The Problem: AI Buzzword Overload – Why Most Companies Get It Wrong
Leadership gets stuck in a top-down directive to implement AI at all costs. They ignore testing, go for full-scale rollouts in high-touch customer journeys... and guess what? It doesn’t work.
AI gets deployed on legacy systems without understanding the underlying data quality, causing more inefficiencies and customer frustration. It's like trying to run a Ferrari engine on a 50-year-old chassis.
The cost-saving fallacy: Most businesses see AI as a way to simply cut costs—but at what price? They end up sacrificing quality, alienating customers, and damaging their reputation.
Here’s the reality: AI takes time, data, and a lot of trial and error to make it work. Like any transformation, it doesn’t happen with a flip of a switch—it’s a test, learn, adapt cycle.
Why Getting AI Right Is Harder Than It Sounds - but not impossible
I’ve worked with companies where a directive came down from the C-suite: “Implement AI—make it happen fast, no excuses.” Sure. No pressure. But here’s where the cracks started to show:
They decided to apply AI to an incredibly high-touch, critical customer journey without testing it first. The idea was to increase efficiency and cut costs—but in reality, they were just rolling the dice and hoping it paid off. The result? They rolled out a half-baked product and alienated their most valuable customers in the process.
We caught it before it hit the revenue drain—thankfully. Testing and learning became the mantra. Instead of blindly pushing through AI without proper experimentation, we ran a lower-risk test, scaled back the customer touchpoints for the AI pilot, and gathered meaningful data on its effectiveness.
We didn’t rush—we tested first, implemented second. And guess what? AI worked when we didn’t treat it like a quick fix. The bottom line improved because we were strategic about scaling and not just cutting costs blindly.
Why Your AI Implementation Needs a Chief of Staff (And Not Just a Budget)
This is where a Chief of Staff comes in. AI is complicated. It requires leadership, foresight, strategy, and pragmatism—which is exactly what a CoS provides. The CoS can manage AI from the top down and bottom up, ensuring that AI isn’t just something you try to shove into your legacy systems or customer experience without a thought.
Here’s how I support businesses in AI adoption:
Define What Success Actually Looks Like – No, it’s not "cutting 20% of employee costs overnight" or "replacing the entire sales team with bots." Define clear, measurable outcomes for AI, like better customer experience, higher engagement, or smarter decision-making.
Testing is Everything – You’re not going to get it right the first time, and that's okay. Run smaller AI pilot programs in lower-risk areas and gather real data. Your AI integration should look like a series of learning sprints, not an all-or-nothing gamble. Embrace the scrappy testing mindset—AI should be about incremental improvements, not instant magic.
Guard the Data – AI needs data to work, but the data needs to be clean, accurate, and secure. This is where the Chief of Staff can oversee data governance to ensure privacy, compliance, and quality. Without that foundation, AI is like trying to drive a Ferrari on a dirt road.
Build or Buy? – Reality check for founders: Building your own AI in-house takes time, resources, and a whole team of engineers who are ready to dedicate themselves to the cause. If you're scaling, do you really want to spend the next 12-18 months building an AI engine or should you buy an off-the-shelf solution that works for 90% of your needs? The CoS helps you evaluate the build vs. buy debate, helping weigh the cost of building against the immediate need for execution.
Actionable Takeaway: Run a Pilot Project to Test AI Potential
Here’s what I recommend: Start small. Implement AI in an area of your business that won’t break everything if it fails. Pick a low-touch customer journey or an internal process that’s manual but not critical. Build an incremental learning framework with defined KPIs.
What are you testing?
How will you measure success?
What data are you collecting?
How will you adjust based on what you learn?
Run it, test it, iterate and then scale once you have real data proving it works.
Reflective Question:
What’s one part of your business that’s running on old processes but hasn’t been updated for years—and if you threw AI at it, what would actually break or improve?