After years of working with clients across various industries at Dreamix, certain patterns repeat. Not the technical work—that varies enormously—but in the conversations that happen before the work begins. The assumptions clients bring into a vendor selection process often shape the outcome more than the technology choices that follow.
Three of those assumptions are worth questioning before signing anything.
1. Don’t design the team before scoping the problem.
A client arrives with a fixed requirement for five senior engineers, a specific tech stack, and product availability by a certain date. The project scope comes later.
I understand their reasoning. Senior engineers are scarce and expensive, and securing them early feels like getting ahead of the problem. What this actually does, however, is optimize for the wrong variable.
Clients are the experts on their business—what needs to be solved, what success looks like, what the constraints are. Translating that into the right team composition is a different kind of expertise. Mixing the two up or doing them out of order often creates problems that show up later.
Senior engineers are built for complexity—ambiguous problems, high-stakes architectural decisions, situations where experience is the differentiating factor. When the work turns out to be well-defined and execution-focused, that same engineer is likely to disengage.
We had one case at Dreamix where a client strongly insisted on a heavily senior team before proper scoping was done. We expressed our reservations, but eventually went along with it. Within months, the lead engineer was visibly demotivated—the work wasn’t complex enough. What began as the client’s ideal scenario became a retention problem, then a restructure. By the time we brought in a more suitable team, the project lost weeks, and a significant amount of institutional knowledge walked out with that engineer.
2. Don’t assume the solution is AI before validating the problem.
Boards are pushing AI initiatives downward, and by the time they reach a vendor conversation, they’ve often hardened into requirements. The problem is that not every process that looks like an AI use case actually is one.
We regularly encounter clients who arrive with an AI brief that, after proper analysis, turns out to describe a rule-based problem—one that a straightforward workflow can solve more reliably, at lower cost, and with less maintenance. Sometimes the response to that assessment is: “Can we still call it AI?”
When the problem genuinely calls for AI, that’s a different conversation entirely. The vendors best positioned to advise here are those whose teams are actively working with AI—building with it, testing its limits, following where the technology is heading. That hands-on exposure is what makes the difference between a recommendation grounded in real experience and one based on what a client wants to hear.
A 2025 MIT study found that 95% of enterprise AI pilots deliver little to no measurable impact on profitability, while the 5% that do succeed share one characteristic: They focused on a single, concrete pain point rather than broad adoption.
A vendor who talks you into AI when you don’t need it is optimizing for their engagement, not your outcome.
3. Don’t leave the business outcomes undefined at kickoff.
Purely technical teams have a tendency to pursue quality beyond what the business actually requires. A system performing at 90% accuracy sounds insufficient until you learn that the previous baseline was 80%. At that point, 90% is already a significant result, and pushing to 95% means spending time and budget on a standard no one asked for.
We had exactly that conversation with a client. The engineering instinct was to keep improving the model, but tracking business results alongside technical ones prompted us to check in first. What we’d already delivered was, in their words, transformational. The more valuable next step was releasing it, not refining it.
Before the build starts, align with your vendor on what success looks like in business terms. What gap are you closing? What are the must-have features versus the ones that can wait? What is the timeline, and why does hitting it matter?
THE VENDOR CONVERSATION IS PART OF THE WORK
These three mistakes usually happen before the first line of code is written, and they set the conditions for everything that follows.
A good vendor partnership runs in both directions. Clients who come with clearly defined business outcomes and openness to pushback tend to get better results, because they create the conditions for honest advice.
Denis Danov is CTO of Dreamix.
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