AI
AI Consulting & Strategy
Decide where AI actually earns its place in your organization, what has to be secured first, and how it will be governed, before money is spent on tools.
Start with the work, not the tool
The best AI strategy is a short list of specific tasks
Useful AI adoption rarely begins with a product. It begins with an honest inventory of the repeated work that consumes staff time: drafting routine correspondence, summarizing long documents, finding the right file, answering the same internal questions, moving data from one system to another. Those are the places AI tends to pay for itself, and they are different in every organization.
The second question is what each of those tasks touches. A task that needs client records, contract data, or personnel files is a different project from one that uses public information, because the data decides which tools are acceptable and what has to be locked down first.
The third is governance: who approves a new use of AI, how it is reviewed, what staff may and may not do, and how a problem gets reported. For a small organization this is a few pages and a named owner, not a committee, but without it AI adoption happens by default.
Scope
What AI consulting includes
Strategy work that ends in decisions and documents, not a slide deck.
AI readiness assessment
Review tasks, data, licensed tools, identity, and permissions to find where AI can help and what must change first.
Use-case discovery workshops
Working sessions with the people who do the work, to surface and rank opportunities by time saved and risk.
Acceptable-use policy
Plain-language rules for approved tools, prohibited data, and how to ask, written for staff rather than lawyers.
AI governance model
Who approves, reviews, and owns AI uses, aligned with the NIST AI Risk Management Framework and scaled to your size.
Data and permissions review
Identify overshared files, missing sensitivity labels, and data that must stay out of AI services before anything is connected.
Tool and vendor evaluation
Compare AI options against your requirements, including data handling terms, security controls, and what you already license.
Regulated and contract data guidance
Map which data categories may be used with which services, for government contractors, public agencies, and regulated sectors.
Roadmap and budgeting
Sequence the work into quick wins and larger projects, with the prerequisites and costs each one carries.
Our approach
How a strategy engagement runs
Scoped before it is quoted, and finished with documents you keep.
- 01
Interview
Talk with leadership and the teams whose work AI might change.
- 02
Review
Examine data locations, permissions, identity, and current tool use, including unofficial use.
- 03
Rank
Score opportunities by value and risk, and identify the prerequisites for each.
- 04
Deliver
Hand over the roadmap, policy, and governance model, and brief leadership on the decisions they need to make.
Business outcomes
What changes
AI decisions become deliberate, documented, and defensible.
Clear priorities
Effort goes to the few uses that matter instead of every demo that looks impressive.
Staff know the rules
People have approved tools and clear limits, so shadow AI has less reason to exist.
Risk is visible
Data that must stay out of AI services is identified before it gets there.
A plan you can budget
Quick wins and larger projects are sequenced with their costs and prerequisites.
Fit
Who this is for
- Owners and executives deciding whether and where to invest in AI
- Organizations whose staff already use AI tools without a policy
- Government contractors and public agencies with data handling rules
- Teams planning an AI assistant rollout who want the risks mapped first
When it may not be the right fit
We would rather tell you up front than sell you something that will not help.
- Individuals seeking help with personal AI tools, which we do not provide
- Organizations that want a tool recommendation without looking at their own processes
Consulting questions
Before you start
What does an AI readiness assessment look at?
Four things: the tasks where AI could save meaningful time, the data those tasks depend on and who can currently reach it, the tools you already license, and the rules you operate under. The result is a short, ranked list of opportunities with the prerequisites each one needs, not a generic maturity score.
Do we need an AI policy if we are not using AI yet?
Almost certainly your staff already are, on their own. A short acceptable-use policy that names approved tools, lists data that must never be entered into any AI service, and says who to ask is the cheapest risk reduction available, and it gives people a legitimate path instead of a workaround.
What framework do you use for AI governance?
We align with the NIST AI Risk Management Framework, a voluntary framework built around four functions: Govern, Map, Measure, and Manage, plus NIST's Generative AI Profile. For a small organization that becomes a practical set of decisions: who approves new AI uses, how they are reviewed, and how problems are reported.
How long does a strategy engagement take?
It depends on the size of the organization and how many teams are involved, so we scope it before quoting. A focused readiness assessment for a single team is a short engagement; an organization-wide roadmap with policy and governance takes longer. Either way you receive written deliverables you keep.
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A consultation is a conversation about how your teams work and where AI could help. You will leave knowing what is worth pursuing and what has to be secured first.