not optional soft skills.

6. Learning agility and resilient self-direction

The World Economic Forum’s Future of Jobs Report 2025 identifies AI and big data, networks and cybersecurity, and technology literacy as among the fastest-growing skills, while also highlighting creative thinking, resilience, flexibility, curiosity and lifelong learning as rising in importance [7]. This is a profound signal to educators, parents and employers. The young person entering work today should not be trained for one fixed role; they should be trained to reconfigure their role repeatedly as AI changes the economics of tasks.

Why companies must invest in young AI-native talent now

For many boards, the current workforce agenda has three pillars: enterprise AI strategy, technology investment and upskilling of existing employees. All three are necessary. None is sufficient. The missing fourth pillar is a deliberate young-talent strategy for the agentic era.

First, existing workforces carry the organisation’s tacit knowledge, customer memory and execution discipline. They must be upskilled, not discarded. Empirical evidence shows why this is valuable: Brynjolfsson, Li and Raymond found that a generative AI assistant in customer support increased productivity by 14% on average, with particularly large gains for novice and lower-skilled workers, suggesting that AI can transmit elements of expert practice and shorten the learning curve [8]. Properly used, AI can make early-career development faster and more equitable.

Second, however, incumbents alone cannot refresh the organisation’s mental model quickly enough. Young people who have grown up with AI-enabled learning, media, coding, creation and collaboration may bring a more natural sense of human-machine teaming. They can challenge legacy process assumptions, prototype agentic workflows, test new customer interfaces and expose where governance is too slow or too brittle. This does not mean putting youth in charge of enterprise risk. It means pairing young AI-native talent with experienced domain leaders in structured apprenticeship models.

Third, companies that ignore young talent will damage their future option value. Agentic AI will create new roles: agent operations manager, AI workflow designer, model-risk analyst, synthetic-data steward, AI product ethicist, human-agent experience designer, automation assurance lead and many more. These jobs will not be filled adequately by waiting for the market to produce mature candidates. They must be cultivated through internships, graduate rotations, apprenticeships, university partnerships, hackathons, internal academies and board-visible talent pathways.

The C-suite action agenda

The practical recommendation is to treat young AI-ready talent as a strategic resource class alongside cloud, data, cybersecurity and AI platforms. This requires five moves.

First, define an enterprise AI skills taxonomy that distinguishes AI fluency, domain expertise, workflow design, governance, data stewardship, cyber awareness, critical reasoning and human collaboration. Avoid vague language such as “digital native”; measure real capabilities.

Second, create agentic apprenticeships. Pair graduates and early-career employees with business owners to redesign real workflows using governed AI agents. Require every project to include a business metric, a risk assessment, a human-in-the-loop design and a lessons-learned artefact.

Third, build a “young talent plus expert mentor” operating model. The strongest teams will combine youthful experimentation with experienced judgement. This mirrors the emerging reality of the next digital workforce: humans at different career stages, agents and systems working in supervised partnership.

Fourth, invest in AI governance as an enabler, not a brake. Young workers must be taught responsible use from day one: privacy, explainability, bias, security, intellectual property, record keeping and escalation. Governance should be embedded into tools and workflows so that responsible innovation is easy to practise.

Fifth, make the CEO and CHRO jointly accountable. This agenda cannot sit only in learning and development or the CIO’s office. It affects productivity, risk, culture, succession, innovation and future competitiveness. The board should ask for quarterly reporting on AI skills coverage, young-talent pipeline, adoption quality, workflow redesign, risk incidents and measurable business outcomes.

Conclusion

The agentic AI era will not reward companies that simply buy tools, announce strategies and hope the workforce adapts. It will reward companies that engineer a new compact between people and machines. Upskilling the existing workforce protects today’s performance. Investing in young AI-native talent protects tomorrow’s relevance. Building both in parallel is how companies maximise their chances of survival, renewal and growth.

For the C-suite, the choice is stark. Either develop the next digital workforce deliberately, or inherit a future in which the organisation has powerful agents, ageing skills, weak supervision and no credible talent bridge between strategy and execution. The companies that win will be those that understand that the scarcest resource in the agentic AI era is not the model. It is the human capability to direct it wisely.

Selected references

[1] Stanford HAI, The 2025 AI Index Report, 2025. https://hai.stanford.edu/ai-index/2025-ai-index-report

[2] McKinsey & Company, Superagency in the workplace: Empowering people to unlock AI’s full potential, 2025. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

[3] Dell’Acqua, F. et al., Navigating the Jagged Technological Frontier, Harvard Business School Working Paper No. 24-013, 2023. https://www.hbs.edu/faculty/Pages/item.aspx?num=64700

[4] Microsoft WorkLab, 2025: The year the Frontier Firm is born, Work Trend Index, 2025. https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born

[5] Green, A., Artificial intelligence and the changing demand for skills in the labour market, OECD Artificial Intelligence Papers No. 14, 2024. https://doi.org/10.1787/88684e36-en

[6] Stanford SALT Lab, Future of Work with AI Agents, 2025. https://futureofwork.saltlab.stanford.edu/

[7] World Economic Forum, The Future of Jobs Report 2025, 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/

[8] Brynjolfsson, E., Li, D. and Raymond, L., Generative AI at Work, NBER Working Paper No. 31161, 2023. https://www.nber.org/papers/w31161

[9] Noy, S. and Zhang, W., Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, Science, 2023. https://www.science.org/doi/10.1126/science.adh2586

Why AI transformation now belongs on the CEO agenda – and why the decisive advantage will be human, not merely technical

The argument

Over the past few years, the AI conversation in business has moved through three distinct phases. The first was fascination: generative AI as an extraordinary instrument for writing, searching, summarising and coding. The second was experimentation: pilots, sandboxes, copilots, innovation days and executive theatre. The third phase, the one now arriving, is more serious. AI is becoming a test of organisational speed. Not speed as haste, not speed as uncontrolled adoption, and certainly not speed as buying every fashionable tool in the market. I mean the deeper speed of an enterprise: the capacity to sense change, decide intelligently, redesign work, govern risk and learn faster than the environment is changing around it.

This matters particularly for large multinational enterprises carrying accumulated technical debt in tools, infrastructure, data and management practice. Many of these organisations were built for scale, resilience and control, not for continuous recomposition. Their ERP estates, data lakes, cloud migrations, procurement cycles, cyber controls, risk committees and legacy applications were not designed for a world in which intelligence is becoming embedded in every workflow. AI has exposed what was already true: the limiting factor is rarely the model. The limiting factor is the operating model.

This is the point I have tried to make in my recent writing on Generative AI and Agentic AI. The interesting question is no longer whether a model can produce an acceptable answer. The question is whether the organisation can turn that answer into a governed action, at scale, in context, with accountability. Agentic AI intensifies the issue because it shifts the discussion from tools that assist people to systems that initiate, plan, call other systems, execute tasks and learn from feedback. That is not a software upgrade. It is a challenge to the organisation’s metabolism.

What has changed

The empirical evidence now shows two truths moving together. First, adoption has accelerated dramatically. Stanford’s 2025 AI Index reported that 78% of organisations were using AI in 2024, up from 55% the previous year, while generative AI investment continued to expand significantly. McKinsey’s 2025 State of AI survey similarly describes wider use of AI and agentic AI, but also notes that many organisations are still struggling to move from pilots to scaled economic value. The pattern is clear: AI has crossed the adoption threshold, but not yet the transformation threshold.

Second, we are learning that value does not arrive evenly. Brynjolfsson, Li and Raymond’s research on generative AI in customer support found average productivity improvements of about 14%, with the largest gains accruing to less experienced workers. Dell’Acqua and colleagues, in their study with BCG consultants, described the ‘jagged technological frontier’: AI can lift performance significantly for tasks within its frontier and degrade performance for tasks outside it. This is crucial for boards. AI is not a universal accelerator. It is a conditional accelerator. It rewards judgement, task decomposition, good data, domain context and feedback. It punishes blind delegation.

This is why so many pilots disappoint. MIT’s 2025 GenAI Divide report argued that many enterprise initiatives fail because they are brittle, poorly integrated into daily work and unable to learn from context. Deloitte’s 2025 enterprise research similarly points to rising investment alongside elusive returns. IBM’s 2025 CEO research found that rapid investment has often created disconnected technology, while IBM’s 2026 CEO research reported that 83% of CEOs believe AI success depends more on people adoption than on technology itself. The message is no longer subtle: AI transformation fails when it is treated as deployment rather than rewiring.

From digital transformation to intelligent transformation

For thirty years, business transformation was largely about digitising existing processes. We put forms online, moved workloads to cloud, integrated channels, automated back offices and introduced analytics. Much of this was valuable, but it often preserved the inherited shape of the organisation. AI is different because it changes the unit of work. It can read, reason, generate, classify, converse, code and increasingly orchestrate. In agentic form, it can become a new participant in the enterprise operating system.

