9 posts tagged with "operations"

An AI pilot is not an operating plan.

It is a container. It tells us that a company intends to try some technology for a limited period. It does not tell us which work will change, who will do less of it, where judgment will remain, or how anyone will know the trial helped.

That missing statement is the operating thesis.

A useful operating thesis sounds like this: routine order-status calls can be handled from approved facts and documented automatically, while exceptions arrive in front of an experienced CSR with the context required to act.

That can be tested. It names the work, the boundary, the human role, and the expected change. "We are piloting an AI phone agent" names a technology and leaves the operation blank.

There is a particular satisfaction in making a business look real on the internet.

The name is settled. The copy explains the offer. The photographs line up. The site loads, the links work, and the rough idea that had been living in notes and conversations finally has a public address.

That moment matters. It is also easy to mistake it for more progress than it is.

Sometimes the right answer is not an agent.

The workflow may be painful. The team may be losing hours to it. A model may even be capable of performing the most visible step. None of that guarantees that an agent is the best first intervention.

"Can we automate the recognition step?"

It was a reasonable request. A civic reporting workflow included manual license plate review. If software could read the plate from an image, return the detected text, and spare someone the repetitive inspection, the team should have more capacity.

The recognition service was technically plausible. It was also not the whole job.

"We need something that handles this."

That sentence can start a useful conversation, but it cannot scope useful work. It points toward pain, not evidence. It says the current process is frustrating, slow, inconsistent, or too dependent on one person. That may all be true. But before anyone can recommend an agent, a software tool, a workflow change, or no build at all, the work has to become visible.

Receiving careful analysis without a recommendation is a special kind of disappointment.

The buyer wanted clarity. Instead they get seven plausible paths, each with pros and cons, each carefully hedged, each leaving the real decision exactly where it started. Build an agent. Buy software. Change the workflow. Hire someone. Document the process. Collect more data. Wait. All of those may be reasonable, but a list of reasonable options is not the same as a useful recommendation.

Assess the workflow before recommending the build.

That sounds obvious until a real AI conversation starts. Someone has a stuck process, a team is losing hours to repeated manual work, and a demo makes it feel possible that an agent could take the whole thing off their plate. The temptation is to jump straight from frustration to build.

Build questions are useful eventually. They are not the first questions.

Submitting a contact form can feel like stepping onto a conveyor belt.

You write a few careful sentences about the problem, hit submit, and wonder what happens next. Will someone try to close you on an agent before they know anything about your business? Will the first call turn into a vague discovery meeting with a proposal waiting at the end? Will the conversation jump ahead before anyone has understood the workflow?

That is not what the first call is for.

The first step with aboriginal armadillo is a 20-30 minute fit call. It is a short conversation about one workflow that is causing friction. The goal is to learn who you are, what work is getting stuck, what has already been tried, and whether there is enough operational reality to justify a deeper assessment.

Most organizations do not have an AI problem.

They have work that waits too long, moves through too many hands, depends on one person's memory, repeats without getting easier, or breaks whenever the usual workaround stops working.