About
Not what AI can do in theory. Not what the demo promised. What value does it actually deliver — to the business, to the team, to the person whose job looks different because of it. That’s the only question that matters, and it’s the one most companies struggle with.
Sales has a number to hit this quarter. Product needs to protect margins. Marketing needs to move leads through the funnel. The CEO needs revenue. Each of them is operating from self-interest. Not cynically. Just honestly. That’s not a problem to manage. It’s a map.
Thirty years across IT, cybersecurity sales, product management, and marketing means I’ve held most of those priorities myself. I’ve had the quarterly number. I’ve built the business case. I’ve sat in the room where the budget got cut and had to figure out how to do more with less. When I’m in a room with your teams, I’m not learning their language. I already speak it.
That’s what makes AI adoption succeed or fail. Not the tools. Not the models. Whether every person in that chain can see, specifically in their context, what’s in it for them, and whether the organization has built the foundation to deliver on that promise.
Here are three examples of AI deployed with clear intent against a solid foundation, and what they delivered.
Instant Clarity
Most marketing teams are sitting on data they can’t actually use. It lives in six different platforms, none of which talk to each other, and the reporting each one provides is optimized to make itself look good. Pulling it into a single analysis covers CRM, marketing automation, ad platforms, web analytics, intent data, and site performance, and it tells a different story.
One campaign consistently flagged as a top performer had a 100% bounce rate. The page load performance was also degraded, almost certainly a contributing factor. Both were invisible inside the individual platform dashboards. Together, they explained everything. The campaign was killed immediately and the ad spend redirected the same week.
That’s the value of a connected foundation. Not a better dashboard. A clearer picture of what’s actually happening, fast enough to act on it.
Vendor Independence
One of the least glamorous, and most valuable, things a solid foundation enables is the ability to swap tools without disrupting the work. By building a structured data layer that abstracted the technology underneath it, it became possible to replace a more expensive platform with a lower-cost alternative without changing a single workflow. The work continued. The invoice shrank.
That’s what vendor independence actually means in practice. Not a philosophical position. A structural decision made early that pays dividends every time the market shifts, a contract comes up for renewal, or a better tool appears.
Staying in the Conversation
Staying current in the news cycle is one of the highest-value content plays a company can make. It’s also one of the first things that gets dropped when resources are tight, because it requires someone to be watching the news, assessing relevance, and producing something worth publishing before the moment passes.
An automated workflow ran every morning, scanning for stories relevant to the company’s market and messaging. When something relevant surfaced, it flagged the story and drafted five potential angles aligned to the company’s talking points. A human reviewed, selected, and refined. The content went out the door quickly, consistently, and on-brand, without anyone spending their day monitoring news feeds.
The human in the loop wasn’t a bottleneck. It was the quality control that made the automation trustworthy. Fast and considered aren’t mutually exclusive when the foundation is right.
The best ideas should reach the audiences that need to hear them. That’s been the consistent thread throughout my career: finding ways to make a compelling case against competitors that had more resources, more brand recognition, or more inertia on their side.
AI changes the equation. It lets smaller, sharper organizations operate with reach and analytical capability that used to require enterprise-scale budgets. But only when the foundation is there to make it real: governance, enablement, clean data, and a clear answer to the question every person in the building is quietly asking. What does this mean for me?
Nearly 30 years.
Every seat at the table.
IT operations, enterprise infrastructure, sales engineering, product marketing, product management, channel marketing, ICS and OT cybersecurity, CMO. Every role was a different vantage point on the same question: how do organizations adopt new technology, and what are the keys to success? The answer is never technical.
Three acquisitions, an IPO, 13-person startups, and 350,000-person enterprises. Roles at companies that competed on merit against better-funded, better-known incumbents, winning by understanding the customer better than the competition did. Most recently: leading company-wide AI adoption strategy across a global organization. Governance, enablement, and data strategy for the entire company, not just the marketing function.
I use my own practice as the proving ground for every strategy I bring to clients. The tools evolve monthly. The only way to advise on AI adoption is to be in it every day.
Let’s Talk
If something here resonated: the challenges, the constraints, the ambition. That’s the start of a good conversation.