Operational reality of AI in service businesses
Posts filed under Operational reality of AI in service businesses.
Topic
-
Making the same work faster is not enough
At Berry, I see the gain in changing the manager's work: AI handles sales evaluations, while managers help salespeople practice. Faster reports alone leave the old work in place.
-
Don't Pick Your First AI Pilot by Performance Alone
Your best performer can belong in your first AI pilot, but performance alone isn't a reason to choose them. I look for people willing to test a different way of working, then check their feedback against the finished work.
-
Nobody Wanted to Write the Meeting Summary
Nobody wants another form between client calls. We use AI to draft meeting summaries, but we still have to define what belongs in them and check them before sharing.
-
AI Enablement Is a Loop, Not a Roadmap
AI enablement means changing the data, the process and the tools until the business gets a result. We had to stop giving a supervisor pages of reports and show him who needed help instead.
-
You Can Afford More Agents. You Can't Afford to Watch Them.
I have spare tokens but not spare attention. I capped the tasks I follow at once, but I stopped counting dropped threads afterward, so I cannot claim the cap improved the work.
-
Cheap Mistakes Go to AI. Expensive Ones Wait.
Routine AI work becomes dangerous when an error passes every control and sits unnoticed. Sort tasks by the thinking they need and the consequence of escape, then measure the checking layer with sampled escape rate and time to detection.
-
The Four Stages of Reviewing AI Work
Reviewing AI work moves from trusting too much to checking everything, then to a written standard and better-aimed review. The company can run slower while it learns, so plan for the tuning.
-
Your Team Isn't Resisting AI. Nobody Gave Them the Hours.
In the rollouts I've helped fix, resistance often turned out to be missing time, poor design or stale training. The calendar needs to change with the tool.
-
Document the Process First, or the Agent Scales the Chaos
Our first agent impressed everybody and changed little because the job still lived in managers' heads. Useful work started when we wrote that knowledge down.
-
The downturn playbook for AI-native firms: cut the fat, not the capability
If revenue falls, I would find waste before cutting the people and systems clients depend on, then give any real AI-saved time a clear job.
-
Constraint Whack-a-Mole: What AI Adoption Feels Like
If AI makes ten reports and your team can safely check five, more drafts won't get more work to the client. Start by managing the whole queue.
-
AI Anxiety and the Output-Validator Workflow
A team can want AI and still worry about where people fit. I would start by checking whether those responsible for the result can shape the work.
-
Delegate the Inputs, Own the Outputs
A task being easy to check doesn't mean anyone checks it. Give agents room to prepare work, with a named owner for the checks and the client send.
-
Shadow AI: The AI Your Firm Already Uses (That You've Never Seen)
Shadow AI is work done with AI tools the firm hasn't approved or can't see. Ask how the last client deliverable was made, then check its sources, conclusions and client-data sharing.