The AI wave is producing plenty of useful first use cases. A team turns on a copilot. A service channel adds an assistant. A function builds an agent to summarise, triage, route, draft or recommend. In one corner of the organisation, the work gets faster. A queue moves. A pilot looks promising.

That is useful.

But it is not yet platform progress.

The harder leadership question is what remains after the first win.

If the next AI use case still needs the same scramble for data access, integration, governance, workflow design, change support and ownership, the organisation may have bought another product.

It may not have built capability.

The Shift

Copilots, assistants and agents are no longer sitting neatly inside innovation teams.

They are appearing across enterprise software, service channels, knowledge bases, sales tools, HR platforms, marketing systems and operational processes. Some are bought directly. Some arrive inside existing SaaS products. Some are built quickly because, quite reasonably, the work still needs to get done.

None of that is automatically a problem.

But each one also asks a quieter question:

does this make the next use case easier, or does it simply add another layer to manage?

That is the product-to-platform shift.

A product solves a local problem. A platform leaves behind something reusable: better data, clearer workflow, tested governance, stronger ownership and less friction for the next team.

The platform is not the dashboard, the product suite, the model or the agent.

It is what the organisation can reuse.

The Real Tension

Product wins are visible.

Platform progress is quieter.

A business unit can point to a working assistant. A team can show time saved. A vendor can demonstrate an agent moving neatly through a workflow, with the confidence only a demo environment can provide.

Lovely.

The frontline team sees whether the tool connects to the system they use every day, whether the answer contradicts last week’s process update, and whether escalation is clear when the assistant gets stuck.

The CFO sees something else again: more licences, more vendors, more claims of productivity improvement — but not always evidence that the organisation now owns something it did not own before.

That is where AI investment drifts.

Not because the tools are useless. Because the value stays local.

The architecture team inherits another connector. Risk inherits another exception. Operations inherits another workflow. The next team inherits very little.

Every AI tool that solves a local problem without improving the organisation’s ability to solve the next one is just a faster way to build the next layer of legacy.

The Ripple Insight

The platform is not the system you buy. It is the capability the organisation can reuse.

This pattern is not new.

Many organisations lived through a version of it during the Customer 360, CRM and personalisation wave. The promise was powerful: one view of the customer, smarter service, more relevant experiences and measurable growth.

The promise was rarely wrong.

The operating model was usually harder than the promise allowed.

Data quality mattered. Identity resolution mattered. Consent mattered. So did integration, ownership, workflow and trust.

The same trap is back, just wearing newer language: copilots, agents, orchestration, embedded assistants, AI platforms, systems of intelligence.

Different labels. Familiar pattern.

A useful tool gets mistaken for reusable enterprise capability.

This is why the data layer cannot be treated as background infrastructure.

The CRM projects that struggled on data quality taught that lesson the hard way.

It is what allows customer journeys to join up, AI tools to reason sensibly, service teams to act consistently and leaders to see whether the operating model is actually working.

If the data layer is fragmented, the experience layer becomes theatre.

A confident answer assembled from inconsistent knowledge is still an inconsistent answer. An agent moving across unclear workflows can move the mess faster.

That is not platform progress.

It is fluent fragmentation.

Moderna is a useful contrast. Its COVID response reflected years of platform capability already in place: reusable scientific methods, tested manufacturing patterns and data infrastructure built before the crisis required it.

The platform did not remove uncertainty.

It reduced how much work had to begin from zero.

A platform makes the next serious problem easier to respond to.

The Move

Leaders do not need to turn every AI pilot into an enterprise architecture ceremony.

Please don’t. Nobody needs that meeting.

Small tests matter. Local productivity gains matter. Teams need room to learn, test and move.

But moving quickly and leaving something useful behind are not opposites.

The best AI use cases do both. They create near-term value and leave something behind the organisation can use again.

So before approving the next AI pilot, agent, assistant or copilot rollout, ask:

what will this leave behind?

Then make the answer specific.

Did it improve the data? Did it clarify ownership? Did it create a workflow another team can reuse, test a governance control, or strengthen a knowledge base? And did it make the second use case faster, safer, cheaper or easier to stand behind?

If the answer is yes, the organisation may be building platform progress.

If the answer is no, the use case may still be worthwhile. But call it what it is: a product win.

There is nothing wrong with product wins.

The problem is pretending they are platform progress.

A product win says:

this worked here.

Platform progress says:

because this worked here, the next thing is easier.

That is the leadership test.

Executive note for leaders

Before approving the next AI use case, ask:

what will this leave behind?

Look for the practical residue: better data, clearer ownership, a reusable workflow, a tested governance control, a stronger knowledge base, or a faster second use case.

A product win proves something worked once.

Platform progress proves the organisation is becoming better equipped for what comes next.

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If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.

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Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.

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Disclaimer

Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.

Reputation used to feel like something organisations accumulated over time: a pattern of delivery, a memory of good service, a sense that the organisation generally did what it said it would do.

That still matters.

But the reading of reputation has sped up. Many organisations have not.

A customer thread starts moving. A support forum fills in some of the blanks. A review gets quoted. An employee post adds colour. Then AI search or an answer engine pulls those scattered signals into one confident version of the organisation.

The organisation may still be working out what happened. The market may already be reading the summary.

That is the shift.

The challenge is not simply to communicate faster.

