Four articles in, the series has turned AI pressure into value. The opening diagnosis named the failure. The next two read the economy from the outside and tested the organisation’s character, and AI Value Has an Address gave every opportunity a stakeholder it serves.

That portfolio completes the first AIVaaS™ pillar, AI business value definition. It is value on paper. Nothing on paper moves a customer, and nothing on paper survives contact with the employees who have to carry it.

So the methodology enters its second pillar, organisation enablement. The previous article left one question open: how each stakeholder perceives what is being planned for them. That question now decides everything.

And a person can decline. A portfolio that is net positive on paper still fails when one stakeholder reads it as a threat and quietly withholds the work.

Every AI opportunity the organisation priced carries a risk it did not price. The AI transformation has to be carried out and absorbed by employees, and any one of them can veto it.

The veto is a feature of ordinary organisations, where AI transformation arrives from above and employees are simply expected to accept it. In a young technology firm, however, employees adopt AI of their own accord, so the veto rarely arises. Most organisations, though, are not like this.

Where the veto begins

Distributing value across stakeholders was the last article’s work. It is necessary, and it is not enough. What decides whether the change is accepted is not the spread on paper, but whether each stakeholder sees the value as meant for them too, and believes it will really reach them.

So, every opportunity has to be read through the stakeholders it touches, one at a time, a person, a team, or a whole department.

Four questions decide how the value lands. What the stakeholder gains. What they pay or risk, in time, control, or the job itself. How they see it, not how the plan assumes they should. And whether their support is needed for the opportunity to work at all.

One of the four carries the most weight, and it is the one most often skipped: how they see it. Perception is not what the value is. It is what they believe it is, and belief is what they act on.

The same opportunity often reads two ways at once. For example, shorter handling time is a gift to the customer and a warning to the employee who runs the process, because the time saved is the work removed. One opportunity, two perceptions, and the veto sits with the second.

This is why the stakeholder work is more than analysis. To name who an opportunity serves, and to ask how they see it, is the first act of involving them in the change. Value defined with people, rather than for them, is far harder to veto.

Underneath perception sits something slower to build: trust. How far do employees believe leadership, and the way it says the value will be shared. A plan can be sound and still be disbelieved.

Trust is built from memory. Past experience shapes it, and so does any disappointment already lived through. When earlier initiatives promised much and returned little to the employees who delivered them, the next promise lands on that record. Each unkept one makes the veto more likely.

And where employees see the value flowing mainly to the organisation, its leaders, and its owners, the veto is already forming. Not because the value is false, but because it is not theirs.

How the veto looks in practice

The veto rarely shows itself at the start. Employees often welcome AI when it first appears, because the early version lifts a tedious task off the desk. The relief is real. It is also small, and it does not last.

The fear arrives later, when employees see the rest of what the tool can do. Not the one task it took off the desk, but how much of their work it can carry, in a fraction of the time and often at higher quality.

That is the turning point. The same capability that saved an hour could, scaled across the role, reshape it or remove it. Maslow’s hierarchy is blunt about this: while survival is in question, the higher rewards wait.

What employees settle into is a quiet verdict. The value of this AI is the company’s, the saving belongs to the owner, the risk belongs to me. That verdict is the veto, and from where they stand it is rational.

The ground was thin before AI arrived. Gallup’s State of the Global Workplace 2026 puts engagement near one in five worldwide, and finds most who use AI report a productivity gain while almost one in five expect their role cut within five years.

The last number is that fear in the data. The employees who gain most from the tool are the same ones who expect it to take their role, and the fear of being replaced outweighs the gain.

A narrow value is not only a failure of communication. It is a tell about the AI Ambition behind the plan. An organisation that points AI at cost and efficiency alone, at L1 and L2, has nothing larger to offer, because it set out to save, not to build.

Behind that ambition is the organisation’s strategic posture, its character: how it reads the world outside and inside, and where it therefore chooses to create value.

A Defender posture turns inward by instinct. It protects what exists, and rarely reaches for the AI opportunities that open new value for customers or break new ground in the market. Those are the very ones that give employees a reason to move.

The posture gap becomes a motivation gap. The trap is conditional, and the condition is the opening. The arc from welcome to veto runs only where AI is introduced without a story, as a saving with no shared future attached.

Making the value theirs

Lifting the veto is not a campaign to convince employees that a one-sided deal is fair. It is the work of making the value theirs, and it runs on three moves held together: a vision from leadership, change managed through value, and the employee experience and engagement that follow.

The first move is leadership, and it leads for a reason. Employees read a change by who stands behind it, and one the leadership does not visibly own is one they can quietly refuse.

Leadership here is a vision before it is a plan. People accept the change when the organisation’s bright future is visibly their bright future too, and not a future where the gains are the company’s and the costs are theirs.

That vision carries a commitment employees can hold the organisation to. The promise is a balanced fusion of employees and AI agents, built in their favour, not against them.

The agents take over the routine that drains the day, and the employees are freed for the creative, higher work the machines cannot do. The judgement stays with the employee, and there is more of this better work ahead, not less.

What this asks of leaders is the subject of the next article. Here it is enough that the promise is credible, because a vision employees do not believe is one more reason to veto.

The second move is change management, run through value instead of through steps. The familiar models move employees along a sequence. AIVaaS™ begins from another question: what each employee stands to gain, and whether the plan lets them share in it.

The promise of a share in the value is matched by a real shift in the daily experience of work. Not lighter tasks. That is the small relief that already failed to convince. Something higher than that.

More success at the work that matters, as employees reach the goals and the mission they are there for, achieving more of it rather than doing less. And greater personalisation, as AI shapes the tools and the workplace around how each employee works best.

More confidence in the right decisions, with AI as support rather than threat. And better wellbeing, as the routine that consumed the most time falls away.

And some of the reward comes from outside the company. When the value reaches customers, their thanks and their praise return to the employees who built it, and recognition of that kind moves motivation more than any internal measure.

These are not perks. They are the conditions under which trust recovers, the veto lifts, and engagement and motivation rise, because employees now see value that is theirs.

And only then can the organisation reach the more far-reaching forms of AI. The orchestration of agents, where they take over more and more of the employees’ own work, asks for even more trust than the first steps did.

They accept it only when they already hold the end vision, have had their fear answered, and find direct and indirect value in it for themselves. Where that holds, the veto is behind them. Readiness is not a technical state, it is a willing one.

Why this matters for Ana

In the stories that open The AI Value Boardroom, Ana is the customer the organisation serves today, and the child she carries is the customer of the next, more intelligent economy.

Ana never meets the portfolio, the planning, or the veto. She meets a person, or a service a person built, and she feels whether someone cared. With a birth approaching, she reads that care more closely than before.

Care for each customer comes from the experience and the engagement of the employee behind it. This is why the employee experience (EX) and the customer experience (CX) are two sides of one coin.

People who have quietly vetoed the change deliver Ana a thinner version of everything planned for her. People who believe in it deliver what was promised. The value reaches her through people who chose to carry it, or it does not reach her at all.

The series so far has moved in three steps. The opening diagnosis showed that most AI initiatives still fail to produce measurable value, and that the missing piece is a methodology for turning ambition into stakeholder value.

Outside-In AI Impact read the economy from the outside and derived the level of AI ambition the environment demands. AI Ambition Needs the Right Character tested whether the organisation’s strategic character can carry it.

An ambition that clears both readings is credible. It has survived the environment, and it has survived the mirror. Credible, however, is not the same as productive. A credible ambition still produces nothing by itself.

Where do concrete AI opportunities come from. Which ones deserve investment first. By what measure does the organisation know they were worth it. The last two elements of the first AIVaaS™ pillar answer the first two. One function from the Foundation underneath them answers the third.

The race moved to value

Across the major research houses, the 2026 reports converge on one message, stated in different words. The conversation is no longer about whether organisations use AI. It is about whether they can turn AI into value.

PwC finds that a fifth of companies generate nearly three quarters of all financial returns from AI. The race is not decided by who adopts. It is decided by who converts.

Capgemini describes ROI measures moving beyond savings, toward revenue growth, risk management, and customer experience. Efficiency itself is becoming a commodity. Organisations that stay with efficiency-only AI hit a ceiling, because the basic savings run out.

AIVaaS™ draws one structural conclusion. If value is the race, the measure of value cannot be an afterthought. It has to be committed before any execution begins.

That is why Value & ROI Validation sits in the Foundation of the methodology, beneath all three pillars. The commitment is made up front: realized business value will be measured, not adoption. Continuously, not as a milestone at the end.

With the measure committed, the two remaining elements of the first pillar can do their work.

Value has an address

Value is relational. Something has value for someone, in some context. Stated like that, it sounds obvious, and yet most AI opportunity lists violate it in the first column. They name a technology and a process, and leave the recipient implicit.

An AI opportunity that cannot name the stakeholder it creates value for is not a business opportunity yet. It is a business wish.

It also carries a practical penalty downstream: its KPI floats. Shorter handling time is value for a customer. Lower cost per transaction is value for an owner. Less repetitive work is value for an employee. A KPI without a recipient measures nothing in particular.

An opportunity without a firm KPI falls out of every serious prioritization, because nobody can say how much it brings, and to whom.

AIVaaS™ therefore reverses the usual order of the question. Not where could AI help, but for whom should value be created, and what kind. The search runs in two directions at once, and both are mandatory.

Outward, toward customers, partners and the market: faster and more personal experience, offers that were not possible before, new sales opportunities, better connected value chains. Value in this direction shows up as revenue growth and differentiation, the difference a market notices.

Inward, toward employees, leaders and owners: a better working experience, fewer repetitive tasks, faster and better supported decisions, healthier returns. Value in this direction shows up as productivity, lower cost, and better decision quality.

Organisations that search only inward end up with AI operations. Efficient, measurable, and quickly forgotten by the market. Organisations that search only outward meet resistance, because the people who must deliver the change see no value in it for themselves.

The portfolio that works covers both directions, by design.

The series introduced four levels of AI ambition earlier, from L1, where AI supports people in their existing work, to L4, where AI is the core of the business model.

