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AI adoption that survives contact with your actual workflows

The hard part of business AI is not the model. It is data access, permissions, and the fact that most pilots never leave the pilot stage because nobody planned for what happens after week one.

Where most AI projects stall

A tool gets turned on for everyone at once, it can see files it should not, and within a month it is either a compliance question or a tool nobody trusts enough to use for anything that matters. Governance and data access have to be reviewed before rollout, not discovered afterward.

What a rollout should include

A short list of use cases worth automating, a review of what data the tool can reach and who currently has access to it, a pilot scoped to a measurable task rather than the whole company, staff training on what the tool is actually good at, and a usage review after thirty days to decide what is working.

What we will not do

We will not recommend AI adoption because it is fashionable. If a scoping call turns up a workflow problem that a spreadsheet or a permissions fix would solve better than an AI tool, we say so, even though it means a smaller invoice.

What's included

Specific deliverables, not a vague promise to "handle IT."

  • A use-case shortlist based on your actual workflows, not a vendor's demo script
  • A data access and permissions review before any tool goes live, covering what it can reach and who currently has access
  • A written governance policy: what the tool can see, what it cannot, and who approves exceptions
  • A pilot scoped to one measurable task on one team, not a company-wide rollout on day one
  • Staff training on what the tool is actually good at and where it is likely to be wrong
  • A 30-day usage review to decide what continues, what changes, and what gets turned off
  • Copilot-specific: licence right-sizing and a review of exactly what content Copilot can index before it goes live

How this engagement works

Step 1

Discovery call

Where time is actually lost in your current workflows, and whether an AI tool is the right fix for it.

Step 2

Governance and data review

What the tool can access, who approved that, and what should be excluded before anyone touches it.

Step 3

Scoped pilot

One team, one task, one defined measure of success, run for a fixed period.

Step 4

Review and decide

Expand it, adjust it, or stop. The decision is based on the pilot's actual results, not a vendor's roadmap.

Questions about ai adoption

Will this replace staff?

Not a call we make and rarely the finding. Most engagements turn up permissions problems and workflow friction, not headcount decisions.

We already tried a pilot and it fizzled out. Can you help with that?

Yes; that is a common starting point. Usually the tool was rolled out before governance and a measurable task were defined, which is fixable without starting over.

Do you only work with Microsoft Copilot?

No. Copilot comes up most often because of Microsoft 365's install base, but any tool gets the same governance-first evaluation before rollout.

How long before we know if it's working?

Most pilots produce a clear signal, positive or negative, within thirty days. If it takes longer than that to tell, the task was probably scoped too broadly.

Other services

Start with an assessment, not a contract

A short scoping call, then a fixed-price review of your security, cloud and support setup. You keep the findings either way.