It is to become coherent sooner.

The question for leaders is no longer only:

what do we say?

It is:

can our operating model stand behind the signal the market is already reading?Ripple Insight

The practical challenge is not faster messaging.

It is faster coherence.

When markets interpret an organisation before internal alignment has caught up, the question changes from:

What do we say?

to:

Can our operating model stand behind the signal we are sending?

That is the shift this article explores.

The Shift

AI search and answer engines change reputation because they do not wait for the organisation to finish its internal alignment.

They assemble. A review site, an employee forum, a support thread, a help page and an old product page can all be pulled into the same answer. The contradictions are rarely marked as contradictions. They are smoothed into a summary and presented as if the organisation’s position were settled.

Sometimes that view is fair.

Sometimes it is incomplete, stale or oddly confident.

Often it is drawn from sources the organisation did not write and cannot fully control.

Reputation is no longer formed only through managed channels. It is shaped through the broader evidence trail the organisation leaves behind.

The homepage says one thing. The support forum says another. The product experience says something else. The employee commentary adds another layer.

AI does not experience that as a governance problem.

It experiences it as source material.

The Real Tension

Most organisations still align sequentially.

Product may be checking the roadmap. Legal may be checking the wording. Customer teams may still be working out what the change means on Monday morning. Employees may be hearing just enough to know they do not yet have the answer.

Meanwhile, the market is not waiting.

Sometimes the product change goes live before the support model is ready. Sometimes the pricing update lands before customers understand the trade-off. An AI-enabled service might give an answer that is technically explainable but publicly indefensible. A platform migration may make sense internally as modernisation while customers experience it as lost functionality.

The same pattern appears in cases such as Sonos’ 2024 app redesign, where internal platform modernisation was experienced by many customers as lost basics and reduced trust.

It can also appear in banking migrations, utility channel rollouts, government service redesigns or AI-enabled support.

What begins as an operating decision can quickly become a customer experience, a market interpretation and, increasingly, an AI-mediated summary.

The Ripple Insight

The operating model shows through.

That is where it gets awkward.

A communications team can make an organisation sound clear briefly. It cannot make an incoherent operating model feel trustworthy for long.

A product team can be technically right and still miss the customer’s lived experience. An executive team can have sound strategic intent and still underestimate what the market will infer from timing, tone or silence.

Reputation coherence is a narrower discipline than brand control. It is about reducing the distance between decision, action, message and experience.

Speed helps with elapsed time. Coherence helps with contradiction. They are related, but they are not the same discipline.

Coherence is not free. Sometimes it means slowing a launch, absorbing short-term support pressure, briefing employees earlier, or accepting a little commercial friction so the organisation does not write cheques the frontline cannot cash.

That is not just reputational caution.

It is operational maturity.

In an AI-mediated environment, inconsistency travels further than intent. If customers, employees, support channels and digital evidence point in different directions, the market has more to work with than the statement.

And the market is getting very good at assembling the pieces.

The Move

The answer is not to treat every reputation moment as a crisis.

That usually creates theatre, hierarchy and a great many calendar invitations.

Some issues are crises. Many are moments where interpretation is simply moving faster than alignment.

For those moments, the useful question is:

who owns the next 12 hours?

Not the whole reputation system. Not a new standing council or brand committee. The next 12 hours.

What are customers actually experiencing? What are support teams, chatbots or service channels saying? What are employees likely to hear or repeat? What are AI search, reviews and forums already surfacing? And what can the organisation say now without quietly walking it back tomorrow?

Reputation coherence is not achieved by centralising every decision. It is achieved by knowing which decisions need enterprise judgement, which can be delegated, and which need a clear line that teams can act on immediately.

Some internal uncertainty is inevitable.

Organisational incoherence is not.

Reputation now forms inside the elapsed time between action and coherence.

The organisations that manage this well will not necessarily be the loudest, fastest or most polished.

They will be the ones whose customers, employees and markets encounter roughly the same organisation wherever they touch it — including when an AI system gets there first.

Executive note for leaders

Before treating a reputation issue as a communications problem, run a fast coherence check across customer experience, support channels, employee signals, legal or policy positions, AI and search visibility, and public messaging.

The aim is not perfect certainty.

The aim is to reduce contradiction before the market — or an AI-generated summary — turns the organisation’s loose ends into the story.

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If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.

Thanks for reading,

Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.

Connect

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Disclaimer

Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.

Springsteen is a Border Collie, which means he combines high intelligence, unreasonable enthusiasm, and the quiet operational belief that everyone else in the household is underperforming.

This becomes especially clear around evening walk time.

If the walk has not commenced within what he considers the acceptable delivery window, he begins stakeholder engagement: eye contact, pacing, strategic sighing, toy placement, and eventually the sort of stare that suggests the roadmap has failed.

The interesting thing is that Springsteen is not especially moved by intent. He does not care that the walk was planned, that the household had competing priorities, or that the delivery team needed five minutes to get organised before mobilisation.

He responds to observable signals: shoes, keys, movement, tone and timing.

Which, unhelpfully, makes him a fairly good market analyst.

There is an exception, of course. If you are a technology company, some customers will analyse your roadmap with the seriousness of an intelligence briefing. Springsteen does something similar when someone picks up a cup of tea during evening-walk hour. To him, that is not a beverage. That is a signal.

