Warum jede KI-Reise ihren eigenen Google-Maps-Moment braucht

Von Oluwole Akinwale, Leiter, Professional Services

I’m still learning my way around my new neighborhood. Because I’m not driving in the city yet, I’ve been using taxis to get where I need to go. Every now and then, the driver confidently tells me, “I know the way.” Sometimes they do. Sometimes they don’t. More than once, a driver has missed a turn, taken the wrong road, or ended up somewhere I didn’t plan to go. The destination stayed the same, but the route changed. What’s interesting is that even when the driver gets lost, I’m still the one who pays with both my time and money.

I’ve since changed my approach.

Now, before I even start the trip, I put the destination into Google Maps. It’s not because I think the driver can’t do it; I just want an extra point of reference. As we go, I watch the route. If we miss a turn, I can catch it early and avoid a costly detour. Eventually, I’ll know these roads well enough that I won’t need Google Maps anymore. Until then, it’s my trusted companion. It doesn’t drive for me, but it helps me find my way in places I don’t know yet.

I think many organizations should approach AI in much the same way.

Today, many organizations are starting their own AI projects. They often turn to technology vendors, consultants, systems integrators, or new AI specialists for help. Most of these partners have real experience and skills. But AI is changing fast, and nobody has a perfect map.

Some partners might suggest technologies that don’t really fit. Others might not realize how complex the work will be. Sometimes, projects or people even lose sight of their original goals. The destination may still be achievable, but the wrong turns can be costly.

And just like in my taxi rides, it’s the organization, not the driver, that ends up paying. The costs show up as delayed projects, wasted money, slow operations, unhappy employees, or missed opportunities to compete.

This is why organizations need their own version of “Google Maps.”

That doesn’t mean you have to become an AI expert right away. It means having a clear plan, knowing what success looks like before you buy any technology, building enough skills in your team to ask good questions, and always checking if your choices are moving you closer to your goal.

Your AI strategy should be your navigation system. It gives you a way to check every recommendation, investment and decision before moving forward:

• Is this taking us where we said we wanted to go?
• Have we drifted from the business problem we set out to solve?
• Are we adding complexity but not creating value?

These moments are like realizing you’ve just missed a crucial turn. But something else happens over time.

As organizations gain experience, they rely less on outside help. Teams build their own AI capabilities. Leaders get better at telling real opportunities from hype. They learn which projects add value, which vendors ask smart questions, and which shortcuts usually lead to costly mistakes. Eventually, they know the roads.

External advisers are still valuable, just like experienced taxi drivers. But now, organizations aren’t just trusting them blindly. They navigate with confidence, backed by their own understanding of the landscape.

Perhaps that’s one of the most overlooked lessons in AI transformation.

The goal isn’t to eliminate external expertise. The goal is to build enough skills inside your organization, so you always know if you’re on track and when it’s time to speak up and say, “We’ve missed the turn.”

Our recent Umfrage zum Automatisierungs-Benchmark 2026 Jitterbit AI showed that AI is delivering measurable results, but planning was key to respondents’ success. In other words, as your organization starts the next phase of its AI journey, it’s important to develop your own version of “Google Maps” to ensure that, even with trusted partners driving, you know when you’ve missed a turn.