tgroenwals shared this post · Sep 23

Your AI rollout isn't failing because of the tech.

It's failing because your leadership team isn't aligned.

They just think they are.

A BCG 2026 survey found that 60% of CEOs believe their boards are pushing AI transformation too fast, while 40% of less-AI-confident board members worry their company is moving too slow.

Same companies. Same meetings and dashboards.

Opposite conclusions.

Now layer in what MIT's NANDA initiative reported: 95% of enterprise GenAI pilots are failing to move revenue. Not because the models don't work. Because the organizations around them don't.

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Michaela Schuler Carolyn, leadership alignment is step one and it always starts there. Love that you put that first.
For the rollout to actually work, the teams need to see the same priorities you do AND be able to act on them without checking back every two days. I have watched aligned exec teams launch rollouts no one downstream could even describe. Most change programs lose people 6 weeks after kickoff, when day-to-day decisions start contradicting the strategy deck. Jun 3

Prasad Mane This is why AI pilots often look successful but don't scale. The technology works, but every team is solving for a different outcome, so the organisation pulls in five directions at once. Jun 3

tgroenwals shared this post · Sep 23

Carolyn Healey

Apr 29 · archived May 13

Move fast with AI and you create risk.

Govern first and you create advantage.

That difference shows up in breach costs, board-level risk and whether AI scales.

Here’s the reality most executives won’t say out loud:
→ 88% of organizations deploy AI in at least one function.
→ Only 1% consider themselves AI-mature. (McKinsey, 2025)

This isn’t a technology gap. It’s a governance gap.

Shadow AI is where that gap becomes a liability.

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Mo Johnson It acknowledges what's actually happening inside organizations, and shifts the conversation from trying to stop it to understanding and shaping it, which is where real advantage starts to show up. Apr 29 2 likes

Clare Kitching Great points Carolyn governance as an enabler rather than a blocker is the real shift. The teams that make it easy to use AI safely are the ones that actually scale it. Apr 29 4 likes

tgroenwals shared this post · Sep 23

Carolyn Healey

Jun 5 · archived Jun 6

Your AI spend per employee keeps climbing.

That does not mean AI is failing.

It means the economics are changing.

For the past 2 years, the business case was framed too simply:

AI replaces headcount. Headcount is expensive. Therefore, AI saves money.

That logic is now being tested.

Not because AI cannot create value.

But because many companies are replacing a cost they understand, labor, with a cost they do not yet fully manage: AI consumption.

CFOs, CEOs, and boards are asking a harder question:

Are we reducing the cost of work? Or just moving it to a different line of the P&L?

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Aarav M. Reddy This is a great point about how we need to stop looking at AI as just a replacement for people. It is really more about shifting how we handle our budgets and our actual work processes. I think a lot of companies are still struggling to make that mental leap from cost-cutting to actual value creation. Do you think CFOs are going to get better at tracking this over the next few quarters? It feels like we are all still figuring out the best way to measure this kind of return. Jun 6

Mohan Nitesh Mallavarapu This distinction uncovers the precise diagnostic failure point that causes modern enterprise AI implementations to experience structural margin drag. When an organization chart permits its leadership tier to confuse raw headcount reduction with genuine operating leverage-simply moving a known labor cost onto an unmanaged AI consumption line of the P&L-it funds an expensive optical illusion of progress. True workforce governance requires human resource and financial leaders to co-engineer the talent strategy: treating token budgets as workforce capacity and explicitly up-skilling the remaining team to operate as high-value workflow architects and output evaluators. Jun 5 1 like

tgroenwals shared this post · Sep 23

Carolyn Healey

May 15 · archived May 15

Your board approved the AI investment.

Your CIO deployed the technology.

Your COO inherited the operational mess.

One question nobody wants to answer:
Who owns the P&L outcome when AI underperforms?

78% of business executives lack confidence they could pass an independent AI governance audit within 90 days (Grant Thornton, 2026).

This is not a technology gap.

It is an accountability architecture gap.

Here's what the executive AI accountability gap looks like inside many enterprises:

1/ The CIO Reports Green. The COO Sees Red.