That creates a dangerous temptation: to insert AI into old processes and call it transformation. A CEO with a heavily indebted technology estate should resist this. If the process is broken, AI will accelerate the brokenness. If the data is fragmented, AI will make the fragmentation visible. If accountability is unclear, AI will amplify ambiguity. If middle management has been trained to protect functional boundaries, AI will not magically create cross-enterprise flow. The organisation will merely become faster at revealing its own incoherence.

The better question is: where are the enterprise constraints that AI now makes negotiable? Which approvals exist because information used to be scarce? Which reports exist because systems could not explain themselves? Which roles exist to reconcile data that should never have been inconsistent? Which customer journeys are slow because the organisation is divided by internal functions rather than external outcomes? Which technical debt has been tolerated because the cost of change was historically too high? AI changes the cost curve of coordination, but only if leadership is willing to challenge the contracts embedded in the organisation.

What it means to build organisational speed

Organisational speed is not the same as moving quickly. Many organisations are already fast in the wrong places. They can launch pilots quickly, buy tools quickly and issue press releases quickly. The more valuable form of speed has five characteristics.

The first is speed of sense-making. Leaders need the ability to detect where AI is changing customer expectations, cost structures, risk profiles and competitive boundaries. This requires external scanning, internal telemetry and board-level fluency. A board that treats AI as a technology topic will be late; a board that treats AI as a strategic discontinuity has a chance.

The second is speed of decision. AI opportunities decay when they are trapped in committees designed for yesterday’s risk. This does not mean weakening governance. It means designing governance that is proportionate, informed and close to the work. Responsible AI, security, data protection and model assurance must be built into the delivery system, not bolted on as a final inspection.

The third is speed of learning. Organisations must move from pilot culture to learning culture. A pilot asks whether a tool works. A learning system asks what changed in the work, what was adopted by people, what risk emerged, what data improved, what should be stopped and what should scale. This is where many enterprises are weakest. They accumulate experiments without compounding knowledge.

The fourth is speed of integration. The next advantage will not come from isolated copilots. It will come from connecting models to workflows, data, controls, APIs, human review, cyber policy, auditability and business outcomes. This is where technical debt becomes strategic debt. Legacy infrastructure is not merely an IT inconvenience; it is a brake on organisational learning.

The fifth is speed of trust. People will not adopt systems they do not understand, cannot challenge or believe are being used against them. Trust is not soft. It is the lubricant of transformation. Without it, employees route around new tools, managers preserve old behaviours and the organisation creates a theatre of adoption while real work continues elsewhere.

Why the deepest transformations are about people

BCG has often framed AI value through a 10-20-70 logic: a smaller proportion lies in algorithms, more in technology and data, and the majority in people, process and change. Whether one accepts the exact numbers or not, the principle is right. The transformation is ultimately human because work is social before it is technical. Decisions are made by people, exceptions are handled by people, customers trust people, risk is owned by people and culture is transmitted by people.

The World Economic Forum’s Future of Jobs Report 2025 expects 39% of workers’ core skills to change by 2030 and estimates that, in a workforce of 100 people, 59 will need training before the end of the decade. That is not an HR footnote. It is a balance sheet issue. Skills are now a strategic asset class. The enterprise that cannot reskill quickly cannot transform quickly. The enterprise that cannot redesign roles cannot capture AI value. The enterprise that treats people as recipients of change rather than authors of change will lose the very intelligence it needs.

This is particularly true for middle management. In many large enterprises, middle managers are the translation layer between strategy and work. They can either become the accelerators of AI transformation or its immune system. If they are excluded, threatened or left untrained, they will slow the transformation in rational self-defence. If they are equipped to redesign work, coach teams, manage risk and interpret AI outputs, they become the most important agents of speed.

The same is true for frontline expertise. AI systems require context. They need to learn from the people who know where processes fail, where customers become frustrated, where data is misleading, where policies contradict reality and where exceptions actually occur. In this sense, AI does not remove the need for human intelligence; it increases the premium on human judgement. The future enterprise is not a machine with people attached. It is a human institution with new cognitive infrastructure.

The CEO agenda

For the CEO of a large multinational enterprise, the practical implications are stark. First, do not allow AI to become another layer of technical debt. Every AI investment should be tested against architecture, data lineage, cyber posture, model governance and integration into real work. Second, move from use-case enthusiasm to capability building. The question is not how many pilots are running, but whether the organisation is building reusable data products, model assurance, workflow orchestration, talent pathways and decision rights. Third, make adoption a leadership discipline. Usage statistics are not enough; measure changes in cycle time, quality, customer outcomes, employee confidence and risk controls.

Fourth, create a strategic map of what must be rewired. Some processes should be automated, some augmented, some eliminated and some protected because human judgement is the source of value. Fifth, put people at the centre without romanticising the status quo. People-led transformation does not mean avoiding difficult choices. It means making those choices with clarity, fairness, participation and investment in capability.

My own view is that AI transformation is entering its second act. The first act was about possibility. The second is about organisational character. The winners will not be the firms with the most pilots, the largest tool catalogue or the loudest AI narrative. They will be the firms that build speed without losing judgement, automate without abandoning accountability, and use AI to enlarge human agency rather than merely reduce human cost.

That is why this is a CEO issue. Technical debt, process debt and skills debt have converged. AI has made the hidden friction of the enterprise visible. The question for the board is not whether the organisation should adopt AI. That decision has already been made by the market. The question is whether the organisation can rewire itself quickly enough, wisely enough and humanely enough to turn intelligence into advantage.

Selected references

Brynjolfsson, E., Li, D. and Raymond, L. R. (2023/2025), ‘Generative AI at Work’, NBER Working Paper 31161 and Quarterly Journal of Economics.

Dell’Acqua, F. et al. (2023/2025), ‘Navigating the Jagged Technological Frontier’, Harvard Business School / Organization Science.

Deloitte (2025), The State of Generative AI in the Enterprise.

IBM Institute for Business Value (2025), CEO Study: CEOs Double Down on AI While Navigating Enterprise Hurdles.

IBM Institute for Business Value (2026), CEO Study: CEOs are Reshaping C-suite Roles for the AI Era.

McKinsey & Company (2025), The State of AI: Global Survey 2025.

MIT NANDA (2025), The GenAI Divide: State of AI in Business 2025.

Stanford HAI (2025), Artificial Intelligence Index Report 2025.

World Economic Forum (2025), The Future of Jobs Report 2025.

Source URLs consulted:

https://hai.stanford.edu/ai-index/2025-ai-index-report

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

https://www.nber.org/papers/w31161

https://www.hbs.edu/faculty/Pages/item.aspx?num=64700

https://www.deloitte.com/uk/en/issues/generative-ai/state-of-generative-ai-in-enterprise.html

https://newsroom.ibm.com/2025-05-06-ibm-study-ceos-double-down-on-ai-while-navigating-enterprise-hurdles

https://newsroom.ibm.com/2026-05-04-ibm-study-ceos-are-reshaping-c-suite-roles-for-the-AI-era

https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

https://www.weforum.org/publications/the-future-of-jobs-report-2025

Why boards and CEOs must look beyond application-layer productivity and ask which hidden contracts in the business technology stack are about to be renegotiated by Agentic AI

The wrong conversation is winning

In recent posts I have argued that Generative AI is not simply a tool for producing more words, more code, more images or more slideware. I have also argued that Agentic AI is not merely “automation with a better user interface”. Agentic AI changes where agency sits in the organisation. It moves decision, orchestration and execution into systems that can observe, reason, act, test, learn and recover. That is why I keep returning to governance, accountability and economic impact. The technology is interesting, but the transfer of agency is the point.

Most board conversations, however, are still stuck in the most visible part of the game. They ask whether developers will be 20% or 40% faster. They ask whether a copilot can clear the technology backlog. They ask whether the customer service team can answer more tickets with fewer people. These are legitimate questions, but they are scoreboard questions. They tell us whether today’s team is running harder under today’s rules. They do not tell us whether the rules of the sport are being rewritten.

The deeper AI story is not that software teams are being given better boots. It is that the pitch, the rules, the coaching staff, the refereeing system, the medical science, the broadcast model and the business model of the club are all becoming writable at the same time. Boards that only watch the striker will miss the change in the entire league.

From productivity story to paradigm story

For fifty years, the technology stack has behaved like a professional sport with rigid divisions of labour. The players play, coaches coach, referees referee, grounds teams maintain the surface, broadcasters package the spectacle, and owners negotiate commercial rights. Each role has its own contract, language, incentives and power base. In technology, those contracts are the application interface (API’s), the runtime, the operating system, the compiler, the instruction set and the silicon. We have treated them as fixed because changing them together was prohibitively expensive.

The application developer did not negotiate with the chip. The chip designer did not negotiate with the customer journey. The compiler expert did not sit in the boardroom discussing revenue leakage. The contracts were not laws of physics. They were historical settlements that lasted so long they started to look inevitable.

Large language models (LLM’s) and Agentic systems now challenge that assumption. They can work across the boundaries that humans built around expertise. They can write application code, generate compiler optimisations, synthesize kernel extensions, assist chip design, support formal verification and help redesign silicon layouts. They do not respect the old dressing-room politics because they did not grow up inside them. They ask a question most organisations have been structurally unable to ask: what happens if several layers of the game can be changed together?

