AI strategy before AI tools
Successful AI integration isn’t about the tech; it’s about starting with three unskippable questions.
If you’re ready for an expensive AI experiment, pick a tool, ask everyone to use it, and watch the chaos unfold.
Sounds a little flippant? Maybe it is, but every conversation about AI in wealth management eventually comes down to the same question: which tool should we use?
That’s the wrong question. And the fact that it is usually the first question explains why so many AI implementations in financial services stall.
The tool is what you choose once the strategy is clear. Get that order wrong, and you are buying yourself a very expensive problem.
The difference between success and failure is never the technology
In the past 12 months, I’ve been in lots of rooms, listening and watching as questions about AI adoption, acceptance, and tool choice bounce around.
Sometimes it goes well, but quite often it goes badly. The difference is never the technology. It is what happened, or didn’t happen, before anyone opened a laptop.
What drives a successful AI integration?
The successful AI projects I have seen in wealth management share a common denominator that gives them the right outcomes from the start:
Three unskippable questions.
Each needs in-depth discussion and careful consideration before any tool is chosen.
But, most firms skip the first two and go straight to the third.
Question 1: What problem are you actually solving?
Not “how do we use AI?” Not “what are our competitors doing?” And not “what does the vendor say it can do?”
What is actually broken, slow, or invisible right now that is costing you commercially?
Early in my career, I worked at a wealth management firm where data was everywhere, and insight was nowhere to be found. Every team had their own numbers. Ask three different divisions for the same figure, and you would get three different answers, all of them defensible, none of them the same. The problem was never the lack of data, but the lack of a single version of the truth that anyone could act on.
That was the problem worth solving. Not “how do we get better technology?”
Technology came later, once we understood what we needed it to do.
The firms I see struggling with AI right now have skipped this step. They have acquired capable tools, pointed them at their business, and then wondered why the output doesn’t quite fit. It doesn’t fit because nobody defined the problem clearly enough for the tool to solve it.
How to make it work:
The starting point is always the same. Sit with the business problem until it is specific enough to be actionable. What decision needs to be made? What information is currently missing? What is the cost of not having it?
Answer those questions first, and the right tool usually becomes obvious. Skip them, and every tool looks like it might be the answer.
Question 2: Who bridges the gap between what the tool produces and what the business actually needs?
This is the question that never gets asked.
It’s also the one that determines whether your AI implementation actually delivers.
Your chosen tool produces output, and businesses need quick decisions; between the two, there is always a gap, and somebody must bridge it.
In my experience, that somebody is the most important person in any data or AI project, and the one most often overlooked.
When I built the commercial analytics function, we had strong technical capability from the start. The developers knew how to build what we needed. The front office knew what they wanted to understand about their clients.
But what we were missing was someone who could translate between the two: someone who could take a question from a relationship manager, convert it into something a developer could build, and then take the output back and make it usable for someone who had never looked at a dashboard before.
The translation role wasn’t glamorous: it didn’t involve building anything or managing the front office. But without it, the technical work would have stayed technical. Insight would have stayed inside the system, and nothing would have changed.
The same gap exists in every AI implementation I have seen: in data, content, client communications, and compliance workflows.
The tool can be excellent, and the output can be accurate. But if nobody owns the step between output and action, if there is no human in the loop whose specific job is to make the tool’s work useful to the business, it’s likely to stall.
How to make it work:
Before you choose a tool, answer these:
Who is that person?
What is their brief?
Where do they sit, and who do they report to?
How will they know if the output is good enough?
If you can’t answer those questions, you are not ready to implement. You are ready to plan.
Question 3: What does good actually look like, before you switch anything on?
AI executes. It doesn’t define. And that matters in wealth management, because the margin between generic and authoritative is where trust is built or lost.
This applies in two ways, and both are equally important. The first is content, and the second is data. Get it wrong, and AI won’t care; it will simply scale the problem.
On the data side, the principle is straightforward but underestimated: the quality of what you put in determines the quality of what comes out. In wealth management, that means client records that are incomplete, inconsistent, or siloed across legacy systems will produce AI outputs that are incomplete, inconsistent, or siloed. AI doesn’t clean data; it inherits it. So before any tool is briefed, the data it will draw on needs to be clean and accurate.
As anyone who’s been following me will know, I use AI tools in my own work. You may have met Margot, my chief of staff, part EA, part strategy partner.
I’m deliberate about where I let her run and where I don’t, because I learned early that the output is only ever as good as the standard I set before I ask the question.
If I haven’t defined what my voice sounds like, what a strong argument looks like, and what a useful insight looks like for my specific audience, Margot might fill that gap with something competent and interchangeable with every other piece of content in the feed.
Margot has a clearly defined remit, rules and a framework within which to work. She has fast become the underlying foundation of my work, and my life.
In wealth management, that problem is acute. Clients are not choosing between firms based on who has the most content. They are choosing based on who they trust. Trust is built on specificity, on consistency, on the sense that the person or organisation communicating with them understands their situation. Generic content, however efficiently produced, does the opposite of that work.
How to make it work:
Before choosing the tool, define the standard. Get crystal clear on
What does strong client communication look like for your firm?
What does useful insight look like for your advisers?
What does your voice sound like at its best, and what tells you when it has drifted?
Is the data underpinning your AI outputs clean, consistent and complete?
Could you use this standard to brief a new team member? If so, brief the tool the same way.
The firms getting AI right in this space are not the ones who moved fastest. They are the ones who were clearest about what they were trying to produce and what they were feeding in before they started.
The sequence that defines success.
Problem. Strategy. People. Standard. Then tools.
Most conversations in our industry start at tools and work backwards, if they work backwards at all. That is why so many implementations look impressive in a vendor presentation and disappoint once they are live.
The technology in this space is remarkable and moving faster than most marketing functions can keep up with. But the firms that will use it well are not the ones with the most sophisticated tech stack; they’re the ones who did the unglamourous work first:
Named the problem
Built the strategy
Identified the person who bridges the gap
Defined what good looks like before they switched anything on.
Listening to the issues around the table amongst my peers and working with Margot has made me realise that AI isn’t a technology project, it’s a leadership one.
The firms that treat it that way will still be ahead in the next few years as tools inevitably change yet again, and those who skipped the unskippable and went straight to procurement will be starting over, once again.
Start with three questions; the tools will follow.