Chapter 020　The Future of Management in the Age of AI

of AI Nineteen chapters have been spent drawing the outline of management in the Age of AI. The definition of management, the role of the executive, decision-making, competitive advantage, organization, culture, people. The questions differed. The answers pointed one way. Whatever AI becomes able to do, the choice of which future to create belongs to human beings. The last chapter of these two volumes asks what lies beyond that. Travel in this direction, and where does management arrive? What follows is not a prediction. It is a view of the ground ahead, and then a call.

1 The question — why it arises now

Chapters about the future usually become one of two things. A prophecy, or an exhortation. We take neither. The reason is simple. Prophecies miss. Exhortations do not change tomorrow’s decision. What an executive needs is neither an accurate forecast nor a stirring phrase. It is a frame for deciding today while the future stays uncertain. So what can be said about the future? Three layers. First, there is what will certainly change. AI capability will keep rising. This is a continuous change already underway, and there is no reason to doubt its direction. Second, there is what is likely to change. How the shape of firms and markets shifts as a result of that rising capability. Here the range is wide. Claims at this layer carry an explicit reservation. Third, there is what does not change structurally. However far the technology runs, some ground does not move. Misread this layer and management loses its footing. Most writing about the future mixes the three. It deduces the probable from the certain, states the probable as though it were certain, and imagines that even the fixed ground is moving. Readers come away either alarmed or reassured that nothing will happen. Neither state is material for a decision. The nineteen chapters behind us were, among other things, an excavation of the third layer. That AI cannot hold a purpose. That trust accrues only through time. That the speed of learning is the competitive advantage. That only human beings can carry responsibility. None of these moves however many model generations pass. Standing on ground that does not move, we can look at what does without flinching. That is why the question arises now. The conclusions of these two volumes are the footing from which the view ahead becomes possible. And this chapter is also a call. The future is not an object of prediction. It is an object of design.

2 Conventional answers and their limits — the three

futures now in circulation As of August 2026, three stories about AI and the future of management are in wide circulation. Each is partly right. Each leads management astray. The first story: technology will solve it The first is technological optimism. Once AI advances far enough, productivity jumps, disease is beaten back, resource constraints loosen. So we can wait for progress. This story is told most often around the technology industry. It has grounds. Technology has in fact solved a great many problems. Pessimists have been wrong repeatedly, and that record is real. Two dangers. First, technology has no direction. AI optimizes against a given objective. Which objective is given lies outside technology. What energy is used for, and who receives a capability, are settled by choice rather than by capability. Second, the story takes responsibility away from the executive. If technology solves it, the executive can wait. Management that waits stops asking about Purpose. As the first of the First Principles states, Purpose Precedes Profit — profit is the result of a purpose society has embraced. Optimism quietly erases that order. The second story: people will not be needed The second is eschatological pessimism. AI passes human intelligence, employment disappears, decision-making moves to machines. Firms concentrate into a few holders of technology, and most people are shut out of the economy. This story has grounds too. History shows that transitions inflict pain. In refusing unconditional optimism it is, in one respect, the more honest position. Two dangers here as well. First, pessimism lowers the resolution of action. If catastrophe is coming, the question of how to change this year’s capital allocation loses meaning. Fear crowds out concrete design. Second, the story wires technology directly to outcome. But as Vol. II, Ch. 015 argued, the outcome for employment is not decided by technology. It is the sum of countless management decisions. Whether the time AI frees is spent on headcount reduction or on creating new value — those choices accumulate into the shape of a society. Pessimism denies that the choice exists at all. That is what makes it the most harmful form of prophecy. The third story: nothing really changes The third may be the story most widely shared inside established enterprises. Technology fashions have come and gone. Each time there was noise, and each time the front line returned to its former shape. This one will be the same. This view has empirical backing. Many technologies did not produce the change expected of them. Organizations are more robust than they are given credit for. The danger lies here. First, the story confuses the speed of change with its depth. Change does not arrive on a single day. Quarter by quarter it looks like noise; add up five years of it and the industry is a different industry. The status quo position is correct every quarter and fatally wrong over a decade. Second, taking this position holds a firm at Level 2 of the Enterprise Redefinition Maturity Model (ERMM) — the Improvement Enterprise. Operations get sharper. AI deployment proceeds. The existing business model is never questioned. The firm becomes increasingly efficient while remaining fundamentally unchanged. That state does not present as a problem while results are good. It presents after the assumptions collapse. Three notes travel with the ERMM and should be stated wherever a level is named. Progression is not linear: organizations frequently display characteristics from multiple levels simultaneously, and the model evaluates organizational coherence rather than isolated excellence. Maturity is assessed across all five dimensions in balance, so exceptional technological capability with weak leadership redesign cannot reach a higher level. And the objective is not reaching Level 5 as rapidly as possible; different industries may require different levels of organizational adaptability. What all three lack The three stories look opposed. They share a premise. In all three, the future is not decided by anyone. It simply arrives. Technology decides it, or catastrophe decides it, or inertia decides it. Only the subject differs. The executive is seated in the audience. Future Value Theory does not accept that premise. Forecasting predicts the future. Future Vision creates it. These are two different activities. Management is the second. As the ninth of the First Principles states, Leadership Means Designing the Future — the right questions and systems rather than the right answers. Getting out of that seat is the practical meaning of these two volumes’ conclusion. As Vol. I, Ch. 008 argued, strategy is not adaptation to an environment. It is an attempt to design the environment. Sit in the audience while the picture of the future is chosen, and every strategy afterward is a reaction. The right posture toward the future is not to guess it correctly. It is to know exactly how far your own decisions reach, and to spend everything inside that range.