That is why this is not primarily a productivity story. It is a paradigm story. Productivity improves the existing formation. Paradigm change alters what a formation is.

The evidence is already on the field

This is not science fiction. At the compiler layer, Meta’s LLM Compiler was trained on 546 billion tokens of LLVM intermediate representation and assembly code. Its published results show 77% of the optimisation potential of an autotuning search and 45% disassembly round-trip accuracy from x86 and ARM assembly back into LLVM IR. In plain English, models are learning the language beneath the language most software teams discuss.

At the operating-system layer, Kgent, presented at the ACM SIGCOMM 2024 eBPF workshop, translates natural language prompts into eBPF programs for the Linux kernel. Related 2025 work on agentic operating systems uses LLM agents to analyse workloads, synthesize eBPF scheduling policies and deploy them through sched_ext, reporting up to 1.79x performance improvement and a 13x cost reduction compared with naive agent approaches. The “grounds team” of the computing world is no longer only repairing turf; parts of the turf-management strategy are becoming agentic.

At the chip-design layer, NVIDIA’s ChipNeMo explores domain-adapted large language models for industrial chip design, including engineering assistant use cases, EDA script generation and bug summarisation. Google DeepMind’s AlphaChip uses reinforcement learning to generate chip floorplans in hours rather than the weeks or months traditionally required, and Google says these layouts have been used in multiple generations of TPUs and in Axion CPU work. At the instruction-set layer, RISC-V research on the XiangShan Nanhu processor extends the instruction set for LLM vector dot-product acceleration, reporting more than four times scalar-method speed on that core operation.

The important point is not that any one of these examples wins the match alone. The point is that they are appearing at multiple layers at once. This is the early sign of what I have called consurgence: new properties rising together because the conditions have changed together.

A sporting metaphor for the boardroom

Imagine a football club that has spent decades improving marginally within known constraints. It hires better strikers, buys improved boots, upgrades nutrition, deploys data analysts and improves ticketing. Each initiative is useful. Some are very profitable. But all assume the same pitch, the same league rules, the same broadcast economics and the same match-day model.

Now imagine a new class of agentic system arrives that can redesign the training plan, simulate match tactics, change the playing surface, alter recovery science, generate new scouting models, negotiate media packages, optimise stadium logistics and propose rule changes to the league. The club that asks only, “Can our striker score 10% more goals?” has misunderstood the opportunity. The better question is, “Which assumptions about the game have become negotiable?”

That is where companies are today. The visible AI market is crowded with applications: copilots, assistants, chatbots, content tools, workflow wrappers and SaaS add-ons. This is the striker market. It is exciting, noisy and overfunded. But the deeper advantage will come from the less glamorous parts of the club: the academy, sports science, data infrastructure, playing surface, referee technology, transfer analytics and league governance. In technology terms, those are compilers, runtimes, verification, operating systems, open silicon tooling, capability security and the governance of agentic action.

Why this matters to CEOs and boards

Boards do not need to become chip designers. CEOs do not need to run compiler teams. But they do need to understand where durable advantage is likely to form. If AI simply improves the application layer, then the winning strategy is adoption speed: choose tools, train staff, measure productivity and manage risk. If AI rewrites the layers beneath the application, the winning strategy is different: identify which hidden contracts shape your economics and decide whether to defend, renegotiate or escape them.

For a telco, the hidden contract may be between network operations, customer experience and vendor equipment roadmaps. For a bank, it may be between risk models, legacy core systems, regulatory reporting and the human sign-off process. For a logistics company, it may be between routing, fleet maintenance, insurance, carbon reporting and customer promises. For a public-sector body, it may be between policy intent, operational data, procurement rules and citizen outcomes. Agentic AI is not powerful because it “does tasks”. It is powerful because it can coordinate across these boundaries and reveal which boundaries were artificial.

This is also why AI governance cannot be reduced to ethics theatre or model-risk paperwork. My position has consistently been that governance must be an operating discipline. When agents can act across systems, governance must define permission, accountability, evidence, reversibility and escalation. The question is not only “Was the model fair?” It is also, “Who authorised the agent to change the play, under what constraints, with what audit trail, and who can stop it when the match changes?”

The capital allocation mistake

The current investment pattern is heavily weighted towards the obvious. Most corporate pilots and much venture capital crowd around application-layer tools because they are easy to demonstrate and easy to sell. The board can see the demo. The CFO can imagine the headcount saving. The press release almost writes itself.

But if the real renegotiation is happening below the application layer, then the smarter capital question changes. Companies should still invest in adoption, but not confuse adoption with advantage. The layers that deserve more attention are the systems that translate new compute into usable business capability: compiler and runtime infrastructure, formal verification, secure agent execution, open silicon design tooling, heterogeneous compute operating models, and hardware-enforced capability boundaries such as CHERI and Arm Morello. These are not fashionable board topics, but neither were cloud control planes, mobile app stores or semiconductor supply chains until they determined who captured the margin.

The UK policy picture illustrates the risk. The AI Opportunities Action Plan and subsequent updates rightly emphasise compute, AI Growth Zones, public-sector adoption, data assets, sovereign capability and skills. UKRI’s 2026 AI strategy commits more than £1.6 billion of targeted AI funding, while the Sovereign AI Unit is backed by up to £500 million. These are serious commitments. Yet the public framing still leans towards bedrock compute and visible adoption. The middle layers, where contracts become moats, receive far less public attention.

That is not a criticism of intent. It is a challenge to worldview. If you believe AI is another wave to be added to the existing stack, the current allocation is coherent. If you believe AI makes the stack itself writable, then the allocation looks incomplete.

The board agenda should change

The practical board agenda should now include five questions.

First: which technology contracts have we treated as fixed because changing them was historically too expensive? These may be vendor contracts, data architectures, operating models, platform dependencies, regulatory workflows or pricing models.

Second: where are we confusing productivity with strategic advantage? A 30% faster process inside a soon-to-be-obsolete model is not transformation. It is a better warm-up before the wrong match.

Third: which agents will be allowed to act, not merely advise? The moment an agent can trigger workflow, move money, reconfigure infrastructure, change a customer promise or alter a policy, the governance model must mature from advisory AI to accountable agency.

Fourth: are we investing only in the striker, or also in the academy, pitch, data room and rulebook? The visible applications matter, but they may become commoditised quickly. The less visible layers may determine lock-in, security, resilience and strategic optionality.

Fifth: what would we do differently if the cost of coordinated change fell by an order of magnitude? This is the question that forces leadership to think beyond process improvement and into new operating models.

A different game is forming

The danger for incumbents is not stupidity. It is success. Incumbents are optimised around the current contracts. Their management systems, budgets, partner ecosystems and board reporting all assume the sport remains recognisable. They will naturally describe change as incremental because their advantage is built inside the old rulebook.

History is not kind to those who manage the visible crisis and miss the structural one. Nokia, Kodak, Xerox and Blockbuster were not short of smart people. They were short of timely courage. They read the current scoreboard well and misread the future league.

The same pattern is visible now. Many leaders are debating whether AI can patch the roof of the current software business model: per-seat pricing, SaaS renewals, developer velocity, customer-service automation. Those discussions matter, but they are not enough. Agentic AI points towards a world where the unit of value may not be the application, the seat or even the workflow. It may be the agentic capability to orchestrate outcomes across systems, with governance strong enough to make that capability trusted.

The CEO takeaway

The old question was: how much faster can AI make our people? The better question is: which assumptions in our business have become negotiable?

The old question was: where can we add GenAI to the existing application estate? The better question is: where are agents about to coordinate across boundaries that our organisation chart still treats as sacred?

The old question was: should we invest in AI tools? The better question is: are we investing in the layers where advantage will compound, or only in the layer where the crowd has gathered?

In sport, the great clubs do not only buy stars. They build systems: academies, analytics, coaching, medical capability, recruitment intelligence, stadium economics and culture. They understand that sustainable advantage is rarely found only in the moment the ball hits the net. It is created in the conditions that make that moment repeatable.

That is the lesson for boards and CEOs. Generative AI has made the visible play more productive. Agentic AI is beginning to change who calls the play, how the play is executed, and whether the rules themselves can be rewritten. The scoreboard will not tell you that early enough. You have to look at the whole game.

My advice is simple: stop watching only the striker. The game is changing under the pitch.

Selected references and further reading

• Meta LLM Compiler: Foundation Models of Compiler Optimization

• NVIDIA Research: ChipNeMo – Domain-Adapted LLMs for Chip Design

• Google DeepMind: How AlphaChip transformed computer chip design

• Kgent: Kernel Extensions Large Language Model Agent, ACM SIGCOMM eBPF 2024

• Towards Agentic OS: An LLM Agent Framework for Linux Schedulers

• RISC-V XiangShan Nanhu LLM acceleration research

• UK Government: AI Opportunities Action Plan – One Year On

• UKRI Artificial Intelligence Research and Innovation Strategic Framework • Arm Morello Program and CHERI capability technology

How national control of compute, data and models will reshape data centre design, location and corporate strategy

The argument

For many years, the phrase “data sovereignty” was treated as a compliance issue: where is the data stored, who can access it, and which regulator has authority over it? AI sovereignty is different. It is about whether a nation can create, operate, audit, secure and adapt the intelligence that will increasingly sit inside public services, critical infrastructure and the balance sheets of private enterprises. In my view, this is not a fashionable policy phrase. It is the next layer of national capability, sitting alongside energy, telecommunications, defence, logistics and financial resilience.