But for most organisations, customers are not studying the roadmap. They are responding to what they can see, feel, use, resolve, buy, access or trust today.

They do not usually know what was discussed in the quarterly planning session, which initiative is in phase two, or that a dependency has been moved into the next release window.

And, in most cases, they do not care.

It is just reality doing what reality does.

A roadmap is comforting because it turns uncertainty into sequence. It gives leaders something structured to point at. It shows intent, ambition and order. Done well, it is useful. Necessary, even.

But the roadmap is not the customer experience, the employee experience or the market signal — it is a theory of movement. Reality is the feedback loop.

Springsteen understands this deeply, although I suspect he would describe it differently, possibly by placing a tennis ball on someone’s foot and maintaining unsettling eye contact.

His view is simple: if the signals suggest walk, then walk should happen. If the signals suggest movement, but no movement follows, confidence begins to fall. If the pattern repeats, escalation becomes reasonable.

In organisational life, the same thing happens more quietly.

A company announces a refreshed customer strategy, but the contact centre still cannot see the right information. A new digital channel launches, but customers still have to repeat themselves when they move to a person. A transformation program says the organisation is simplifying, while employees experience more tools, more meetings and more internal friction.

No one is necessarily being dishonest. The roadmap may be real, the intent may be good, and the sequencing may even make sense internally.

The roadmap explains what we meant to do. The market responds to what it can see.

That does not mean leaders should abandon roadmaps and chase every signal that moves. That would be exhausting. Springsteen would probably support it, but he is not the Chief Operating Officer.

The more useful reminder is smaller: leaders need to hold the roadmap lightly enough to notice when reality has started giving feedback.

Sometimes the signal is customer behaviour. Sometimes it is frontline stress, reputation moving faster than expected, adoption lagging behind a product everyone believed was ready, or a Border Collie standing in the hallway, making it abundantly clear that the current operating model lacks credibility.

The leadership work is not to throw away the plan every time the room changes; it is to notice when the room has changed. Because the market does not care that the roadmap was carefully considered. It cares what is landing.

And if what is landing does not match what was intended, the useful response is not to point harder at the roadmap. It is to read the signal, adjust the work, and keep moving.

Preferably before the Border Collie escalates.

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The Ripple Effect is written for leaders navigating AI, digital modernisation and organisational change inside complex organisations.

Practical judgement, useful observations and clearer ways to think through change.

With occasional help from Springsteen, my Border Collie, who reminds me that clarity often comes from movement 🐾.

Stuart Gonsal MAICD

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There is a question that often gets skipped in a successful AI pilot: who approved the knowledge the assistant is using, and who keeps it current when the pilot team moves on?

In the pilot, everything feels close enough to manage. The knowledge base is small, the scenarios are familiar, and the project team can still spot the awkward answers before they become awkward in public.

Production is less polite.

Policies change. Service definitions shift. Customers arrive with disputes, hardship issues, accessibility needs, language barriers and circumstances that have shown very little interest in the pilot plan.

That is when a useful AI assistant becomes something more serious.

Pilot success proves the capability works. Production rollout proves whether the organisation is ready to stand behind it.

And those are not the same thing.

The Shift

The specific AI capability here is not a general chatbot making things up from the internet.

It is a grounded AI assistant: a system that generates answers from approved organisational knowledge sources such as policy libraries, service guides, hardship procedures, complaint pathways, internal knowledge articles and customer service scripts.

In practice, that might mean supporting a contact-centre agent, helping a chatbot respond more consistently, guiding a customer through an after-hours service issue, or summarising the next best step from approved material.

For high-volume service environments, the appeal is obvious: faster answers, more consistency, and less dependence on every frontline person remembering every policy variation on a difficult Tuesday.

Lovely, in theory.

But grounding the answer does not settle the leadership question. It makes it harder to avoid.

Once approved knowledge becomes response logic, the organisation is no longer just managing content.

It is operationalising its promises.

The Real Tension

A grounded assistant is only as reliable as the knowledge it is allowed to read.

Simple sentence. Unhelpfully large implication.

A hardship policy changes, but the source document is not refreshed. A complaint pathway is updated on the website but not in the internal knowledge article. A customer asks a question that is technically answerable, but sensitive enough that a human should be involved.

In each case, the assistant may be doing exactly what it was designed to do.

That is the uncomfortable part.

The model can be grounded while the organisation around it is not.

Customers do not see the retrieval pathway, confidence score, or internal debate about whether the pilot was “production-ready.” They do not admire the architecture. Reasonable, really.

They experience the organisation speaking to them.

If the answer is wrong, outdated or poorly escalated, the issue is no longer an AI-output issue. It is an institutional-readiness issue.

This is why many AI programmes slow down between pilot and rollout. The capability has not necessarily failed. The organisation has reached the accountability threshold.

The Ripple Insight

Most AI programmes do not stall because the model suddenly becomes useless.

They stall because the organisation reaches the question of what it is prepared to own.

A grounded AI assistant can retrieve, draft, summarise and respond. It can make service language more consistent. It can also expose long-standing gaps in policy, content ownership and channel governance that have been sitting there quietly, looking harmless, because no machine had tried to operationalise them at scale.

Useful, yes.

Also diagnostic.

Once knowledge becomes response logic, content governance is no longer housekeeping. It is part of the operating model.

