There’s a growing tension at the heart of AI adoption, and it’s showing up in the data, in community forums, and in conversations I have with IT leaders every week.
The promise of AI is as a productivity tool. The reality, for many teams, is something closer to the opposite.
This isn’t only anecdotal, either. A recent Anthropic study found that while AI can accelerate certain coding tasks by up to 80%, it can simultaneously erode the core problem-solving capabilities that make engineers effective over the long term.
Chief Product Officer at SolarWinds.
Harvard Business Review went further, identifying a phenomenon they’ve labeled “AI Brain Fry” — the cognitive fatigue that results from sustained, unstructured AI exposure.
And, our own research at SolarWinds found that nearly three in four IT professionals say AI is making their roles more demanding, with only one in five reporting any meaningful reduction in mental load.
This is a paradox the industry needs to take seriously.
Impacts on cognitive load and productivity
IT teams are already operating at capacity. They’re managing fragmented environments spread across cloud, on-premise, and hybrid IT infrastructure, which are often across geographies and time zones. Layering AI into that complexity without structure isn’t a solution. It’s an acceleration of an existing problem.
The core issue is signal overload without context. Many AI tools generate significant volumes of insights, flags, and recommendations, but without the connective tissue to make those outputs actionable. The result is more noise, more decisions, and more cognitive work — not less.
Compounding this is tool sprawl. Organizations that have introduced multiple AI tools into their IT environments often find those tools have overlapping capabilities, inconsistent outputs, and competing interfaces. Each tool demands context-switching. Each generates its own stream of recommendations to evaluate, verify, and act on.
Swings and roundabouts
For anyone who has been in that position, the sense of being overwhelmed can be suffocating, making the results harder to interpret and act upon.
But even if IT teams could address this concern, far too many are faced with yet another obstacle – double checking AI output. Hallucinations still exist even in the best LLM or SLM models. IT teams sit at the front line of system stability, security, and compliance, so every AI output carries an implicit cost: the time and judgment required to validate it before acting.
I think of this as a “trust tax.” It’s a real overhead that accumulates with every tool, every alert, and every AI-generated recommendation. Until organizations reduce that tax through better structure and governance, AI adoption will continue to feel like two steps forward, one step back.
AI Effect on IT Roles
The good news is that some IT organizations are getting this right, and there are clear patterns in how they operate.
The most effective teams are consolidating, not expanding. They’re working with a smaller, deliberately chosen set of tools rather than adopting new ones constantly as they emerge. They’ve established clear governance frameworks that define where AI acts autonomously, where human judgment is required, and how outputs are validated. And they’ve built explicit lines of accountability around AI deployment, monitoring, and escalation.
This isn’t about limiting ambition. It’s about building the foundation that makes AI genuinely transformative rather than operationally disruptive.
A Framework for IT Leaders
If you’re navigating AI adoption in your organization, I’d encourage you to start with these three priorities:
1. Invest in training before you invest in tools.
There’s a meaningful gap between frontline IT awareness of AI risks and the understanding at senior leadership levels. That disconnect creates organizational friction that compounds over time. Teams need to understand how to become orchestrators, not just operators.
2. Define guardrails before you scale.
Governance shouldn’t be an afterthought, and it will be expensive to unwind in the long term. Build the structure first, then expand from within it.
3. Simplify the environment before you add intelligence to it.
AI is only as effective as the data and infrastructure it operates on. Observability should be a priority for all environments, but especially environments where systems span cloud, on-premise, and legacy infrastructure.
I believe AI has real potential to elevate what IT teams can accomplish, especially if it is applied intentionally. Get the foundation right, and the technology delivers on its promise. Get it wrong, and “AI Brain Fry” may be the least of your concerns.
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