Best practice is already converging around a wider definition. The OECD argues that national AI compute plans need to address capacity, effectiveness and resilience, including “security, sovereignty, sustainability”. [1] The UK Government has committed to expand “sovereign compute capacity by at least 20x by 2030”. [2] The European Commission’s AI Continent plan is even more explicit: AI Gigafactories are intended to train complex models, with up to five facilities mobilised through InvestAI, while the proposed Cloud and AI Development Act aims to triple EU data centre capacity in the next five to seven years. [3]

This changes the question for governments and companies. Sovereignty is no longer achieved by placing a server in a capital city and declaring victory. It requires control over the stack: data, connectivity, chips, energy, cooling, model governance, cyber resilience, jurisdiction, procurement and the ability to operate under stress.

From centralised cloud to sovereign AI fabric

The early cloud model favoured concentration: a few hyperscale regions, massive efficiency, global platforms and standardised operating models. AI will not abolish that model, but it will stretch it. Training frontier models may still require enormous specialised clusters. Inference – the daily running of AI inside customer service, fraud detection, logistics routing, insurance pricing or network operations – will be persistent, distributed and latency-sensitive.

That distinction matters. A country may not need every frontier model trained entirely inside its borders, but it will increasingly ask that sensitive inference happens under local law, on trusted infrastructure, with auditable model behaviour and operational continuity. This is why AI sovereignty will manifest as a network of national and regional AI facilities, not merely one national supercomputer.

The IEA has warned that global data centre electricity consumption is projected to more than double to around 945 TWh by 2030, “slightly more than Japan’s total electricity consumption today”. [4] This turns data centre strategy into energy strategy. The best locations will not simply be near financial districts or existing cloud hubs. They will be near power, water, fibre, subsea cable routes, heat reuse opportunities, skilled labour and politically acceptable land.

The new design principles

Sovereign AI will push data centre design in five directions. First, resilience by geography. Nations will not want a single point of compute failure. They will favour dispersed clusters: primary AI factories, regional inference nodes, edge compute in telecom networks, and fall-back capacity in allied jurisdictions.

Second, energy coupling. Data centres will be planned around renewable generation, grid constraints, private wire connections, battery storage, modular power and demand response. The UK’s emerging AI Growth Zone model points in this direction: designated zones are intended to accelerate planning, power access and data centre build-out. [5] In practice, the winning design will look less like an isolated technology park and more like an integrated energy-digital-industrial campus.

Third, sovereign operations. The debate will move from “where is the building?” to “who operates it, who has administrative access, which law applies, what foreign dependencies exist, and what happens during sanctions, cyber conflict or supply chain disruption?” This explains why Europe’s recent digital sovereignty procurement has introduced assurance concepts such as Sovereignty Effectiveness Assurance Levels, with requirements around legal, operational and supply-chain resilience. [6]

Fourth, sector-specific enclaves. Banking, telecoms, logistics, healthcare and defence will not all use the same sovereignty pattern. A national model will have to support regulated partitions: confidential computing, private LLMs, verifiable audit trails, model lineage and controlled data sharing between government and industry.

Fifth, social licence. Reddit and developer forums reveal a healthy scepticism: if the chips, cables, software and cloud control planes are owned elsewhere, is sovereignty real or just branding? One Reddit contributor put the issue bluntly: “Does that count as sovereignty. Debatable. I’d say no.” [7] That sentiment matters because communities will be asked to accept more data centres, more grid reinforcement and more land use change. The value exchange has to be visible: jobs, heat reuse, skills, regional regeneration and cheaper, cleaner power.

Consequences for international companies

For telecommunications operators, sovereign AI is both a threat and an opportunity. It threatens the old carrier model where connectivity is sold as capacity alone. But it creates a new role for telcos as national AI fabric operators: providing low-latency edge compute, secure data exchange, identity, lawful intercept governance, IoT intelligence and resilience across critical networks. Operators that own fibre, mobile edge locations, data centres and trusted enterprise relationships can become anchor institutions in sovereign AI ecosystems.

For banks and financial services firms, the implications are equally material. AI models will sit inside credit, fraud, trading surveillance, customer vulnerability, cyber defence and regulatory reporting. Boards will increasingly ask: can we explain where the model ran, what data it touched, whether the regulator can audit it, and whether we can continue operating if a foreign cloud service is disrupted? The likely response is hybrid: global cloud for scale, sovereign cloud for regulated workloads, and private model environments for the most sensitive functions.

For logistics companies, AI sovereignty intersects with physical sovereignty. Ports, warehouses, shipping lanes, border systems, customs data, predictive maintenance and fleet optimisation all depend on real-time data. If AI controls routing or risk scoring, then data centre geography becomes part of supply chain resilience. A logistics group operating across Europe, Asia and the Middle East may need regional AI nodes aligned to trade corridors, not merely corporate headquarters.

For hyperscalers and international technology firms, the message is clear: sovereignty cannot be dismissed as protectionism. It is becoming a customer requirement. The winners will be those able to offer sovereign controls without destroying interoperability: local legal entities, transparent operational models, encryption and key sovereignty, local support teams, exit rights, open standards and credible partnerships with national champions.

The policy choice

There is a danger that governments confuse sovereignty with autarky. Full national independence across chips, models, cloud software, energy systems and talent is unrealistic for most countries. The better goal is strategic optionality: enough domestic and allied capability to avoid coercive dependency, enough openness to remain innovative, and enough governance to earn trust.

In practical terms, the next generation data centre will be judged not only on power usage effectiveness, cost per rack or GPU density. It will be judged on sovereign value: does it strengthen national resilience, support local industry, protect sensitive data, reduce carbon intensity, improve public services and give domestic firms a route into the AI economy?

AI sovereignty therefore marks a behavioural shift. Data centres will stop being hidden technical real estate and become visible instruments of industrial policy. Their geography will follow power and politics as much as fibre and land. Their design will embed trust, auditability and continuity. And for international companies, the strategic question will no longer be whether they use AI, but whether their AI can operate legitimately, resiliently and locally in every jurisdiction that matters.

That, I suspect, is the real meaning of AI sovereignty. It is not the ownership of a machine. It is the ability of a nation, and the companies operating within it, to shape the intelligence on which their future depends.

References and source notes

[1] OECD, “A blueprint for building national compute capacity for artificial intelligence”, 2023; OECD AI Compute topic page, 2025/26. Quote: national plans should address “security, sovereignty, sustainability”. https://www.oecd.org/en/publications/a-blueprint-for-building-national-compute-capacity-for-artificial-intelligence_876367e3-en.html and https://www.oecd.org/en/topics/ai-compute.html

[2] UK Government, AI Opportunities Action Plan: government response, 13 January 2025. Quote: “expand our sovereign compute capacity by at least 20x by 2030”. https://www.gov.uk/government/publications/ai-opportunities-action-plan-government-response/ai-opportunities-action-plan-government-response

[3] European Commission, AI Continent Action Plan factpage, 7 May 2025. Quote: AI Gigafactories will be “4x more powerful than AI Factories”; the Cloud and AI Development Act aims to “Triple the EU’s data centre capacity in the next 5-7 years”. https://digital-strategy.ec.europa.eu/en/factpages/ai-continent-action-plan

[4] International Energy Agency, Energy and AI: Executive summary, 2025. Quote: data centre electricity consumption is set to more than double to around “945 TWh by 2030”. https://www.iea.org/reports/energy-and-ai/executive-summary

[5] UK Government, AI Opportunities Action Plan: One Year On, 29 January 2026. Quote: the UK has “designated 5 AI Growth Zones, unlocking investment and accelerating data centre buildout”. https://www.gov.uk/government/publications/ai-opportunities-action-plan-one-year-on/ai-opportunities-action-plan-one-year-on

[6] ITPro, “European Commission awards digital sovereignty contracts”, 20 April 2026. Reported reference to Sovereignty Effectiveness Assurance Levels (SEAL) and digital sovereignty criteria. https://www.itpro.com/cloud/cloud-computing/european-commission-awards-digital-sovereignty-contracts-backs-google-cloud-involvement

[7] Reddit discussion on sovereign AI and infrastructure ownership, 2026. Quote: “Does that count as sovereignty. Debatable. I’d say no.” https://www.reddit.com/r/I_DONT_LIKE/comments/1r76rac/idl_how_everyones_talking_about_ai_data/

[8] NVIDIA / World Government Summit coverage, 2024. Quote attributed to Jensen Huang: “Every country needs to own the production of their own intelligence.” https://www.financemiddleeast.com/fintech/every-country-needs-sovereign-ai-says-nvidias-huang/

In my recent blogs on the Agentic Digital Workforce, I have argued that the next phase of digital transformation will not be defined simply by smarter software, larger language models or more autonomous agents. It will be defined by how intelligently organisations design the relationship between people, process, data and machine intelligence. The danger for 2026 is not that companies will ignore AI. The greater danger is that they will adopt it too quickly, too narrowly and with too little thought about human judgement, organisational accountability and the long-term development of human capital.