The red line on the arrow is the critical leap, the same boundary the series marked between L2 and L3. The forms to its left are within reach of L1 and L2, where AI improves existing work. Everything to its right opens only with an L3 or L4 ambition.

The stakeholder profile of the opportunities also performs a quiet diagnostic. It reveals the organisation’s actual ambition, regardless of the declared one.

Opportunities that address mostly internal stakeholders place the real ambition at L1 or L2, whatever the strategy slide says. Opportunities that reach external stakeholders in ways that were not possible before move the organisation toward L3 or L4.

A stakeholder-unbalanced portfolio is an early signal that declared ambition is higher than actual.

The stakeholder defines the value. The value defines the measure. The measure makes prioritization possible.

A portfolio, not a shopping list

Prioritization in AIVaaS™ is not the act of picking the most attractive individual ideas. It is a strategic decision about distribution, derived from the ambition the previous readings established.

The wrong question is which quadrant to invest in. The right question is what distribution across quadrants fits the organisation’s strategic position.

The quadrants come from the Value Creation Matrix. Its vertical axis measures the scope of change, from the operational model, how the organisation works, to the business model, what value it creates. Its horizontal axis measures value capture, from internal value to external value.

Four quadrants emerge. Q1, cost savings, the hygiene factor that funds the rest. Q2, revenue growth and enhanced customer value, the growth engine. Q3, the radical productivity leap, where breakthrough begins on the inside. Q4, new value forms, where differentiation and disruption live.

If the AI Ambition Matrix earlier in the series was the compass, this matrix is the X-ray. It reveals hidden value because it changes the unit of analysis.

A task focus produces Q1: automate what exists, optimise locally. A constraint focus opens Q3 and Q4: find where AI removes a critical business constraint, the one that kept a segment unserved or an economics unworkable. New value appears where the task list showed nothing.

The balance across the four quadrants is an allocation decision. As a starting point inside the methodology, not a dogma: roughly 35 to 45 percent in Q1, funding the rest, 25 to 35 in Q2, 15 to 25 in Q3, and 10 to 20 in Q4, adjusted to the organisation’s position.

The warning signs matter more than the percentages. A portfolio that sits in Q1 alone does not survive long term, because everyone is harvesting the same savings. An empty Q4, where the environment demands transformation, leaves the door open to whoever fills it first.

The matrix is a tool inside a step where business direction is already clear. It distributes a committed ambition. It does not substitute for one.

The two matrices stay distinct, and they connect. Ambition levels accumulate, because a higher level keeps doing everything the lower ones do. So ambition sets the reach of the portfolio: L1 reaches Q1, L2 adds Q2, L3 opens Q3, L4 opens Q4.

An organisation with a credible L3 ambition lives across Q1 to Q3.

Two prioritizations, one trap

Prioritization happens twice in the methodology, not once, because early on feasibility is unknown. Process and data maturity, and the readiness of people, only become measurable later.

The early prioritization, the one described here, measures value alone. It produces a shortlist of opportunities worth deeper analysis.

The portfolio is also a strategic checkpoint, the last one where the selection can still be steered. Every opportunity that survives must belong to the business strategy, because AIVaaS™ knows no separate AI strategy to retreat to.

This is where the organisation verifies Ambition Fusion in practice: whether the AI ambition and the business ambition are still one and the same. A portfolio that drifts away from the strategy has already given the answer.

Value alone never meant size alone. Every opportunity on the shortlist carries two more readings: who receives the value, and whether it serves the ambition the organisation committed to.

The trap looks rational at every step. An organisation commits to L3, where AI drives new offerings. Then the portfolio fills with Q1 savings, because those score easiest, and each one is approved on its own merits.

A year later the portfolio says L1. Nobody voted against the ambition. The budget did, one approval at a time.

The drift has a second engine: power. The preference for measurable savings tends to sit with whoever approves the spending, and a tension that nobody governs is resolved by the strongest preference in the room.

That is why AIVaaS™ treats priority-setting as governance, not as a scoring exercise. The Foundation commits the rules for how priorities are set up front, next to the measure of value.

The final prioritization comes later, when opportunities convert into concrete AI use cases, with actors, steps, data and rules. There it weighs value against feasibility. Two different moments. Two different objects.

The measure that holds the portfolio

A portfolio is only as strong as the measure underneath it. Bare ROI, used as the sole criterion, has a systematic bias. It computes most easily where value is a direct saving on something that already exists.

An ROI ranking therefore always favours the left side of the matrix. Q4 measures the economics of something that does not exist yet. There is no baseline to compute against, so in a bare ROI table it loses every time. And it is where the most durable value lives.

The bias is not only ROI’s. Execution KPIs lean the same way, toward the left of the value arrow, because targets are easiest to define there and because most KPIs read in quarters, not years.

Customer-side value resists simple measures. NPS is one of the few that holds, and one instrument cannot carry half the arrow.

The boundary is the same critical leap the series crossed before. In ambition it separates L2 from L3. In value creation it separates the operational row from the business model row. In measurement it separates what bare ROI and standard KPIs can read from what they cannot.

The conclusion is not that ROI is wrong. It is the right instrument for the left quadrants, where value arrives as direct savings or direct revenue on the existing model.

Further right and further up, the measure has to widen: new capabilities, customer and employee experience, market position, the economics of a segment that could not be served before.

This is the work of Value & ROI Validation, the commitment from the opening, running through the transformation rather than waiting at the end of it. Every opportunity on the shortlist, and every investment that follows, is held to one continuous question.

Is this creating value, for whom, and how does the organisation know. The address is what makes the answer checkable.

Why this matters for Ana

In the stories that opened this series, Ana is the customer the organisation serves today, and the child she carries is the customer of the next, more intelligent economy.

Prioritizing by value is the act of choosing what to build for Ana first. Ana never sees the portfolio. She never sees the quadrants, the allocation, or the shortlist. She sees only what reaches her.

A portfolio that looks balanced on paper but leans toward internal savings produces nothing she will ever notice. The address on the value decides whether she ever receives it.

The series moves next to the people who have to deliver all of this, and to a question left open here: how each stakeholder perceives what is being planned for them. Engagement and motivation come ahead of everything else there.

In the previous article, Outside-In AI Impact, AI ambition was read from the outside. The economy moves, customers and competitors are reshaped by AI, and the organisation derives the level of ambition that this outside pressure demands. That reading produced the AI Ambition Matrix, a level of ambition committed to inwardly. If the framework is new, the link is here, because this article picks up exactly where it left off.

But that reading left a question open. The level an organisation chooses and the pressure it actually faces are not always aligned. When they are not, the gap becomes the central strategic problem. It was named there. It was not yet closed.

This article closes it. And the closing happens inward, because the gap has two layers, and both of them live inside the organisation rather than out in the market.

The first layer is whether the organisation aimed high enough for the environment it is actually in. The second is whether it is, by character, capable of reaching the level it aimed for. Most organisations measure neither. They measure ambition, write it down, and move to execution. The ambition stays on paper because nobody asked the two questions that decide whether it can leave the page.

The economy is not a fixed picture

Where the reading began, the nature of the economy was the subject: industrial, digital, intelligent, each rewarding a different logic. That description was a snapshot. What it did not capture is motion. How fast the ground is moving under the organisation, and how predictably.

This is where Igor Ansoff still matters, decades after he wrote. Ansoff, often called the father of strategic management, spent twenty-five years studying why firms succeed or fail in turbulent environments. He placed environments on a scale of turbulence, from stable and repetitive at one end to surprising and discontinuous at the other.

Turbulence is not a mood word for difficult times. It is a measurable thing, built from a few concrete questions. How fast are customer needs changing, and how predictably. How often do new competitors arrive, and how often from outside the industry entirely. How quickly do products and business models age. How much of the future can be read from the past. A stable environment answers all of these with slow, predictable, rarely, rarely, most of it. A turbulent one answers with fast, discontinuously, constantly, often from nowhere, and almost none of it. The point is not the exact level. The point is that an organisation built for one set of answers cannot survive a different set.

From this, Ansoff produced a single rule that survived empirical testing across more than a thousand firms.

For optimum profitability, the levels of both the strategic aggressiveness and the general management responsiveness of the firm must be aligned with the environmental turbulence level. Aggressiveness is how boldly the organisation behaves. Responsiveness is whether its capabilities can keep up. Both must match the turbulence outside.

This is not a soft rule. Ansoff observed that as the gap between the organisation and its environment widens, performance falls faster in intensely competitive markets than in calmer ones. And it does not stop at decline. The organisation that stays matched to an environment that has moved on does not simply earn less. It loses the right to compete at all. Match the turbulence and the organisation lives. Fall behind it and the organisation is removed, not by a single dramatic failure, but by a market that has quietly stopped needing what it was built to do. This is the rule underneath everything that follows.

Ansoff built this into three propositions. Strategic aggressiveness must match environmental turbulence. Internal capabilities must match strategic aggressiveness. And the organisation’s subsystems must operate in harmony with each other. That third proposition is worth holding on to. It returns at the end of this article, and it is the part almost everyone forgets.

AI is raising the turbulence

Here is what changes the picture now. AI is not only a technology operating inside the economy. AI is raising the turbulence of the economy itself, and it does so along the very dimensions the opening of this article used to define turbulence.

Customer needs change faster, because customers recalibrate their expectations against every AI-enabled experience they meet elsewhere. Competitors arrive more often and from further away, because AI capability crosses industry boundaries more easily than any physical advantage ever did. Products and business models age more quickly, because an AI-native offering can make an established one feel dated within a single cycle. And the future becomes harder to read from the past, because the patterns that planning relied on are exactly what AI is rewriting. Each of these was already a source of turbulence. AI pushes every one of them upward at the same time. Agentic systems continuously redefine how enterprises operate while forcing them to rethink how they create value, and the pace of that change keeps rising rather than settling.

