Why AI Projects Are Different – And How Spryfox Helps You Get Them Right
By Dr. Christian Debes, Head of Data Analytics & AI
At Spryfox, we spend our days deep in the trenches of AI engineering, helping organizations across the globe transform how they operate, compete, and grow.
But if there’s one thing we’ve learned over the years, it’s this: AI projects are not just “harder IT projects” (or if you believe the hype - easier ones!) They’re fundamentally different, and if you approach them like traditional software initiatives, they’re likely to fail…. A whopping 67% of them in fact, according to our research.
To better understand this gap, we recently ran a survey focused on what truly separates successful AI initiatives from those that fall short. One question asked, “How do AI projects fundamentally differ from traditional IT projects?”
The responses painted a clear picture, one that matches what we’ve seen firsthand.
AI Is a Different Beast
Unlike traditional IT projects, which are often linear, deterministic, and requirements-driven, AI projects are probabilistic, experimental, and data-dependent. In other words, you’re not building from a fixed blueprint; you’re exploring possibilities, testing hypotheses, and iteratively refining results based on messy, real-world data.
Here are some of the key differences our survey highlighted:
- Uncertainty and experimentation: AI involves building models that learn patterns, not executing logic that always behaves predictably. That means outcomes can be fuzzy and require ongoing iteration, and failure is part of the process.
- Data over code: In AI, code isn’t king, data is. Poor data quality or access issues can break even the most sophisticated models before they leave the lab.
- Cross-functional collaboration: AI projects need diverse teams; data scientists, engineers, domain experts, UX designers - all working together in a way that most IT projects don’t require.
- Trust and user experience: AI systems can be unpredictable. Getting users to trust and understand AI behavior and outcomes requires intentional design, clear communication, and often, cultural change.
- Ongoing maintenance: Models degrade. Data drifts. Successful AI systems require long-term monitoring, retraining, and recalibration, not a “set it and forget it” approach.
It’s no wonder so many companies struggle to realize ROI from AI. But that’s exactly where Spryfox steps in.
Improve Your Odds of Success with Spryfox
We didn’t just jump on the AI bandwagon, we helped build the road. Max, Johannes and I, spent decades delivering AI and machine learning systems that solve real problems at scale before we joined forces to found this company. And we’ve built Spryfox from the ground up, preserving what’s important in any professional engineering organisation like data privacy and security, for example, but tackling the specific challenges that make AI hard to get right..
Here’s how:
- Engineering excellence meets business reality: We combined deep technical expertise with commercial focus. Every AI solution we design is meant to work in your world and at scale, driving real value, not just technical novelty.
- Data-first, API-driven: We start where it matters: your data. And we build modular, scalable systems that are easy to integrate, adapt, and expand as your needs evolve.
- Not tied to vendors: We don’t push platforms or sell licenses. We’re independent, which means we choose the best tools, models, and infrastructure for your context.
- Battle-tested methodology: Over the years, we’ve developed proprietary frameworks, toolkits, and processes to accelerate delivery while ensuring quality. Whether it's anomaly detection, virtual personas, models that have to move quickly from lab to product or AI-powered decision support, we know how to get there fast, and get it right.
- European roots, global impact: Based in Germany, we apply rigorous privacy, security, and compliance standards to every project so you can scale AI across your business without compromise.
- Real experts, not junior squads: When you work with Spryfox, you get senior engineers and AI specialists solving your problems, complete with their combined, real world experience and expertise.
Moving from Potential to Impact
AI holds immense promise, but only if it’s approached with the right mindset, methods, and partners. At Spryfox, we’re not here to sell hype. We’re here to help you explore what's possible, overcome what's difficult, and turn your data into meaningful, measurable outcomes that last.
Whether you're just getting started or scaling up, we're ready to guide you every step of the way. Let’s build AI that works. for your business, your users, and your future.
Or, want to see the full report? Contact us and we’ll happily share it with you and take you through the outcomes.