We are entering an era in which AI agents can plan, reason, retrieve information, trigger workflows, monitor exceptions and increasingly act across enterprise systems. This is powerful, but it changes the question leaders must ask. The question is no longer, ‘Can we automate this?’ The better question is, ‘Should this decision, interaction or process be fully automated, partially automated, or deliberately retained as a human-led activity supported by AI?’ That distinction is where value, trust and resilience will be created.

The lesson from the first wave of enterprise AI adoption is clear: technology is rarely the hardest part. McKinsey’s 2025 workplace research argues that the challenge of AI at work is a business and leadership challenge, not merely a technical one. Employees often want support, training and permission to use AI productively, while leaders must rewire operating models rather than simply buy tools. Stanford HAI’s AI Index similarly shows the accelerating reach of AI across business and society, but also underlines the need for thoughtful governance as capability advances faster than many institutions can absorb.

This is why I prefer to frame the Agentic Digital Workforce as augmentation, not replacement. An agentic workforce should be a designed collaboration model: human professionals setting intent, defining boundaries, exercising judgement and taking accountability, while AI agents perform high-volume analysis, orchestration, monitoring and administrative work. In this model, the human is not a decorative approval step placed at the end of an automated process. The human is part of the system architecture.

Human-in-the-loop must therefore be more than a slogan. It should mean that suitably skilled people have context, authority and time to intervene. A nominal human checker, overloaded with machine-generated outputs and no practical ability to challenge them, is not governance. It is ‘theatre’. The EU AI Act’s approach to high-risk systems is instructive here: human oversight is intended to prevent or minimise risks to health, safety and fundamental rights. NIST’s AI Risk Management Framework also places governance, mapping, measurement and management at the centre of trustworthy AI. Both point to the same conclusion: oversight has to be designed into the lifecycle, not bolted on after deployment.

Global best practice is now converging around a few important principles. First, classify AI use cases by risk and materiality, rather than treating every AI experiment as equal to mirror George Orwell’s words “All AI models are equal, but some are more equal than others!’. Second, define decision rights: what the agent may recommend, what it may execute, and what must be escalated to a human. Third, maintain auditability: the organisation must be able to explain what data, rules, prompts, models and human approvals shaped a decision. Fourth, invest in capability building, because a workforce that does not understand AI cannot govern it effectively.

The World Economic Forum’s Future of Jobs Report 2025 makes this human capital point very strongly. It anticipates substantial labour market disruption by 2030, with both job displacement and job creation, and highlights the continuing importance of reskilling. The most responsible organisations will not interpret AI productivity as a licence to hollow out their talent base. They will use AI to raise the quality, reach and speed of human work while creating new roles in assurance, data stewardship, model supervision, customer empathy, domain expertise and AI-enabled service design.

There is also a strategic restraint argument. Not every process should become agentic. Some customer interactions are emotionally sensitive. Some decisions carry moral or legal consequences. Some knowledge work depends on tacit understanding, institutional memory, negotiation, persuasion or trust. In these domains, the right answer may be AI-supported human excellence rather than full automation. The organisation that knows when not to automate may be more mature than the organisation that automates everything it can.

Deloitte’s 2026 analysis of agentic AI makes a similar point: the winners will not be those that simply replace people with machines, but those that create new forms of human-AI collaboration. The OECD and G7 work on human-centred adoption of safe, secure and trustworthy AI in the world of work reinforces this direction, emphasising inclusion, worker engagement, risk management and social dialogue. This is not anti-technology; it is pro-value. Technology that weakens trust, increases regulatory exposure or degrades human capability is not transformation. It is operational debt.

For boards and executive teams, the practical agenda is now urgent. Every significant agentic AI initiative should have an accountable business owner, a defined human oversight model, a risk classification, a data governance assessment, a skills plan, an incident response process and a benefits case that includes human impact. Productivity should be measured not only by cost reduction, but by better decisions, faster learning, improved customer outcomes and stronger organisational resilience.

The Agentic Digital Workforce, properly understood, is not a cheaper digital substitute for people. It is a new operating model in which human capital becomes more important, not less. AI can process at scale; humans provide purpose. AI can identify patterns; humans understand consequences. AI can accelerate execution; humans carry accountability. The companies that fall into the AI trap in 2026 will be those that confuse automation with transformation. The companies that lead will be those that place people, governance and judgement at the centre of agentic design.

In short, the future is not human versus machine. It is human judgement amplified by machine intelligence, governed by clear accountability and directed toward outcomes that customers, employees, regulators and society can trust. That is the real promise of the Agentic Digital Workforce.

References and supporting evidence

• Stanford Institute for Human-Centered AI, AI Index Report 2025, https://hai.stanford.edu/ai-index/2025-ai-index-report

• McKinsey & Company, Superagency in the Workplace: Empowering people to unlock AI’s full potential at work, 2025, https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

• World Economic Forum, The Future of Jobs Report 2025, https://www.weforum.org/publications/the-future-of-jobs-report-2025/

• NIST, Artificial Intelligence Risk Management Framework, https://www.nist.gov/itl/ai-risk-management-framework

• European Union Artificial Intelligence Act, Article 14: Human Oversight, https://artificialintelligenceact.eu/article/14/

• Deloitte, Tech Trends 2026: The agentic reality check, https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2026/agentic-ai-strategy.html

• OECD/G7, Compendium of Best Practices for the Human-Centered Adoption of Safe, Secure and Trustworthy AI in the World of Work, 2025, https://www.oecd.org/

• ISO/IEC 42001:2023, Artificial Intelligence Management System standard.

Over the past decade I have written extensively about the rise of Software‑as‑a‑Service (SaaS) and how it reshaped the structure of the digital economy. In several earlier blogs I explored what I described as the “vulnerability of SaaS” in the emerging world of Agentic AI.

At the time, some readers interpreted that argument as a criticism of SaaS itself.
That was never the intention. SaaS was one of the most powerful technology and commercial innovations of the last twenty years. But every technology wave eventually becomes infrastructure for the next one. What we are now witnessing is precisely that transition.

The shift underway is not simply about better software. It is about the emergence of digital workers – autonomous, AI‑driven agents capable of performing tasks, coordinating processes, and increasingly making operational decisions. In other words, we are moving from software that people use to systems where software itself does the work.

This is the real meaning of Agentic AI. And if that trajectory continues – which the evidence increasingly suggests it will – the dominant commercial model will evolve from Software‑as‑a‑Service to something far more profound: Digital Workers‑as‑a‑Service.

The End of the Software Interface Era

To understand why this matters, we need to step back and look at how enterprise technology has evolved. For decades, enterprise software was designed around the assumption that a human user would sit at the centre of every process. Software provided the tools, dashboards, and workflows, while humans executed the tasks. SaaS refined that model brilliantly. Instead of installing complex enterprise systems, organisations subscribed to cloud platforms that were continuously updated, scalable, and relatively easy to integrate.

• Salesforce transformed customer relationship management.
• Workday modernised HR systems.
• ServiceNow digitised enterprise workflows.

But in every case the operating model remained the same: people used software. Agentic AI disrupts that assumption.

In an agentic system, the software no longer waits for instructions.  It observes data, interprets goals, and executes actions autonomously.  Human involvement shifts from execution to supervision. The implication is profound: the primary “user” of enterprise systems may increasingly be another piece of software. When that happens, the entire design logic of SaaS begins to change.

From Applications to Digital Labour

What makes this moment particularly interesting is that we are already seeing early evidence of the transition. In China, logistics giants such as Alibaba and JD.com have deployed AI systems that autonomously optimise supply chain routing across thousands of delivery points in real time. The system continuously adjusts warehouse allocation, delivery routes, and inventory positioning without human intervention.

In the financial sector, JPMorgan’s COiN platform analyses complex legal contracts using machine learning, performing in seconds tasks that previously required thousands of hours of manual legal work.

Meanwhile, in Europe, telecommunications operators are increasingly deploying AI agents to manage network optimisation. Rather than engineers manually monitoring network performance, autonomous systems detect anomalies, predict congestion, and automatically adjust network parameters.

Even in customer operations the shift is visible.
Swedish fintech company Klarna recently reported that its AI assistant now performs work equivalent to hundreds of customer service agents, handling millions of conversations with customers across multiple markets.