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Balance Sheet A thoughtful perspective on how organizations should approach AI beyond just investment and hype. Great insight! May 15

Read Startup Many companies invest in AI tools, but few focus enough on implementation and long-term value. Excellent point. May 15

tgroenwals shared this post · Sep 23

Carolyn Healey

Aug 12 · archived Aug 13

Your AI governance policy exists.

Your organization's behavior doesn't match it.

McKinsey just put a number on the gap.

Enterprise Responsible AI maturity averages 2.3 out of 4 in 2026, up from 2.0. But fewer than a third of organizations have reached level 3 in the dimensions that matter most: strategy, governance, and agentic AI controls.

Most executives think of "AI governance readiness" as binary: you either have it or you don't. That's the wrong frame.

Readiness is a ladder, and most organizations are standing on a lower rung than the board deck suggests.

Ángel Cárdenas-Bahena I like the distinction between having governance and being able to demonstrate that it actually operates in practice.

The progression toward adaptive and accountable governance is particularly important. But accountability may raise another question: is knowing who is accountable enough to understand whether a decision was sound?

An autonomous action can have a named owner, follow established controls, and remain fully traceable, while still being influenced by weak assumptions, incomplete evidence, or biases embedded in the reasoning behind the decision.

As AI becomes more involved in consequential decisions, governance may need to examine not only who authorized an action and whether the rules were followed, but also the quality of the reasoning that led to it.

Perhaps maturity will increasingly depend on making that reasoning visible enough to challenge, review, and learn from.

Paul Souhuwat This is a useful distinction, Carolyn.

The gap between policy and behavior is, perhaps, the more important governance measure.
An institution can have an excellent governance framework and still behave as though it has none.

The real test is what happens when the policy meets an actual decision: who has the authority to act, who owns the consequence, and whether the control is embedded in the workflow rather than left in a document.

In that sense, governance maturity is less about having more rules and more about making the right behavior increasingly institutional rather than dependent on individual vigilance.

AI simply makes that distinction much harder to hide.

tgroenwals shared this post · Sep 23

Carolyn Healey

Jul 14 · archived Jul 15

Most AI programs still aren't hitting the P&L.

The ones that are all made the same early decision.

They stopped treating AI as a board-level response and started treating it as an operating model redesign.

88% of enterprises now use AI in at least one function. Yet only 39% report EBIT impact (McKinsey, 2025).

This isn't an AI problem. It's a rollout problem.

Here's what's actually happening inside most enterprises:

The Broken Playbook: How Most Enterprises Roll Out AI

1/ The Board Mandates It

→ Competitive pressure hits the boardroom
→ "We need an AI strategy" lands with the CEO
→…

Bhavik Shah The point about redesigning workflows really stands out. We've seen that AI delivers value when it's built around a business outcome, not when it's added as another tool. Technology is only one part of the equation. Execution is what ultimately shows up in the P&L. Jul 15

Jessica Williams The biggest takeaway is that AI rarely fails because of the model- it fails because the workflow never changes. Technology amplifies processes; it doesn't fix broken ones. Jul 15

tgroenwals shared this post · Jun 16

Carolyn Healey

Jun 15 · archived Jun 16

Companies winning with AI aren’t better at choosing tools.

They’re better at redesigning how work gets done.

PwC’s 2026 AI predictions put a hard number on it:
~20% of value comes from technology.
~80% comes from how work is redesigned.

Most enterprises have it reversed.

They’re spending 80% of their energy evaluating tools and running pilots, and 20% asking the only question that matters:

“How does this change the way we operate?”

Here’s the 80/20 AI strategy framework:

1/ Start With the Workflow…

Carolyn Healey Author Holly Moe - spot on and this is why pilots often look impressive but stall at scale. The demo proves what is possible, but the business still runs on old habits. Value shows up when AI becomes part of the way work actually moves.

Dr. Devraj Bikash Das Perhaps the most important shift here is moving from asking, "What can AI do?" to asking, "What should work look like in an AI-enabled organisation?"
Too many companies are trying to fit AI into existing operating models, when the real opportunity lies in redesigning those models altogether.
Technology is becoming increasingly commoditised. Organisational adaptability, workflow design, and the ability to translate capability into business outcomes may prove to be the more enduring competitive advantage.

tgroenwals shared this post · Jun 13

Carolyn Healey

Jun 12 · archived Jun 13

Your AI spend per employee keeps climbing.

