AI isn't a tool you roll out. It's a mindset you need to cultivate.

Technology is becoming faster and smarter by the week. The shortage is shifting toward people who can make decisions, design, and steer, rather than build.

Turner provides its clients with timely, fresh, and relevant insights into their markets and the Strategy Execution challenges they face. In this blog series, Frits Korthals Altes, Director of Digital Transformation, AI & Innovation at Turner, explains why exceptions are no longer more expensive than standard cases, how standardization is shifting toward data, infrastructure, and AI capabilities, and what design choices are currently on the table.

This blog explores the shift from minimized datasets to context-rich information processing, the role of multimodal AI in this shift, and the implications for decision-making processes, customer experience, and fraud management at insurance companies and pension funds.

“Innovation happens with innovative people. It really takes a different mindset to design AI-first processes.”

Your AI projects don't fail because of technical issues, but because of a decision that no one is willing to make

Take a look at an AI initiative that has stalled in your organization and see where it’s getting held up. Often, it’s not the technology. The model works, the data is there, and integration is feasible. Where it’s stalled is on a pending decision: Can this system independently approve an insurance policy or a mortgage, or does a human need to review it? What are our priorities, and what must not be sacrificed in the process? Who is accountable if things go wrong? The project isn’t waiting for development. It’s waiting for a decision.

That’s new. For years, technology was the bottleneck. Software development was expensive, slow, and reserved for specialists, so that’s where the bottleneck was—and that’s where the focus went. The decision-making process came later, because there wasn’t really anything to decide on yet.

That dynamic has been reversed. Building with AI is becoming faster and cheaper, and that is precisely why a new bottleneck is emerging. It’s not a question of whether it’s technically possible, but whether the organization can make the decisions that the technology requires. And those decisions don’t rest with the people who are building it. They rest with people who can make decisions, design, and provide direction—and with an organization structured to support that.

Now that technology has become the easy part, decision-making is the limiting factor

This is where the mechanism changes. In the old paradigm, the sequence was: first build the technology, then find the people to operate it. Technology was the scarce resource; human labor followed. Whoever controlled the technology set the pace.

AI-first flips that ratio on its head. Technology is no longer the bottleneck; rather, it is becoming a commodity that grows more accessible by the week. What is becoming scarce is the human ability to steer technology in the right direction: refining the core decision, defining the scope of the mandate, designing the data logic, and establishing governance. This isn’t a matter of managing things after the fact, but of designing them in advance, and it requires a different kind of person than the organization is accustomed to employing.

That’s why this is a mindset issue, not a tool issue. A tool is something you roll out: you buy it, implement it, and train people to use it. A mindset is something you cultivate: you bring in people who think in a data-driven way, give them the mandate to design rather than just execute, and structure collaboration so that business, data, IT, and risk work together to create a single design rather than just passing tasks back and forth. Innovation comes from innovative people, not from a license.

A critical executive rightly objects: this is implementation, and implementation is a matter for specialists and technology, not for people with a certain mindset or for senior management. That’s true for the construction phase, but it overlooks what’s currently shifting. Precisely because the technical side is becoming the easier part, the scarce resource is the human judgment that guides the technology. An organization that invests only in tools—and not in the people and the mindset that use them to design—does not build better. It builds the wrong things faster.

In the workplace, the bottleneck is clearly shifting from technology to decision-making

The evidence isn’t found in a single case, but in a pattern that Frits Korthals Altes sees time and time again on the job. Developing processes with AI is happening faster and faster. What took months to build just a year ago can now be done in weeks. And that’s exactly what reveals where the real bottleneck lies: no longer in the technical implementation, but in the decision-making surrounding it.

What strikes him is not that things are moving faster. What strikes him is that organizations aren’t set up to handle it. Technology is waiting for the clear frameworks needed to continue building, but organizations aren’t used to establishing these frameworks. Decision-making on this matter is complex and time-consuming because it’s far from always clear who is responsible and who should take the lead. That’s where things come to a standstill—not because of an inability to act, but because the organization isn’t yet set up for an AI-first world.

That is why the sequence of an AI-first redesign is the insight itself. You don’t start with the technology, but with the core decision and the KPIs: what does this process need to decide, and what are we using to steer it? Then comes the mandate and the decision-making scope: where is Agentic AI allowed to make autonomous decisions, and where should it escalate? Only then do you address the decision logic and the necessary data. At the same time, you ensure controls, monitoring, and explainability. And then, once the choices are set, comes the implementation: integration into the workflow, a working version with real data, validating and scaling up what works. The technology is the second half of the story, not the first.

Judge for yourself: the relevant baseline isn’t how many AI pilots you’re running, but what percentage of your AI initiatives are currently stalled due to a pending decision rather than a technical problem. In most organizations, that percentage is high, and it explains why the technology is ready but the value hasn’t materialized.

Three Key Considerations When Implementing an AI-First Redesign

Since design and construction are intertwined and the implementation process is iterative, issues arise that did not exist in a traditional project.

The first point to consider is that an MVP without governance is not a harmless experiment. Working iteratively with real data means that you’re experimenting with real customer data and making real decisions early on. Without controls and governance in the first cycle, even a working version poses a compliance and privacy risk.

The second point to consider is that learning without documentation turns into a black box. Speed and learning should not mean that decisions are not documented. Anyone who iterates without documenting why a mandate or KPI was chosen will not be able to explain to DNB and AFM afterward how the process came about.

The third point to consider is that a mindset cannot be rolled out; it can only be organized. A multidisciplinary team succeeds or fails based on the “soft” aspects: whether business, data, IT, and risk truly understand one another and share ownership. Anyone who approaches this as a technical task and underestimates the importance of collaboration will end up with four disciplines working at cross-purposes.

What this requires of you

Three insights to take away:

“AI-first” is not a tool but a mindset. You can buy the technology, but you have to organize the people and the way of working that will derive value from it.
The bottleneck has shifted from technology to decision-making. If you don’t treat decision-making as the scarce resource, you’ll end up with technology that’s ready to go but has no value.
The approach is iterative and multidisciplinary. A single team that engages in learning with real data outperforms four departments that spend a year planning.

Three questions for your organization:

Which part of your stalled AI initiatives is held up by a pending decision, and which part by a technical problem?
Do you have the new skills—decision logic, data-driven thinking, and AI governance—in-house, or do you rely on the people who build the models to make the decisions that are ultimately yours?
Does your AI development team operate as a single Multidisciplinary team, or as separate departments that pass results back and forth through handoffs?

Most financial institutions recognize that an “AI-first” approach is on the horizon. Fewer, however, have prepared for the shift this entails: from technology as a scarce resource to people, mindset, and decision-making.

Want to learn more about Turner Financial services?

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AI isn't a tool you roll out. It's a mindset you need to cultivate.

Technology is becoming faster and smarter by the week. The shortage is shifting toward people who can make decisions, design, and steer, rather than build.

“Innovation happens with innovative people. It really takes a different mindset to design AI-first processes.”

Your AI projects don't fail because of technical issues, but because of a decision that no one is willing to make

Now that technology has become the easy part, decision-making is the limiting factor

In the workplace, the bottleneck is clearly shifting from technology to decision-making

Three Key Considerations When Implementing an AI-First Redesign

What this requires of you

Three questions for your organization:

Want to learn more about Turner Financial services?

Would you like to improve, innovate, and transform faster?

Make an appointment

Insights

All of our specialists' insights on Education

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