Use AI to Draft Your Scorecard. Use Your Team to Get It Right.

The requirements that matter aren’t written down

AI works from what it’s given: your documented processes, your operational reports, and vendor info. The requirements that actually separate a good-fit platform from a bad one rarely live there. They live in how your team works day to day. The workaround your email team runs because the current tool cannot do one specific thing. The approval step that exists because of a single incident three years ago. The handoff that breaks every peak season. That is tacit knowledge, and it sits in people’s heads and habits, not in a document a model can read. Give AI only the documented material and it returns a competent list that leaves those out. Give it the workarounds, the incident history, and the peak-season problems, and the draft improves a lot. Getting that knowledge out of people’s heads is the step that comes before the prompt.

Where the blind spots are

What AI produces is plausible. It’s trained heavily on published material, a lot of it vendor marketing, so it tends to absorb the industry’s framing of what matters, which isn’t exactly the framing you are trying to evaluate independently.

It can’t weigh what it doesn’t know: your competitive advantages, your political realities, the fact that one stakeholder’s must-have is another’s nice-to-have.

When the evaluation is challenged, nobody can defend a decision with ‘the AI suggested it.’

Use it for the mechanical work instead. AI can draft a first structure for the team to react to, turn requirements people have already surfaced into a clean scorecard, check a draft for obvious gaps, compare vendor documentation against your criteria, and turn a long working session into clear notes. Used this way it saves time and frees your team for the judgment calls.

The process is doing more than producing a document

Here is the part that gets lost when a tool generates the scorecard in minutes. The requirements-and-weighting process is not only how you produce a scorecard. It is how a group of stakeholders who see the problem differently argue their way to a shared view of what the organization is buying for. The marketing-ops lead, the email team, the analytics partner, the security reviewer, and the executive sponsor do not start aligned. Sitting together and working out which requirements are must-haves, and what the weights should be, is how they get there. The document is the result of that conversation, but the alignment is the real output. Use AI to prepare for these sessions and to write them up afterward. Keep the sessions themselves human.

Skip the work and you skip the buy-in

When AI replaces the process instead of supporting it, you get a scorecard nobody argued over, and it shows.

People defend what they helped build and disown what was handed to them.

A team that weighted the requirements together will stand behind the recommendation when it is questioned, push the proof-of-technology sessions to be rigorous, and adopt the platform they helped choose. A team handed a generated scorecard treats it as someone else’s homework. They are less invested in the process, less invested in the outcome, and quicker to second-guess the decision the moment the new platform hits its first rough patch. The time you saved by skipping the sessions comes back as disengagement and a decision no one owns. If the tool decides, there is no real role for the people affected, and people care less about decisions they didn’t help shape.

It is also how an executive’s favorite wins by default. When the committee has no shared conviction of its own, because it never did the work to build one, the loudest preference in the room fills the vacuum.