01 · TOOL
Hallucination Checker
Finds claims that deserve verification and turns them into a documented human-review workflow.
Use the tool →Good AI starts with good human habits.
I help organizations use AI without surrendering human judgment.
I build practical tools and teachable controls for workforces where mistakes matter, so people use AI openly, check what matters, keep control of the work, and own the final result.
I learned technology adoption from the other side: as the operator who had to use whatever was handed down, in rail, safety-critical IoT, and Cisco's most regulated accounts. Today I turn that experience into tools, workshops, and adoption systems that people can understand, trust appropriately, and actually use.
New working tool
Hallucination Checker
AI can be wrong in language polished enough to make the mistake disappear. This free browser tool marks the claims most likely to need verification, turns them into a review checklist, and gives the user a disciplined way to challenge the output before it is sent.
It does not pretend to determine truth. It shows where human judgment is required. The checker works offline, without an account, and the document never leaves the device.
Works offlineNo accountNothing uploadedHuman stays accountable
What the tool notices
According to the Journal of Organizational Behavior, hybrid teams are always more productive and report 23% higher engagement between 2019 and 2023.
A coherent body of work
Tool + training + adoption method.
The software is not the whole answer. People need a way to understand the risk, a practical control they can use at the moment of work, and an adoption method that survives after the workshop ends.
02 · WORKSHOP
You Are Smarter Than AI
A plain-language AI literacy session that replaces fear and overconfidence with four memorable operating rules.
Explore the workshop →03 · METHOD
Turning Skeptics into Champions
My field method for earning trust, finding the real objection, and turning the loudest skeptic into the quality authority.
Read the field method →The operating standard01 02 03 04
Good AI is visible in the habits around it.
AI should be used. It should also be used openly, checked where the stakes are real, and kept under clear human ownership. The goal is not to make AI-assisted work look less like AI. The goal is to make it good enough to disclose proudly.
Use it openly.
Do not hide the tool. Be clear when AI assisted the work and who reviewed the result.
Check what matters.
Verify factual claims, numbers, dates, sources, and commitments before they leave the organization.
Keep control.
AI drafts and suggests. People decide what changes, what stays, and what the final message means.
Own the result.
The final reviewer should be able to defend every sentence carrying the organization’s name.
A practical disclosure: AI-assisted. Human-reviewed and approved.
The through-line
The common thread was never an industry. It was stakes.
Rail operations where safety was life or death. Safety-critical IoT. Cisco accounts for banks, hospitals, government, and military organizations, where process mistakes carried legal and operational weight.
That is why skeptical rooms believe me - and why I design AI adoption around behavior, judgment, and practical controls rather than hype.
What I do
Make AI useful, understandable, and safer to use.
I work at the point where tools, process, and human behavior meet. The engagement can be a role inside an organization, a focused consulting project, or a workshop with a customized tool left behind.
Practical AI oversight tools
Simple controls that surface risk at the moment of work: factual claims, unauthorized rewrites, unsupported sources, commitments, or sensitive information.
Role-specific training
Workshops and plain-language guides built around what a team actually does, not generic prompting demonstrations.
Adoption programs that stick
Champion networks, train-the-trainer, promptathons, communications, and follow-through after the launch.
Workflow and process design
Find the failure point, write the rules, rebuild the handoff, and make the safe behavior easier than the unsafe one.
Human-in-the-loop governance
Review standards, escalation paths, evidence requirements, and clear ownership for AI-assisted work.
Measurement and refinement
Track usage, listen for friction, identify where people are improvising, and adjust the tools and training until the behavior holds.
Proof
The work is easier to trust when you can open it.
Product judgment, demonstrated
From an operational risk to a working tool in days.
I saw that most "hallucination detectors" made a promise they could not keep. Instead of pretending software can determine truth, I built a private, offline control that identifies claims requiring human verification and carries the user through the review. Real testing immediately exposed mobile and onboarding failures, and the tool was revised around what users actually saw.
Conceived, built, tested, and shipped as a working browser applicationHostile rooms, turned into daily users
Nine years of adoption where nobody asked for the technology.
I traveled the US, Canada, and Mexico teaching skeptical teams at Amtrak, VIA and commuter operators: mechanics, technicians, supervisors, and executives. The people I trained did not merely attend. Their usage increased and skeptical accounts expanded their deployments.
Internal adoption data showed substantially higher platform usage among trained customersAI used on regulated work
Processes built in dialogue with AI, then audited by people.
At Cisco, I wrote custom migration processes for highly regulated accounts while using AI to interrogate the logic. I then trained an India-based delivery team to use generative AI for customer communications and to audit every output before it reached a customer.
120-140 enterprise migrations per month · checkpoints caught every issue before it reached the customerExperience
Getting people to actually use what is in front of them.
Cisco Systems
Business Operations Leader
2020-2025
Ran revenue-lifecycle operations across a global, regulated environment. Built decision and accountability systems that removed leaders from daily exception handling, reduced legal exposure, and trained delivery teams to use generative AI safely on live customer work.
Wi-Tronix
Customer Support Manager / Senior Customer Support Manager
2010-2020
Built the training function from scratch and drove adoption of safety-critical rail IoT across North American passenger, commuter, and freight operations. Turned resistant teams into daily users and enablement into an expansion lever.
Canadian National Railway
Locomotive Engineer, then Acting Trainmaster
Earlier career
Operated freight and passenger trains under strict safety and regulatory requirements, where decisions were time-critical and mistakes had immediate consequences. The operator's-eye credibility underneath everything I do now.
Credentials
Formal grounding, put to work.
AI & Generative AI
Specializations from Vanderbilt, Wharton, and IBM, plus PMI's AI in Project Management suite.
Instructional Design
Instructional Design Foundations & Applications, University of Illinois.
Professional
Project Management Professional (PMP), PMI. Six Sigma Green Belt, University of Illinois.
Education
B.S., Business Management, Western Governors University.
About
Why the skeptics believe me.
I have spent my career in places where being wrong had a measurable cost. I also spent years as the operator expected to use technology designed by someone else. That combination changed how I approach adoption: the people are rarely the problem. The process, the explanation, or the handoff usually is.
I do not try to make AI sound magical. I explain what it can do, where it fails, and who remains accountable. Then I build the smallest practical control that makes the right behavior repeatable.
My rule is simple: if I cannot explain it plainly, I do not understand it well enough. A workforce will not adopt what it cannot understand.
What people say
From people I have worked with.
“
He belongs in AI enablement, and I'm the one who pushed him toward it, because I'd already watched him do the job before it had a name. At Cisco he turned skeptics into confident daily users of AI, without any mandate telling him to do it.
Simone Richards, M.S., PMP, CSM
Project Manager, Cisco
“
He brought structure where there wasn't any, kept the project on track, and helped align multiple stakeholders around shared goals without overcomplicating things. He was the one people turned to for solutions that actually worked.
Tim Collins
Principal Consultant, Business Development
“
He leads by example, earning respect through his actions and clear communication. Always asking insightful questions and ensuring processes are well-documented and easy to follow.
Renee Spencer
Business Operations Manager
Get in touch
Let's talk.
I am open to roles and focused consulting engagements involving AI adoption, employee enablement, workflow design, or practical human-oversight controls. If your organization needs people to use AI well without trusting it blindly, that is the work I want to do.