These examples are not isolated experiments.
They represent early manifestations of a new organisational capability: digital labour. Lowering the Barrier to Adoption. Despite the promise, however, the deployment of agentic systems remains uneven. Research across global enterprises consistently shows that while organisations are experimenting heavily with AI, relatively few have managed to scale autonomous agents across their operations. The reasons are not difficult to understand. Building agentic systems requires a combination of capabilities that many organisations simply do not possess: data infrastructure, orchestration frameworks, governance models, and the ability to continuously train and monitor AI systems. This is precisely where a new commercial model begins to emerge. Instead of building digital workers internally, organisations can increasingly subscribe to them. In the same way that SaaS allowed businesses to consume software without managing infrastructure, Digital Workers‑as‑a‑Service allows organisations to deploy autonomous agents without building the underlying AI architecture themselves.

The analogy with cloud computing is striking. Few companies today build their own data centres. Instead they rely on cloud providers such as Amazon Web Services, Microsoft Azure, or Google Cloud. The same dynamic is beginning to appear with agentic AI.

Specialist providers are developing domain‑specific digital workers that can be deployed across industries: compliance agents, procurement agents, supply chain optimisation agents, and financial reconciliation agents. For smaller organisations in particular, this model dramatically lowers the barrier to entry. A mid‑sized manufacturer, for example, may never build an advanced AI operations platform internally.  But subscribing to a digital supply chain agent that continuously optimises production schedules is entirely feasible.

New Business Models Emerge

This is where the real strategic opportunity lies. In previous technology waves, the companies that dominated were those that recognised how to translate technical capability into scalable commercial models. Google and Amazon emerged from the early internet economy. Salesforce and ServiceNow defined the SaaS era. Agentic AI will produce its own generation of platform leaders. But the opportunity is not limited to technology companies. One of the most interesting possibilities is that organisations will begin to package their own operational expertise as digital workers. Consider a global logistics firm that has spent decades refining supply chain optimisation algorithms. Instead of simply using those capabilities internally, the company could offer autonomous logistics agents to other businesses as a service.

A legal consultancy could deploy AI agents trained on its regulatory expertise to act as automated compliance advisors for smaller companies. A cybersecurity firm could provide continuous AI‑driven threat monitoring agents that operate across thousands of client networks simultaneously. In each case, the company is no longer selling software. It is selling operational capability. That distinction matters enormously.

Governance and Trust

Of course, the rise of digital workers also introduces new governance challenges. In earlier writing I have argued that AI governance must evolve beyond traditional IT risk management.  When organisations deploy autonomous agents capable of executing decisions, oversight frameworks must address transparency, accountability, and human supervision. Encouragingly, regulators and international organisations are already moving in this direction. The European Union’s AI Act establishes risk classifications for AI systems and mandates governance controls for high‑impact deployments. Similarly, the OECD and various industry bodies have developed frameworks for responsible AI deployment that emphasise auditability, human oversight, and ethical safeguards.

In practice, organisations adopting digital workers will need new internal capabilities: AI supervision roles, model validation processes, and operational guardrails. Digital workers may perform tasks, but accountability will always remain human.

Why Leaders Should Pay Attention Now

One of the most consistent lessons in technology history is that early signals of structural change are often underestimated. Cloud computing initially appeared to be simply a more convenient way of delivering software. In reality it reshaped the economics of the entire technology sector. Agentic AI may prove to be an equally transformative shift.

When digital workers become widely deployable through service models, the cost structure of organisations begins to change.  Routine operational tasks can be automated at scale, allowing human employees to focus on creativity, strategy, and complex decision‑making. Importantly, this does not imply the disappearance of human work.  Rather, it signals the emergence of hybrid organisations where human and digital workers collaborate.

In many ways, the future enterprise may resemble a mixed workforce composed of people and autonomous systems working together. For business leaders, the strategic question is not whether this shift will occur. It is how quickly.

Organisations that begin experimenting with agentic systems today will develop the operational knowledge needed to manage digital workforces tomorrow. Those that delay may find themselves competing against companies whose operational efficiency has been radically transformed by autonomous systems.

Conclusion

When I wrote about the vulnerability of SaaS in the age of Agentic AI, the argument was not that SaaS would disappear. Far from it. SaaS will remain a critical foundation of enterprise technology. But its role is changing. Instead of being the destination, SaaS increasingly becomes the infrastructure layer upon which autonomous digital workers operate. We are witnessing the emergence of a new organisational paradigm: the digital workforce. And just as cloud computing democratised access to computing power, Digital Workers‑as‑a‑Service may democratise access to advanced AI capability. If that happens, the next decade of business innovation will not simply be driven by better software. It will be driven by autonomous systems that work alongside us, augmenting human capability and reshaping how organisations operate. The companies that recognise this shift early will not just adopt new technology. They will redesign how work itself is done!

Why AI will make great consulting more valuable, not less — and why Bolgiaten’s AI Maturity Assessment is becoming an essential boardroom tool.

The old consulting pyramid was built on leverage. The next generation of consulting will be built on judgment, governance, enterprise design, and the human leadership needed to turn AI from a tool into a transformation.

For decades, the consulting business was built on a familiar structure: a broad base of junior analysts and associates feeding insight upward to a narrow band of partners and senior advisers. That model rewarded scale. Firms could deploy teams of smart graduates to gather data, build decks, perform benchmarking, document processes, and power the analysis behind recommendations. It was efficient, profitable, and deeply entrenched.

Artificial intelligence is now breaking that structure apart.

The market has been quick to notice the obvious part of the story: work once assigned to junior consultants can increasingly be completed faster, cheaper, and often more consistently by AI-enabled tools. Research synthesis, first-draft presentations, pattern recognition, market scanning, scenario generation, and parts of due diligence no longer require the same labor model they did even two years ago. In professional services, this is not a marginal productivity gain. It is a structural shock.

Yet this is only half the truth. The deeper truth is more important for clients, advisers, and firms deciding what kind of business they want to become. The same force that is eroding the old consulting pyramid is creating a much larger market for a new kind of consultancy: one built on judgment, enterprise architecture, governance, change leadership, and the disciplined translation of AI capability into operating reality.

This is the paradox at the heart of consulting’s AI moment. AI destroys low-level advisory work while simultaneously expanding the need for high-value advisory work.

The New Scarcity Is Not Analysis. It Is Integration.

The analytical scarcity that once justified large consulting teams is fading. What organizations increasingly lack is not information, but the ability to integrate AI safely, strategically, and at scale. Many enterprises now have pilots, proofs of concept, and isolated use cases. Far fewer have an enterprise-wide model that links AI strategy to governance, process redesign, workforce capability, data readiness, risk controls, and measurable commercial outcomes.

That gap is where the next generation of consulting value sits.

Recent global research points to the same conclusion from different angles. McKinsey has reported that while almost all companies are investing in AI, only a tiny minority describe themselves as genuinely mature in adoption, and the major barriers are leadership alignment, operating change, and scaling discipline rather than employee enthusiasm alone. NIST’s AI Risk Management Framework reinforces that AI deployment is not simply a technical issue but a governance and lifecycle challenge. The OECD’s AI Principles and its recent work on enterprise adoption likewise emphasize trustworthy governance, human-centered design, transparency, and capability-building as prerequisites for durable value creation. In Europe, the phased implementation of the EU AI Act is pushing organizations to translate AI ambition into documented controls, accountability, literacy, and risk-based operating practices.

Taken together, these developments point to a simple reality: enterprises do not need more AI theatre. They need AI orchestration.

This is why senior advisory work is becoming more valuable. The enterprise challenge is no longer “Can AI do this task?” It is now “How should this business redesign itself so that AI creates measurable value without creating unmanaged risk, fragmented workflows, regulatory exposure, or employee resistance?”

That question cannot be answered by a chatbot alone.

From Project Work to Enterprise Transformation

The strongest global practice is moving beyond isolated use cases towards enterprise transformation. Leading organizations are not treating AI as a bolt-on technology layer. They are redesigning decision flows, clarifying governance, upgrading data foundations, defining accountable ownership, and investing in AI literacy across both executives and delivery teams.

In practical terms, best practice now rests on six connected disciplines.

First, strategy. High-performing organizations are explicit about where AI will create value and where it will not. They prioritize a small number of mission-critical business outcomes rather than chasing dozens of disconnected experiments.

Second, operating model. AI needs a home inside the organization. That means clear sponsorship, role definition, investment logic, model ownership, and a decision-rights framework that prevents innovation from becoming chaos.

Third, data and technology foundations. AI maturity is constrained by the quality, accessibility, and governance of enterprise data. No amount of enthusiasm compensates for poor metadata, fragmented systems, or weak integration architecture.

Fourth, governance and trust. Responsible AI is no longer a compliance side note. It is a business requirement. Firms need controls around model risk, human oversight, security, auditability, third-party tools, and policy compliance. This is especially urgent for regulated sectors and for organizations operating across jurisdictions.

Fifth, workforce and change. The organizations that succeed treat AI adoption as a human transformation. They redesign roles, reallocate work, retrain managers, and engage employees early. Change management is not the packaging around the transformation; it is the transformation.

Sixth, value realization. Mature adopters define metrics in advance. They measure cycle-time reduction, cost-to-serve, quality uplift, revenue impact, risk reduction, and adoption depth. Without this discipline, AI becomes another innovation story rather than a business result.