3 Redefinition — toward an economy measured by

Future Value Where, then, is management headed? Future Value Theory gives an answer. We are moving toward a Future Value Economy. The claim is that the axis of evaluation shifts not only for firms but for the economy as a whole. What counts as valuable activity. Where capital goes. What is called success. Those criteria move from past performance toward the capability to create the future. Why the axis moves The reason is that scarcity changes address. In the industrial era, productive capacity was scarce. So volume and unit cost measured value. In the information era, information and the capacity to process it were scarce. So the accumulation of data and the efficiency of processing measured value. In the Age of AI, analysis, prediction, optimization, and the enumeration of design options are cheap. Capabilities that were once scarce become standard equipment. As Vol. I, Ch. 006 argued, a capability that has been democratized is no longer a competitive advantage. What becomes scarce instead? The power to decide what should be created. Which societal challenge to take on. Which future should exist. That choice, and the capability to keep realizing it, are the remaining scarce resources. When scarcity moves, measurement follows. That is the skeleton of the Future Value Economy. The three layers keep their order It is worth restating a structure already set out. Value has three nested layers. At the top is Future Value — the capacity to create value that does not yet exist. Beneath it is Enterprise Value (the middle layer of value) — competitive capability, brand, people, and the capacity to leverage AI and earn trust. Beneath that is Financial Value — revenue, profit, cash flow, valuation, and share price. Each layer encompasses the one below. Financial Value is a result, not a cause. A Future Value Economy is the state in which that order becomes the ordinary sense of the economy. Today, most institutions are designed around the first layer. Quarterly disclosure, credit assessment, performance appraisal, the standard format of a business plan. Each treats past figures as a proxy for the future. That proxy worked because change was slow. The faster change runs, the less past figures explain the future. Institutions follow late. They do follow. Principle 10 — the highest purpose of the enterprise At the center of this chapter we place the last of the First Principles. Future Value Is the Highest Purpose of Enterprise. Future Value is the highest purpose of the enterprise; everything else follows. That line is the consequence of the other nine. Purpose precedes profit (Principle 1). Future Value precedes Enterprise Value (Principle 2). Capital exists to create possibility (Principle 3). The enterprise exists to redefine itself (Principle 6). Social challenges are future opportunities (Principle 7). Lay those over one another, and the final purpose of the enterprise appears. A firm does not exist in order to make a profit. Profit is the condition of continuing to exist. A firm exists in order to bring into the world value that does not yet exist. This may sound idealistic. Its practical implications are severe. If Future Value is the highest purpose, then the agenda of the executive meeting, the allocation of capital, and the appraisal system must all be consistent with it. A firm without that consistency is contradicting its stated purpose every day. Moving toward a Future Value Economy is the work of closing those inconsistencies one at a time.

