AI strategy and rollout your team will use
Where AI pays in your business, and a rollout your team uses.
How we ship AI
- 1TestYour real tasks, run through Claude, GPT and Gemini. Scored before anything is built.
- 2BuildInside Gmail, Slack, your CRM or Sheets. Agents, MCP connections, retrieval on your documents.
- 3GuardLimits it cannot cross, a person on the exceptions, every action logged.
- 4MeasureHours and error rate before and after. If it does not pay, it comes out.
ClaudeGPTGeminiMCPAgentsRetrieval (RAG)Evalsn8n / ZapierYour CRM
Where would AI pay here?
One task, four questions. It says which shape of AI fits, or that none of them do yet.
1. What do you get at the end?
Pick the thing that comes out at the end, not the effort that goes in.
2. If it gets one wrong, how bad is that?
Think about the wrong answer nobody notices, not the one that falls over loudly.
3. Is what it needs written down anywhere?
Written down means an assistant can read it without asking a person.
4. How often do you do it?
Frequency decides whether it is worth building anything at all.
Where it lands
Answer the four questions
The shape of AI that fits depends on what the task produces, what a wrong answer costs, and whether the material it works from is written down anywhere.
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What to watch
A second opinion
Want this checked against what ships?
We reply within one business day.
What this usually leads to
AI laid over a process that does not work makes the mess faster and harder to see. Process automation is the step before, and data and reporting is what makes the result measurable rather than merely impressive in a demo.
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Everyone is telling you to use AI. We find the one task where it pays, build it in your systems, put a person on the output, and measure the hours it gives back. The rest can wait.
Is it a fit?
Hours
One task is eating real hours every week, not once a quarter.
Judgment
A person can still own the output and be answerable for it.
Evidence
You can write down what it costs today, before anything changes.
What the engagement includes
A clear answer
Where AI helps, where it does not, and where it creates risk you do not want.
Prioritized use cases
Ranked by hours saved and effort to ship, with the numbers behind the ranking.
Working pilots
We build the first two or three, in your systems, with your data.
Policy and guardrails
What staff can put into which tools, written plainly enough to be followed.
Where AI lands first
The highest-return first deployments we see, in rough order of payback speed.
Where AI earns its place
AI is chosen for one task, after the hours are measured, and only where a person still owns the final call.
Start the other way round. Find where the hours go, find the tasks that repeat, then ask whether a model is the right tool for any of them. Often the biggest item wants a process fix or a plain rule, which is cheaper and more predictable than a model.
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How it runs
Diagnose, build, run.
How engagements are set up
Assessment and roadmap run as a single piece of work. Pilots are scoped separately, once the use cases are agreed and worth committing to.
- Scope
- Agreed before work starts
- First call
- 20 minutes
- Reply time
- 1 business day
What you have at the end
Three things, in writing.
One task, chosen by the numbers
The task where AI pays, picked from where the hours go, not from a vendor demo.
A pilot people use
Built in your systems, on your data, inside the work as it is done.
An owner and a number
Somebody accountable for hours saved or errors cut, and the before-and-after to show it.
We map the operation first, pick the narrow slice where AI pays, and prove it with before-and-after numbers before anything scales.
Questions we get
Where should we start with AI?
With a measurement of where the week goes, not with a tool. The tasks worth giving to a model are identifiable from the data, and they are usually not the ones people nominate.
We already bought licenses and nobody uses them. What now?
That is the most common situation we are called into and it is recoverable. The licenses are rarely the problem. The absence of a defined task, a defined output and a reason to change how somebody works is.
Is our data ready for this?
Less of a blocker than vendors suggest for most uses, and more of one than people expect for anything touching your own records. Drafting and summarizing need almost nothing. Anything answering questions about your business needs that information to be findable and correct.
What should we not use AI for?
Anything where genuine judgment is the point, anything that changes every time, and anything where an error is expensive and hard to spot. Those get better process and better tooling around a person.
More questions? AI in the business, answered Read the guide: How to bring AI into your business all areas
Read next
How this runs, every question we get, and the work that usually sits next to it.
Shaheer leads the work, with engineers, writers, filers and analysts behind him. C-suite operations for a San Francisco AI company, Six Sigma on the process side, Anthropic certified on the Model Context Protocol, ten years across eight industries. See what we have built