Your AI Vendor Can't Save You
And why AI is the end of the insights industry's oldest constraint
I think most of the people that read the articles on this Substack are more than well aware that the pace of change in the AI space is ridiculously fast. It’s so blazingly fast that one tends to think you’ll never catch up. For many insights professionals, the temptation is to just sign a multi-year deal with Microsoft, Google, OpenAI, or Anthropic and be done with it. Let them bring AI engineers to the table and solve your problems.
Some have already taken this path, but in a market this volatile, does locking in early really help?
There’s an interesting memo that was leaked from Google in early 2023 called “We Have No Moat. And Neither Does OpenAI.” The memo highlighted how the rules of the game had changed. Open-source models were catching up to the giants, not by outspending them, but by leveraging the free labor of a global community. It’s hard for any single company, no matter how big, to compete with tens of thousands of researchers creatively solving the same problem from their home offices.
It didn’t take long for this observation to be proven right. In the last year, open-source models from companies like China’s DeepSeek have been released that rival the quality of the big, proprietary players. The moat is gone.
In a race this fast, with the leaderboard changing every few months, what sane company would lock themselves into a single AI vendor and outsource their strategy? It’s a mistake.
Strategy First, Vendors Second
The reality of the insights space is that it’s not dominated by powerful tech innovators. It’s full of researchers who think more about collecting data than they do about implementing state-of-the-art technology. So, before you pick a vendor or sign a contract, you need to figure out your own strategy.
And that strategy should be grounded in a first-principles approach to a simple question: Why is AI so transformational for the insights industry?
From what I’ve seen, the answer isn’t about chatbots or synthetic respondents. It’s about solving the one, massive problem that has been the industry’s Achilles’ heel for decades.
AI creates scale.
The Industry’s Achilles’ Heel
For as long as the insights industry has existed, it has been defined by its human limitations. I recall sitting in a company meeting with Bob Myers, former CEO of Millward Brown, who proudly claimed we had more dialing centers than the competition, creating a substantial competitive advantage. That advantage quickly fell away and was replaced by online surveys and, later, sample exchanges. Each step was a leap forward, a way to talk to more people, faster.
But we always hit the same wall.
Despite decades of innovation, all these advancements really did was bring down the cost of an interview. This was an optimization of the data collection pipeline, not an optimization that opened the aperture for more consumer feedback.
As a result, we never escaped the fundamental scale constraint: Clients can’t use insights for every problem. They’re forced to choose when and where to use consumer insights, limiting them to the few big problems that can justify the cost and effort of finding a few hundred respondents.
AI is the first new technology to come along that offers a genuine solution to that constraint. Once you see AI as a tool for creating scale, your strategy becomes clearer and more actionable. It opens the door to an age of abundance, an abundance of problems that can be solved, an abundance of consumers you can “talk” to, and an abundance of valuable work that can be done.
Picking Your Major: Refining Your Scale Strategy
So, if your strategy is to use AI to create scale, the next question is: where, specifically, do you apply it?
It’s hard for any company to be good at everything. Most successful companies have something they do really well; that’s their major. “Picking your major” is the process of refining your scale strategy. It’s about identifying the specific area of your business where AI-driven scale will create the most value and getting the entire organization aligned behind it. This focus prevents you from wasting time and resources on minor projects that don’t move the needle.
For example, is your major going to be scaling decisions for clients? This could mean building systems that help marketers make thousands of micro-decisions, like optimizing ad creative at a volume that human-led research could never handle. Or is your major scaling expertise for your own teams? This might involve using agentic AI to automate the 70% of operational work that bogs down your best people, freeing them to deliver more strategic advice.
These are just two examples. The key is to choose. Once you know your major, you know what to build, what to own, and where your true competitive advantage lies.
This clarity is your best defense against vendor lock-in. The parts of your business that are core to your major are the parts you need to understand deeply and control. This is where you invest your internal resources and build proprietary knowledge. For everything else, the “minors”, you can confidently partner with third-party AI companies. You can license their tools for non-core functions without risking your strategic future, because you haven’t outsourced the one thing that makes you unique.
The Strategic Question
It’s easy to get lost in the technical details of AI. We can debate whether synthetic respondents are “real” or if an LLM is biased. These are valid questions, but they can distract from the bigger picture.
History shows that the companies that win are the ones that master the benefits of scale:
Scale in relational databases gave us the internet.
Scale in cloud computing gave us YouTube.
Scale in cameras in cars gave us self-driving technology.
AI is waiting to give the insights industry scale.
So, go back to your strategic planning documents. Do they talk about new AI features, or do they have a clear answer to the fundamental questions:
How are we going to use AI to scale? And what major area in our business are we going to scale first?
If you can’t answer those questions clearly, you don’t have an AI strategy. You have a list of ideas. If you start from a foundation of optimizing for scale, your strategy will be inherently linked to the biggest unlock AI is delivering.
So, stop asking which AI vendor will give you an edge, they’ve already admitted they have no moat. Instead, start asking what part of your business you are going to scale. That’s the only question that matters.