I’d be lying if I said AI hasn’t created some anxiety for me.
Like a lot of business owners, I’ve had that tight-chest feeling of wondering how fast this is going to move, what parts of our work might get automated, and whether competitors who adopt it faster are going to gain an edge before we’ve fully figured it out.
There’s a real fear in that. You start to wonder whether this thing is going to hollow out a lot of the value you’ve spent years building. You wonder whether clients are going to look at ChatGPT or Claude and think, “Why am I paying for this anymore?”
That fear is real.
Moving from Fear to Practical Use
But the more we’ve worked with AI inside Sanctuary, the more I’ve come to a different conclusion.
AI can absolutely speed up the work. It can remove friction. It can improve throughput. It can help a team get more done with less drag. But from what I’ve seen so far, it still can’t replace strategy.
And that distinction matters. What changed for me was moving from abstract fear to practical use.
At Sanctuary, we’re not treating AI like some magic answer machine. We’re using it where it can actually help. It has become useful in competitor reviews, note-taking, quality assurance, presentation development, and digesting large volumes of information from research sources, technical documents, and content blocks. It helps us gather, sort, normalize, and accelerate.
That’s real value.
Data Is Not Direction
Take competitive research as one example. AI can help us collect a discrete set of materials from a client’s competitors, organize them into a more apples-to-apples view, surface missing variables, and make it easier to compare patterns.
That saves time. It helps us get to the evidence faster. It helps us see the field more clearly.
But that is not the same thing as deciding what matters.
The real value still comes after the data is organized.
It comes from a strategist looking at the landscape and asking: Where is the opening? What are competitors missing? What does this target buyer actually care about? Which gap is real, and which one just looks interesting on paper? How do we thread the needle in a way that helps this specific company beat its actual competitors, not just produce a prettier report?
That’s the part I don’t think AI replaces.
The Real Opportunity: Removing Friction, Not Replacing People
If anything, AI is teaching me to value that layer of work even more.
A lot of the current hype makes it sound like the big opportunity is replacing people. I think that’s where leaders may be led astray.
The bigger opportunity is not replacing the uniquely human parts of the work. It’s removing friction around them.
That’s a very different mindset.
It’s the difference between asking, “How do we automate strategy?” and asking, “How do we automate enough of the prep, organization, and low-level drag that our people have more time for the work that actually creates competitive advantage?”
That’s how we’re trying to approach it. In that sense, AI is not the strategy. It’s a process multiplier.
Strategy Still Requires Human Judgment
That logic isn’t new to manufacturing. It’s how you think about any process improvement worth making.
The closest analogy that comes to mind is root cause analysis. AI can help surface patterns, summarize inputs, and move through evidence faster. But root cause analysis is never just about spotting a symptom and calling it a day. It’s about asking the next question, and then the one after that, until you get past the obvious answer and closer to the real constraint.
That’s what strategy feels like too.
Information vs. Insight
A lot of companies use some version of an intake form. It captures the basics: what the company sells, who they sell to, some history, some context. That information matters. You need it to begin. But it is just the start.
The form gives you information. The conversation starts to uncover conviction.
That’s where some of the most important strategy work happens.
You hear something in the room. You see a shift in energy. A client starts talking about a product, a customer segment, a service issue, or a market angle with a level of passion they never would have put in a form. Other people on the team react. A thread starts to emerge. Someone asks a second question, then a third, then a fourth. Suddenly, what looked like background detail starts to feel like the real story.
That kind of moment is hard to reduce to a transcript. It’s even harder to reduce to a generic prompt.
The Gap in AI-Generated Output
All the information can be technically correct and still miss the point. A spreadsheet can be accurate. Notes can be complete. A summary can sound polished. And yet none of it may capture the thing that will actually help a human buyer choose one company over another.
That’s the gap.
And I think it’s one of the reasons so much AI-generated output feels fine but forgettable.
If everyone is using the same tools and asking average questions, they’re going to get average answers. The output may be efficient. It may even be clean. But it tends to flatten into the lowest common denominator.
That’s not how competitive advantage is built.
Where Competitive Advantage Actually Comes From
Competitive advantage comes from seeing the strategic opening others miss. It comes from choosing which tradeoff is worth making. It comes from reading the room, making a judgment call with incomplete information, and sometimes spotting the emotional truth behind a buying decision before it’s obvious on paper.
That’s why I don’t believe you can outsource strategy to AI.
Better Questions Beat Faster Answers
AI tends to be most valuable when it helps you ask better questions, not just generate faster answers.
If the questions are sharp and the context is thoughtful, AI becomes a real advantage. If they’re not, it just helps you get to the wrong answer faster.
You can outsource pieces of preparation. You can accelerate research. You can streamline production. You can improve QA. You can use AI to summarize, organize, and pressure-test.
We’re doing all of that, and we should.
But those are not the same thing as judgment. AI output is a starting point, not an ending point, and the leaders who treat it that way will get far more out of it than the ones who don’t.
The Businesses That Will Win
The real promise isn’t that AI makes people irrelevant. It’s that it can make good people more effective.
So no, I don’t think manufacturers are going to replace real marketing strategy with ChatGPT and call it a day.
Some may try. Some probably already are.
But I think many of them will eventually run into the same wall: faster output is not the same as better direction.
The businesses that win here won’t be the ones that ignore AI out of fear. They also won’t be the ones that hand over their thinking to it.
They’ll be the ones that integrate it carefully, use it to speed up the work, and protect the parts of the process that are still deeply, stubbornly human.