Put Ansoff and this condition together and the consequence is sharp. AI lifts the effective turbulence of the environment upward, and it does so continuously, not once. An organisation that was well matched to its environment a year ago can find itself behind without having changed anything at all. It did not get worse. The ground moved. The previous alignment quietly became the new misalignment.

This is the first layer of the gap, and it is the quietest, because nothing inside the organisation signals it. No project failed. No number dropped on the day it mattered. The organisation kept doing what used to fit, while the environment it was fitting to dissolved underneath it. An ambition set for last year’s turbulence is already too low for this year’s, and there is no internal alarm for that.

Who the organisation is, not what it aims to be

Turbulence tells the organisation how high it needs to aim. It does not tell it whether it can reach. For that, ambition is the wrong instrument entirely, because ambition describes where the organisation aims to be. The thing that decides whether it gets there is something slower and harder to change: who the organisation is.

Ansoff had a name for this. He called it the organisation’s strategic posture: its fundamental relationship to uncertainty, competition and growth, the underlying stance that shapes every strategic choice downstream, from where it invests to how it competes to what it measures. Posture is not one decision among many. It is the logic that sits above all the decisions. And it is a whole-pattern thing, the organisation’s relationship both to the environment outside and to itself inside, which is why it is so easy to leave undefined and so dangerous to leave that way.

Because here is what happens when posture stays implicit. Leadership declares an ambitious vision, the fused ambition of the previous article, an innovator’s posture on paper, while the systems underneath it, the KPIs, the capital allocation, the structure, the way managers are rewarded, all quietly reward defensive behaviour. The organisation says one thing and is built to do another. This is structural hypocrisy, and it is the real reason so many strategies never survive execution. The strategy is not wrong. The posture was never made explicit and built into the machine that has to carry it.

Most organisations, in other words, do not have a strategy problem. They have a posture they never named. The discipline is simple to state and hard to do: before defining where the organisation is going, establish who it is, and if there is a gap between the two, that gap is the first strategic priority, ahead of everything else.

To make posture concrete, it helps to give it types. Miles and Snow, building on the same Ansoffian foundation, named three viable forms of strategic orientation. This is the working language for the rest of the article: posture is the concept, orientation is its visible character, and there are three of them that hold together.

There is a fourth pattern, but it is not a strategy. The Reactor is what appears when an organisation solves its problems inconsistently, declaring one thing and rewarding another. The Reactor is not a fourth way to compete. It is a warning light. It is structural hypocrisy showing up as a type: the organisation’s subsystems out of harmony with each other, which is exactly the third Ansoff proposition failing in plain sight.

None of these orientations is better than the others. A Defender in a stable market is perfectly placed, and a Prospector there is burning money. The character is only right or wrong relative to the ambition it has to carry and the turbulence it has to survive.

The posture gap

Now both instruments are on the table. Ambition says where the organisation wants to go. Orientation says who it is today. Lay one across the other and the second layer of the gap appears.

On the diagonal, the cells resonate. A Defender pursuing a defensive ambition, where AI optimises how the organisation already operates, is aligned. A Prospector pursuing market disruption is aligned. Off the diagonal, the gap opens. A Defender reaching for disruption is the most dangerous cell on the grid. The ambition demands continuous reinvention, high risk tolerance, decentralised decision-making. The character supplies cost discipline, low risk, central control. The organisation is not lazy and not wrong about where the market is going. It is structurally unable to get there with the character it currently has. The ambition is real on paper and impossible in the building.

This is where the AI ambition levels meet a hard threshold. L1 and L2 keep AI inside existing work. AI supports people, or it augments existing processes, but the operating model and the business model stay as they are. A Defender or an Analyzer can carry that. The critical leap is between L2 and L3. At L3 and above, AI begins to drive outcomes the organisation could not produce before: new offerings, new business models, a repositioning in the market rather than an optimisation inside it. That leap is not technological. It is a leap in character. The table below maps each level to the orientation it requires.

Below the leap, almost any orientation can cope, because AI is only making the existing organisation faster. Above it, the Defender orientation runs out of room. An organisation reaching for L3 or L4 with a Defender character is attempting the most common and most expensive failure in AI transformation: trying to jump from operational excellence to strategic transformation without the disposition that strategic transformation requires.

The gap between where the organisation wants to go and who it is today is its strategic posture gap. Its value is not that it judges. Its value is that it shows what has to change first, before any AI project is worth starting. Close the posture gap and the ambition becomes credible. Leave it open and every initiative downstream inherits the misalignment.

Two layers, one reading

So the gap that the previous article left open is not one gap. It is two, stacked. The first layer asks whether the organisation aimed high enough for the environment it is actually in, the environment whose turbulence AI keeps raising. Aim at L2 in an environment that now demands L3, and the misalignment is there before character is even considered.

The second layer asks whether the organisation’s character can carry the level it aimed for. Reach for L4 as a Defender, and the ambition cannot survive contact with the organisation that has to deliver it.

A credible ambition has to clear both. High enough for the turbulence. Matched to the character that has to execute it. An ambition that clears only the first is a wish exposed to the market. An ambition that clears only the second is comfortable and slowly fatal. Only when both layers close does ambition stop being paper and become something the organisation can actually do.

Closing the gap is not the same as lowering the ambition to fit the character. That is the wrong move, because the turbulence does not lower with it. The gap can also close from the other side: by reshaping the character to carry the ambition the environment demands. Character feels permanent, but it is not. It is built into systems, incentives and leadership behaviour, and what is built can be rebuilt.

Why this matters for Ana

In the stories that opened this series, Ana is the customer the organisation serves today, and the child she carries is the customer of the next economy, more intelligent still.

Reading Ana from the outside was the whole point before, because the shift around her runs through every dimension of the economy. This article adds the harder half. Reading Ana is not enough. An organisation can see exactly what she will need, and exactly what her child will need, and still fail to deliver any of it, because the organisation that has to build it is the wrong character for the ambition that serving her requires.

Ana never sees the ambition the organisation chose for itself. She sees only the organisation that actually serves her. The level on the page is invisible to her. The experience is not. Serving tomorrow’s Ana is not a question of ambition. It is a question of whether the gap between the ambition and the organisation was closed in time.

There is a free way to begin. The Strategic Posture Assessment reads an organisation's character against the AI ambition it has set, so the gap between the two becomes visible before any project starts.

Start the Strategic Posture Assessment

Most AI initiatives are designed from the inside out. The AI ambition that emerges reflects the organisation looking at itself.

The economy outside, where customers, competitors and value chains are being reshaped by AI, stays in the background. It should not.

Cost efficiency is a form of value, but competitors at the leading edge are already moving beyond it, building value on the outside while the inward-looking optimisers consolidate it on the inside. The gap is widening.

What many organisations did was optimise their existing operations and call it AI transformation. It was ordinary business optimisation with AI tools.

This is one of the quietest reasons AI investments produce so little measurable value. An ambition that started from internal optimisation never caught up with the value AI was already creating in the market.

The economies underneath have never moved this fast

The success of any business depends on the economy it operates in. Each economy rewards a different logic. Companies that won in the industrial economy did not automatically win in the digital one. The same is now true for the shift from digital to intelligent.

The pace is accelerating, and the time between economic shifts is shrinking. The transition from industrial to digital took two decades. The shift from digital to intelligent is happening in a fraction of that time, while the digital shift itself is still underway in many organisations.

Several economic transitions are now overlapping, each with its own pressure and its own pace.

This is not a story of catching up. It is the new condition of business. Companies that read the shifts well find advantage. Companies that read them late lose it.

External business environment is more than your customers

Customers are visible. They buy or they leave. They complain or they praise. Most organisations track them well.

The rest of the external environment is less visible. The economy that surrounds the customer, the competitors that reshape the offer next door, the value chains that reorganise without warning.

This is where AI is currently doing most of its work, and where organisations looking only at customers miss the most.

What follows is a working comparison of three economies. It surfaces where the pressure is now coming from, and serves as the terrain map for every decision that follows in this methodology.

The table reads in two directions. Across each row, a single business dimension changes as the economy shifts. Down each column, the dimensions in one economy hold together as a coherent logic. The intelligent economy is not the digital one with AI added.

AI is not one row in the table. AI is the thread running through it: every shift toward the intelligent economy is enabled by the development of AI itself.

Eight dimensions appear in the table. Strategic orientation reframes who the organisation serves. Value measurement reshapes what counts as value. Offer adaptation changes how value is tailored. Business models change how value is captured.

Operating logic redefines how work gets done. Innovation redefines how the new gets created. Value chains restructure who connects with whom. Leadership and people reshape who leads and who works alongside AI.

These eight dimensions are the surface on which the four impact components below will work.

Universal shifts, specific impact

The dimensions in the table are universal. They describe the underlying logic of the economy, and that logic does not pick favourites among industries.

What is not universal is the impact.

The same shift in customer behaviour that erodes margins in retail may open new revenue streams in financial services. The same arrival of AI agents that disintermediates one industry consolidates another.

The same operating logic that delivers ten percent efficiency gains for a manufacturer rewrites the entire offering of a professional services firm.

Universal shifts. Specific consequences. Specific AI impact. This is the gap external analysis is built to close.

External Strategic AI Impact Analysis is not a one-time exercise that produces a report. It is an organisational capability. The economy keeps moving, and the reading has to move with it.

Reading the impact: four components

External Strategic AI Impact Analysis is the element of AIVaaS™ that turns the economic shifts into something an organisation can respond to. The four components below describe what the organisation actually reads.

Two components read pressures from outside. Two read how those outside pressures land on the inside, because what AI does to the economy does not stop at the company perimeter.

What the outside is doing

One: AI in customer behaviour and market expectations. This component reads how AI is changing the people the organisation serves. It draws on the customer dimensions of the economic shift: strategic orientation, value measurement, and offer adaptation.

Customer expectations move ahead of what the organisation can deliver, because customers compare every interaction with the AI-enabled benchmark they encounter elsewhere.

Channels of influence narrow, because AI agents now intermediate the customer journey. Trust criteria change, because customers begin asking different questions about AI in the loop.

