What to tell your board about AI

Your board keeps asking what you're doing with AI. It's a fair question, and it rarely comes with the patience for a long answer. What they want, usually inside the time it takes to clear one agenda item, is confidence that the company isn't falling behind and isn't burning money to find that out.

The instinct is to answer with a list. Three pilots in procurement. A copilot rollout in finance. A proof of concept somewhere on the plant floor. The list feels like progress because it's made of nouns and it's easy to read aloud. But a list of pilots answers a different question than the one the board is asking. It tells them what you're trying. It says nothing about whether any of it will drive business value.

That gap is where most AI programs quietly stall.

The pilot list is not a strategy

The numbers on failed AI work have gotten hard to ignore. Look closely, and a pattern shows up. The trouble isn't a shortage of ideas or appetite. Companies are piloting plenty. What's missing underneath the pilots is the thing that lets any of them hold up in the real business: a foundation.

So here is the more useful answer to give your board. The credible case for AI right now isn't built on pilots. It's built on a foundation: Grounding, capability, and a deliberate rate of building both. The CFOs who can describe that foundation are the ones who answer the board's question well, because they're describing something durable instead of something in flight.

Two parts of that foundation are worth spelling out, because they're where the real work lives. For most industrial and manufacturing companies, that foundation runs through the ERP system already sitting at the center of the business, not a new platform bolted on top of it.

Grounding: Your own knowledge as a working asset

Everyone talks about clean data, and clean data matters. But grounding runs deeper than a tidy data warehouse. The asset that actually drives AI value inside your company is your company's own knowledge. It's the operating know-how and the specific way you run things that no vendor model shows up already understanding.

The useful way to picture it is a loop. Work happens, the organization learns from it, and that learning feeds back into how the next round of work gets done. An enterprise that captures its own knowledge this way gets sharper with each cycle. An AI system sitting on top of a company like that has something real to reason with. One sitting on top of scattered, undocumented, contradictory information mostly produces confident nonsense, faster.

Grounding is the discipline of turning what your organization already knows into something the business, and the systems you point at it, can actually see and use.

Your ERP is becoming a system of action. But is it ready to act?

AI is turning the ERP from a tool that records what happened into one that acts on it — holding a disputed payment, rerouting a stalled line, resolving an exception before a person ever gets involved. This insight breaks down the five shifts that decide whether this AI shift scales or leads to another stalled pilot.

Capability: Treat AI spend like a resource you manage

The second part is capability. AI spend behaves like a new resource category. Call it token capital. Like any resource on your income statement, it should be planned, measured, and allocated on purpose, not handed to whoever files the most enthusiastic request.

The aim is balance between token capital and the human capital already doing the work, so the two reinforce each other on a given process instead of doubling up. A CFO is already fluent in this. Capital allocation under constraint is the job. AI just adds a line item that behaves a little differently and moves faster than most.

Framed this way, capability stops being an IT abstraction and becomes something you can govern with mechanisms you already trust.

The move that separates real ROI: Redesign, not automation

Here is the point most likely to change what your company does on Monday. There are two ways to put AI to work on a process, and they pay out very differently.

The first is to automate a task inside a process you already have. This works. You will see efficiency gains, and they are real. But you are pouring new capability into a container that was shaped around human limits: the handoffs, the approvals, the workarounds that exist because people needed them.

The second is to rebuild the process itself around what AI now makes possible, stripping out the constraints that were baked in back when only people did the work. That is harder. It is also where the higher returns live. The companies pulling meaningful ROI out of AI are not the ones that bolted it onto yesterday's workflow. They are the ones that asked what the workflow should look like now and rebuilt accordingly.

This is the line between an activity and a foundation. Automation gives you a faster version of what you already had. Redesign gives you something the old operating model could not produce at all.

One more question the board will eventually ask

AI also shifts the security and governance picture. The risks are not the same ones you have managed before, and not every control you already own transfers cleanly to them. It is a real topic and worth its own conversation, but it sits alongside the foundation rather than at the center of this answer.

So what do you actually say?

When the board asks what you're doing with AI, skip the pilot count. Describe the foundation instead. Tell them what the company can now see on that it couldn't before. Tell them how you're allocating AI spend as deliberately as any other capital. Tell them which processes you're rebuilding rather than merely speeding up, and how fast you intend to keep building.

That is an answer a board can trust, because it describes something that lasts. The pilots will come and go. The foundation is what decides whether any of them were worth running. The CFOs who can describe it are the ones who make the AI question look easy. Not because they ran better pilots, but because they built the thing underneath.

Wondering why this keeps happening even when a rollout looks clean on paper?

We wrote a guide that breaks down why change management, not technology, is the variable that decides whether an ERP program pays off.

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Luis Solano

Senior Director

Luis brings more than 30 years of experience helping manufacturing and industrial organizations improve performance through AI, digital transformation, process improvement, and intelligent manufacturing. He combines operational experience with technology expertise to help executive teams identify high-value opportunities, modernize operations, and translate digital investments into measurable business outcomes.

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