Transformation Should Start With a Business Problem, Not With Technology
We keep meeting companies that bought an AI platform before they knew what problem it was meant to solve. It rarely ends well, and it is entirely avoidable.
A pattern we see constantly, across industries and company sizes, goes something like this. A leadership team reads about AI, or automation, or some new data platform, feels pressure to look modern, and buys a solution before they have clearly defined the problem it is supposed to solve. Six months later, the tool sits mostly unused, the budget is spent, and the original inefficiency is exactly where it was before. This is not a technology failure. It is a sequencing failure.
The right order of operations is almost always the reverse of how most companies approach it. Start with the friction. Where is the business actually losing time, money, or customers? Where does a process break down, where does a team spend hours on something that should take minutes, where does a customer walk away frustrated? Only once that friction is clearly named and measured does it make sense to ask what kind of solution might address it, and only then does it make sense to evaluate whether that solution should involve AI, automation, better data infrastructure, or sometimes nothing more sophisticated than a clearer internal process.
We spend a lot of time with founders and operators walking through this exercise, and it is remarkable how often the exercise itself reveals that the flashy technology solution was never the right answer. A company convinced it needed a machine learning model to predict customer churn sometimes discovers, once they actually map the problem, that their churn is driven by a slow onboarding process that a simple workflow fix would solve for a fraction of the cost. The AI model might have worked eventually, but it would have been solving the wrong layer of the problem.
This is not an argument against AI or automation. It is an argument for discipline in how a company decides to use them. The businesses that get real value from these tools are the ones that can articulate, in specific and measurable terms, what is broken before they start evaluating how to fix it. What does the friction cost per month. How many customers does it affect. What would it mean, in dollars or in retention, if it were solved. If a company cannot answer these questions, they are not ready to select a solution, no matter how impressive that solution's marketing looks.
There is also a cultural dimension to this. Buying a shiny new platform feels like progress. It generates a press release, an internal announcement, a sense that the company is moving forward. Doing the harder work of mapping a business problem in detail, interviewing the people who deal with it every day, and quantifying its cost, feels slow and bureaucratic by comparison. But that slower work is what determines whether the eventual solution actually gets used, or whether it becomes another unused subscription that shows up on next year's budget review with nobody able to explain why the company is still paying for it.
We have also noticed that companies who skip this step tend to select technology based on what a vendor's sales team emphasizes rather than what their own business actually needs. A platform's demo is built to show its best features in the best light, and it is easy for a busy executive to walk away convinced the tool solves their problem without having done the work to confirm that it does. The businesses that avoid this trap are the ones who walk into vendor conversations already knowing exactly what they need solved, with numbers attached, so they can evaluate a pitch critically instead of being swept along by it.
For founders building technology companies, this has a direct implication. The products that succeed are not the ones with the most advanced technology. They are the ones built by teams who deeply understand a specific business problem and built the simplest possible solution to it. We have seen founders spend a year building an elaborate AI system for a problem that a well-designed dashboard would have solved. The sophistication of the technology is not the value. Solving the actual problem is the value, and sometimes the actual problem does not require much sophistication at all.
When we evaluate an investment, one of the first questions we ask a founder is what specific, measurable business problem their product solves, and how they know it is the right problem to solve. Founders who can answer this crisply, with numbers and customer evidence, tend to build companies that grow in a straight line. Founders who answer with a description of their technology, without grounding it in a business problem, tend to struggle to find product market fit, because they built a solution before they fully understood what it needed to solve.
The advice here is simple, even if it goes against the current excitement around new technology. Before you buy a platform, before you hire a data science team, before you commission an AI proof of concept, sit down and map exactly where your business is losing value. Put a number on it. Talk to the people closest to the problem. Only then start evaluating how to fix it. The technology should follow the problem, never the other way around, and the companies that internalize this end up spending less money and getting far better results than the ones that do not.
