Garry M Consulting

Intelligence & Operations

Business intelligence and AI integration for growing businesses.

Turn scattered business data into useful decisions, and apply AI to clearly defined business workflows. Start with the business case, test a practical solution, and measure its value before expanding.

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The problem

Interest in AI, without a clear business case

Sales, customer, marketing, and operating data sit in different tools that do not agree. Meanwhile AI ideas arrive from vendors and headlines rather than from the workflows where they would help, and it is hard to tell which are worth a trial.

You might recognize:

  • Reports from different tools disagree about the same number.
  • You want to use AI but cannot say which workflow, or what success looks like.
  • Nobody owns data quality or definitions of key measures.
  • Past tool trials produced demos rather than decisions.

Intended outcome

Clearer decisions and better-supported operations, with any AI use tested on a small scale and measured before it expands.

What is included

The scope of the work.

  • Data and workflow assessment

    What data exists, how reliable it is, and which workflows depend on it.

  • KPI definitions

    Agreed definitions of the few measures that matter, so everyone reads the same number.

  • Reporting and dashboard requirements

    What the owner and team need to see, and how often.

  • AI use-case priorities

    Candidate uses ranked by business value, feasibility, data readiness, and risk.

  • Scoped pilot

    One well-defined pilot with a baseline, a test window, and a success threshold.

  • Evaluation, human review, and training

    How results are judged, where a person stays in the loop, and how the team is trained.

Deliverables

What you receive.

These are the documents and working files you keep. For each one: what it is, what it looks like, and what it does for the business. The layouts shown are illustrative and use a fictional business. No client material is shown.

Data and workflow assessment

What it is
A review of what data you hold, how reliable it is, and which workflows depend on it.
What it looks like
A table of data sources: where each one lives, who owns it, how reliable it is, and what it feeds.
What it does for the business
Shows whether your data can support the reports or AI use you want, and what to fix first.

KPI definitions

Also called key performance indicator (KPI) definitions

What it is
Agreed definitions of the few measures that matter, so everyone reads the same number the same way.
What it looks like
A table: each measure, exactly how it is calculated, where the data comes from, and who owns it.
What it does for the business
Ends arguments about whose number is right, and makes reports comparable from one period to the next.

Reporting and dashboard requirements

Also called a dashboard specification

What it is
A description of what the owner and team need to see, how often, and from which data, ready to hand to whoever builds it.
What it looks like
A table of the tiles on the dashboard: what each shows, how often it refreshes, and who owns it.
What it does for the business
Means a report or dashboard is built to answer your real questions, not to display every number that happens to be available.

AI use-case shortlist

Also called ranked AI use cases

What it is
Candidate uses of AI in your workflows, ranked by business value, feasibility, data readiness, and risk.
What it looks like
A ranked table that scores each use on those four points and gives a recommendation for each.
What it does for the business
Points you to the one or two uses worth a trial and shows why the rest should wait, so you do not buy tools that produce demos instead of decisions.

Scoped pilot plan with evaluation criteria

What it is
A plan for one small, well-defined test of a chosen AI use: the starting baseline, the test window, and the result that counts as success.
What it looks like
A one-page plan: the task, the baseline, the test window, who reviews the output, the success threshold, and the decision at the end.
What it does for the business
Lets you judge an AI use on evidence from a small trial, and expand or stop on purpose.

Human-review procedure

What it is
A written procedure for where a person checks AI output before it is used, and what they check for.
What it looks like
A one-page checklist: the checkpoint, what the reviewer looks for, what to do when output is wrong, and how the check is logged.
What it does for the business
Keeps a person accountable for anything AI produces, and catches errors before they reach a customer.

Also delivered

Built or set up for you, rather than handed over as a document.

  • Team trainingYour team trained to use the reports and to review AI output.

How the work proceeds

Four steps, each ending in a decision.

Every engagement follows the same six-stage process, applied to this service.

  1. Assess data and workflows

    Review sources, quality, and the decisions and tasks they support.

  2. Define measures and priorities

    Agree KPIs and rank the reporting and AI opportunities worth pursuing.

  3. Run a scoped pilot

    Test one use case with a baseline, safeguards, and a clear success threshold.

  4. Evaluate, then decide

    Continue, adapt, expand, or stop based on evidence, and train the team on what remains.

Read the full process

Illustrative example

Example opportunities (not past results)

Illustrative example, not a client result

Typical candidates include triaging inbound leads, retrieving answers from internal documents, assisting with recurring reports, and drafting product content for review. Each would start with a business case and a small test, not a company-wide rollout.

Scope and boundaries

What we name before any pilot starts

  • Success criteria, and how they will be measured.
  • Where human review is required.
  • How business and customer data will be handled.
  • Recurring tool costs.
  • Technical dependencies and who is responsible for them.
  • Custom engineering responsibilities, which are agreed per project.

Related experience

Where this shows up in my work.

  • Product management role

    Plat.AI loan origination system

    Product-management experience directing an AI-enabled loan origination system from zero to one, with reported outcomes on the case page. It was a product role, not an AI consulting engagement.

FAQ

Scope questions.

What is digital intelligence?

Bringing together sales, customer, marketing, and operating data so the owner can see what is working, where value is being lost, and what to improve next.

Have you delivered AI projects for clients?

I do not publish AI client outcomes on this site yet. The closest evidence is my product-management role directing Plat.AI’s AI-enabled loan origination system, with the reported outcomes on its case page. That was a product role, not an AI consulting engagement. What I bring to client work is a method: business case first, defined success measures, human review, and measurement before expansion.

Does my data need to be clean first?

Not perfect. The assessment checks whether the data is reliable enough for the use case. If it is not, the first project is fixing that.

Related services

Test whether AI belongs in your workflows before you invest.

Start with a free, 20-minute introductory call to talk through your business, the challenge, and what a sensible next step looks like.

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