Every one of these domains is advisory-intensive. None can be solved by technology procurement alone. This is why consulting is not disappearing. It is being re-priced around deeper capability.

Why the Old Pyramid Is Collapsing

The traditional consulting pyramid assumed that clients would continue paying for labor-intensive analytical assembly. That assumption no longer holds. If AI can compress work that once took five analysts and two weeks into a few hours of guided review, then the economics of leverage change dramatically. Clients will be less willing to fund armies of junior staff producing outputs that can now be generated, compared, and refined by machines.

This does not mean junior talent becomes irrelevant. It means the apprenticeship model must change. Tomorrow’s consultants will need stronger problem framing, industry context, facilitation, governance awareness, and data fluency much earlier in their careers. The premium will shift away from producing slides and toward shaping decisions.

For consulting firms, this creates a stark strategic choice. They can defend the old model and watch margins erode, or they can redesign around senior expertise, domain-led teams, AI-enabled delivery, and repeatable transformation frameworks. The winners will not be those with the largest bench. They will be those with the clearest method for helping clients move from experimentation to enterprise maturity.

The Bolgiaten Proposition: AI Maturity Assessment as a Strategic Entry Point

This is exactly why Bolgiaten’s AI Maturity Assessment is not a nice-to-have diagnostic. It is an essential executive instrument.

Most enterprises are currently trapped between ambition and execution. Boards want AI value. Business units want faster tools. Risk teams want assurance. IT wants standardization. HR worries about capability and workforce impact. Legal and compliance want clarity on obligations. Everyone is right, but very few organizations have a common picture of where they actually stand.

An AI Maturity Assessment solves that problem.

At its best, such an assessment gives leadership a clear, evidence-based view of current capability across the dimensions that matter most: strategy, governance, data readiness, technology architecture, operating model, workforce capability, responsible AI controls, and value realization. It reveals where the enterprise is genuinely ready, where it is exposed, where investment should be prioritized, and what sequence of actions will unlock scale.

For Bolgiaten, this creates a compelling market proposition.

First, it establishes a trusted advisory entry point. Instead of selling abstract AI transformation, Bolgiaten can begin with a structured diagnosis grounded in enterprise reality.

Second, it converts uncertainty into a roadmap. Clients do not simply receive a score; they receive a staged transformation pathway tied to business outcomes, risk posture, and organizational readiness.

Third, it creates board-level relevance. AI has now moved into the language of competitiveness, resilience, compliance, and workforce redesign. An assessment translates technical noise into executive decisions.

Fourth, it opens downstream consulting opportunities. Once maturity gaps are visible, the follow-on demand becomes clear: governance frameworks, operating model redesign, use-case prioritization, AI policy development, vendor evaluation, workforce capability building, and enterprise change management.

In other words, the assessment is both a client value tool and a consultancy growth engine.

Why This Is a Massive Consultancy Opportunity

The opportunity is massive because nearly every medium and large enterprise now needs the same sequence of support. They need to understand their AI maturity. They need to prioritize use cases. They need to redesign processes. They need to establish governance. They need to upskill leaders and teams. They need to embed trust, compliance, and accountability. And they need to prove measurable value.

That demand is horizontal across industries and vertical within them. Financial services, telecoms, public sector, logistics, infrastructure, energy, health, and professional services all face the same core challenge: AI cannot remain a pilot portfolio. It must become an enterprise capability.

This is precisely the territory where seasoned consulting earns its keep. The work is cross-functional, politically sensitive, operationally complex, and deeply human. It requires facilitation, judgment, pattern recognition, and the ability to move senior stakeholders from fragmented enthusiasm to coordinated action.

That is why the future consultancy will look different. It will be smaller at the base, stronger at the center, and far more valuable at the top. It will use AI aggressively in delivery, but it will sell wisdom, not labor. It will package diagnostics, roadmaps, governance architectures, and transformation methods. It will blend technology fluency with organizational design and change capability.

The Bottom Line

The consulting industry is not facing extinction. It is facing selection.

The firms under pressure are those still organized around work that AI now performs adequately. The firms that will grow are those that understand AI as a force that raises the premium on human judgment. As analytical work becomes automated, the value migrates upward to synthesis, leadership, architecture, governance, and change.

The pyramid is collapsing. But what rises from its foundations will be something more strategic and more durable: a professional services model built not on scale, but on wisdom; not on volume, but on vision.

And in that new model, tools such as Bolgiaten’s AI Maturity Assessment will become indispensable. They provide the starting point every serious enterprise now needs: an honest view of readiness, a practical route to maturity, and a disciplined bridge from AI ambition to enterprise performance.

That is not simply a service offering. It is the gateway to the next great consultancy market.

Bolgiaten Offer a free one hour consultation with Professor Paul Morrissey to discuss this and other related AI issues across your organization please send a request to PJM@bolgiaten.com

Whenever technology evolves, cyber threats evolve alongside it. The arrival of autonomous and agentic artificial intelligence is accelerating that evolution in ways that many organisations are only beginning to understand. The real shift is not simply the automation of attacks, but the emergence of penetration at scale across multiple attack surfaces.

In practical terms, this means attackers will increasingly be able to automate the entire attack cycle—from reconnaissance and vulnerability discovery to credential compromise, data extraction, and deception-based intrusion. AI systems can simultaneously probe identities, applications, networks, cloud environments and human decision-makers. The result is not a single attack vector but a coordinated campaign that unfolds across an organisation’s entire digital ecosystem.

This represents a profound departure from the traditional model of cyber intrusion. Historically, human attackers focused their attention on a limited number of targets, investing time in reconnaissance before launching an intrusion. Artificial intelligence changes that equation dramatically. Autonomous tools can continuously scan for vulnerabilities across thousands or millions of potential targets, learning from each interaction and refining their approach in real time.

The implication is clear: the future threat environment is defined by scale, persistence and simultaneous pressure across multiple attack surfaces.

Penetration at AI Scale

Human cybercriminals have historically been constrained by time and operational capacity. Identifying vulnerable systems, crafting convincing phishing campaigns, or attempting credential theft required careful manual effort. AI-enabled systems remove many of these constraints.

Autonomous tools can perform reconnaissance continuously, mapping attack surfaces across identities, APIs, cloud infrastructure, and enterprise systems. They can generate and test thousands of phishing messages, automatically adapt social engineering techniques, and exploit exposed credentials within minutes of discovery.

The attack does not occur in a single place. Instead, it unfolds across multiple surfaces simultaneously:

• Identity systems such as authentication platforms and privileged accounts
• Cloud infrastructure and software-as-a-service environments
• APIs and interconnected digital services
• AI models and data pipelines themselves
• Human users targeted through increasingly convincing deception

This is what penetration at scale looks like: not one entry point, but many potential openings tested continuously until one succeeds.

And once access is achieved, AI-driven tools may accelerate lateral movement, privilege escalation and data discovery far more quickly than human attackers could manage. Sensitive data can be identified, aggregated and exfiltrated automatically, while malicious software can be inserted to enable future exploitation.

At the same time, organisations themselves are rapidly deploying AI agents across their operations—from customer service and internal knowledge management to supply chains and decision support. While these systems deliver clear efficiency gains, they also introduce new vulnerabilities and attack surfaces that traditional cybersecurity frameworks were not designed to address.

In particular, researchers have highlighted the risk of prompt injection attacks, data poisoning, model manipulation and agent misalignment. These vulnerabilities allow malicious actors to manipulate AI systems themselves, turning internal automation tools into potential attack vectors.

In short, the defensive environment is becoming more complex at the same moment that offensive capability is becoming more automated.

A New Cybersecurity Landscape

We are therefore entering a new phase of cybersecurity where defence must operate at the same scale and speed as AI-enabled threats. Reactive models of cybersecurity—where incidents are analysed and mitigated after detection—will increasingly struggle to keep pace with automated attacks unfolding in real time.

Governments and regulators are already recognising this shift. Emerging initiatives such as AI risk management frameworks, secure AI system development guidance, and new cybersecurity standards are being developed to help organisations manage these risks. The direction of travel is clear: cybersecurity must become more proactive, predictive and resilient.

For businesses, this means developing a cybersecurity playbook designed specifically for the AI era.

A Cybersecurity Playbook for the Agentic Era

Every organisation should now be developing a strategic framework that prepares it for penetration attempts occurring simultaneously across multiple attack surfaces.

The first element of such a playbook is governance. Organisations deploying AI systems must implement clear policies defining how those systems operate, what data they can access, and how their actions are monitored. Robust identity and access management is essential, alongside detailed logging and audit mechanisms capable of tracking both human and machine decision-making.

Second, incident response strategies must evolve. Traditional response processes assume that human analysts investigate threats and then take action. When attacks unfold at machine speed, that model becomes increasingly impractical.

Defensive systems will need automated containment capabilities capable of isolating compromised services, revoking credentials, and limiting lateral movement in real time. This raises an important governance question for leadership teams: when should automated systems be authorised to take disruptive action in order to protect the organisation?

