McKim & Co. · An operations advisory practice

Operations leadership, turnarounds, and AI operational readiness.

I fix operations that are breaking and scale the ones that are working.

Two ways to work with me.

McKim & Co. is an operations advisory practice, run by an operator. The assessment and the governance tools exist because the work needed them.

One if you want to find out where you stand, one if you want someone to come run it.

AI Operational Readiness

For owner-operated businesses, 5⁠–⁠200 people (roughly $2M⁠–⁠$50M)

For a business that’s using AI, or about to, and isn’t sure what it’s exposed to.

  • Free: 30 questions, 8–12 minutes, and a written report on where you stand — AI use (including the shadow AI nobody logged), compliance and insurance, infrastructure, and how fast you actually need to move.
  • If it’s warranted: a Briefing that ranks what to fix first, and governance policies your team can run.
Take the free assessment →

Interim & Integration Leadership

For midsize and PE-backed companies, $5M–$250M

For a company that needs someone in the operating seat: after a close, during an integration, or when margins slip.

  • I take the role and run it: post-close stabilization, post-merger integration, margin recovery, scale-up.
  • Interim or project-scoped.
  • Led the operational merger of a $100M healthcare enterprise: full platform migration in eight months, $4M in annual savings found line by line.
Start a conversation →

The receipts

A career of fixing operations, before the tool.

>$4M
Annual savings · Post-merger integration
200%
EBITDA expansion · Churchill Downs subsidiary
24×
Platform scale · 500 → 12,000+ units

Every number above has the same person behind it. Mike McKim — operations executive across healthcare services, regulated gaming, and multi-site logistics; engineering-trained; COO and VP Operations seats in PE-backed and founder-owned companies. Built the systems, not just the slide decks. More about Mike →

I build the thing I advise you about.

Everything on this site — the readiness assessment and its scoring, the surface scanner, the pattern analysis, the reports, the payment handling, and these pages — I designed and built, working with AI the same way I’d tell you to work with it.

That means it runs the way I’d want your system to run. Content lives in the database rather than in the markup, so a copy change ships as a tracked migration instead of an edit somebody forgets. The structured data is driven by config rows with kill switches, so a bad value gets switched off rather than redeployed. Nothing reaches production without being verified somewhere else first. The AI wrote a great deal of the code; I wrote the specification, and I checked what came back against it.

It’s a small system, and that’s the point. The discipline doesn’t change with size — and most AI projects fail for want of exactly this: documented decisions, a governed path for changes, and somebody who can tell whether the output is right.

The market without operational preparation

This is how AI implementation is going for most.

$252B
Invested in AI in 2024
Total global corporate AI investment, private funding up 44.5% — Stanford HAI AI Index, 2025
74%
Show no tangible value
Survey of 1,000 CxOs across 59 countries — BCG, “Where’s the Value in AI?”, October 2024
42%
Of companies abandoned most AI initiatives
Up from 17% the year before; 1,000+ enterprises in North America and Europe — S&P Global Market Intelligence, March 2025
61%
Report no bottom-line impact from AI
Just 39% attribute any EBIT (operating profit) impact to AI, and most of those put it under 5%; 1,993 respondents in 105 nations — McKinsey Global Survey on the State of AI, 2025

In my experience, that gap traces to one thing: the operation wasn’t evaluated before the tool was purchased. McKinsey found that fundamentally redesigning workflows made one of the strongest contributions to business impact of all the factors it tested — and that the organizations seeing real value were nearly three times as likely to have done it. Redesign the work first, then choose the tool. That sequencing is what this practice does.

Every one of these is solvable. In sequence. Before you buy anything.
The pattern across all five

Five reasons AI fails. All of them operational.

These are the five I find most often. None of them are technology problems — they’re operations problems the wrong tool makes visible, expensively, at scale.

01
The process was never documented.
Two people run it from memory. There is nothing for AI to follow — only a person to replace. You can't automate what isn't written down.
02
Shadow AI created exposure nobody audited.
Staff was already using ChatGPT with client data before any policy existed. The insurer is now asking. The audit trail doesn't exist.
03
Handoffs weren't owned by anyone.
Sales tells ops verbally. Ops tells billing by memory. When nobody owns the workflow, nobody owns the failure — and AI executes those gaps at machine speed.
04
The tool was bought before the operation was evaluated.
Vendors assessed the tech stack. Nobody walked the floor. The organization couldn't absorb what it just paid for.
05
Capability left with the consultant.
The engagement ended. Nothing ran without outside help. The organization was dependent by design, not by accident.

In their words

He wasn't someone who solved problems in isolation, but someone who built the capability of the people around him.
Mark Schlepphorst Solution Security Officer, former colleague
He has a rare ability to identify gaps in technology infrastructure before they become problems — and to build plans that close them.
Syaidah Awls HR Administrator, former colleague

Upcoming

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Operational readiness

You don't have to know exactly what's broken. That's what I'm for.

Start a conversation →Take the free assessment →


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