If you’re part of an IT organization today, chances are you’re wrangling more AI tools than you have coffee mugs (and let’s face it, IT people have a lot of coffee mugs). From ticketing bots that auto-triage incidents to predictive analytics tools promising clairvoyant insights, there’s an AI for everything.
Tool Overload
On paper, it all sounds magical. One AI tool summarizes support tickets. Another forecasts system outages. A third one “learns your environment” and recommends security patches while whistling Beethoven (ok, not literally…yet).
But throw all these tools into a real-world IT environment, and suddenly you’re playing conductor to an AI orchestra where everyone’s playing a different symphony. One tool runs on OpenAI’s GPT, another relies on a homegrown model trained on last year’s logs, and yet another has a user interface seemingly designed in 1998.
They don’t always talk to each other. They don’t always agree with each other. And worst of all, sometimes they hallucinate, giving you confidently wrong answers with the certainty of a toddler explaining how planes work.
“According to AI Tool #3, Our Datacentre is in Narnia”
AI hallucinations are no joke. One moment your tool is summarizing service logs, and the next it’s describing a major outage in a location you don’t even operate in. Meanwhile, another bot might misinterpret a spike in disk usage as an alien invasion (or worse, a ransomware attack) and flood your Slack with red alerts.
Miscalculations and inconsistent data interpretations can make your IT team feel like they’re debugging the AI, not the actual infrastructure. And who watches the watchers when they all have different dashboards?
Integration Frustration
Many of these AI tools are amazing on their own. But when you try to string them together in a workflow, things can go sideways fast. APIs don’t align, event triggers misfire, and your automation pipeline becomes a Rube Goldberg machine powered by duct tape and hope.
Think of it this way: each tool is a talented musician, but without a conductor, or at least a shared sheet of music, your AI ensemble turns into a jazz improv session during a production outage.
So what’s an IT leader to do?
Instead of chasing the newest AI shiny object, organizations need to focus on orchestration. That means asking:
- How do these tools talk to each other?
- Where does our data live, and who’s interpreting it?
- Are we automating chaos or streamlining it?
The most successful teams treat AI like a team of interns. Promising, energetic, but in need of guidance. With thoughtful integration, clear workflows, and regular review, you can harness their power without descending into madness.
Final Thoughts: Herding Digital Cats, With a Plan
Using multiple AI tools doesn’t have to feel like herding cats in zero gravity. But it does require strategy.
Focus on interoperability, transparency, and setting limits (yes, even digital interns need boundaries). Leaders should scrutinize the way in which their AI thinks, decides and acts on the information being shared with it, especially in conjunction with the workflows that are connected to it. These strategic workflows will quickly become the most powerful tool in your AI stack, feeding it, orchestrating it, and ensuring visibility and compliance into the future.