In many cases, cybersecurity platforms will need authority to shut down systems or restrict operations temporarily to prevent wider compromise. Determining where those boundaries lie will become a critical leadership decision in the coming years.

Third, organisations must prioritise workforce awareness. AI-powered deception techniques—including deepfake audio, synthetic video, and highly personalised phishing—are becoming increasingly sophisticated. Security awareness cannot remain confined to IT departments; it must become a universal organisational capability.

Employees need training to recognise emerging forms of manipulation and to understand the role they play in maintaining cyber resilience. Just as importantly, training programmes must evolve continuously as new attack techniques emerge.

Finally, organisations must remain aligned with emerging standards and frameworks. Cybersecurity policies that remain static will rapidly become obsolete in a rapidly evolving threat environment. Continuous review against global best practices ensures that defensive strategies remain current.

The Strategic Message

If there is one central message for business leaders, it is this: the emergence of AI-enabled penetration at scale across multiple attack surfaces represents more than simply another cybersecurity threat.

It represents a transformation of the entire threat landscape.

Defensive strategies built for a slower, more predictable era of cyber intrusion are no longer sufficient. Organisations must now prepare for a world in which attacks occur continuously, adapt dynamically, and operate simultaneously across infrastructure, software, identities, data and human behaviour.

In such an environment, cybersecurity resilience depends not only on stronger tools but on stronger strategy.

The organisations that succeed will be those that recognise the scale of this transformation early, rethink their security playbooks, and build defences capable of operating at the same speed and scale as the threats they face.

Why the Real Economic Impact of AI Is Harder to Measure Than You Think.

Over the past year I have had many conversations with executives, board members, and investors about Agentic AI and the profound changes it promises to bring to organisations. The tone of these discussions is usually enthusiastic, and understandably so.

We are told that AI agents will unlock new revenue streams, dramatically increase productivity, and automate complex workflows across the enterprise. Marketing teams expect faster campaign creation, customer service leaders expect 24-hour support automation, finance departments expect automated reconciliation, and operations teams expect continuous optimisation. In short, everyone is focused on the upside.

But there is a question I often ask in boardrooms and strategy sessions that tends to bring the conversation to a pause:

How do you actually measure the real economic value of AI?

Because while everyone is excited about the promise of increased revenue and operational efficiency, far fewer organisations are measuring the full economic impact of AI — including the hidden risks that come with deploying autonomous or semi-autonomous AI agents. And those risks can be significant.

The Problem with Simplistic ROI Thinking

Most AI business cases presented to CFOs follow a predictable format.

They focus on two numbers:

1. Revenue growth
2. Operational efficiency

This is a reasonable starting point. AI can absolutely help organisations generate new revenue opportunities and reduce operational costs. But it is only part of the picture. What is often missing from these models is a third and much more complex factor: Intangible Benefits. (IB)

These can be positive — such as improved customer experience, faster innovation, or stronger competitive positioning.

But they can also be negative, — And when negative intangibles occur in the context of AI systems, they can escalate quickly. Before discussing those risks, it helps to introduce a simple framework I often use when discussing AI economics with executive teams.

A Practical Metric for Measuring AI Value

One way to frame the discussion with finance leaders — particularly the CFO, who is usually the most sceptical person in the room — is to express the impact of AI in terms of Economic Impact  (EI) relative to the organisation’s financial scale.

The metric I use is the following:

Economic Impact (EI) = (∆ Revenue + ∆ Efficiency + Intangible Benefits) / EBITDAR

Where:

• Δ Revenue represents the incremental revenue generated by AI initiatives (Use Cases)
• Δ Efficiency represents measurable improvements in productivity or cost reduction
• Intangible Benefits (IB) capture both positive and negative strategic effects
• EBITDAR represents Earnings Before Interest, Taxes, Depreciation, Amortisation and Restructuring (or Rent), which effectively normalises the organisation’s operating scale

Why divide by EBITDAR?

Because doing so contextualises the Economic Impact (EI) relative to the size of the organisation. A £5 million efficiency gain means something very different to a company with £20 million EBITDAR than it does to one with £500 million.

This framework gives the CFO a common financial language in which to evaluate AI initiatives. But the most important component of the equation is the one that is most frequently ignored. Intangible Benefits. (IB)

The Hidden Side of Intangible Benefits (IB)

When organisations present AI initiatives internally, intangible benefits are usually framed in positive terms:

• improved decision-making
• faster response times
• enhanced customer experiences
• stronger brand perception

All of these are real.

However, what is often underestimated are the negative intangible impacts that can emerge from poorly supervised AI systems. Particularly when organisations begin deploying autonomous AI agents.

AI agents are powerful because they can act independently — analysing information, making decisions, and executing tasks across multiple systems. But autonomy without governance creates new categories of risk.

Three deserve careful attention.

1. Data Leakage

AI systems depend heavily on data.

When those systems are connected to internal knowledge bases, customer records, contracts, or intellectual property, the risk of data leakage becomes significant.

This can occur in multiple ways:

• sensitive data being exposed through prompts or responses
• proprietary information being incorporated into external models
• confidential customer data being accessed or transmitted improperly

The consequences can range from regulatory breaches to loss of competitive advantage. In highly regulated sectors — such as telecommunications, healthcare, or finance — the reputational damage alone can be considerable.

2. Hallucination and Customer Trust

Large language models and AI agents can sometimes generate hallucinations — confident but incorrect responses.

In internal workflows this may simply create inefficiencies.

In customer-facing systems, however, the consequences can be more serious.

Imagine an AI agent:

• giving incorrect billing information
• misrepresenting product capabilities
• generating misleading compliance guidance

The immediate impact is poor customer experience. But the deeper issue is trust erosion.

Trust, once lost, is extremely difficult to rebuild.

3. Model Drift

AI systems are not static.

Over time, models can experience drift — where their behaviour gradually deviates from expected performance.

This may occur because:

• the underlying data environment changes
• feedback loops alter model behaviour
• system updates introduce unintended bias or errors

If drift is not detected early, the organisation may continue operating under the assumption that AI outputs remain accurate. In reality, decision quality may already be deteriorating.

Reputation: The Fragile Asset

When organisations discuss AI benefits, they often overlook the fact that reputation is one of the most valuable assets any company possesses.

And reputation behaves asymmetrically. One bad event can wipe out thousands of positive interactions. I often summarise it in very simple terms:

One negative event can wipe out 10,000 positive ones.

In the context of AI, this could be:

• a widely reported data breach
• an AI-generated decision perceived as unethical
• a discriminatory algorithmic outcome
• a regulatory violation resulting from automated decision-making

These events do not just affect operations. They affect brand trust, customer loyalty, regulatory scrutiny, and investor confidence. All of which belong squarely within the Intangible Benefits (IB) component of the economic impact equation.

Why Governance Matters

None of this should be interpreted as an argument against AI. Far from it.

AI will undoubtedly become one of the most powerful productivity tools organisations have ever deployed. But the organisations that succeed will not simply deploy AI faster than others. They will deploy it more responsibly and more intelligently.

That means introducing:

• strong AI governance frameworks
• human oversight for critical decisions
• continuous model monitoring
• robust data protection mechanisms
• clear ethical guidelines for AI deployment
•  

In other words, AI should augment human judgement — not replace it entirely.

The Conversation CFOs Need to Have

Whenever I present the Economic Impact (EI) equation to executive teams, I emphasise one point. The equation is not just a financial model. It is a governance conversation.

It forces leadership teams to ask:

• What new revenue can AI truly create?
• What measurable efficiencies will it deliver?
• What positive intangible benefits will it generate?
• And critically, what negative intangible risks might it introduce?

Only by considering all four elements together can organisations measure the true economic value of AI. Because if the numerator in the equation includes hidden risks that no one is monitoring, the apparent economic impact may be overstated.

And when those risks materialise, the consequences can be sudden and severe.

Final Thoughts

AI agents will undoubtedly transform how organisations operate. They will create extraordinary opportunities for automation, innovation, and growth. But as with all powerful technologies, the benefits must be balanced with careful governance and realistic economic measurement. The organisations that thrive in the AI era will not be those that chase automation blindly. They will be those that understand both the upside and the downside and measure the true economic impact accordingly.

Why This Thinking Matters in AI Readiness.

 This type of thinking is precisely why I developed my AI Readiness Assessment methodology. Too many organisations approach AI adoption as a technology deployment exercise rather than a strategic capability transformation.

The purpose of the AI Readiness Assessment is to help organisations understand:

• where they currently stand with AI maturity
• how strong their governance and risk frameworks are
• whether their data foundations are ready for AI deployment
• how AI initiatives can be measured in terms of real economic impact

More importantly, it allows organisations to design an AI journey that is measurable, risk-aware, and sustainable. In other words, it helps organisations capture the upside of AI while ensuring the hidden risks — the ones that often sit inside the “Intangible Benefits” component of the equation — are properly understood and managed.

Because the real challenge of AI is not deploying it.

The real challenge is deploying it responsibly, strategically, and in a way that strengthens the organisation rather than exposing it to unnecessary risk.