Here’s a depressing stat for you: 95% of executives say they’re struggling to see any ROI from their AI initiatives.
That’s not because AI doesn’t work; it’s because most organizations are applying it in the wrong place.
They’re working harder. They’re buying tools faster. They’re automating everything they can touch.
And yet… nothing really changes.
If that sounds familiar, the solution isn’t another shiny AI platform. It’s a systems-thinking concept from a book written in 1984.
Yes, really.
It’s called the Theory of Constraints, and once you understand it, you’ll never look at your marketing workflow – or your AI roadmap – the same way again.
The Theory of Constraints comes from Dr. Eli Goldratt’s book The Goal. It was originally written for manufacturing, but don’t let that fool you. The ideas apply beautifully to modern marketing, especially now that our systems are more complex than ever.
The core premise is that every system has at least one constraint at all times.
A constraint is the single biggest thing limiting your ability to achieve your goal. And if you don’t identify and address it explicitly, the system will never improve.
Here’s the part most teams miss:
Improving anything that is not the constraint does not help. In fact, it can actually make the whole system worse.
This is exactly why so many AI initiatives fail.
Teams add AI “where it fits,” or where someone got excited, or where a vendor demo looked impressive.
But they never stop to ask: Is this where our system is actually stuck?
Let’s make this concrete.
Picture a five-lane highway that suddenly narrows down to one lane.
Traffic backs up. Cars crawl. Everyone is frustrated.
That single-lane section is the constraint.
Now imagine trying to fix the problem by:
None of that helps – it just creates more pile-up.
Your marketing workflow works the same way.
Your process is only as fast and effective as its slowest point. Until you fix that point, nothing else matters.
In marketing, constraints show up in lots of places. Here are some of my least favorite:
AI can help with many of these, but only if you apply it at the actual bottleneck.
Slapping AI into a random step of the process is like widening the wrong part of the highway. It looks productive. It feels busy. And it accomplishes nothing.
So, what do we do instead?
After identifying your actual goal, this is the most important step. So do not rush it.
Ask questions like:
That spot – the place where work consistently slows down – is your constraint.
And here’s the hard truth: Working harder anywhere else will not help.
It may even make the constraint worse by pushing more work into an already jammed lane.
“Exploit the constraint” is a terrible phrase, so let’s start by clearing something up: It does not mean chaining someone to their desk.
It means making the constraint as efficient as possible without changing the system yet.
Some low-disruption ways to do that:
This is often where AI can help – carefully.
For example:
The goal here is simple: The bottleneck should only be doing bottleneck-specific work.
This is where systems thinking really shows up.
Once you’ve identified the constraint, the entire system must support it.
That means:
Everyone benefits when the bottleneck moves faster, so everyone has to adapt their behavior.
There’s nothing that’s “the bottleneck’s problem.” If it affects the bottleneck, it’s the system’s problem.
Only after you’ve exploited and subordinated do you move here.
Elevating the constraint means changing the system itself. We do this last because it’s usually the most expensive and the most disruptive, and we want to make sure we try the easier stuff first.
This could include:
This is where AI often shines, but only because you’ve done the thinking first.
Good AI use cases at this stage include:
Remember, in this and all use cases, we aren’t wielding a sledgehammer. We should consider systems adjustments a precision exercise.
Make small adjustments, see what happens, and then move forward from there.
Once you fix one constraint, another will appear.
That’s not failure, that’s progress.
You’re not trying to create a perfect system. You’re making it less broken, one bottleneck at a time.
Over time, you also get better at this process. You learn where AI actually helps. You stop chasing hype. And you start seeing real ROI – because you’re applying effort where it matters most.
AI doesn’t eliminate constraints. It shifts them.
Think of it as widening a bridge to ease traffic – not magically teleporting everyone to the other side.
Once you fix your current bottleneck, new ones will emerge:
The good news is, you already know what to do about these new bottlenecks.
That’s how you build a marketing system that actually improves over time, and avoid becoming another cautionary tale of expensive AI tools with nothing to show for them.