An assistant explaining hardship eligibility may shape what a customer believes they can access. A portal recommendation may influence whether they escalate, complain, wait or give up.

The production decision sits one level higher than answer quality.

The question is not only:

is the answer right?

It is:

are we prepared to own the answer when the context changes?

That means knowing who owns the approved knowledge, when the assistant must stop answering, who can see the audit trail, and who fixes the source when yesterday’s correct answer becomes today’s problem.

That is the ownership test.

Good enough to ship is not the same as safe enough to sleep.

The Move

Before moving a grounded AI assistant from pilot to production, run a simple readiness check.

Not a theoretical governance review. Not a 47-page framework quietly hoping no one asks who updates the hardship policy.

A practical check.

Start with three questions.

Who owns the knowledge?
Not the vendor, the project team or “the business” in the abstract. The actual person or function accountable for the policy, service rule or customer commitment being expressed.

Where must the system stop?
Some answers can go directly to a customer. Others should only support an agent. In hardship, vulnerability, complaints, disputed charges, accessibility needs and grey areas of policy, the handoff should be designed before launch, not discovered after it.

Can the answer be traced later?
If a customer challenges a response six months from now, the organisation needs to show what the assistant used, what it produced, when the source was current, and who owned the decision path.

If those questions do not have named owners, the organisation may not have a production system yet.

It may have a pilot wearing a production badge.

Confidence comes when the organisation knows what it is prepared to own — and who wakes up when something goes wrong.

That is the difference between good enough to ship and safe enough to sleep.

Executive note for leaders

Before a grounded AI assistant moves from pilot to production, ask:

who owns the knowledge, where must the system stop, and can the answer be traced later?

If those questions do not have named owners, the organisation is still carrying pilot risk into production.

The practical test is not whether the assistant can answer.

It is whether the organisation is ready to stand behind the answer when the context changes.

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If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.

Thanks for reading,

Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.

Connect

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Disclaimer

Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.

The productivity case for AI copilots is not hard to see. The appeal is easy to understand. The briefing note appears faster. The contract summary takes minutes. The first draft is ready before the meeting has finished. A senior person can ask for the comparison, the synthesis and the tidy next-step email without waiting for someone junior to pull it together by hand.

That is useful.

But there is a quieter question underneath the efficiency gain.

What if some of the work organisations are most excited to remove is the work people used to learn from?

Not the glamorous work. Not the work anyone put on a career brochure.

The messy first pass. The weak draft. The boring comparison table. The document review where someone slowly learned what mattered and what was noise.

AI is starting to take more of that work.

It may be the moment organisations accidentally make the future expert pipeline thinner while everyone is congratulating themselves on throughput.

Always a fun little governance surprise.

The Shift

The specific AI capability here is not vague “automation.”

It is embedded workflow AI: copilots and assistants that draft, summarise, compare, analyse, review documents, prepare notes and produce first-pass thinking inside the tools people already use.

AI is no longer only helping people do the work. In some workflows, it is doing the first pass, the messy pass and sometimes most of the pass.

That matters because first-pass work has never only been output. It has also been apprenticeship.

People learn by doing the clumsy version first. They learn what a bad brief looks like by writing one. They learn what a weak argument feels like by trying to defend it. They learn which details matter by including too many and having someone more experienced quietly circle the three that count.

Not always gently, admittedly. Many of us have the tracked changes to prove it.

But that repetition did something.

It built judgement.

The Real Tension

Most organisations are not trying to weaken their future capability.

They are trying to move faster, reduce cost, improve consistency and make better use of experienced people. That logic is reasonable.

The tension is that the short-term productivity gain is visible, while the long-term capability loss is harder to see.

If AI absorbs routine drafting, research, synthesis and document review, the organisation may still get the work done. It may even get it done better.

But who is learning how the work works?

A junior employee who only reviews AI output may move faster. They may also skip the part where pattern recognition forms. They can approve a summary without wrestling with the raw material. They can edit a recommendation without knowing how the options were weighed.

That is the supervision paradox.

We may ask people to check work they have never really done.

And then, a few years later, wonder why the mid-career bench feels thinner than expected.

The Ripple Insight

This is not mainly a jobs argument.

It is a question about how organisations keep building judgement.

Entry-level roles have always done two jobs. They produce work today, and they produce the people who will carry harder work tomorrow.

The first job is easy to measure. The second is easier to forget.

That is why the AI productivity conversation can become misleading. It asks how much faster the task can be completed. Fair question. But it often misses the better one:

what developmental work was hidden inside that task?

Because sometimes the inefficiency was the point.

The slow draft, the repeated review, the awkward first client note, the analysis rebuilt after a senior person asked one annoying but correct question — these were not just delays in the system. They were how the system produced judgement.

No one wants to preserve busywork for sentimental reasons. I certainly do not.

But leaders need to know the difference between removing waste and removing practice.

Those are not the same thing.

If organisations remove repetitive low-value work, good. Please do. There is plenty of it.

But if they remove the early exposure that teaches people how to think, interpret, challenge and recover, they may be borrowing capability from the future.

And the future, irritatingly, tends to send the invoice later.

The Move

Before automating or compressing early-career work, leaders should ask a more concrete question:

Where does judgement currently form?

Not in the values statement. Not in the learning platform. In the actual work.

Pick one function, role or pathway. Look at the tasks AI is now absorbing: drafting, analysis, research, document review, meeting preparation, customer summaries, case notes, code review, contract triage.