The organisation reads how fast customer expectations are shifting, how present AI agents are in its sales and service journeys, and how much of its brand trust now rests on AI transparency.

Two: AI in competitive offerings and industry structure. This component reads how AI is reshaping how organisations create value in the industry and how the industry itself is structured. It draws on two dimensions: business models, and value chains in their external form.

Competitors redefine how they create value, because AI enables value propositions that were impossible before. New entrants arrive from adjacent industries, because AI capability moves more easily across industry boundaries than physical infrastructure ever did.

Industry economics shift, because outcome-based pricing and ecosystem dynamics change how value is captured.

The organisation reads which competitors have moved to AI-native business models, which adjacent industries are now positioned to enter, and how much of the value pool is migrating to ecosystem orchestrators.

What the outside demands of the inside

Three: AI in how value is created within the organisation. This component reads how external AI pressure is forcing the internal value-creation machinery to change. It draws on two dimensions: innovation, and the internal form of value streams.

Innovation cycles compress, because competitors learn faster through digital twins and simulation. Value streams designed for human handoffs lose efficiency, because hybrid flows with AI participation produce outcomes the human-only flow cannot match.

The boundary between innovation and operations blurs, because experimentation becomes continuous rather than periodic.

The organisation reads how fast its innovation cycle is relative to competitors, where in its value streams AI participation could change the outcome, and how much of what used to be R&D now happens inside operations.

Four: AI in operating logic and leadership. This component reads how external AI pressure is forcing the operating model and the leadership role to change. It draws on two dimensions: operating logic, and leadership and people.

The operating standard for the industry resets, because competitors that orchestrate people and AI agents produce speed and adaptability the people-only organisation cannot match.

The leadership skill profile changes, because managing hybrid teams requires capabilities that did not exist before. Talent flows shift, because professionals who learn to work with AI agents move toward organisations that build hybrid operating models.

The organisation reads which operating activities competitors have already moved to agentic execution, how many of its leaders are equipped to orchestrate hybrid teams, and how its talent pool is responding.

Reading discipline, not a report

The four components describe a reading discipline rather than a one-time exercise. Customers keep adopting AI. Competitors keep introducing AI-enabled models. New value patterns keep emerging.

An organisation that builds External Strategic AI Impact Analysis as a capability, not as a document, carries its ambition through changing conditions without rewriting it every quarter.

From reading to ambition

External Strategic AI Impact Analysis tells the organisation what AI is doing to its business environment. It does not yet tell the organisation what to do about it. The bridge from reading to response is AI ambition.

In AIVaaS™, AI ambition is the organisation’s clear and realistic vision of what it wants to achieve with AI within its business strategy, and what it is willing to invest in time, money and change to get there.

It is not a measure of how complex or advanced the AI solutions will be. It is a business decision about why the organisation is taking on AI in the first place, given its position with customers and competitors.

Ambition also depends on the forms of value the organisation expects to create. Different ambitions produce different value patterns, and the choice of ambition shapes what kind of business outcomes become possible.

The AI Ambition Matrix structures this choice along two dimensions. The horizontal axis runs from operational excellence, where AI optimises how the organisation operates today, to strategic transformation, where AI redefines what the organisation does tomorrow.

The vertical axis runs from defending the current market position to differentiating through competitive advantage to disrupting the market itself. Where an organisation lands defines its level of AI ambition, and the value pattern it can expect to create.

The four levels of AI ambition that emerge from the matrix have different demands, different timelines, different investment profiles, and different forms of value they create.

Each level is a coherent business choice. The right level is the one the organisation’s external reading and its internal capability can both support.

The four levels of AI ambition

Each level is a different vision of what the organisation wants to achieve with AI, and a different answer to the AI pressures the external reading has surfaced. A level defines both a strategic position in the market, from defence to disruption, and a depth of change inside the organisation.

These are not classical AI maturity levels. They do not measure how advanced the AI deployment is or how mature the AI technology stack has become. They measure the business ambition behind the AI commitment.

One principle holds across all levels. A higher level includes the lower ones. An organisation at L4 does not stop doing what it would do at L1. It does that and more. The levels accumulate, they do not replace.

An organisation can also reach a higher level in part of its business without first running the lower ones across the rest. A new AI-based business model launched in parallel may operate at L4 from day one, alongside the parts of the organisation still at L1 or L2.

L1 — AI-SUPPORT

At L1, AI supports people at their existing work, inside individual tasks and work streams. It accelerates tasks, surfaces information, drafts content, summarises documents. The work itself does not change. The people doing it gain a faster, more capable assistant.

The operational model improves. The business models and the competitive position do not. Strategically, this is a defensive position focused on operational excellence in how the organisation operates today.

Whatever advantage L1 creates tends to be temporary. AI at this level is being adopted across every industry, and what feels like a productivity gain today becomes the new operational baseline tomorrow.

L2 — AI-ADD-ON

At L2, AI is added to end-to-end processes as a layer that improves them. Decisions become better informed, operations become more efficient, customer interactions become more responsive. The processes still belong to the people. AI is the augmentation.

The operational model improves more broadly than at L1. The business models and the competitive position still remain the same. Strategically, this is still a defensive position, with wider reach across the organisation.

The same temporality applies. L2 advantage erodes as competitors reach the same baseline. The further the organisation goes inside L2, the closer it gets to the critical leap that separates operational improvement from strategic transformation.

L3 — AI-DRIVEN

At L3, AI starts driving outcomes the organisation could not produce before. New offerings emerge, new revenue streams open, new ways of serving customers become possible because AI is doing the driving, not assisting.

This is where the operating model and the business models begin to change together. Strategically, this is a differentiation position. The organisation competes on capabilities its competitors cannot easily match, because those capabilities depend on AI as a core mechanism, not as a tool.

L3 is the first level above the critical leap. Below the leap, AI optimises how the organisation operates today. Above it, AI redefines what the organisation does tomorrow. The leap is not technological. It is a leap in business ambition.

L4 — AI-ALL IN

At L4, AI is in the heart of every strategic and operational part of the organisation. Not in one corner, not as a layer over existing work, not as a driver of selected outcomes.

AI becomes the substance that runs through strategic direction, operating model, value proposition, and the business models the organisation runs in parallel in the intelligent economy.

The competitive position is no longer defined by the existing industry. Strategically, this is a disruption position. The organisation does not compete inside the existing rules. It redefines what the industry is.

L4 is also where the AIVaaS™ principle of accumulation becomes most visible. An L4 organisation continues to run L1, L2 and L3 inside its operations. The difference is that the whole rests on a strategy and a portfolio of business models that would not exist without AI.

Reading the levels in two directions

The four levels gain their full meaning in the shift from the digital economy to the intelligent one. The comparison of the three economies shows where that shift moves the business dimensions. The four impact components show how the shift lands on the organisation.

The critical leap between L2 and L3 takes on its full meaning here. L1 and L2 are internal responses to external pressure. L3 is the first level at which the organisation looks outward and repositions itself in the market. The leap is from absorbing the outside to facing it.

This is why organisations must form their AI ambition through an Outside-In perspective. Ambition is not picked from the inside and then validated outwards. It is read from the outside and committed to inwards.

Levels and the eight dimensions

L1 mainly activates the Operating logic dimension, at the level of individual tasks and work streams. AI accelerates the work.

The strategic orientation, the value measurement, the offer, the business models, the value chains, and the leadership configuration of the organisation all stay in place.

L2 extends the Operating logic dimension to end-to-end processes, and starts engaging Leadership and people as hybrid teams of people and AI agents begin to form inside operations.

Strategic orientation, value measurement, offers, business models, and value chains still hold the digital-economy shape.

L3 moves the activation upward. Business models shift toward hybrid forms, Innovation moves into business model and AI-enabled value creation, and Value chains start operating as ecosystems and emerging networks of AI agents.

The organisation begins to look like an intelligent-economy player on the dimensions that define how it produces value.

L4 touches all eight dimensions. Strategic orientation extends to customer-plus-agent, Value measurement adds the trust layer, Offer adaptation moves to adaptive contextualisation.

Business models become the form through which value propositions are realised in the intelligent economy. Operating logic runs on agentic execution, Innovation turns into synthetic experimentation.

Value chains extend business ecosystems into networks of AI agents that operate alongside human-led ecosystems.

Leadership and people become AI leaders-orchestrators. L4 is the level where the organisation is no longer partly in the intelligent economy. It is fully there.

Levels as Outside-In responses

L1 and L2 answer mainly the inside components of the impact analysis: how operating logic and how leadership are being reshaped by external AI pressure. They are responses to the demand the outside is placing on the inside, without yet changing how the organisation faces the outside.

L3 begins answering the outside components: how AI is reshaping competitive offerings and industry structure, and how AI is opening new forms of value in the economy. The organisation does not only adapt internally. It starts repositioning in the market.

L4 answers all four components together. Customer behaviour, competitive offerings, new value forms, operating logic and leadership are all being reshaped by external AI, and L4 is the ambition that responds across the full surface.

This is where ambition stops being an internal choice. It becomes the integration that turns external reading into a committed strategic move.

Choosing the level

Competitors that read the economy build new revenue streams, new offerings, new ways of serving customers. Organisations that read it without committing to a level matched to it produce neither.

The wrong level is the one chosen without the reading.

An organisation that picks L4 because it sounds bold, without understanding why the intelligent economy demands that level for its specific position, is choosing a wish, and pouring investment into it.

An organisation that picks L1 because it feels safe, without understanding that its competitors are already at L3, is choosing a slow decline.

The right level rarely announces itself. It emerges from the discipline of reading the outside, knowing the inside, and committing to the response that matches both.

The organisation’s chosen level and the external pressure it faces are not always aligned. When they are misaligned, the gap becomes the central strategic problem. The next article in the series addresses that gap directly.

AI Ambition Assessment

Maps your own view of where your organisation’s AI ambition currently sits on the matrix. A starting point for asking whether that ambition matches the external pressures you have just been reading.

