Who is actually advising your organisation on AI?
Something I have been noticing in the market is that every leader, executive, and transformation professional needs to be aware of. It is about who is shaping the AI conversation inside organisations right now, and whether the advice being given is grounded in anything beyond theory.
TL;DR: AI is on every board agenda (despite what others might argue that it should or shouldn't be). But the quality of those conversations depends entirely on who is advising the organisation. Right now, most AI advice falls into one of 3 categories, and only one of them is built on the kind of depth that will actually help you execute. This newsletter walks through all 3 so you can make informed decisions about whom you listen to and work with.
The board question is not whether AI belongs. It is how it gets framed.
Every board I have worked with or advised in the past 18 months has AI on the agenda. That is not going to change.
AI has strategic, financial, ethical, reputational, and workforce implications. Boards have a responsibility to engage with it.
Where it gets interesting is in how the conversation is framed. Most board-level AI conversations start with a version of “what are we doing with AI?” It is a natural question. It is also one that tends to pull the room toward a technology briefing: tools, platforms, vendor capabilities, what competitors have announced.
The board hears a lot of information but walks away without a clear way to evaluate whether the organisation is actually positioned to execute.
The shift I have seen work is when the conversation moves from “what are we doing with AI?” to “are we structurally ready to deliver on AI? And subsequently, what business capabilities can be elevated using AI?”
That is a different kind of question. It opens up the dimensions that actually determine success or failure:
Whether the data foundations are in place
Whether governance is designed to enable speed rather than create bottlenecks
Whether leadership is aligned with what AI is being asked to deliver
Whether the workforce transitionhas been thought through beyond a training plan
Whether the operating model can absorb what AI changes about how work gets done
Those are not technology questions. They are organisational readiness questions. And they give the board something they can actually govern: are we making the right structural investments to ensure this delivers?
When a board is having that conversation, they are not trying to understand the technology. They are holding the organisation accountable for being ready to use it. That is exactly where a board should be.
The quality of what a board can govern depends entirely on the quality of the question they are given to work with.
Three categories of AI advice in the market right now
As AI transformation becomes a bigger part of the market, more people and firms are positioning themselves as experts. That is expected, and there is genuine expertise out there.
What I think is worth paying attention to is depth and real experience in the trenches. Not credentials. Not confidence. Not how clean the framework looks on a slide. Not conceptually sound, the talk is.
From what I am seeing, most AI advice tends to fall into three broad categories.
Category 1: technology-led
This is advice that comes from a technology lens. Platforms, tools, infrastructure, what is technically possible.
Some of this is excellent. If you need help with technology selection, architecture, or engineering, this is the right place to go.
Where it gets complicated is when technology-led advice becomes the only input
for transformation decisions. Technology selection is one dimension of an AI agenda. But there are equally critical dimensions that need a different kind of expertise:
Operating model design
Governance architecture
Workforce readiness and transition
Adoption and change capability
Business capability architecture
Knowing what AI can do and knowing how to land it inside a complex organisation are two different skill sets.
Category 2: conceptually sound, not yet battle-tested
This is the growing category I think is worth being most thoughtful about, because it is the hardest to evaluate from the outside.
These are advisors, thought leaders, and owners of boutique consulting companies who are clearly intelligent, well-read, and articulate on AI. Their content is structured. Their thinking is logical. It makes sense in a presentation or a boardroom briefing.
But if you are an experienced leader or practitioner, you might notice something after sitting with the advice for a while. It starts to feel like a well-organised version of what you already know. Sound principles of good strategy, repackaged with AI terminology. The kind of insight that lands well at a high level but does not go deeper than what most experienced professionals could arrive at on their own.
This is not a question of intelligence or intent. It is the difference between studying a discipline and having practised it under pressure.
There is knowledge that only comes from being inside the work:
Making decisions that do not have a clean answer
Watching a well-designed plan meet the complexity of a specific organisationand having to adapt in ways no framework anticipated
Learning what governance actually needs to look like at different stages of maturity, not just on paper
Navigating the resistance that comes with workforce transition, not just planning for it
If you want to test for depth, ask questions like:
What happens when a technically successful pilot still fails to get adopted?
How does governance need to shift as an AI program matures from experimentation to scale?
How do you scale an AI pilot to a full-scale production-ready implementation?
How do you measure AI cost and benefitsin the short and long term?
What did you change your mind about after something did not go the way you expected?
Where there is depth, the conversation quickly gets specific. Where there is not, it stays at a level of generality that feels safe but does not move the needle.
Category 3: built from the work itself
This is the smallest category and the hardest to find.
These are people who have led AI transformation from the inside. They have been accountable for outcomes, not just recommendations. They have built programs, hit walls, revised their thinking, and refined their approaches based on what they learned when things went sideways.
Their frameworks carry the weight of real experience. Every structure, every step, every decision point exists because it was tested, broken, and rebuilt until it held under pressure.
What sets this category apart:
They talk about failure with specificity
They talk about governance as something they have had to design under real constraints, not as a concept
They describe workforce transition in detail because they have navigated it and learned what actually works versus what looks good in a change plan
They can explain what they would do differently if they ran the same program again
The sign of genuine depth is not confidence. It is nuance. It is the willingness to say “it depends” and then explain what it depends on.
What to take from this
If you are a leader or executive evaluating AI advisory or consulting support, here are the questions worth sitting with:
Is the advice I am receiving specific to my organisation’s context, or could it apply to any organisation with minimal adjustment?
When I push past the framework and ask about what went wrong, what changed, what surprised them, does the conversation deepen or deflect?
Am I hearing about governance, workforce transition, adoption, and operating model design with the same rigour as the technology conversation?
If you are a transformation or change professional being asked to lead or contribute to AI programs, the same filters apply to the methodology you invest in and the people you choose to learn from.
The market will keep growing. The noise will keep increasing. Your ability to distinguish depth from packaging is one of the most valuable skills you can sharpen right now.
I have been building a practical and comprehensive playbook and methodology specifically for this space, designed for experienced practitioners and leaders who need enterprise-level AI transformation frameworks they can actually use. I am currently building this program to fill the gap end-to-end, drawing on practical experience from working hands-on for more than 3 years at the deep end of AI transformation programs that actually scaled and delivered solid ROI across 5 organisations, 3 continents, and 4 industries.
I will be sharing more in the coming weeks, and I am genuinely excited about what is taking shape.
For now, I want to leave you with this:
The best test of any AI advice is whether it still holds when the room gets complicated, the stakeholders disagree, and the plan meets reality. That is where depth earns its place.
I would love to hear what you are seeing. When you look at the AI advice in the market right now, what has been genuinely useful to you? And where have you felt like something was missing?
Send me an email and let me know.
