A lot of teams were told AI would remove repetitive work and give people more time back.
What I keep seeing instead is people doing the old work plus reviewing AI output, learning new tools, and being expected to move faster because AI exists.
For leaders who have rolled it out successfully, what work did you actually remove from people's plates instead of just adding AI on top?
We have people building small AI automations around spreadsheets, docs, email, Slack, and internal tools.
Individually they seem harmless. But six months later, who owns them, who knows what data they touch, and who shuts them down when the employee leaves?
For CIOs dealing with this already, are you tracking AI tools centrally or still handling them case by case?
One thing I keep seeing is teams start with "what should replace this platform?" before anyone has a clear picture of what actually depends on it.
Then halfway through the project someone remembers backup, monitoring, licensing, an old integration, or a workload that can't move.
I've started thinking the dependency map might matter more than the shortlist at the beginning.
Do you normally map all of that first, or does it happen during evaluation?
We’re at the point where people aren’t just using ChatGPT or Copilot anymore. Teams are building little automations around them too.
Nothing huge on its own. Someone connects one to a spreadsheet, someone else has one reading docs, another team has something tied into Slack or email.
The part I keep thinking about is what happens 6–12 months later.
Who still owns these things? Who knows what they’re connected to? Who remembers why they were built in the first place?
It feels a lot like shadow IT all over again, except now some of these tools can actually do things instead of just storing data somewhere.
For anyone dealing with this already, are you tracking these centrally or just handling them as they come up?
A lot of IT risk sounds “theoretical” until something breaks. How do you explain cybersecurity, downtime, vendor risk, or technical debt in a way leadership actually takes seriously?
A lot of IT risk sounds “theoretical” until something breaks. How do you explain cybersecurity, downtime, vendor risk, or technical debt in a way leadership actually takes seriously?
A lot of IT risk sounds “theoretical” until something breaks. How do you explain cybersecurity, downtime, vendor risk, or technical debt in a way leadership actually takes seriously?
Response time sounds good in a contract, but real incidents are messy. What do you ask before trusting a cybersecurity vendor with incident response?
C-suite executives keep Slack-ing my mid-level admins directly for quick favors or pet projects, completely blowing up our sprint planning and ticket queues. The admins feel uncomfortable saying no to a VP. I've asked leadership to use proper intake channels, but old habits die hard. How do you strictly enforce project boundaries without looking unhelpful?
Sitting on a core ERP system that hasn't had major modernization in 8 years. It runs, but every integration takes triple the time, support talent is retiring, and a single patch can crash dev for days. Leadership refuses capital budget because "it works fine today." How do you translate technical debt into a language that makes non-tech execs care before a catastrophic outage forces their hand?
Trying to get approval for upgraded backups and disaster recovery infrastructure. The CFO views it purely as an expense line item with zero ROI because "we haven't had a major outage yet." I've tried showing calculated hourly downtime costs, but it gets dismissed as theoretical. What analogies or metrics have actually landed with your finance leaders?
Caught a team using an off-the-shelf AI tool hooked into our CRM via API keys they generated themselves. When called out, their response was "it saves us 10 hours a week." I don't want to be the department of "No," but the data exposure risk is wild. How are you setting boundaries on rogue AI tools before legal or security has a meltdown?
Every pitch promises "native integrations" and "5-minute setup," but during implementation, we end up needing custom API work or paid add-ons just to get basic features running. We’re losing hours sitting through 5+ polished demos per category only to find out the tool can't even handle our basics. What's your filter process before you actually let a vendor book time on your calendar?
Just opened our renewal questionnaire and the goalposts moved again, MFA on local admin, strict data retention, and strict endpoint isolation times. Keeping this policy would require doubling our security spend.
Are you guys actually checking every single box on these impossible questionnaires, or are teams just dropping coverage at this point?
Some teams score vendors before demos. Others just talk it out after the calls. What works better in real life? Does a scorecard actually help, or does it become extra admin?
Some security tools are easy to approve. Others get stuck because leadership sees cost before risk. What kind of cybersecurity purchase has been the hardest to get approved?
not naming vendorsust curious what caused the mismatch. was it bad onboarding, weak support, missing integrations, uncle. jar scope, or something the sales process didn’t reveal?
some gaps can be handled in-house while others slow the whole project down. for IT managers here what usually triggers the decision to bring in a consultant, vendor, or outside specialist?
sometimes the best-fit vendor is not the loudest, cheapest, or the one an executive already knows. for CIOs and IT leaders how do you keep vendor selection focused on actual fit instead of internal pressure?
there’s always something that looks harmless at signing and painful later. what clause do you now check every time?