Then separate the work into two piles.

One pile is genuine waste: repetitive, low-learning work that nobody needs to defend for sentimental reasons.

The other pile is different. It may look inefficient, but it teaches pattern recognition, context, trade-offs and professional instinct.

That second pile needs redesign, not disappearance.

It may mean structured first-pass ownership before AI assistance. It may mean requiring juniors to explain what the AI missed. It may mean pairing AI output with senior review conversations or rotating people through messy real cases before they supervise polished machine output.

The goal is not to slow the organisation down for the romance of apprenticeship.

The goal is to keep producing people who can make good calls when the answer is not in the tool.

The quiet risk is not that AI makes people faster.

It is that organisations may stop noticing where expertise used to be made.

Executive note for leaders

Before removing a task with AI, ask:

is this task only producing output, or is it also producing judgement?

If it is only output, automate it carefully.

If it is also building judgement, pause before removing it and redesign the pathway around it.

Identify where first drafts, analysis and review are disappearing — then decide how future experts will get the repetition and feedback they still need.

Organisations do not inherit expertise.

They design the conditions under which it forms.

Subscribe

You can subscribe for free. Every edition is delivered directly to your inbox and published here on Substack.

If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.

Thanks for reading,

Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.

Connect

LinkedIn – Follow for practical leadership in the AI era. ↗

Disclaimer

Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.

Spend enough time around a border collie and you realise something fairly quickly. They’re always watching you.

Not in a demanding way. More in a quietly attentive way - as if they’re constantly checking in on what you might be about to do next.

My border collie Springsteen does this every morning. Before work, he’ll sit near the door, looking at me with that very particular collie expression that seems to say:

So… are we doing something interesting today?

Border collies are remarkable animals. But what makes them remarkable isn’t just intelligence. It’s the relationship they build with their person.

They read tone.
They notice small changes in movement.
They pick up signals you didn’t realise you were sending.

And over time, something interesting happens.

You start reading them too.

Without thinking about it much, you develop a kind of quiet coordination. A glance, a shift in posture, a change in pace - and both of you know what’s happening next.

It’s less like giving instructions and more like being a small two-person team that understands each other.

Recently, while studying Organizational Leadership at Harvard Business School Online, a theme kept surfacing: as leadership scales, it becomes less about authority and more about calibration.

People sometimes assume leadership at the senior level becomes more about command - clearer direction, stronger decisions, sharper strategy.

And those things absolutely matter.

But what colleagues consistently describe when they talk about the leaders they trust most are slightly different qualities:

Empathy.

Presence.

Curiosity.

Clarity of direction.

And perhaps most importantly - the ability to genuinely listen - to catch the signal before the noise.

In other words, leadership isn’t just about directing energy.

It’s about tuning into it.

Which brings me back to Springsteen.

Border collies don’t work well with constant instruction. In fact, if you try to over-control them, things tend to fall apart pretty quickly.

What works much better is awareness.

You pay attention.
They pay attention.

And somewhere in that space, a kind of rhythm forms where both of you move in the same direction.

The longer I work in organisations, the more I suspect the same thing is true there as well.

The strongest teams rarely form because someone is simply issuing orders from the front.

They form because people feel understood. Because trust builds. Because the leader creates enough space for the unspoken signals to be noticed.

Which means leadership, at its best, is a little less like command…

…and a little more like a really good border collie partnership.

I sometimes joke that I’m Springsteen’s GM.

But in reality he’s a constant reminder that leadership often starts with awareness.

And on most mornings, watching how closely he reads the world around him, I’m reminded just how powerful paying attention can be. 🐾

Leadership, it turns out, often starts with paying attention.

If this perspective on leadership and partnership resonates, join the conversation.

The Ripple Effect is a space for leaders navigating digital transformation and organisational change in complex environments.

No hype, no trend lists - just one leadership pattern, carefully observed.

— Stuart

The interesting problem with AI measurement right now is that most organisations are not short of data.

They are short of evidence.

They can count licences, logins, prompts, active users, pilot numbers and time-saved anecdotes. They can show adoption dashboards. They can point to teams using copilots, assistants, workflow tools and AI-enabled features across the business.

They can usually produce a slide proving that activity is increasing.

Lovely.

But the harder question is still sitting there, looking mildly inconvenient:

did any of this change an important decision?

That is the confidence gap underneath a lot of AI strategy right now.

The practical question is more specific:

what is AI improving, who owns the outcome, and can the organisation stand behind the result?

The Shift

This is less about one tool than the spread of AI across everyday work.

Enterprise copilots, GenAI assistants, AI-enabled workflow tools and embedded features are helping people draft, summarise, analyse, compare, search, recommend and decide faster.

That matters because these tools create a lot of visible activity.

Someone used the assistant. A team generated summaries. A pilot reduced drafting time. A workflow produced recommendations. A dashboard showed usage going up and to the right, which is always comforting and occasionally meaningful.

But activity is not the same as value.

A tool can be popular and still have a vague relationship with the decision it was meant to improve.

A team can save time on drafting while the approval still sits in someone’s inbox for two weeks.

A model can produce a useful recommendation that nobody trusts enough to act on.

A pilot can look successful because it made part of the process faster while leaving the actual business decision untouched.