4 Structure — what determines the future economy

Future Value Theory places an equation at the level of the economy as well. Future Economy = Purpose × Future Capital × AI × Human Creativity × Trust This is the Future Economy Formula. As with the other equations, it is a product. Not a sum. That property is decisive. In a sum, a weak term is covered by the others. In a product, the moment one term reaches zero, the whole reaches zero. The five terms in order. Purpose. Without a settled answer to what the economy is for, the other four have no direction. An economy with purpose at zero achieves nothing, however much it grows. Future Capital. Not financial capital alone. People, learning, trust, AI, knowledge, ecosystem, and purpose. Only when these are integrated do they become capital directed at the future. AI. The capability for execution and optimization. This term is likely to keep rising. It cannot compensate for any other term on its own. Human Creativity. The power to frame questions, choose meanings, and generate options that do not yet exist. The more AI rises, the greater the relative weight of this term. Trust. Transactions, employment, and investment all stand on trust. With trust at zero, no mechanism is accepted by society, however well it performs. The warning in this equation is plain. Growing AI alone does not grow an economy. However large the AI term becomes, if Purpose or Trust approaches zero, the product approaches zero. This is exactly the structure technological optimism overlooks. The Future Value Cycle — value circulates One more structure is indispensable for thinking about the future: the Future Value Cycle. Societal Challenges → Purpose → Future Value → Enterprise Value → Capital

→ New Challenges → Societal Progress → Greater Future Value

What the cycle shows is that value grows in a circle rather than along a line. A societal challenge gives rise to purpose. Purpose creates Future Value. Future Value becomes Enterprise Value, and Enterprise Value attracts capital. The capital attracted goes to the next challenge. The challenge resolves the problem, and the resolution creates greater Future Value. As the seventh of the First Principles states, Social Challenges Are Future Opportunities — the origins of future markets, industries, and capital. The entrance to this cycle is always a societal challenge. And the cycle stops if it is cut anywhere. It stops if Enterprise Value attracts no capital. It stops if capital never reaches a new challenge and runs only to buybacks and dividends. It stops if the challenges resolve nothing, because trust from society is lost. A Future Value Economy is that cycle turning at the scale of a society. That is where we should be going.