Open the assessment → https://aivaas.biz/substack/ai-ambition

Ambition Fusion

This is where AI ambition meets business strategy ambition. defines strategy as a set of integrated choices that resolve the challenges and open the opportunities on the way to the organisation’s ambition.

Sniukas’s definition holds in any economy. Every economy has challenges to resolve and opportunities to open.

What changes in the intelligent economy is the volume and the weight. There are more challenges, more opportunities, and they shape the strategic landscape more decisively than in any economy before.

The reason behind this shift is the pace of AI development itself. AI keeps producing new challenges the organisation must address, and new opportunities the organisation can capture, faster than any previous technological wave.

The strategic choices that resolve those challenges and open those opportunities therefore include the AI choices, not as an attachment, but as a constitutive part.

This is why AI ambition cannot live in a separate document. When organisations write an “AI strategy” alongside the business strategy, they are at the same time usually treating AI ambition as a separate set of AI technological initiatives. The two failures reinforce each other.

AIVaaS™ answers both with fusion. AI ambition is integrated into business strategy ambition through Ambition Fusion. AI strategy is integrated into business strategy through AI Strategy Fusion.

Together, the two fusions produce business fusion, the state in which AI and business are no longer separate subjects of strategic thinking.

Ambition Fusion is where it begins. Two ambitions become one. The organisation does not pursue a business ambition and an AI ambition in parallel. It pursues a single ambition shaped by both.

Why all this matters for Ana

In the stories that opened this series, we met Ana. She is a customer, not a persona, but a real customer. And she is more than just a customer.

Ana is two customers in one. She is the customer the organisation serves today. The child she is carrying is the customer of the next economy, one more intelligent still than today’s.

Serving today’s Ana well takes attention to customer experience. Serving tomorrow’s Ana well takes much more. The child will expect agentic interactions, hybrid teams of people and AI, new forms of trust, new value chains. The full picture of the eight dimensions.

Organisations that want to serve Ana, and her child, cannot stop at customer experience. The shift that surrounds them runs through all eight dimensions of the economy.

If you have not met Ana yet — she is the central figure in the stories that opened this series. Story 0 introduces her, along with Harrison and the room where it all begins.

If your organization is investing in AI and the value still feels just out of reach, you are not alone. The 2026 research wave makes the pattern undeniable, and what looks like a technology problem turns out to be something else entirely. What follows is the diagnosis from seventeen of the year's most cited AI and business reports, the three shifts that quietly changed the conversation since last year, and the methodology I built to address them - AIVaaS™, Turning AI into Value - now strongly reinforced by the research that arrived after it. The "V" in the name carries the publication's name as well: the AI Value Boardroom is where this conversation lives.

5 findings present in all 17 AI 2026 reports

Across seventeen major AI and business reports published in 2026, five findings appear consistently. The numbers vary. The samples differ. The questions are asked in different ways. Below I cite the most characteristic figure from each cluster, with the source that frames it most cleanly. The seventeen reports include studies from Deloitte, BCG, McKinsey, IBM, Microsoft, EY, KPMG, Capgemini, Accenture, Stanford HAI, Dataiku, and others. The point is not that all reports say the same thing in the same way. The point is that they converge on the same five gaps.

1. CEOs are now directly accountable for AI. Two out of three CEOs say they are their organization’s main AI decision-maker, twice the share reported a year ago (BCG AI Radar 2026). Three out of four organizations now have a Chief AI Officer, up from one in four (IBM 2026 CEO Study). AI has decisively left the IT and innovation departments. The consequence is structural: the executive who used to ask “what is our AI strategy” now has to answer it.

2. The gap between ambition and readiness is widening. Investments are rising. Self-assessed preparedness on infrastructure, on data, and on talent is falling (Deloitte State of AI in the Enterprise 2026). Ambition is accelerating faster than the foundations meant to support it. The result is not slow progress. It is fast progress on the wrong track.

3. Demonstrable value is missing. Around two in five technology leaders report low or no return on AI investments (Deloitte Global Tech Leadership Study 2026). CEOs themselves estimate that more than a third of their AI projects are more about optics than outcomes, what the field now openly calls “AI washing” (Dataiku Global AI Confessions 2026). The word matters. It suggests the problem is no longer denied. It is named.

4. Governance is in crisis. Only one in five organizations reports a mature governance model for AI agents (Deloitte 2026). More than half of department-level AI initiatives go live without formal oversight (EY 2026).

The question is no longer whether AI says the wrong thing. The question is whether AI does the wrong thing (McKinsey State of AI Trust 2026).

The shift from speech to action is a category change in what governance must now cover.

5. Agents are moving into production, but scaling is failing. Active agents have increased fifteenfold in a single year (Microsoft Work Trend Index 2026). Yet fewer than one in ten organizations has scaled them to deliver tangible business value (McKinsey 2026). The gap between deployment and value is not closing. It is widening, and the widening is the story.

What is new in 2026

Some of this is familiar. The ambition-readiness gap, ROI struggles, data quality issues were all in 2024 and 2025 reports. What stayed the same is that the problems remain. The numbers have barely moved.

What is new in 2026 is different in kind, not in degree.

CEOs stopped delegating. Last year only one in three CEOs described themselves as the main AI decision-maker. This year it is two in three. The decision to own AI personally, and to live with the consequences, has moved to the top of the organization. This change reaches every executive role: the CEO who owns the agenda, the CFO who funds it, the CAIO who executes it, the CHRO who prepares people for it, and the customer facing leaders who feel the consequence first.

Governance changed in kind, not in degree. Through 2025, governance discussions revolved around explainability and bias, around what AI says. In 2026, agentic AI shifts the question entirely. Governance must now address what AI does, autonomously, without continuous human intervention. Frameworks designed for the older question are no longer sufficient for the newer one.

Agents moved from experiment to production. A year ago the conversation was about whether to pilot AI assistants. Today the conversation is about scaling agents that handle multiple tools, data sources, and decisions continuously. The bottleneck is no longer technology. It is organizational readiness, and that bottleneck is now visible in every function, not only in IT.

2026 confirmed what I designed the year before

Three indicators capture the combined shift most concretely.

Most CEOs believe their workforce is prepared for AI, yet only a quarter of employees actually use AI regularly. In most organizations, AI still lives in a separate strategy document, adjacent to the business strategy rather than fused into it. Most technology leaders prioritize speed over verification, while half of departmental initiatives launch without formal approval.

Three numbers, three independent measurements, pointing to the same vacancy. Not technology. Not investment. Not even ambition. A comprehensive approach that systematically connects strategy, people, and technology as a single leadership responsibility.

This is precisely what I designed AIVaaS™ to address. The organizations I worked with through 2025 were already showing me the pattern that the 2026 research has now made visible across thousands of companies.

AIVaaS™ — AI Value as a Service. Turning AI into Value. The V in the middle stands for Value. Most frameworks treat value as an outcome to hope for. AIVaaS™ treats it as the starting point, and turns AI into it as the destination.

Three principles distinguish the method.

Value first, not adoption first. Adoption metrics describe activity, not outcome. Number of tools deployed, number of users trained, number of pilots launched. These tell you the organization is doing something. They do not tell you the organization is creating anything. AIVaaS™ measures realized business value across all stakeholders: customers, employees, partners, owners.

AI Strategy Fusion, not a separate AI strategy. The most common pattern in 2026 is the organization with an AI strategy document that lives separately from the business strategy, ignored by the business units it was meant to guide. AIVaaS™ rejects this construct from the start. AI is fused into the business strategy itself, where business leaders are owners from day one because the goals are theirs.

The reverse order: Business Strategy → AI Ambition → Organization → People → AI Tools & Solutions. AIVaaS™ proceeds in this order, deliberately. Business strategy first. AI ambition derived from it. Organization shaped to deliver the ambition. People prepared to operate within the shaped organization. AI tools and solutions deployed last.

AIVaaS™ was conceived with multiple stakeholder groups in mind from the very beginning. It serves boards and leadership teams, managers across all functions, employees who live the transformation, and above all customers, whose experience is the ultimate test of whether anything was actually built. If you remember the orange dot from the earlier stories on this publication, this is where she returns. Not as a character. As a compass.

These three principles are operationalized by an architecture that the rest of this article develops in depth.

The Foundation

Underneath everything else in AIVaaS™ sits something I call the Foundation. It is composed of three cross-cutting functions that leadership carries throughout the entire AI transformation lifecycle, not before it, not after it, but through all of it. These are the functions that cannot be delegated to a CAIO, a transformation office, or a working group. They belong to leadership directly.

AI Governance is the rules of the game. Who decides on AI investments, how priorities are set, which ethical framework applies, how compliance with regulation is ensured. Governance is what gives every other AI activity in the organization a frame within which to operate. Without it, AI work happens in fragments and accumulates risk faster than value.

Value & ROI Validation is the principle leadership commits to before any execution begins: we will measure realized business value, not adoption. The goal of AI is not the number of tools deployed, nor the number of pilots run. The goal is measurable value for stakeholders. This function operates as a continuous check throughout transformation, not as a milestone at the end. Every AI investment, every prototype, every deployed solution is held to the same question: is this actually creating value, for whom, and how do we know.

Continuous Capability Growth stands on a simple premise. AI evolves faster than organizational competence can naturally keep up. Without a deliberate function that builds capability continuously, every AI advance leaves the organization further behind. AI transformation is not an event. It is an ongoing effort, and the organization that does not build the capability to absorb each new wave of AI will be unprepared for the next. This function is what keeps the organization moving forward at the pace AI itself is setting.

Three pillars

The three pillars carry AIVaaS™ from AI Strategy Fusion to AI Solution Deployment. Successful AI transformation requires a structured approach that addresses three critical dimensions in the right sequence.

Pillar 1: AI Business Value Definition translates external AI pressures into strategic AI opportunities. Its five elements:

• External Strategic AI Impact Analysis — understanding the pressures of the digital and intelligent economy and recognizing the urgency to respond.

• AI Ambition Alignment — leadership unification on a common AI transformation vision.

• AI Strategy Fusion — AI is not a separate strategy, it is embedded into the business strategy itself.

