AI without Adults: How to Deliver When Leadership is Checked Out
A Survival Guide for the Doers, Not the Talkers
Like many people, I like to poke around Reddit to see what is going on outside of the world I am currently living in. I came across a thread recently about “The Great IT-Divide: Why Adoption in enterprises is failing” posted in the r/Futurology subreddit.
The article that generated the discussion is interesting, but the comments were much more so. The one I screenshotted above was particularly interesting since it seemed so human:
…middle management hyping up AI use mindlessly (and annoyingly), while upper management and the stakeholders have been more or less quiet on the topic (at least publicly).
As someone who has spent their entire life figuring out how technology can solve problems and improve business outcomes, I think there is a lot to unpack here.
One of my last articles that I posted was around how to ensure you can scale your AI pilot into production and across the organization, because there is nothing quite so demotivating as a project that fails. Sure, there are times when projects should be shelved, but it is important to do so for the right reasons, not because an executive found a new shiny object to chase.
Add onto that, there has been a number of articles about the current dilemma of AI-Generated “Workslop.” Yikes. According to a recent article in Harvard Business Review, they (so-far) have surveyed 1,150 US-based full-time employees. Of those who took the survey (which can be found here), 40% report having received workslop in the last month. This has pushed a large amount of rework downstream — wasn’t there a saying about stuff that rolls downhill?
I like to look for ways to empower all levels of the organization so that everyone not only feels more engaged, but more in control of outcomes.
So, I began working on a playbook that people could use to build real AI wins in any organization — even when leadership has mentally checked out.
Why Most AI Initiatives are DOA
Here is an example of a complaint that I hear all-to-often: the EVP reads a McKinsey report about AI transformation over the weekend. Monday morning, there’s an all-hands about “our AI journey.” By Tuesday, middle management is scrambling to show they’re “AI-forward.” By Friday, you’re sitting in your third committee meeting about forming a committee to discuss AI governance.
Meanwhile, the actual work that people are working, like the problems that need solving, the processes that need fixing, and the customers who need serving all sit untouched while everyone plays corporate theater.
I’ve seen this movie before. During my time leading the Orion Crew Module Software Integration at NASA, we had a saying: “No one wants to be responsible for mission failure, but everyone wants to be in the room where decisions are made.” Replace “mission failure” with “AI failing,” and you’ve got today’s corporate landscape.
1. Recognize the Signs the Initiative is Doomed
Before you raise your hand to lead that AI initiative, look for these warning signs:
The Vanity Metrics Trap: Leadership asks “How many AI projects do we have?” instead of “What problems are we solving?”
The Committee Carousel: More than two committees involved in approving a simple proof of concept. (Remember the OSS sabotage manual I wrote about? This is literally page one.)
The Phantom Budget: Lots of talk about “investing in AI” but no actual budget codes or spending authority.
The Accountability Void: Ask who owns the P&L impact if your AI project succeeds. If you get silence or “we all do,” run.
The Data Desert: No one can tell you what data you’re allowed to use, where it lives, or who owns it.
If you see three or more of these signs, your organization isn’t ready. That doesn’t mean you can’t succeed, it just means you need a different playbook.
2. How to Scope an AI Pilot That Won’t Blow Up Your Life
Remember my manufacturing colleague whose company spent $500K on an AI pilot that’s been sitting on a shelf for 18 months? That pilot tried to solve everything. Don’t be that pilot.
Make It Embarrassingly Small
Your first win should be so small that people mock it … until it works. Think:
Automating one weekly report (not all reporting)
Flagging anomalies in one data stream (not predictive maintenance for the entire factory)
Summarizing one type of customer feedback (not sentiment analysis for all channels)
The 3x3x3 Rule
Your pilot should solve 3 specific pain points (no more, no less), show measurable results in 3 weeks, cost less than 3% of what the problem currently costs the organization.
Co-ownership is Non-negotiable
Every pilot needs two owners: a technical owner (probably you — yay!) and a business owner who feels the pain you’re solving daily
If you can’t find someone willing to co-own, the problem isn’t painful enough. Move on.
3. How to Demand Sponsorship (Without Getting Fired)
Here’s a script that has helped to keep me out of trouble many times:
“I’m excited about this initiative. Before we begin, I need clarity on three things:
Which VP/other leader will make the final decision if we recommend scaling this?
Who signs off on data usage and security protocols?
If this saves $X or generates $Y in revenue, which budget does it credit to?”
