Executing AI Strategy: A Corporate Guide to Building GenAI Ventures

This is the second part of our two-part podcast episode, discussing how corporates can leverage their data to create new revenue streams with AI Ventures. If you haven’t read the first post, you can check it out here.

In this episode, we’re moving the conversation from strategy to execution. Once again, we're joined by Jasper Wilmes, Venture Architect at MVP Factory, who specializes in transforming AI opportunities into scalable ventures.

Baby Jessi Parker: Welcome back, everybody. This is part two of our discussion on the generative AI boom and the opportunities it brings. In the last episode, we covered the history of AI, the startup ecosystem, and the big question for corporates — should they build or buy?

Today we’re diving into how companies actually get started. Jasper, you mentioned offline that this is where a lot of companies are looking for answers — and it’s easy to see why. It’s everywhere. But how do they go from intention to action? Where do they start?

Jasper Wilmes: It’s a good one. And as many in innovation or venture building already know, you always start with a problem.

The reason is simple — if you start with a solution, the odds of building something no one wants is close to 100%. Most innovation frameworks start with empathy for that exact reason: who has the problem, how big is it, and what’s the current experience?

And here’s where GenAI throws a wrench into things — because it’s a solution. If you say “We want to use GenAI,” you’re starting with the solution. That’s where things fall apart.

So the method I follow is to first deeply understand the solution — its capabilities. That means understanding what models are available and what tasks they can perform better than a human.

You also need to know what the model requires in order to work. For instance, ChatGPT is good at writing essays but poor at basic math. It’s not general intelligence — it's narrow AI.

Baby Jessi Parker: Yeah, and I think that’s one thing people overlook — training the model. It’s a huge lift.

Jasper Wilmes: Exactly. And every model has different requirements. You need to understand what it can do, and what data it needs to do that.

Once you have a shortlist of models and tasks they can perform, then you bridge back to the problem. You look across your company — or even the market — and ask, where do these tasks show up? Can they be automated? Do we have the data to support that automation?

That’s where corporates have an edge. They sit on mountains of data. Maybe it's messy or unstructured, but it’s there — and with some work, it can be cleaned and used.

Baby Jessi Parker: Right. So once you’ve identified a process that fits a model, and you know you’ve got the data — what next?

Jasper Wilmes: Then you validate it the same way you would any other venture: desirability, feasibility, and viability.

Desirability — is it something the user or customer wants?
Feasibility — can we actually build it with the current tech and data?
Viability — does it make commercial sense?

If you get green lights on all three, you start building. But before jumping into an MVP, you usually begin with a proof of concept to test if the model can deliver accurate outputs.

Baby Jessi Parker: So it’s still the classic build-measure-learn loop, right?

Jasper Wilmes: Absolutely. And to your point, you want to start small. Go for use cases that are low-risk, repeatable, and highly standardized. Things like answering “What’s my balance?” in customer service are perfect.

You don’t want your GenAI giving safety instructions for repairing appliances right out of the gate. Start where a mistake won’t be catastrophic.

Baby Jessi Parker: Makes sense. So build the first small process, test, iterate, and expand. But how do you stay ahead of the curve once you’ve built something?

Jasper Wilmes: That part is the same as for any venture. Keep learning from users, adapt based on feedback, and improve the product with every iteration. Stay close to the problem. Stay close to your users.

What’s different with AI is the speed of advancement. It’s exponential. So the faster your feedback loops, the better your odds of staying relevant.

Baby Jessi Parker: And now to one of my final questions — what are the biggest pitfalls companies should avoid when building GenAI ventures?

Jasper Wilmes: First, don’t fall in love with the solution. Fall in love with the problem. Start there and stay there.

Second, understand that data is your only true source of competitive advantage. The models are democratized — everyone has access to them. What sets you apart is the data you can feed into those models.

And third, move fast. Work like a startup. The biggest blocker for corporates is speed. You need to build quickly, test constantly, and make decisions based on evidence.

Baby Jessi Parker: So basically — act like a curious kid. Play, learn, test, and don’t be afraid to fall down a few times.

Jasper Wilmes: Exactly. Be a bit more childish. It’s the best way to learn.

Baby Jessi Parker: One thing we didn’t cover — your whitepaper! It’ll be live by the time this episode airs. Want to give people a quick teaser?

Jasper Wilmes: Definitely. The whitepaper is called Monetizing Data with GenAI: Building Scalable AI Ventures for Industry Leadership. It goes deeper into everything we’ve discussed today — how to identify AI opportunities, evaluate feasibility, and monetize corporate data through scalable ventures.

Baby Jessi Parker: Amazing. Also, if you haven’t heard Part 1, make sure to go back and listen. Jasper — thank you for your time. Always a pleasure.

Jasper Wilmes: Thanks a lot, Baby Jessi. Always great to be here.