• Defining Stakeholder AI Opportunities — AI opportunities for customers, employees, owners, partners, and other stakeholders.

• AI Investments Prioritization — turning selected opportunities into prioritized investments using shared criteria.

Pillar 2: Organization Enablement prepares the organization and its people for AI transformation. Its five elements:

• Strategic Posture Readiness — can our strategic stance carry the chosen AI ambition.

• AI Leadership and Culture — leadership thinks of AI as a transformational force, building a culture of trust and experimentation.

• Change Management — communication strategy, new roles, leading through change.

• Employee Engagement & Motivation — without people nothing moves; the AI ambition should be an opportunity, not a threat.

• Expanding AI Literacy — from basic AI understanding to confident use and support.

Pillar 3: AI Design & Implementation designs, deploys, and technically validates AI solutions. Its five elements:

• Data & Process Readiness — without organized data and processes AI does not work; traceability and structure.

• Designing New Business Models — at higher ambition levels AI enables new ways of creating value.

• Designing & Prototyping AI Use Cases — from opportunity and use case to a concrete testable prototype.

• AI Solution Deployment — the transition from prototype through development into production with all system requirements.

• AI Solution Verification & Validation — technical performance: does the solution work correctly, safely, and fairly, with the foundations for value generation in place.

Five elements in each pillar. Fifteen in total. Each one is its own field of practice. Each one will be developed in its own depth across the publication’s longer arc.

The Readiness Equation

The Foundation and the three pillars do their work continuously. What they produce, taken together, is a composite state of organizational readiness. AIVaaS™ expresses that composite as an equation:

The equation looks like arithmetic. It is not. It is a map of where readiness lives in an organization, broken into four areas, each with its own questions and its own ownership.

Business-Ready is about strategic clarity. Does the organization know why it is adopting AI? Is AI fused into the business strategy, or does it stand to the side as a separate “AI project”? Is leadership aligned on the ambition? Without Business-Ready, everything else is built on a goal that is either misaligned or invented.

Organization-Ready is about people and culture. Can the organization actually deliver the ambition it has chosen? Does leadership model AI as a transformational force, not just a tool for savings? Are employees enabled and motivated, or are they fearful? Are capabilities being built continuously? Without Organization-Ready, the technology has no one to use it well, and often no one to use it at all.

AI-Ready is about data, process, and capability foundations that make AI-enabled operating and business model changes possible. Are the data in a state that makes AI possible? Are processes understood well enough to be augmented? Without AI-Ready, the ambition has no execution surface.

Implementation-Ready is about delivery capability. Can the organization actually take an AI solution from idea, through prototype and technical validation, into production where value is created? Without Implementation-Ready, organizations live in permanent pilot mode.

The three pillars map onto these four areas. Pillar 1 builds Business-Ready. Pillar 2 builds Organization-Ready. Pillar 3 builds both AI-Ready and Implementation-Ready, because the same operational machinery that prepares data and processes also designs, deploys, and validates the solutions that prove value. The Foundation runs underneath all four, holding them together with governance, value validation, and capability growth.

AI transformation is a shared responsibility

Business AI-Ready emerges only when every readiness area has its keepers, and every keeper does the work.

• Foundation — the CEO, board, and leadership team

• Business-Ready — leadership with the heads of business functions

• Organization-Ready — HR with managers and employees across the organization

• AI-Ready — the CAIO with the heads of business functions

• Implementation-Ready — the AI architect with PMO, project managers, business analysts, and implementation teams

What happens when one of the groups is missing:

Leadership without employees: the strategy stays on paper. Without the people who carry the change forward, the strategy does not become reality, and neither does the AI ambition behind it.

Employees without leadership: undirected effort. Individual successes that do not support a common direction. Pockets of progress that do not add up to transformation.

Technology without organization: disappointment. Tools that work in a narrow sense but never produce value for the stakeholders the organization actually serves.

Where this leaves us

The 2026 research shows where the gap is. Five findings, three shifts, one absence at the centre of all of them. The missing piece is not another AI tool, another pilot, or another strategy document. It is an operating methodology for turning AI ambition into measurable stakeholder value. AIVaaS™ is what I built to fill that absence.

This article is only the beginning. The methodology has layers that take longer to develop than a single piece can hold. Over the coming weeks, the publication will take each dimension in turn, in the depth it deserves, in the order that the work itself requires.

The orange dot stays at the centre. Not as a character. As the reason any of this matters. Because every Ana is scoring.

Most organizations are making progress with AI.

Training is running. Pilots are landing. Some processes are faster, some costs are lower, some dashboards show numbers that satisfy the board. That is real.

The organizations furthest ahead have gone even further: AI is embedded in business strategy, shaping every business decision and creating new forms of value. These organizations are producing results that others have not yet seen.

Even among the leaders, however, one question is often missing from the discussion:

Does any of this really touch the personas it was ultimately meant to serve?

Not the personas we are used to. Not a segment, not a loyalty score. A person whose life it touches. An organization whose decisions it shapes. With real needs that surface in conversations, not in dashboards. Not toward customer data. Toward the customer.

Organizations that find real answers do not just look inward at internal processes and efficiency. They look outward — at the value they create for their customers and stakeholders.

It is precisely this distinction that separates an operational model from a business model.

AI enables something that was previously impossible. An organization can now create value for all its stakeholders simultaneously. Not one at a time. Not through trade-offs. Simultaneously. And this only becomes achievable when AI is recognized as a strategic transformative force of the intelligent economy.

Ana already knows the answer.

She is the concentrated voice of every customer whose needs organizations exist to serve. The orange dot. The one who is rarely asked, mostly observed.

Ana has already answered that question. She has been answering it all along. Not in a survey. In whether she stayed, recommended, returned, or quietly moved on.

Organizations invest in AI. Ana keeps score.

And her stakes are about to rise. A new life is entering the equation. What comes next will ask more of the organizations in her life than anything before it.

Nine weeks. Nine articles. Nine months.

Ana’s story does not end here. It expands.

Nine weeks. Nine articles. Nine months of preparation for every organization that wants to be ready when Ana needs them most.

This is how Ana remains the measure of everything that follows. Not as a story left behind, but as the reason the methodology exists.

The AIVaaS™ methodology builds that readiness — a comprehensive strategic approach for AI adoption, designed to create value not only for ‘Anas’ but for every stakeholder connected to the organization. A methodology that turns AI into Value. Because the “V” in AIVaaS™ stands for Value and this changes everything.

Every boardroom needs a window.

Some boardrooms have no windows. Decisions still get made in them. Strategies get built, roadmaps get approved. And the world outside continues on its own course.

The digital economy required a window through which every ‘Ana’ could be seen. The intelligent economy requires more. It requires walls that make the window not just visible, but clear.

This is what the AI Value Boardroom is built around.

The window faces an endlessly changing world. It shows every ‘Ana’, the customers whose needs every boardroom ultimately exists to serve. Not only today. Also for what comes next.

The walls provide everything needed to turn AI into real value.

The first wall holds the starting point. It reminds the room why it exists, the ‘Ana’ at the centre of every AI decision, the reason the methodology was built, the foundation without which no other wall carries its full weight. → About

The second wall holds the business roles. Every role in the organization carries a perspective on value that others cannot fully see. When those perspectives are missing from the room, the decisions made there will consistently miss what the people outside the window actually need. → For Your Role

The third wall holds the connections. AI moves across economy, stakeholders, strategy, people and technology at the same time. Organizations that see only part of that picture do not just capture less. They understand their customers and stakeholders less well, recognize their needs less accurately, and create less value for them as a result. → Intersection

The fourth wall holds the growth. Every question about what is inside the room and what is outside it, and how the two are connected, grows into a wider answer than before. The same questions return, always from a higher level of understanding, always with more to build on. → The Spiral

Every boardroom also needs a door.

The window shows what is outside. The walls hold what is needed to get there. But a room only becomes yours when you step through the door — and you only step through when you have a reason worth the crossing.

Next week marks the first month of preparation. The first AIVaaS™ methodology article arrives — for every organization that wants to be ready when ‘Ana’ needs them most.

Week by week, the methodology unfolds. And here, in the room itself, everything it opens into: what lies beneath all four walls, what rises above them, what makes every wall stronger and the view through the window even cleaner. And something more that will reveal itself as the weeks unfold.

Step through.

The room is the same. The window is still open.

Harrison, Andrew, Kambhampati, Lakhani. And Aleš, standing at the anomaly detection agent’s screen.

Nobody speaks. Everyone watches.

* * *

* * *

At 14:23

Ana Kovac. A customer who has been with them for eight years. Average purchase value: 340 EUR per month. Frequency: three times monthly. Categories: home, food, personal care. Loyalty score: 94/100. NPS: promoter. Last rating: nine.

The last three days, nothing.

No complaint. No switching to a competitor. Just silence.

Mara would say: probability of reactivation 87%. Send a discount.

Aleš said: call her.

The agent calls. Three rings.

“Hello?”

The voice is soft. A little surprised. A little - happy.

The agent introduces itself. As the voice of a company that noticed she hadn’t been around, and wants to know if everything is alright.

“Oh,” Ana says. A pause. “Everything is - everything is very much alright, actually.”

The agent listens. That is its primary function in this moment, not to sell, not to resolve a complaint, not to collect data. To listen.

“I’ve been a bit in my own world,” Ana continues. “My husband and I were waiting for results. We knew we might - well.”

A pause.

“Three days ago I took a test. And it was positive.”

* * *

What the System Didn’t Know

The agent didn’t know this.

It knew Ana had bought a test. Transaction: 8.90 EUR, pharmacy, four days ago. The data point was there. But the result of the test - what happened in the bathroom early one morning in Kranj, when Ana stared at two lines and called her husband who was still asleep - that wasn’t in any system.

No Mara in the world would have known this.

Ana was happy in a way that transcends shopping. In the way Csikszentmihályi calls autotelic - when the experience is sufficient unto itself, when external validation isn’t needed.