5 What it looks like in practice — what changes in ten

years, and in twenty What follows sketches a concrete range. These are not predictions. They are directions with high likelihood, and we do not assert them. Speed and degree will differ sharply by industry, and there will be periods of reversal. With that reservation stated once, four changes. Change 1 — the speed and unit of decision-making First, decisions get faster. This has already begun. The time consumed by analysis, scenario generation, and impact estimation is being compressed. Ten years out, the waiting time before a judgment may have largely disappeared in many firms. The constraint on management then moves from information to will. “We cannot decide because we lack the material” stops being available as an explanation. At the same time the unit of decision changes. Judgments handled by people are selected by weight rather than by count. Routine operating judgments are carried by systems, and people concentrate on the design layer — purpose, boundaries, and the allocation of responsibility. The division of roles set out in Vol. I, Ch. 005 and Vol. I, Ch. 009 becomes standard rather than exceptional. Twenty years out, the concept of formal approval may itself have changed character. From a chain of sign-offs toward mechanisms that act autonomously inside designed constraints. This change depends on institutions and law. It will very likely run behind the technology. Change 2 — the boundary of the enterprise Second, the outline of the firm blurs. Firms have defined their boundaries by employment contracts and asset ownership. Employees inside, contractors outside. But as Vol. II, Ch. 011 argued, the components of a value-creating system have already spread. People, AI, partners, universities, customers. Ten years out, it may become ordinary for a substantial share of the agents creating value to sit outside the legal entity. The organization chart moves from a map of command toward a map of collaboration. There is a caution here. The more the boundary blurs, the more a criterion is needed for what counts as this company. That criterion is Purpose. Not employment, not assets. Purpose draws the outline of the enterprise. A firm with a vague purpose loses its shape when the boundary dissolves. Twenty years out, whether “the firm” still means what it means today is not knowable. The structure in which a collective sharing a purpose creates value is likely to remain. Change 3 — where capital flows Third, the axis on which capital is evaluated moves. Capital markets today measure firms mainly by past financial results and by projections extrapolated from them. As intangible assets take a larger share, the explanatory power of that method is falling, and this has been widely observed. Ten years out, indicators that attempt to measure the capability to create future value may be in practical use. The VURA Future Index (VFI), which this series takes up, is one such attempt. A complete indicator may never appear. The recognition that financial statements alone are insufficient will keep spreading. When that happens, capital flows differently. Away from businesses that reproduce past success, toward businesses attempting territory with no market yet. As the third of the First Principles states, Capital Exists to Create Possibility, not merely to maximize return. Twenty years out, the assessment of capital allocation may be spoken of in terms of what possibilities were opened rather than what was protected. Institutional change is slow. This is the area where the range of uncertainty is widest. Change 4 — how human time is spent Fourth, and perhaps most important, the allocation of human time changes. AI returns time. Time from doing the work, from checking it, from coordinating it. The question is what the returned time is used for. Ten years out, the gap between firms is likely to show up not as a gap in AI performance but as a gap in where the returned time went. Firms that book the saving and stop, and firms that route the time into learning and creation. The first heads toward an efficient status quo. The second accumulates Future Value. This is the idea of Future Time. Future Value = Future Time × Future Capability If Future Time is zero, Future Value is zero however great the capability. Twenty years out, the meaning of the word work may also have changed. From payment for effort toward the activity of framing questions and choosing meanings. This transition depends heavily on the design of education, employment institutions, and social security. Firms cannot complete it alone. What does not change Four changes have been named. Two things that do not change deserve the same weight. First, human beings choose the purpose. This is not a limit of technology. It is a property of the concept of purpose. A purpose is a choice of meaning, and a choice of meaning is the assumption of responsibility. AI cannot assume responsibility. A choice with no subject to carry it is not a purpose. It is a calculation. However far models evolve, that structure does not move. AI Optimizes. Humans Define. AI optimizes; humans define value, purpose, and direction. Second, trust grows only through time. Trust accumulates through the repetition of promise and delivery. Repetition takes time. This is the one domain where acceleration by AI does not apply. Analysis gets faster. Design gets faster. Execution gets faster. But the reputation that this firm does what it says is a function of the number of years it has done what it says. There is no shortcut. The eighth of the First Principles states that Trust Compounds Faster Than Capital and becomes the last durable advantage. Speed here means the strength of compounding. The firm that starts accumulating earlier is the firm that gains. Lose it once, and recovery takes a long time. Holding something that can only be bought with time, in an era when everything else moves fast. That is what tells twenty years out. These two fixed points carry a practical implication. Investment in the things that change can be recovered late. Technology can be bought later; so can mechanisms. Purpose and trust cannot be bought. So an executive must move quickly on what changes while starting first on what does not. A firm that reverses the order does not catch up.

