Rethinking Technology Evaluation:
Why the Early Assumptions Decide Everything

Plenty of technology decisions look solid when they’re made. The goals seem clear, the research feels thorough enough, and the teams involved genuinely want to fix something that isn’t working. Everyone leaves the room thinking the choice is a good one. .

Then the system enters the day-to-day and the cracks appear. A workflow that seemed straightforward in a walk-through turns into a weekly choke point. A feature meant to “simplify” a task adds two new steps. The people who were supposed to feel supported end up doing more work to keep the system afloat.

What’s striking is how often this happens even in well-run organizations. In almost every case, the issue traces back to something simple: the decision was made in the wrong order and without enough context from the people who understand the work at its most practical level. By the time their perspective enters the room, the direction is already set, and the outcome reflects that.

This article looks at how those early assumptions form, why they harden so quickly, and how leaders can bring more truth into technology evaluations so decisions actually support the work they’re meant to improve.

How Early Assumptions Derail Technology Decisions

The earliest definition of the problem sets the ceiling for the solution. When that definition is shaped too far away from the real work, it produces a version of the process that looks accurate but isn’t. That version becomes the foundation for the entire decision. Teams adopt it quickly because it gives them something to align around, and alignment feels like progress.

The drift starts there.

The early framing strips out details that matter. It leaves out irregular steps, workarounds, and the judgment calls that hold the real process together. Not because anyone is ignoring them, but because those details aren’t visible from the vantage point where early conversations happen. What survives is only a simplified outline that seems stable enough to move forward.

That outline solidifies fast. It gets written into requirements, comparison grids, and vendor discussions. It becomes the reference point for what a “solution” should address, even when the shape of the real work doesn’t match it.

The deeper issue is that those early assumptions stop looking like assumptions. Once they’re embedded in the process, people stop questioning them. The evaluation begins to measure tools against the simplified model instead of the conditions the system will actually face.

By the time a system is selected, the decision feels clean and well-reasoned. But it’s built on a frame that was never accurate, and every downstream choice inherits that gap.

Where Technology Evaluation Breaks Down in the Real Work

The consequences of a misaligned decision rarely appear in the software itself. They appear in how the organization starts behaving around it.

The first shift is behavioral. People adjust their work to match the system’s logic because the system can’t match theirs. You start to notice small accommodations: steps that once fit smoothly into the day take longer because the tool dictates a different pace or sequence. People create extra pauses, reorder tasks, or add small guardrails simply so the work doesn’t fall out of sync with the system. The work bends, even if no one names the bend.

The second shift is cognitive. Instead of removing friction, the tool introduces new decisions and approval paths that weren’t part of the original process. People spend time interpreting what the system is asking of them rather than relying on their understanding of the work. The added mental load slows everything down long before it shows up in metrics.

The third shift is directional. The intent behind the work begins to drift. Teams lose the context that once signaled how a task should move forward, and workflow rules can’t carry that meaning. Downstream teams act on what they receive, but the reasoning behind earlier steps isn’t visible to them. They fill the gaps as best they can, often relying on assumptions the upstream team never intended. Small variations compound until coordination feels inconsistent, even when everyone believes they’re following the same steps.

None of this registers as “technology failure.” It looks like hesitation, inefficiency, or a lack of ownership. But these patterns are the predictable result of a system designed around an incomplete version of the work.

Over time, these shifts reshape the organization more than the tool itself. Cycles lengthen. Decisions take longer. Teams lose the clarity they once had. The work didn’t change. The system altered how the organization engages with it.

A Better Model for Making Technology Decisions

A stronger evaluation process depends on bringing the right perspectives in at the right stages and using a structure that makes the real work visible. That visibility comes from understanding how the work actually operates — the flow, responsibilities, dependencies, and natural points of variation. A shared baseline like this anchors every later requirement.

• Translate goals into operational terms. Terms like “faster” or “more consistent” only matter once they’re tied to specific steps, volumes, and decision points.
• Bring practitioners in early. Their experience shapes the definition of the need, not just the validation of a solution.
• Pressure-test assumptions with realistic scenarios. High-volume days, incomplete information, competing priorities. These are everyday conditions, not outliers, and any proposed model has to hold under them.
• Fold implementation planning into the decision. Sequencing, data flows, feasibility, and change impact belong in the evaluation itself, not in the phase that follows.
• Align leadership around accuracy. Precision here avoids friction later and shortens the time from decision to adoption.
• Run a decision rehearsal. Walking through a representative week of work using the proposed model surfaces gaps sooner than demos or comparison grids ever will.
• Use a structured method like REJ. Rapid Economic Justification (REJ) forces clarity around operational impact, time-to-value, and what improvement should look like in concrete terms. REJ forces teams to articulate what “better” actually means and prevents decisions from being shaped by assumptions that don’t survive contact with real work.