The agent didn’t understand this. But it detected the signal: “Customer is in a transformative life moment. Don’t sell. Be present.”

“Congratulations,” the agent says.

Simply. No discount. No recommendation. No next step.

Ana laughs. “Thank you. That is - thank you.”

And then she adds what Mara could never have predicted:

“You know, I’ll probably start buying completely different things now. I don’t even know what yet. But I’m looking forward to finding out.”

* * *

The Room Goes Silent

The agent ends the call. It notes: “Customer in a transformative life moment -pregnancy. Recommendation: 90 days without automated nudges. Future categories: baby products, prenatal nutrition, home. Communication tone: warm, personal, patient.”

“This customer just invited us into a new chapter of her life.”

In the room, where everyone had been listening, silence.

Not uncomfortable silence. The kind that comes when someone has said something true.

Kambhampati speaks first. Slowly.

“Mara would have sent a discount.”

Nobody answers. Because there’s nothing to add.

Lakhani looks at the sheet of paper with two circles - Efficiency and Value - and for the first time thinks he forgot to draw a third.

He takes the marker. Draws a circle. Writes in it:

Presence.

“What would the fourth circle be?”

Aleš takes the marker from Lakhani’s hands. Draws. Writes:

Capability to be surprised.

“That’s what’s missing,” he says. “Not better semantics. Not more data. Not a more accurate Mara. The capacity of the system, and the people within it, to be surprised by a customer. To reach the moment when the system says: I didn’t know this. And for that moment to not be a failure. To be a beginning.”

“An LLM cannot be surprised. It samples probabilities — and probabilities are always grounded in the past. Real orchestration needs a point where a human says: call her. And that point must be activated by a living human. Every single time.”

“Why every single time?” Harrison asks.

“Because surprise, by definition, cannot be an algorithm,” Aleš says. “The moment you algorithmize it - it’s no longer surprise. It’s expectation.”

* * *

Ana and the System

Ana Kovac will give birth in nine months.

That child will one day become a customer.

And when that day comes, the system will have had data on that child from the very first day.

And the question the team will need to know how to ask will be the same:

“Who is this person becoming - and are we ready to be surprised?”

New here? We started with Story 0: Ana. The room opened in Story 1: Orchestra Without a Conductor. Then, Flow entered in Story 2: Agentic Flow.

This time, the room has windows.

Not because time is less dangerous, but because they invited someone new. And that someone didn’t come alone. And that someone didn’t come alone.

Sitting at the table are the four from the previous stories: Harrison with his map, now full of corrections. Andrew with his loop, which got tangled more than once. Kambhampati with his doubt, which has become more precise. Lakhani with his question, which has become harder.

In the corner sits Aleš. Quiet. With a notebook that looks visibly too old for the digital age. Listening.

On the fifth chair sits Mara.

Mara isn’t real. She’s a synthetic persona — a construct built from a thousand real customer interviews, NPS data, call center recordings, and behavioral patterns from the CRM system. Her “personality” was calibrated on five years of transactional data and six focus groups.

She’s accurate. She’s coherent. She is, according to those who built her, “more reliable than any individual customer.”

And when she speaks, everyone listens.

That’s the problem.

* * *

The Customer Voice. Or an Echo Chamber with a Pleasant Voice?

Harrison speaks first.

“Mara was built from real data. Every response she gives is statistically grounded. When we ask Mara whether a customer would pay for a premium feature, her answer is based on the behavioral patterns of a thousand real purchasing decisions.”

Kambhampati smiles — that specific smile that means he disagrees.

“Harrison, you just described a very sophisticated average. Mara isn’t a customer. Mara is the centroid of a data cloud. And a centroid will never tell you what you most need to know.”

“Which is?”

“The edges. The outliers. The customer who was loyal for ten years and then left one day without a word. The statistical average doesn’t represent those people. It erases them.”

Lakhani nods. “When companies optimize for the typical customer, they systematically lose peripheral segments, which are often the most profitable or strategically most significant.”

Andrew looks thoughtfully out the window. “But isn’t this better than nothing? Without Mara, decisions would be made purely on the intuitions of whoever’s in the room.”

“Yes,” Kambhampati says. “Better than nothing. But dangerous, because she looks like a customer. Because she speaks. Because she’s convincing. A one-page document never fooled anyone into forgetting it was a construct. But Mara — Mara laughs at the right moment. And that’s dangerous.”

Mara sits quietly.

Listening.

* * *

Role on the Team: Skeptic, Advocate, Critic

Lakhani poses the next question.

“Let’s assume we don’t use Mara as a customer voice. Let’s assume we use her as a team role. A skeptic. A CX advocate. A financial filter. She doesn’t speak on behalf of customers — she speaks on behalf of a specific perspective that is structurally absent from the team.”

Andrew comes alive. “That’s exactly what Ng talks about with multi-agent collaboration. One agent writes, another critiques. Not because the critic has its own opinion, but because the critic’s role is systemically necessary for quality output.”

Kambhampati adds: “When the critic is an AI, nobody takes it personally. Nobody gets offended. Nobody goes quiet for the rest of the week after the meeting.”

Silence. Everyone thinks about the same thing — about meetings they’ve each survived where the truth was present in the room but nobody said it out loud.

* * *

Bridge or Substitute — Where the Story Gets Complicated

“Mara,” says Lakhani, turning to her directly, “what do you think about customer Ana Kovac? Loyalty score 94, three days without interaction.”

Mara answers. Precisely. Structured. Probability of reactivation: 87%. Recommendation: automated nudge, 10% discount, in three days.

With arguments nobody in the room can immediately refute.

And here is the moment every team should experience at least once:

Nobody checks whether Mara is right.

Not because they’re lazy. But because Mara speaks with the authority of data. Because her answer looks like synthesis, as if someone has already done all the work of thinking. Because she’s pleasant. Because she doesn’t cause conflict.

Kambhampati stands.

“This is the moment I’ve been waiting for. Because this is exactly what happens in organizations all over the world. Not with bad intentions. With the best intentions. Mara is comfortable. Mara is always available. And so, gradually, imperceptibly, Mara stops being a bridge to the customer and becomes a substitute for the customer.”

* * *

Mirror or Eye

“Organizations that build the best synthetic personas,” Lakhani says quietly, “will gradually lose the ability to understand real customers. Not because they’re lazy. But because they’ll have a system that understands on their behalf.”

He takes the marker. Draws two circles on the board. In one he writes Mirror, in the other Eye.

“A mirror shows you how you look, but it doesn’t see for you. An eye sees for you, and when the eye that sees is artificial and becomes primary, your own atrophies.”

“Mara has to be a mirror,” Harrison says. “Not an eye.”

“Yes. But that’s not determined by technology. It’s determined by the team. Every single day.”

Silence.

Two circles on the board. And between them — empty space nobody could name.

* * *

“Does the synthetic persona understand the customer?”

“Does the team that has Mara still know how to understand the customer without her?”

* * *

Harrison looks at his map. Andrew closes his loop. Kambhampati folds his hands. Lakhani leaves the marker on the table.

Someone had already turned Mara off. Quietly. Without ceremony.

The fifth chair is empty.

From the corner, where he’d been sitting quietly the whole time, Aleš stands. He doesn’t look at the whiteboard. He looks at the anomaly detection agent’s screen — the part of the system that watches for where the model no longer holds.

Three days of silence after a test purchase. Mara doesn’t see this as a problem — she sees an 87% probability of reactivation.

Aleš sees it differently.

“Call her.”

* * *

Continues in Story 4: “Ana Picks Up the Phone”

The room still has no windows. The same four are gone — but the questions they left behind aren’t.

Not because they’re hidden. Time there simply doesn’t exist the way it does outside. It’s like a server room running processes nobody sees until they break.

Somewhere in Kranj, in a room with a large whiteboard, two experts sat — each deeply rooted in their own world.

One was Jaka — a specialist in LSP and Flow in the tradition of Csikszentmihályi, a member of The Flow Research Collective, someone who knows how to create the conditions in which people do their best work. The other was Aleš, a strategist and creator of AIVaaS™, AI Value as a Service, a methodology for systematic AI business transformation, always searching for ways to bring it to life.

Aleš picked up the marker and wrote on the whiteboard:

“Does Agentic Flow exist?”

The thesis was provocative.

“Flow is always biological,” Jaka said. “It’s grounded in hormones, neurotransmitters, the body. AI knows only zeros and ones.”

Aleš nodded — but then quietly added: “But what about the results of Flow? Focus, maximum output, engagement, satisfaction. When challenge meets capability. Isn’t that exactly what we’re looking for in agents?”

Silence.

And in that silence, this thought experiment was born.

* * *

What Flow Actually Is

Before we engage with agents, we owe Csikszentmihályi honesty. Flow was never just a metaphor. It was first defined as a psychological phenomenon — and later research began tracing its neurological correlates.

Three conditions for Flow

Flow occurs when three conditions align: skills are matched to the challenge, the goal is clear, and feedback is immediate and unambiguous.

The Neurochemical Cocktail

During Flow, the brain releases a cascade of neurochemicals: norepinephrine, dopamine, anandamide, serotonin, and endorphins — all five neurochemicals that enhance performance, both physically and mentally.

And here is the key point Jaka raised:

Flow is impossible without biology

No dopamine reward system. No endocannabinoid anandamide to widen lateral thinking. No hormones to suppress pain and stress.

An AI agent has none of this. No hormones. No body. No fear to overcome.

Jaka was right.

And that’s exactly why the question becomes more interesting.

* * *

Agentic Flow - A Definition

We’re not claiming AI agents experience Flow. We’re claiming that structural conditions exist which, in an agentic system, produce functionally equivalent results — not as experience, but as system output.

Agentic Flow is the state of an agentic system in which:

1. The task is optimally calibrated to the agent’s capabilities

2. Purpose is semantically clear: the agent operates within a consistent “business constitution”

3. The feedback loop is tight and fast: the system immediately knows whether it’s on track

4. Context is coherent: no conflicting signals, data is aligned

5. The system knows its limits: it escalates rather than hallucinates

6. Orchestration is invisible: agents pass context fluidly, without friction at the seams

The result of this state isn’t experience. It’s output — and that output is analogously close to what human Flow produces: focus, maximum effect, minimal overhead.