6 Questions for the executive — three things to begin

now The future looks far away. But Future Value accumulates only from tomorrow’s decisions. Three things that can be started now. First, decide where the returned time goes. Few firms track where the hours freed by AI actually went. Untracked, that time is almost certainly absorbed by existing work. It begins with measurement. Then an explicit share of it is allocated to learning and experiment. That is the entrance to Future Time. Second, read the budget from the future backward. A budget is the record of which future a firm has chosen. Spending that maintains past success, and spending on territory with no market yet. Produce the ratio. If it is close to last year’s, the firm has chosen last year’s future. Third, name one assumption that is going obsolete. This is the central question of Recognize, the first stage of Enterprise Redefinition. What assumptions about our enterprise are becoming obsolete? One is enough. If it can be named, redefinition has begun. If it cannot be named, it has not. None of the three requires a large investment or a reorganization. All three can be started at the next executive meeting. To Volumes III and IV Volumes I and II drew the outline of management in the Age of AI. An outline does not move a business. From here, a system and a procedure are needed. Vols. III and IV (Ch. 021–040) develop the system of Future Value Theory. What Future Value is. How it differs from present value. How it can be measured. The definitions of the VURA Future Index (VFI) and of Future Value Management are fixed there. How investors might evaluate Future Value is taken up as well. Vols. V and VI (Ch. 041–060) handle the practice of Enterprise Redefinition. What is redefined, in what order, and how. How a firm locates itself on the Enterprise Redefinition Maturity Model. Vols. VII and VIII (Ch. 061–080) take up enterprise value in the Age of AI directly. What determines enterprise value, if not revenue and not profit. How far trust and people can be spoken of as value. Vols. IX and X (Ch. 081–100) examine real companies. The work of testing whether the theory explains reality, on the basis of public information. If Vols. I and II handled why, everything after handles what and how. The point of this chapter, in one line. The future of management in the Age of AI does not arrive. We choose it. All three stories seated us in the audience. Technology decides, catastrophe decides, inertia decides. We decide. AI computes the future. People choose the future. Enterprises create the future. Capital flows toward the future. Society inherits the future. Nobody knows what the economy looks like in ten years. The one thing known is that it will be the sum of countless management decisions. One of them, tomorrow, is ours. Future Value is the highest purpose of the enterprise. Connect that line to tomorrow’s agenda. Management in the Age of AI begins there.

In brief

The future of management in the Age of AI does not arrive. It is chosen, as the sum of countless management decisions.
Scarcity moves from productive capacity to information, and then to the power to decide what should be created. Measurement follows.
Three things can be done tomorrow: decide where the returned time goes, reread the budget, and name an obsolete assumption.
Institutions follow late but they do follow. Quarterly disclosure and credit assessment will eventually be rebuilt from the side of Future Value.

Key concepts

Future Value Economy / Future Value / Enterprise Value / Financial Value / Future Value Cycle

The chain of ideas

Future Resource → Purpose → Future Value → Enterprise Value → Financial Value

Related first principles

Principle 10 — Future Value Is the Highest Purpose of Enterprise. Principle 7 — Social Challenges Are Future Opportunities. Principle 9 — Leadership Means Designing the Future. Principle 3 — Capital Exists to Create Possibility.

Related chapters

Vol. II, Ch. 019 “What Is the Management Model for the Age of AI?” — the skeleton of the model that carries us forward
Vol. III, Ch. 021 “What Is Future Value Theory?” — the entrance to the next volume, and the whole of the system
Vol. III, Ch. 023 “What Is Future Value?” — where the definition is fixed
Vol. IV, Ch. 040 “Where Future Value Theory Is Headed” — the destination of the Future Value Economy

Papers and companion volumes

Kadowaki, N. (2026a). Future Value Theory: A Management Framework for Enterprise, Capital, and Society in the Age of AI. VURA Working Paper Series. SSRN: https://ssrn.com/abstract=7120980 / Zenodo: 21255662 https://doi.org/10.5281/zenodo.
Kadowaki, N. (2026b). Enterprise Redefinition: Toward an Enterprise Evolution Theory for the Age of AI. VURA Working Paper Series. (Published on Zenodo; under review at SSRN)
100 Questions on Management in the Age of AI, #100 “What Future Will You Create in the Age of AI?” / #099 “What Kind of Enterprise Creates Future Value in the Age of AI?”

Chapter 020 The Future of Management in the Age of AI