That is why the next useful shift in AI measurement is not another dashboard of tool usage.

It is a better view of what happens between AI-assisted work and an actual decision.

The Real Tension

Adoption data can be reassuring for a while.

A high licence activation rate can make an AI programme look healthy. So can prompt volumes, pilot counts, internal showcases and enthusiastic stories from early adopters.

Some of those signals are useful. None should be dismissed.

But they do not answer the question a board, CFO, executive team or business owner will eventually ask:

what changed?

That question has a way of clearing the room slightly.

Often, the value is probably there. A customer issue was resolved with better context. A manager had a clearer view before making a call. A piece of analysis that once took days took hours.

But if the organisation cannot trace that value to a decision, an outcome or an accountable owner, the story becomes hard to defend.

This is where AI strategy can start to wobble.

The technology may be working. The dashboard may be accurate. The pilot may have genuinely helped.

But if the dashboard can tell you who used the tool and not what decision improved, it may be measuring attendance, not value.

A neat little trap, really.

Shadow AI makes this harder. Useful work may be happening in tools the organisation cannot see, while the official dashboard confidently reports on the smaller system it can.

That is not measurement.

That is a torch pointed at only part of the room.

The Ripple Insight

The real gap is not measurement first.

It is ownership.

Someone buys the licence. Someone runs the pilot. Someone reports adoption. Someone else is expected to realise the benefit.

Then, when scrutiny increases, everyone can explain their part.

No one can quite explain the outcome.

That is not just a measurement problem.

It is an operating model problem.

Name the decision, not the tool.

If AI is supposed to improve customer retention, reduce risk, sharpen pricing, speed up triage, support forecasting or improve service recovery, then the relevant business leader needs to own the decision AI is meant to influence.

Otherwise, AI becomes another layer of activity sitting across an already complicated organisation.

More tools. More dashboards. More governance meetings.

Not necessarily more confidence.

The useful question is not:

how much AI did we use?

It is:

what decision changed, who owns it, and can we stand behind the result?

That question moves AI strategy away from technology consumption and toward decisions someone actually owns.

AI does not create value in the abstract. It creates value when it changes a decision, improves a process, catches a risk earlier, reduces rework, or helps a team act with better judgement.

If none of those things can be named, measured or owned, the strategy may still be active.

It may not yet be useful.

The Move

Before the next AI steering committee, board update or investment case, pick one important decision stream.

Not the whole AI portfolio. That way lies a long workshop and a very tired room.

One decision stream.

A customer escalation. A pricing approval. A field response. A claims decision. A campaign allocation. A workforce planning call. Something repeatable and close enough to the operating model to reveal the truth.

Then ask four questions.

How was the decision made before AI?

Where is AI now involved?

What changed in speed, quality, rework, escalation or confidence?

Who owns the result?

The point is not to build a perfect measurement system in one quarter. Good luck to anyone attempting that with a straight face.

The point is to create a sharper link between AI activity and organisational consequence.

Some AI use will prove valuable. Some will not. Some will simply show leaders where the organisation had learned to live with a bottleneck.

That is useful too.

The leaders who make progress from here will be the ones who connect AI to decisions the organisation actually cares about.

The practical test is simple:

what decision changed, who owns it, and can we stand behind the result?

Executive note for leaders

Before scaling the next AI pilot, dashboard or licence rollout, ask:

what decision is this meant to improve?

If the answer is unclear, the organisation may be measuring activity rather than value.

Name the decision, assign the owner, then decide what evidence would prove AI changed the result.

Name the decision, not the tool.

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If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.

Thanks for reading,

Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.

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Disclaimer

Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.

Years ago, I wrote a feature for Desktop magazine about the global shopper: the customer who crossed borders, compared at speed and expected digital commerce to keep up.

The channel changed. The shopper’s orientation did not.

Customers kept getting better informed, less patient with friction and more willing to compare across borders, brands and platforms.

Now the shift has moved again.

The customer’s journey may still end on your website, in your app, in your store or with your service team. But increasingly, part of that journey starts somewhere else: inside a GenAI tool, answer engine or AI browser that has already formed a view before the customer arrives.

That is the important part.

The customer is not just searching anymore.

They are asking a tool to interpret the market for them.

And that tool may be reading your product pages, reviews, policies, delivery promises, support content and third-party commentary before deciding what kind of organisation you appear to be.

Always nice to meet the customer after someone else has explained you first.

The Shift

The specific AI capability here is not “AI in shopping” in some broad, shiny sense.

It is GenAI-mediated discovery: tools that summarise, compare, interpret and recommend before the customer reaches an owned channel.

A customer no longer needs to begin with a brand name, category page or traditional search result. They can start with a rough question:

I need something reliable for this use case. I care about price, delivery, returns and trust. What should I be looking at?

The tool starts assembling an answer.

It may compare specifications, summarise reviews, read return policies, scan support pages and decide which trade-offs matter. It may decide which brands seem credible enough to mention.

Sometimes that answer will be useful.

Sometimes it will be incomplete, stale or oddly confident.

But either way, it may shape the customer’s first understanding before your website has had a chance to do anything at all.

The website is not dead.

Brave little thing.

But it may no longer be where the customer’s first understanding is formed.

The Real Tension

Most organisations are still designed around the customer who arrives.

They optimise content, imagery, navigation, offers, service prompts and checkout flows. That work still matters.