At what point does it cost less to keep the human in the loop?

CFOs are starting to run that math.

For the past two years, the pitch was simple:

AI replaces headcount. Headcount is expensive.

So AI saves money.

That logic is starting to wobble.

Not because AI does not create value.

Because the cost model is more complicated than the sales deck made it sound.

Here’s what’s actually happening:

1/ The swap is becoming explicit

→ CFOs are prioritizing AI and technology investment while headcount growth slows
→ AI budgets are increasingly…

Dr. Markus Limberger One part often gets overlooked.

Even when AI creates productivity gains, the value only shows up if the organization can actually absorb the freed-up capacity.

In many cases, the work doesn’t disappear. The bottleneck just moves to coordination, approvals, exception handling, and adoption.

Helmut Hubmann A valuable perspective.
Much of the AI discussion still focuses on productivity gains, while the operational costs of governance, oversight, quality assurance and risk management receive far less attention.
The point that replacing a salary does not automatically eliminate the work is particularly important. In many cases, organisations are not removing effort but redistributing it across AI infrastructure, human review, monitoring and governance functions.
The long-term economics of AI adoption will likely depend as much on operating models and governance as on the technology itself.

tgroenwals shared this post · May 11

Carolyn Healey

May 11 · archived May 11

Most AI programs still aren’t hitting the P&L.

The ones that are all made the same early decision.

They stopped treating AI as a board-level response and started treating it as an operating model redesign.

88% of enterprises now use AI in at least one function. Yet only 39% report EBIT impact (McKinsey, 2025).

This isn’t an AI problem. It’s a rollout problem.

Here’s what’s actually happening inside most enterprises:

The Broken Playbook: How Most Enterprises Roll Out AI

1/ The Board Mandates It

→ Competitive pressure hits the boardroom
→ “We need an AI strategy” lands with the CEO
→…

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Holly Moe AI creates real business impact when it is connected to measurable operational outcomes rather than isolated experimentation.

The strongest companies are usually redesigning workflows, decision systems, and execution processes instead of focusing only on impressive demonstrations. Carolyn Healey May 11 1 like

Mo Johnson The pilot is only useful if it changes the work people return to every day. Value shows up when the outcome, owner, and workflow are clear enough to move together. May 11 1 like

tgroenwals shared this post · Apr 27

Carolyn Healey

Apr 26 · archived Apr 27

Companies winning with AI aren’t better at choosing tools.

They’re better at redesigning how work gets done.

PwC’s 2026 AI predictions put a hard number on it:
~20% of value comes from technology.
~80% comes from how work is redesigned.

Most enterprises have it reversed.

They’re spending 80% of their energy evaluating tools and running pilots, and 20% asking the only question that matters:

“How does this change the way we operate?”

Here’s the 80/20 AI strategy framework:

1/ Start With the Workflow…

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Serge E. Kouevi La règle 10-20-70 de BCG citée au point 5 devrait être affichée dans toutes les salles de comité. En marketing et en développement commercial, on voit très concrètement ce que ça donne quand on automatise des tâches sans avoir redéfini le rôle autour : les équipes gagnent du temps sur des tâches à faible valeur, mais elles ne savent pas quoi faire de ce temps parce que personne n'a posé la question de ce que le rôle devrait produire différemment. L'automatisation sans redesign de rôle, c'est de l'efficacité sans direction. Apr 26

Rupesh Dandekar Agree the value sits in the work redesign, not the tools. The piece worth adding: the reason pilots collapse at deployment isn't measurement discipline, it's that the delivery model that built the pilot was never structured to redesign the operating model around it. Discovery team, build team, change team — three handoffs, three context losses. The companies actually capturing value have collapsed the build and the redesign into the same unit, with the same people accountable for both. Apr 26 3 likes

Author posts

Your AI governance policy exists.

Companies winning with AI aren’t better at choosing tools.

Your AI spend per employee keeps climbing.