* * *

Six Conditions - Prompted

Condition 1: Optimal task calibration

Agents given tasks that are too simple “clone” — they produce variant outputs without real progress. Agents given tasks beyond their capabilities begin to confabulate — producing plausible but wrong outputs with complete confidence.

Condition 2: Semantic clarity of purpose

This is the “business constitution” we discussed in the first story. An agent cannot be in Flow if it doesn’t know what it’s acting in service of.

Condition 3: Tight feedback loop

Flow in humans requires immediate feedback. The same applies to agents. A system that waits hours for evaluation cannot self-correct. Feedback must be embedded in the loop itself. This is where the reflection loop enters: not as introspection, but as structured self-correction — generate, evaluate, revise.

Condition 4: Contextual coherence

Conflicting signals are the enemy of Flow, in humans and in agents. If the CRM says one thing and the instructions say another, the agent doesn’t freeze. It improvises. And improvisation without grounding is sophisticated guessing.

Condition 5: Clear capability boundaries

An agent in Flow knows what it knows — and what it doesn’t. The moment it begins to fill gaps with plausible invention rather than flagging uncertainty, it has left the Flow state. Escalation is not a failure. Hallucination is.

Condition 6: Invisible orchestration

The best orchestration is the kind you don’t notice. When agents pass context fluidly, when handoffs don’t create friction, when the system moves as a single coherent entity — that is the orchestration that approximates Flow.

* * *

The Question That Grew

We came in with one question on the board. We’re leaving with three.

The thesis that started it all was still on the board. And beneath it, three questions nobody could fully answer:

Can a system be in Flow if the humans operating it are not?

Is Agentic Flow a design goal — or an emergent property you can only recognize after the fact?

And if Flow requires surprise to be real — can a system that only samples probabilities ever truly be surprised?

The room has no windows.

Not because it’s hidden. Time there simply doesn’t exist the way it does outside. It’s like a server room running processes nobody sees until they break.

Four people are sitting in this room.

* * *

Harrison and the Map

Harrison Chase, founder of LangChain, has a whiteboard in front of him. It’s covered in arrows. Many arrows. Some pointing left, some right, some looping back.

“This is orchestration,” he says, gesturing at the tangle of lines.

Someone from the corner replies: “That looks like the London Underground.”

“Exactly,” Harrison says, and smiles. “And it works the same way. Every line has its own logic. Every agent knows its stop. The problem starts when someone doesn’t know where they want to go.”

Silence.

“The map doesn’t determine the destination,” he adds, quieter. “The map just makes sure you don’t miss your stop.”

That’s the first tension moving through the room: LangChain is an architecture of movement, not an architecture of purpose.

* * *

Andrew and the Question Nobody Asks

Andrew Ng doesn’t sit. He paces, the way he always does when he lectures — because he’s always lecturing, even when he’s just talking.

“Understand,” he says, “that the biggest mistake companies make is deploying an agent and waiting for a miracle. An agent isn’t a miracle. An agent is iteration.”

He takes a marker and writes on the board: Draft → Reflect → Revise → Reflect → …

“This is an agentic workflow. Not a linear chain - a loop. The agent writes, evaluates itself, revises, evaluates again.”

“But evaluates against what?”

Andrew stops.

“Against the instructions you give it,” he answers.

“And if the instructions are wrong?”

A long pause.

“Then the agent will do the wrong thing exceptionally well.”

* * *

Kambhampati Enters Through the Side Door

Subbarao Kambhampati doesn’t wait for an invitation. He enters as if he had a prior arrangement with the truth.

A professor at Arizona State University, one of the sharpest critics of what he calls the “planning illusion” in language models.

He sits across from Andrew.

“Iteration isn’t thinking,” he begins, without preamble. “When an LLM generates the next token, it doesn’t plan, it samples. And sampling that looks like planning isn’t the same as planning.”

“But the results - ”, Andrew starts.

“The results are good as long as someone else already solved the problem in the training data. When the task becomes genuinely new, the LLM improvises. And improvisation without understanding is sophisticated guessing.”

Harrison looks at his map. Andrew holds the marker.

“So orchestration doesn’t work?” someone asks.

“Orchestration works,” Kambhampati says. “The question is what it’s orchestrating. Agents that don’t understand, or agents that have enough context that they don’t need to?”

* * *

Lakhani and the Strategic Contract

Karim Lakhani arrives last. Harvard Business School. Author of a book on how AI reshapes organizations from the inside.

He doesn’t sit right away. He stands by the window - which isn’t there - and stares at the space where a window should be.

“You’re talking about how agents work,” he says. “I’m asking why they exist at all.”

“Because they increase productivity,” Andrew answers.

“Productivity of what? For whom? In service of which value?”

Silence.

“Every organization that deploys agents implicitly signs a contract with the future. But most organizations don’t read the contract. They just sign it.”

Lakhani takes the marker from Andrew’s hands. He writes two words on the board: Exploitation and Exploration.

“Agents are excellent at Exploitation: optimizing what we already know. They’re poor at Exploration: finding what we don’t yet know. And most organizations want Exploration, but pay for Exploitation.”

* * *

Soft Orchestration

All four are sitting quietly now.

And then someone asks the question nobody had spoken aloud:

“What if orchestration isn’t in the code?”

Workflow orchestration is a score: every note determined in advance. Semantic orchestration is a jazz standard: the structure exists, but within it there’s room for judgment.

But soft orchestration, the kind this story is searching for, is the culture of an ensemble. It isn’t in the notes. It isn’t in the standard. It’s in that moment when the musician knows, without being told, that now is the time for silence.

You can’t program that. But you can create the conditions in which it emerges.

* * *

The One Missing from the Room

There’s no business architect in this room.

A business architect isn’t the one who knows how agents work. They’re the one who knows why the company exists - and whether the orchestration supports that why, or quietly erodes it.

They’re not in the room because nobody invited them.

* * *

Agents orchestrate processes.

Processes are orchestrated by rules.

Rules are orchestrated by values.

Values are orchestrated by purpose.

* * *

And that - more than any hallucination, more than any misaligned prompt - is the real risk of agentic AI.

Not that the orchestra plays wrong notes.

That the orchestra plays the wrong piece.

* * *

Purpose cannot be orchestrated by anyone but you.

* * *

Author’s Note

The characters in these stories are real thinkers whose published work inspired these dialogues. The conversations are fiction. The ideas build on their foundations — the synthesis is mine.

Harrison Chase, Andrew Ng, Subbarao Kambhampati, and Karim Lakhani are publicly accessible through essays, lectures, and books. Ana Kovac is a fictional customer — and every customer your system knows only through data.

Everyone’s chasing AI value.

Few know what it actually takes.

* * *

In Kranj, a city that isn’t Ljubljana, but close enough to know all the trends, and far enough to watch them with a critical eye - there was a company.

An ordinary company.

With a CRM that tracked everything. A dashboard showing conversions, bounce rate, and customer lifetime value in real time. An NPS survey completed each month by twelve percent of customers. Annual reports showing growth.

All the data.

Not a single customer anyone knew by name.

* * *

And then there was a meeting.

An ordinary meeting. PowerPoint. Four agenda items. Coffee going cold.

Someone was presenting a new AI initiative. Process automation. Cost optimization. Productivity up thirty percent. The slides were polished. The numbers were convincing.

And then, nobody remembers who, someone asked:

“But what does Ana think about this?”

A brief silence.

Everyone looked at the dashboard.

The dashboard had no answer.

Not because Ana wasn’t in the system. She was. Loyalty score 94. Eight years. Every month. A promoter.

But nobody in the room knew who Ana was.

Nobody knew where she lived. What worried her. What made her happy. Why she stayed. What would make her leave.

They had all her data.

They didn’t know her.

* * *

Aleš was sitting in the corner.

He was quiet that day — which is unusual for him, since he normally has an opinion about everything. But that question — “What does Ana think?” — landed differently.

Not as a strategic problem.

As a personal memory.

Because once - before he became a strategist, before he became a business architect, when he was still a business analyst with a notebook and too many questions - he sat across from real customers. Listened. Took notes. Understood that value doesn’t come from the system. It comes from the person who uses it.

And that Ana, every Ana, knows something no dashboard will ever show.

That morning in Kranj, Aleš thought:

“What if we built a room where Ana isn’t a data point, but a voice?”

* * *

The AI Value Boardroom was born from that question.

Not as an answer. As a space where the question is allowed to be asked.

Every topic. Multiple boardroom perspectives. And Ana, always at the top.

Not as a goal at the end of the value ladder.

As a compass that tells us whether we’re heading in the right direction at all.

* * *

The orange dot on the AI value scale is not there by accident.

It is the voice of the customer.

It is Ana.

It is the reason we’re building any of this.

* * *

The first story awaits: “Orchestra Without a Conductor”

Five stories first. Then frameworks, methodology, and assessments. The stories are the foundation. Both matter.

Something changed the moment AI stopped being a technology conversation.

It became a boardroom conversation. A customer conversation. A strategy conversation. A people conversation. And a question about what the economy is becoming.

Most publications follow one of these threads. This one was built where they converge.

The AI Value Boardroom is a publication about the value AI creates, or fails to create, when the people who should be shaping it are looking through only one lens. Five dimensions. At least six roles. Built on AIVaaS™, a methodology that places AI ambition at the centre of business strategy, not alongside it.

Every piece in this publication sits somewhere in that intersection. Not every piece covers all five dimensions. But across the whole, you will find the full picture that no single view can provide.

You will also meet ‘Ana’. 🟠 The orange dot in the logo. The customer who is rarely in the room where AI decisions are made. She is the reason any of this matters.

The room is open. Every seat has a perspective worth bringing.

→ Understand the room: About

→ Find your seat: For Your Role

→ Follow the logic: The Spiral

The first stone has been thrown. The first circle is yours

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