But GenAI discovery changes the start of the journey.

The customer may arrive already carrying an interpretation of the organisation: what the product is good for, what the trade-offs are, whether the returns policy is clear, whether support seems reliable, whether the price premium makes sense.

The organisation may not have written that interpretation.

It may have supplied some of the source material, of course. Product pages. FAQs. Policy pages. Help content. Delivery information.

But the final version may have been assembled somewhere else.

That is where the gap opens.

The AI may describe a slightly different promise from the one the organisation believes it made. The customer may arrive expecting the AI’s version. The service team may inherit the mismatch.

Everyone has had a lovely time except, perhaps, the customer.

Visibility matters. Being found and misrepresented is something else entirely.

A premium brand may be flattened into “similar but more expensive.” A service promise may lose an important condition. A product may be recommended for a use case it was never designed to serve.

That is not just a marketing issue.

It is a customer experience and service coherence issue.

The Ripple Insight

AI-mediated shopping shifts the problem from persuasion to interpretation.

For a long time, organisations could assume they would get a chance to explain themselves. The customer would arrive, browse, read, compare and decide. Even when the journey was messy, the brand still had moments to shape understanding.

Those moments are now moving upstream.

If a GenAI tool forms the first summary, the organisation needs to know whether that summary is accurate enough to stand behind.

Not perfect. That is not the world we live in, sadly.

But accurate enough that the customer is not being sent into the wrong expectation before the relationship even begins.

The leadership question is not simply whether GenAI will send more or less traffic.

It is whether the organisation is legible enough to be represented fairly when it is not in the room.

Product data is no longer just back-office hygiene. Policy content is no longer fine print nobody wants to revisit. Service promises cannot quietly vary between the homepage, the FAQ and the support script.

In a GenAI-mediated journey, those inconsistencies become source material.

And source material has consequences.

The organisation may think it is managing a customer journey.

The customer may be arriving from a market interpretation journey the organisation has not yet mapped.

The Move

Before building a large strategy around AI discovery, pick one product category, service journey or customer need.

Then ask major GenAI tools the sort of questions a real customer would ask.

Not brand-safe prompts. Real ones.

Which option is best for me? What should I watch out for? Who has the clearest returns policy? Which provider is most reliable? What is the trade-off between these three choices?

Then watch carefully.

What does the tool get right?

What does it flatten, invent or omit?

Where does it send the customer?

Where should the customer be handed back to an owned channel, a specialist, a human or a clearer policy?

The point is not to optimise immediately.

Don’t optimise.

Don’t automate.

Just watch.

That first audit costs very little. The difficult part is having the stomach to read the results.

The value is in seeing the gap between what the organisation believes it is saying and what GenAI is actually telling the customer.

That is where the work begins.

The global shopper has changed again.

This time, the customer may not just arrive informed.

They may arrive already shaped by a version of you that you did not write.

Executive note for leaders

The practical test is simple:

what is GenAI telling customers before they reach you?

Pick one product category, service journey or customer need and ask GenAI tools the questions a real customer would ask.

Then compare the answers against your product data, service promises, policy content, support scripts and frontline reality.

The aim is not to control every answer.

The aim is to find the gaps before customers arrive with expectations your organisation has not had the chance to shape.

Subscribe

You can subscribe for free. Every edition is delivered directly to your inbox and published here on Substack.

If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.

Thanks for reading,

Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.

Connect

LinkedIn – Follow for practical leadership in the AI era. ↗

Disclaimer

Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.

Executive Brief

Most organisations already have an AI strategy. They just may not have seen it yet.

It is sitting in browser tabs, AI note-takers, unofficial prompt libraries, pasted documents, side experiments and small workarounds people are using to get through the day.

In one team, someone uses ChatGPT to summarise a policy because the internal search is hopeless. In another, an AI note-taker appears in a meeting because the follow-up actions keep disappearing. A useful prompt library starts circulating in Teams before the official training has arrived. A personal tool sits open beside the approved one because, awkwardly, it gives the better answer.

None of that means the organisation is full of reckless rule-breakers.

Usually, it means people are trying to solve the work in front of them.

The quiet experiment is not always the problem.

Sometimes it is the receipt.

The Shift

The specific AI capability here is not one platform.

It is the easy availability of copilots, public GenAI tools, AI note-takers, research assistants, prompt templates and lightweight automations that people can use before formal strategy catches up.

That changes the starting point for leaders.

AI strategy no longer begins when the board approves the roadmap, IT selects the platform, or the steering committee agrees the acceptable-use policy.

By then, the organisation may already have developed a shadow operating model.

Always calming.

The workarounds matter because they are not random. They tend to appear where the organisation is slow, unclear or under-supported.

Poor knowledge access creates unofficial summarising. Too many meetings create silent note-takers. Slow review pathways create AI-assisted drafts. Confusing policy creates personal judgement calls. Weak training creates prompt libraries built by whoever happened to get useful first.

Some of it is useful. Some of it is risky. None of it should be invisible to leadership.

The Real Tension

Leadership often sees quiet AI use first through the risk lens.

Fair enough.

Customer data in a public tool is not a cute innovation story. A meeting bot recording sensitive conversations without consent is not a cultural breakthrough. A prompt library full of unreviewed legal, HR or customer language can scale poor judgement very efficiently.

So yes, the risk is real.

