Over the years, I’ve seen plenty of tech trends come and go. Each promised to change everything, and for a moment, it felt like they might. Investment poured in, expectations skyrocketed, and for a while, the buzz drowned out the caution. But over time, reality set in: results were underwhelming, teams burned out, and the excitement quietly faded.
That’s why I look at the AI boom with both interest and realism. The potential is huge, but so is the risk of doing too much too fast — chasing shiny pilots, collecting more data than anyone can use, and ending up with little to show for it.
The real challenge isn’t adopting AI; it’s finding small, focused ways to make it truly useful — where it saves time, reduces risk, and creates measurable value from day one.
That’s exactly where Agentic AI comes in.
The shiny object trap
Too often, companies jump on new technology simply because it’s the next big thing. It’s the classic shiny object syndrome. Teams rush to experiment before anyone agrees on the specific problem they’re trying to solve. The outcome? Expensive pilots, frustrated teams, and little real progress.
That’s why the smarter approach is the opposite: start small, keep it controlled, and focus on a real pain point. When you do that, you give AI a chance to prove its value early — and to grow naturally from there.
Why Agentic AI is different
Unlike generic AI systems that try to do everything, Agentic AI focuses on doing one thing really well. It lets you define the boundaries of your solution before you even finalize the problem statement.
The best use cases have a few things in common: they rely on high volumes of structured data, involve repetitive analysis, and often depend on human reviewers who are prone to fatigue or error. Sound familiar? If so, you’re probably already sitting on several opportunities where an AI agent could make life easier and results more reliable.
Think of an agent as a digital colleague — one that handles the repetitive, data-heavy parts of the job so your team can focus on higher-value work. It streamlines analysis, supports smarter decision-making, and keeps processes moving efficiently.
Low cost, low risk — high potential
One of the biggest advantages of Agentic AI is how accessible it is. You don’t need to rebuild your systems from scratch. Building and deploying an agent typically costs between €5,000 and €10,000, and because it runs alongside your existing setup, it can be tested, scaled, or rolled back without disruption.
It’s a way to experiment safely — to learn fast and adapt quickly. You see results, adjust as needed, and avoid the risk of large, slow-moving projects that eat up months of effort with uncertain payoffs.
From idea to impact: What we’re building at Connective Payments
At Connective Payments, we’ve already developed several practical AI agents that support critical processes such as scheme pricing and management and transaction monitoring.
These agents analyze large datasets, detect anomalies, and simplify complex reporting — all without adding headcount or increasing workload.
Our experience confirms it: Agentic AI isn’t about hype. It’s about making work simpler, smarter, and more precise — starting small and building from real wins.
Curious which processes in your organization could benefit from an Agentic AI approach? Don’t hesitate to reach out — we’d love to explore the possibilities with you.