This model grounds technology evaluations in the realities the tech must support.

What This Looks Like When It’s Done Well

Grounding decisions in reality makes every downstream step easier.

The system stops drawing attention to itself and blends into the day because people use it without negotiating every step or questioning whether they’re working around it. You see small confirmations of that fit: work that used to prompt clarification moves forward without follow-up, and routine steps stop generating side questions because the system captures the context people rely on.

Conversations shift. Instead of debating interpretations or tracing breakdowns, teams talk about the work itself. People spend less time reconstructing intent and more time deciding on the next move because the system carries over the cues that used to require explanation.

Leaders get a clearer line of sight. The information coming out of the system matches what they hear from the teams doing the work. They don’t have to translate between the two. The signal is cleaner. Patterns that once blurred together — changes in pace, pressure points, emerging bottlenecks — show up early enough to address rather than interpret.

Coordination tightens. Handoffs don’t wobble as much because the steps leading into them are more predictable. Small issues stay small instead of compounding into something larger. Teams settle into the same understanding of how the work moves because the system reinforces the practical flow they already use, not some abstract model of it.

And the next decision is easier. Once the organization has a reliable way to define needs and test assumptions, it doesn’t have to rebuild the process each time. The debate shifts from “what do we mean?” to “what matters most?”

What stands out most is that people trust the decision. It feels earned, not optimistic. And that trust carries into the work that follows. The system supports how the organization functions at its best, which is the clearest sign the choice aligned with the reality of the work.

Closing Thought

Every technology decision rests on assumptions about how the work functions, and the strength of the outcome depends on how close those assumptions are to reality. When the early framing comes from distance, the system amplifies errors that were built in from the start. When the framing reflects the lived conditions of the work, the decision gains a level of accuracy no feature list can create.

Beginning with the real flow of work gives leaders a grounded way to evaluate options, test constraints, and set realistic expectations for what a system can deliver. It replaces abstraction with clarity and creates a model that holds up under real conditions instead of collapsing into exceptions and workarounds.

A strong technology decision comes from clarity about the work, not volume of features or depth of comparison. That clarity is what keeps the decision honest once it meets the day-to-day.

FAQ

What’s the most reliable way to evaluate technology before buying it?

The most reliable evaluations start by documenting how the work actually functions. That includes the real flow of tasks, dependencies, handoffs, and natural variation that shape daily operations. With that foundation in place, leaders can translate goals into specific operational requirements and evaluate systems against conditions the tool will actually face. Bringing practitioners in early, testing assumptions under realistic scenarios, and defining expected impact through methods like Rapid Economic Justification keeps the decision rooted in operational truth rather than guesswork.

Why do technology decisions break down even when the requirements seem clear?

Breakdowns usually stem from requirements built on an oversimplified model of the work. Early assumptions often ignore exceptions, judgment calls, and informal steps that hold the real process together. Once those assumptions become “requirements,” they stop being questioned. The system is then judged against an idealized version of the work rather than day-to-day conditions. Misalignment only becomes visible after implementation, when teams stretch routines or add checkpoints to compensate. The requirements weren’t wrong. They were incomplete.

What should be included in a technology evaluation to avoid surprises later?

A reliable evaluation includes everything the system will face in real conditions. That means mapping workflows in detail, identifying where variation occurs, outlining dependencies, and understanding how decisions are made when information is missing or priorities conflict. Assumptions should be tested against realistic scenarios such as peak-volume periods or exception-heavy days. Implementation planning — data flows, sequencing, feasibility, and change impact — must be part of the decision rather than work postponed until after selection. This removes surprises because the evaluation mirrors the actual environment.

How can leaders tell whether a technology decision aligns with real work?

Alignment shows up in everyday behavior. When a decision reflects the real work, people use the system without building workarounds or pausing to interpret each step. Handoffs stabilize, decisions move faster, and conversations focus on the work rather than on decoding the tool. Leaders also see consistency between what the system reports and what they hear from teams. When the data and the lived experience reinforce each other, the system is aligned with reality rather than with early assumptions.