AI adoption for companies that can’t afford to get it wrong.
I help operating companies find the workflows where AI actually saves time — and put the guardrails in place so quality, privacy, and human accountability hold up.
What this looks like
Three things, in order.
Most AI projects fail because they start with a tool instead of a task. The sequence matters more than the software.
Find the work worth automating
Interviews with the people doing the work, and a written workflow inventory. A prioritized list, not a wish list.
Set the guardrails
Acceptable-use policy, data boundaries, tool approval, and human review standards — before anything goes live.
Prove it in one department
A scoped pilot with a measure of success defined up front, before anyone signs a platform contract.
The method
The AI drafts. A person decides.
Every system I build follows the same shape. The tools change from one business to the next. The pattern doesn’t.
Ingest → Process → Generate → Human Approves
Information comes in from the systems you already use. The AI structures it and produces a draft. A named person reviews and signs off. Nothing goes out the door unreviewed — and accountability for the finished work stays with a human being.
What I do
Three kinds of engagement.
AI readiness & adoption roadmap
- Stakeholder interviews across the departments that would actually use it
- A written workflow inventory with time cost and risk noted for each
- Prioritized use cases — scored on effort, impact, and risk
- A tooling recommendation with the reasoning and the cost implications
- A pilot plan for one or two departments, with success defined before it starts
This is not a licensing resale. I don’t take vendor commissions.
AI governance & acceptable use
- An acceptable-use policy your staff can actually apply
- Data classification boundaries — what may and may not be entered, with examples
- A tool approval process, so shadow AI stops being the default
- Human review standards for anything customer-facing or published
- A one-page quick reference for daily use, not just a compliance document
This is not a template with your name pasted on it. It reflects your workflows and your risk.
Workflow automation builds
- Document and report generation from information you already hold
- Turning unstructured input — email, notes, forms — into structured output
- Knowledge capture, so critical process knowledge stops living in one person’s head
- Human approval built into the architecture, not bolted on afterward
This is not an autonomous system. Every output is reviewed by a person before it goes anywhere.
How it goes
Four phases, fixed scope.
- Intro call — freeYou describe what’s eating your team’s time. I tell you honestly whether AI helps. Sometimes the answer is no, and you should hear that before you spend anything.
- Scoped assessment — fixed feeInterviews, inventory, prioritized use cases, guardrails, and a pilot plan. Defined deliverables and a defined end date. No open-ended engagements.
- Build and deliverI build the pilot. You see and approve the output before anything goes live.
- HandoffA working system, written documentation, and a way to reach me when something needs attention.
Where I draw lines
What I won’t do.
- I won’t recommend a tool I haven’t used myself.
- I won’t build a system that publishes, sends, or acts without a person reviewing it first.
- I won’t take a commission from a software vendor. My recommendation isn’t for sale.
- I won’t tell you AI can solve a problem when it can’t.
AI use & data handling
My own policy, published.
If I’m going to write your AI policy, you should be able to read mine first.
- I use AI tools in my own work.
- Drafting, research, analysis, and code. Every deliverable I hand you has been reviewed and edited by me, and I am accountable for it. No exceptions, and no output goes to you unread.
- Your information stays in controlled systems.
- Client material lives in my business Microsoft 365 tenant on commercial terms — not in consumer AI accounts, not on personal storage, and not on personal lab hardware. Commercial and enterprise plans do not train models on customer content; consumer plans have different terms, which is exactly why I don’t use them for your work.
- I’ll tell you what I need and nothing more.
- Most assessment work needs conversations and process descriptions, not your data. Where real records are genuinely required, we agree in writing first — what, why, where it lives, and when it’s deleted.
- Nothing I build acts on its own.
- Every system I design puts a named human between the AI’s output and the outside world. That’s an architectural commitment, and it goes in the contract.
- This site collects almost nothing.
- No cookies, no third-party scripts, no advertising trackers, no forms. If you email me, I keep the correspondence for as long as it’s useful and delete it on request.
About
Who you’d be working with.
Micah Bias · York County, Pennsylvania
I’ve spent about eight years building and operating technology systems in environments where mistakes are expensive — healthcare, manufacturing, and public-sector security operations. I currently work in cybersecurity operations, and I’ve built and deployed production AI automation that runs in daily use, not as a demo.
I also develop and teach a non-credit AI-for-business course for a Pennsylvania community college, which means I spend a lot of time explaining this work to people who don’t care about the technology and shouldn’t have to. That turns out to be most of the job.
Micah Bias Consulting is a one-person practice, deliberately. You work with me directly — the person who does your assessment is the person who does your implementation. There’s no team to hand you off to, which is the point.
Contact
Start with a conversation.
No pitch deck, no aggressive follow-up. Tell me what’s taking your team too long and I’ll tell you honestly whether this is worth your money.