Beyond the AI hype. On the choices that really matter.
AI is everywhere. New models, tools, and agents follow one another at a rapid pace. Experimenting is getting easier all the time, and the technical possibilities grow almost daily.
But technology alone doesn’t make an organization successful with AI.
Where do you use AI, and where don’t you? When is an experiment successful? Do you build in-house or buy technology? How do you deal with employees who use their own AI tools? And how do you make sure the data all those applications run on is actually in order?
In MacGyvering AI, Jaspar Roos and Stefan Wijers discuss the choices behind successful use of Data & AI. No stories about what might be possible five years from now, but open conversations about the dilemmas organizations are already facing today.
Watch the episodes below.
First episode: The Value of AI Pilots
Experimenting is easy. Realizing value is a lot harder.
Organizations are launching AI pilots left and right. But when is an experiment actually successful? How do you determine which applications add value and which are better stopped? And how do you get from a promising pilot to an application that works at scale?
Jaspar and Stefan discuss why a good experiment starts with a concrete problem, why saving time doesn’t automatically make a business case, and why AI doesn’t have to be used everywhere it can be.
Watch the full video here:
This video is part of a broader series of discussions on the practical application of Data & AI within organizations. In the coming months, Jaspar and Stefan will delve deeper into topics that are currently on the minds of many organizations. When should you choose to develop AI solutions in-house, and when is an off-the-shelf solution a better choice? How do you deal with “Shadow AI,” where employees use tools on their own outside the organization’s purview? And what role do data governance, ownership, and architecture play in successfully scaling up AI? In each episode, we combine real-world experiences with concrete insights to help organizations deploy AI in a responsible and valuable way.
From Experimentation to Lasting Value with Data & AI
The topics differ, but every episode comes back to the same challenge: technology is evolving faster than organizations can absorb it.
Success with Data & AI therefore takes more than technology. It takes sharp choices about where you want to create value, a foundation you can trust, and the ability to make successful applications a real part of the organization.
We help organizations do this through four interconnected Data & AI services:
- Data & AI Strategy
Determine where Data & AI create value.
Make deliberate choices about ambition, positioning, investments, and risks, and translate them into a clear direction. - AI Value Delivery
Turn promising ideas into applications that work.
Take AI use cases from experiment to daily processes and measurable business results. - Effective Data Foundation
Lay the foundation for reliable, scalable Data & AI.
Organize data, governance, quality, and technology so applications can grow safely and be reused. - Data & AI Execution Power
Turn ambition into action.
Accelerate delivery with leadership, program management, and hands-on support where it’s needed.