This does three thing, it forces accountability without being confrontational, removes ambiguity down the road when you are in the thick of the messy middle, and gives leadership an easy out if they’re not serious.
4. Data Reality Check — The Hard Questions No One Wants to Ask
Data is an everlasting problem that haunts efforts across every industry. Every AI pilot dies one of three data deaths:
Death by Privacy: “Oh, we can’t use customer data for that.”
Death by Quality: “Turns out 40% of our data is garbage.”
Death by Politics: “That data belongs to another department.”
Death by Regulation: “That data needs to adhere to the laundry list of regulatory requirements for our industry (GLBA, GDPR, HIPAA)”
In order to avoid the data morgue, ask some questions upfront, like:
“What data are we explicitly cleared to use for this pilot?”
“Who signs the data usage authorization?”
“What happens if we discover PII in the training data?”
“Which legal/compliance stakeholder needs to review our approach?”
Get the answers in writing. Seriously, that casual “yeah, sure, use whatever data you need” becomes “I never authorized that” real quick when something goes wrong.
5. Build Political Cover (The Art of Strategic Documentation)
In aerospace, we documented everything because lives depended on it. In corporate AI, you document everything because your career depends on it.
The Decision Log
Create a simple spreadsheet that includes decisions made, who made them, why and when. Update it after every meeting. Share it on the server. When things get a bit chaotic and folks are all working to make it all come together, remembering when, why and who can be very helpful.
The Escalation Framework
Before you start, establish clear escalation triggers. Here are a couple of examples, but every organization is different:
Budget overrun > 10%: Escalate to sponsor
Timeline slip > 1 week: Escalate to stakeholders
Scope change request: Escalate to committee
Technical blocker: Escalate to IT leadership
And — this is important — identify how you are going to address it: by getting it back on track or other critical decisions.
The Exit Strategy
Just like Kenny Rogers used to say, “you gotta know when to hold ‘em, know when to fold ‘em, know when to walk away, know when to run…” Before you start any pilot, know your exit criteria. What does success looks like (for the company)? What signals will you watch out for that signal that it is time for a pivot? What are the non-negotiable red lines that indicate it is time to drop your cards and run?
6. Deliver a Small Win That You Can Actually Finish
Remember the 88% of AI pilots that never reach production? They all had something in common: they tried to boil the ocean.
The “Holy Crap, It Actually Works” Moment
Your first win should create this reaction. Not “this will transform our business” but “wait, it actually did the thing?”
Examples of perfect first wins:
Cut manual data entry time from 2 hours to 10 minutes for one specific form
Flag the three fraudulent transactions that got missed last month
Generate the first draft of that mind-numbing compliance report everyone hates
7. Career Strategy: Make Every Pilot Add to YOUR Portfolio, Not Just Their KPIs
Here’s the truth no one tells you: Most AI pilots fail because of politics, not technology. No matter the outcome of your pilot (shelved or scaled), you need to walk away with more than experience.
Document Your Wins (Even if the Project Loses)
Create a personal portfolio that include problems you identified (with quantified impact), solutions you designed (with technical architecture), results you achieved (even if just in testing), and lessons you learned (especially what NOT to do).
The Uncomfortable Truth About AI Without Adults
After twenty years in technology transformation — from building systems to detect anomalies in satellites to helping enterprises modernize their entire stack — I’ve learned that organizations don’t fail at AI because they lack technology or talent. They fail because they lose sight on the outcomes they are trying to achieve.
When leadership is checked out, you have two choices: Wait for them to check back in (spoiler: they won’t), or build something so undeniably valuable that they can’t ignore it.
But here’s the thing (and this might be the most important paragraph in this entire article) you don’t need permission to solve problems. You need clarity on constraints, access to resources, and protection from political BS. Everything else is just noise.
Action Plan
Identify one problem that costs your team at least 4 hours per week
Find one ally who experiences this pain and has budget authority
Scope a solution that could show results in 2 weeks with existing tools
Document everything like your career depends on it (because it does)
Build the thing, even if it’s held together with duct tape, prayers and a questionable Slack DM
Demo the value, not the technology, the VALUE
Let them take credit, your portfolio is your real win
The Final Reality Check
You know what’s worse than an AI pilot that fails? An AI pilot that succeeds but goes nowhere because no one with authority cared enough to scale it. I’ve seen too many brilliant technologists burn out trying to push a rope uphill.
Also, it never hurts to remind leadership that AI is a tool, not a destination.