But if leaders stop there, they miss the more useful question:

what problem was the workaround trying to solve?

That question changes the conversation.

If people are pasting documents into public AI tools, maybe the internal knowledge system is not doing its job. If employees are using personal subscriptions alongside approved enterprise tools, maybe the approved tool does not meet the work. If teams are building their own prompt libraries, maybe capability is forming faster than the organisation can package it.

The issue is not simply that people are using tools without permission.

It is that the organisation may be learning about AI faster than its leaders are.

And much of that learning is happening in places leaders are not looking.

The Ripple Insight

Quiet AI experimentation is a mirror held up to the operating model.

Not always a flattering mirror, admittedly.

It shows where the work is too slow. Where systems are too hard to use. Where people do not know the rules. Where the official process is technically correct and practically avoided. Where the approved tool exists, but the real work has quietly moved elsewhere.

That is why the first leadership move should not be panic.

Nor should it be applause.

It should be curiosity with a backbone.

The question is not:

who is breaking the rules?

At least, not first.

The better question is:

what are people trying to get done that the current operating model is not helping them do safely?

That does not remove the need for governance. It makes governance more useful.

A good AI policy should not simply say “no” from a safe distance. It should help people understand what is allowed, what is not, which tool to use, what data can go where, when a human must stay involved, and who to ask when the work falls into the grey zone.

Governance shifts from:

no, unless

to:

yes, if

That is not a soft position.

It is a more honest one.

The Move

Before writing another AI policy, leaders should map what people are already doing.

Not as a witch-hunt. Good luck getting honesty that way.

As an operating scan.

Pick a few teams and ask simple questions.

Where are you using AI today? Which tools? For what work? What data are you putting in? What problem is it solving? What feels unclear? What are you hiding because you are not sure how it will be received?

That last question may do more useful work than the whole policy.

Then do the slightly dull but very useful work of sorting what you have found.

Stop now: too much data, too little control, too much exposure.

Govern better: useful behaviour, but it needs clearer rules, approved tools or better data boundaries.

Legitimise: the workaround is solving a real problem and should no longer be treated as unofficial.

Scale carefully: the experiment is narrow enough to govern, useful enough to matter and clear enough for people to understand.

The practical habit is simple: create a small, regular review with technology, risk, customer or operations, and one business owner close to the work.

Thirty minutes.

Actual examples.

No theatre.

The point is not to celebrate every quiet experiment. Some need to stop.

The point is to stop pretending the formal AI strategy is the only one that exists.

The real strategy may already be forming in the workarounds.

Leaders should read it before it becomes liability — or before the best learning walks out the door.

Executive note for leaders

Before tightening AI governance, ask:

what are people already using AI for, and what is that behaviour trying to tell us?

Quiet experiments can reveal risk. They can also reveal slow systems, poor knowledge access, unclear policy, weak training and useful capability forming before the organisation has named it.

Map the actual use first.

Then decide what to stop, govern, legitimise or scale.

The aim is not permission without control.

The aim is governance that starts from reality.

Subscribe

You can subscribe for free. Every edition is delivered directly to your inbox and published here on Substack.

If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.

Thanks for reading,

Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.

Connect

LinkedIn – Follow for practical leadership in the AI era. ↗

Disclaimer

Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.

Welcome - and thanks for being here

Every meaningful transformation I’ve been part of - whether inside large organisations or in external projects - has started the same way: not with a dramatic leap, but with a quiet, intentional shift.

• A clearer question.

• A reframed assumption.

• A small spark that ripples outward into something bigger.​

That’s the idea behind The Ripple Effect - and it’s why I’m starting this publication for executives and senior leaders who carry responsibility for digital, CX and AI‑enabled change.

Why this space exists

We’re entering a moment where AI isn’t just a technology conversation - it’s a leadership conversation. It is reshaping how organisations design, build, learn, collaborate and make decisions.

For leaders with P&L, portfolio or transformation accountability, that shows up as a different kind of pressure: where to place the next dollar, how far to lean into AI, how to bring people with you while the ground is moving.

Here’s what I’ve learned along the way:

AI can accelerate possibility, but it doesn’t replace perspective.

Tools evolve fast; understanding, culture and capability take time.

My view is simple:

• We don’t need more hype.

• We don’t need to pretend everything is changing overnight.

• And we don’t need to abandon what makes us human to embrace what makes us capable.​

What you can expect

I’ll share reflections and practical ideas for executives and senior leaders across:

• AI and human-centred leadership

• Digital and CX transformation in complex organisations

• Strategy, systems and design thinking

• How small changes create meaningful momentum

• The mindset shifts leaders need as AI becomes everyday

• Stories from people doing interesting, impactful work around the world​

I try to write the way I lead - calm, curious and practical, with a touch of creative spark, always aiming for clarity and momentum over noise.​

Some posts will be short. Some deeper. Some conversational. Occasionally, I’ll explore big questions I’m wrestling with myself.

And over time, you’ll see more interviews, perspectives and ideas from beyond my own experience - because the most interesting insights often come from places we don’t expect.

Why subscribe?

If any of this resonates, subscribing is the best way to stay connected.
Every edition arrives directly in your inbox and is also available here on Substack.​

I write this to think in public, and I appreciate hearing how it lands for others.

Thanks again for joining me at the start of this.
I’m glad you’re here.​
- Stuart

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