Set Shared Criteria for a Platform Comparison Across Multiple Labs

When three laboratories evaluate the same research platform, the conversation can quietly turn into one lab's workflow standing in for everyone's. A research account executive who lets that happen ends up defending a single configuration that only half the group actually needs, and the deal stalls the moment someone points out the mismatch. The fix starts before the comparison call, with a shared set of criteria every lab agrees to and a named owner for every fact that nobody has confirmed yet. Get this right and the evaluation feels fair to all three sites. Get it wrong and you spend the next month refereeing a dispute you created.

Why this conversation goes wrong

A research lead under pressure to move fast will ask you to name one configuration for three labs. The easy answer feels confident and specific, and it is tempting to give it. But a single recommendation almost always borrows its assumptions from whichever lab spoke up first or whichever lab's data happened to be ready. The other two labs did not agree to those assumptions. They just did not object out loud, and silence gets mistaken for consensus.

This goes wrong in a specific way. One lab tracks samples in a system built around individual researcher workflows. Another tracks by batch, with a technician logging results once a day. The third has no consistent tracking at all, just spreadsheets that vary by project. If you build a recommendation around the first lab's habits, the second lab will need a different data output format and the third will need training nobody scoped for. Worse, someone in the room will have quietly assumed a sample volume that nobody verified. That number then drives every downstream configuration choice, and it is often wrong by a wide margin.

The account executive who avoids this problem is not the one with the fastest answer. It is the one who names the missing facts out loud, before the group builds a plan on top of them.

How to prepare

Before you get three labs on a call together, do the work that makes the call useful instead of chaotic. Write down the actual decision questions the group needs to answer. These usually cover sample types, tracking needs, staff roles, data outputs, local constraints such as space or existing instruments, and what evidence each lab will accept before it signs off.

Send each lab a short request for its current workflow, in its own words, not filtered through what you think the platform should do. Ask directly whether anyone has a confirmed sample volume estimate, and if not, ask who owns getting that number before the joint session. A missing volume assumption is not a small gap. It shapes storage needs, throughput requirements, and staffing, so it deserves a name and a deadline, not a placeholder.

Build a simple comparison sheet with a column for each lab and a column for open questions with an owner listed next to each one. Leave blanks where you do not have facts. A sheet with three or four honest blanks is more useful than one filled in with guesses, because it tells the group exactly where the disagreement is going to surface.

If your deal touches procurement decisions that span sites, it helps to understand who actually holds budget authority before you propose consolidating anything. The post on how to Map Campus Procurement Authority Before Proposing Consolidation Across Sites walks through that groundwork in more detail, and it pairs well with the criteria setting described here.

What to say

In a planning call, the research lead pushes for a shortcut. The lead says, "We have three labs and one budget cycle. Just tell us which configuration works for everyone so we can move."

You answer, "I can walk you through the documented options today, but I do not have comparable workflow facts from all three sites yet. Your downtown lab tracks samples by batch, your east campus lab tracks by researcher, and I still do not have a confirmed volume estimate from the new satellite lab. If I recommend one setup now, I am guessing on behalf of two labs that have not weighed in."

The lead pauses, then asks, "So what do you actually need from us?"

You say, "Thirty minutes with someone from each lab, ideally the operations lead who knows the daily numbers, not just the site director. We agree on the criteria that matter to all three labs first, sample types, tracking needs, data outputs, then we look at where the labs actually differ. Your satellite lab's operations lead can bring current sample counts, even rough ones, so we stop guessing on that front."

The lead agrees to set up the joint session. On that call, it becomes clear within minutes that the downtown lab's batch tracking would force the satellite lab into a workflow change nobody had scoped or budgeted. That difference was invisible until someone asked the right question in front of all three labs at once.

Mistakes to avoid

The most common mistake is treating one lab's workflow as the default because it was the first one you talked to. It rarely is the default. It is just the first data point you had.

The second mistake is accepting an unverified sample volume as if it were confirmed. Once that number gets used in a slide or a configuration proposal, it hardens into fact even though nobody checked it. Ask who owns the number before you build anything on top of it.

The third mistake is running the joint call without a shared list of criteria on the table. Without that list, the loudest lab in the room sets the agenda by default, and the quieter labs go along with it until implementation, when their actual needs surface as complaints instead of requirements.

The fourth mistake is closing gaps with your own assumptions instead of naming them as open questions with an owner. A blank in a comparison sheet is honest. A guess dressed up as a fact is not, and it will cost you credibility later when the gap becomes visible anyway.

The fifth mistake is skipping the operations staff and only talking to site directors. Directors often do not know the daily sample counts or the actual tracking habits their teams use. The people closest to the bench have the numbers you need.

How to practice and score it

A manager can run this as a short drill with three conflicting lab briefs, each written to include a plausible but different workflow, along with one lab missing its volume data entirely. Have the account executive build a comparison sheet with an owner column for every unknown, then run a mock joint call.

Score the call on a few specific things. Did the executive name the decision criteria before offering any configuration opinion. Did they catch the missing volume number and assign an owner instead of guessing. Did their questions actually surface a real difference between the labs, or did they accept surface level answers that left the real gap hidden. Did they resist the pressure to give a single fast answer when the facts were not there yet.

DealSpeak lets you rehearse this exact scenario against an AI counterpart that plays a research lead pushing for a quick answer, then scores your discovery questions and your handling of the pressure to converge early. Running it more than once, with the missing data moved to a different lab each time, tests whether the account executive is following a real process or just remembering the first script. If your team is building out a broader set of Biotechnology practice conversationspair this drill with a session on qualifying a brand new site, covered in Qualify a New Laboratory Account Application Before the Lab Openssince both scenarios hinge on getting real facts before committing to a plan.

Closing

A fair multi lab evaluation depends on naming the criteria before naming a winner. The account executive who slows down long enough to ask each lab for its actual workflow, and who assigns an owner to every unverified number, ends up with a recommendation the whole group can trust instead of one they quietly resent.