What does an AI Strategist do?

We want to go beyond making a stand-alone plan. We want a plan to be very concrete, actionable and pragmatic. And then we also want to put our money where our mouth is, by helping the client after we have developed the strategy.
We do this in the form of coaching programs in which, for example, we work with the client one day a week to monitor the implementation of that strategy or to implement specific technical use case.
The nice thing about it is that it is not just code, programming, and implementing tooling, it is much more about how you ensure that value is actually obtained from that piece of code and application.
The perspective needs to be broader. You have to look and say “OK, how are we going to ensure that that application will soon be used by people on the work floor”, and then include the people in the process.
A certain role may be that you are a reviewer, so you are asked to assess an organization’s current performance. In a very short and intense period, you will determine how that organization is doing when it comes to people, technology, data and processes. Based on the findings, you can advise how that organization could improve on these areas. That is the role of reviewer, I would say.
If you can then help a customer implement your action plan or steps, you will become a bit more of a coach over time.
If you really work together on a strategy for longer, I would call myself more of a partner. Where we really spar with the customer at a high level about how we are going to make data successful within the company.
What appeals to me most is the trust you get from clients when you can really show that you bring knowledge of the field and can apply it to the client’s domain. Being a sparring partner appeals to me. What really helps in this is that helicopter view of how how a particular client is performing and how the field is moving. Bringing all this together in one case is fantastic.
AI Myths! I think that many organizations still do not know very well what data and AI can and cannot do and what it means. I don't even think I knew it that well three years ago. I have a lot more experience now, and it just takes a lot of time and experience to understand what makes a project successful and what doesn't. There are certainly a lot of myths involved. What it mainly comes down to is managing expectations very well. Hiring one data scientist straight out of university, gives little perspective on a successful implementation of data science for an organization. A larger playing field of people with different skills is required to do that. Getting that message across, that is my daily work.
The biggest misconception I've come across about AI is that an organization thinks it's more of a project than a transformation. “As long as we hire an external vendor, we can buy AI and it will work itself out. We don't have to worry about that anymore," they think. Precisely because of the nature of data that changes continuously, your artificial intelligence applications will continuously change and it is therefore a lasting investment. That's really the biggest misconception I regularly deal with.