Shadow usage
Teams use tools without disclosed guidance on data privacy, IP, or client commitments.
01
Position
Your team is running AI experiments.
No one owns the outcomes.
I help leadership turn scattered AI activity into a managed program. Use cases that matter, guardrails people actually follow, and a way to tell whether any of it is working.
Veteran Owned Business
Ex-VP AI services
ISO 42001
NIST AI RMF
02
Diagnosis
AI is already inside your organization.
Employees are testing tools. Leaders are asking for productivity gains. Vendors are bolting AI onto every feature. The problem isn't curiosity — it's ownership.
Tool sprawl
Departments buy overlapping AI tools with no central view of cost, risk, or productivity.
Training gaps
Employees aren't sure how to use what they're given, resulting in dollars spent for underutilized tools.
No operating rhythm
No standing review of usage, value, or risk. No reporting cadence. No path forward.
The question is not whether you are using AI. It is whether you are running a program - or just funding experiments.
03
Engagements
A practical ladder for AI adoption, governance, and leadership ownership.
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01
Executive AI briefing
A focused session for leadership that needs a shared, honest read on AI calibrated to your situation, with three to five specific recommendations to act on.
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02
AI program discovery sprint
Three to four weeks of stakeholder interviews and assessment to surface where AI is already happening, where the risk sits, and what to actually do next.
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03
Fractional Head of AI
Embedded program leadership for organizations that need it before, or instead of, a full-time hire. Governance, vendor evaluation, program design, and stakeholder navigation.
04
Method
Simple enough to understand. Strong enough to operate.
AI adoption does not need theater. It needs a practical operating model. Discover what is happening, decide what matters, put guardrails around it, train people, and keep improving.
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01
Discover current usage
Find the tools, prompts, vendor features, and informal habits already inside the organization.
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02
Map use cases
Separate interesting experiments from work that can scale and operate at value.
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03
Prioritize value
Choose the use cases with the clearest business value, feasibility, risk profile, and executive support.
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04
Define guardrails
Create practical policies, review paths, data guidance, and accountability before adoption scales.
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05
Train teams
Teach people how to use AI in their actual roles — with examples, constraints, and working patterns.
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06
Build operating rhythm
Standing reviews, usage tracking, and a reporting cadence so the program doesn't drift back into ad-hoc activity.
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07
Measure and improve
Track adoption, value, quality, and risk. Balance learning loops against retiring pilots that never matured.
05
About
Matthew Guenther
I am not interested in AI theater.
I help leaders figure out where AI is useful, where it creates risk, where it should not own the work, and how to turn scattered experiments into a program people can actually operate.
My work sits between strategy and execution. I can help an executive team get oriented, help a department identify practical use cases, help a company write the guardrails, or step into the operating role long enough to build momentum.
The goal is not to make AI sound impressive. The goal is to make it useful, governed, and measurable.
06
Questions
Questions leaders ask before turning AI activity into an AI program.
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Yes — and the cost of putting it in late is much higher than putting it in early. Governance does not need to be heavy at the experimentation stage; it needs to be enough to prevent experiments from creating commitments you didn't mean to make.
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Both, but the work I am best at is helping organizations adopt and govern AI thoughtfully. I have built and run AI platforms; I am not selling one.
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That is the executive briefing. Ninety minutes, plain language, focused on what matters for the decisions in front of you.
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Owns the AI program until you are ready to hire someone full-time. Sets priorities, writes guardrails, runs the operating rhythm, builds the case for the permanent hire — and hands it over cleanly.
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Yes. Acceptable use policy work folds into the discovery sprint or the retainer — sized to the organization and reviewed against your existing policies, not lifted from a template.
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By writing policies that fit on a page, putting decision rights with the people closest to the work, and reviewing the system on a cadence rather than escalating every case. Governance that nobody reads is worse than no governance at all.
Next steps