More tools, more processes touched, same bottlenecks - just with an AI label on them now. This diagnostic helps you see where the friction actually lives.
Run the diagnosticWhat came up across conversations with IT directors and ops leads wasn't that AI tools failed. It was that they got applied to the wrong layer. The fragmentation was already there. AI landed on top of it, added new surface area, and left the root cause untouched. After enough of those conversations the contributing factors became consistent enough to put into a diagnostic.
The problem isn't AI itself. It's where it got applied. Most deployments landed on top of broken processes instead of inside them. The bottlenecks are still there - they're just harder to see now.
Problem 01
Even with AI search tools in place, employees still can't find the right answer reliably. Information lives across Confluence, Google Drive, Slack threads, and old email chains. AI didn't consolidate it - it added another search box to the pile.
Problem 02
AI tools lowered the threshold for asking questions - employees now expect instant answers, and when AI fails them, they escalate to IT anyway. There's also a new category of tickets that didn't exist two years ago: questions about the AI tools themselves.
Problem 03
Onboarding, access requests, approvals - these still require someone to chase someone. AI didn't get wired into the actual workflow, so handoffs still happen over Slack DMs and tickets. Someone in IT is still the connective tissue.
Free · 5 questions · instant result
Problem 01
Even when the answer exists, employees can't reliably navigate to it. After a few failed searches, most people learn to skip the tools entirely and go straight to a colleague or a Slack DM. That DM eventually becomes your ticket queue.
The result: your team spends meaningful time answering questions that have already been answered somewhere - just not somewhere findable.
"The answer is there - it's in the portal - but people can't navigate to it. So they come to us instead, every single time, asking the same things."
HR Operations Manager · B2B software company, 150 employees
"Most of the time when someone's looking for an answer, we don't even know they're searching. They go off on their own and come back with something they found - and half the time it's just wrong."
IT Compliance Manager · Infrastructure software company, 300 employees
"What worries me most is we have no real control over what it tells people. The last thing I want is a manager acting on a wrong answer without checking with the people team first."
People Operations Lead · E-commerce platform, 400 employees
"Six of our strongest engineers spent weeks on it and still couldn't stabilize the output. Same prompt, same inputs - completely different results every single run."
HR Operations & Analytics Manager · Fintech company, 400 employees
Problem 02
AI tools made it faster to ask a question, not faster to find a reliable answer. When Copilot or an internal chatbot gives a wrong or incomplete response, the employee escalates to IT anyway - having lost more time than if they'd just asked directly.
There's also a new ticket category that didn't exist 18 months ago: "why did the AI tell me X?" Your team now triages AI mistakes on top of everything else.
Problem 03
Onboarding a new hire still requires someone to manually trigger provisioning, chase approvals, and confirm everything completed. AI was never wired into the handoff - so the handoff still happens over Slack DMs and tickets.
Engineering bandwidth for internal tooling is almost always zero. The people team has the clearest view of where the process breaks, but no way to fix it themselves.
"Every hire starts with a notification and ends with me doing everything by hand - provisioning access, checking what devices are free, uploading all their documents."
HR Manager · Data services company, 80 employees
"Engineering is always focused on revenue-generating features. Anything for internal teams sits at the bottom of the backlog - if it makes it there at all."
Head of People · B2B SaaS company, 200 employees
5-question diagnostic
Answer 5 questions. Get a specific estimate of hours lost per year and a fragmentation score for your setup.
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Count everything - Slack, Confluence, Jira, Notion, Google Drive, email, wikis, ticketing systems, shared drives. Each one counts.
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Tickets, Slack DMs, email questions - anything that requires a team member to respond.
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Copilots, AI search, chatbots, automation tools - count each deployment, even pilots that didn't fully roll out.
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Your estimate
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hours lost per year to avoidable IT requests
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While you wait - our AI at Work 2026 research covers the broader data behind what this diagnostic is measuring.