Lessons from onboarding

Most onboarding journeys hit an early inflection point: a moment when it becomes clear whether the software will actually stick. And, more often than not, that moment has little to do with the product itself. At Kaleidoscope, we’ve noticed a set of early warning signs that signal when onboarding is starting to go off track – and, crucially, when both customers and vendors still have time to hit the brakes and course-correct.

Starting too big

A common assumption is that serious teams roll software out broadly across every workflow straight away. In practice, the teams that succeed usually do the opposite: they start narrow on purpose.

The most effective pattern looks something like this:

• Kick-off: pick one workflow and one team – something real, not a toy example. A single area of experiments, a single program slice, one functional group that already has enough motion to generate data and decisions.
• Initial several weeks: get that workflow into a steady rhythm where updates and outputs happen inside the system, not as a parallel “extra step.”
• Week N+: expand outward using the first team as the reference point, because they can now teach the rest of the organization what “good” looks like in context.

This sequencing matters because early adoption is less about coverage than trust. When teams attempt to onboard everyone at once, the system becomes everyone’s “other tool.” People ask, reasonably, whether it’s updated, whether it reflects reality, whether it’s safe to rely on. If the answer is “not yet,” they return to what they trust: spreadsheets, inboxes, side conversations.

A small rollout creates a contained environment where the system can earn credibility. Once one group is using it as the truth, the rest of the organization stops treating it as theory.

A trial without criteria 

One of the fastest ways to stall onboarding is to treat the decision like an opinion poll. It usually looks like this:

• multiple stakeholder groups are invited to trial
• feedback is collected in a doc (maybe)
• the feedback is contradictory, because it’s filtered through different goals
• no one has defined what the organization is optimizing for
• a meeting is scheduled to “align,” then another

By the time the group reconvenes, everyone has returned to old habits and the ‘trial’ becomes something that happened, not something that changed anything. When onboarding works, the trial has clear boundaries:

• Why the organization is making a purchase at all
• Why status quo isn’t acceptable (not in a vague sense; specifically!)
• What success looks like in a way that can be evaluated
• Who owns interpretation of feedback

This doesn’t eliminate disagreement; it makes disagreement usable. Instead of “I don’t like this UI,” feedback becomes “this workflow makes it harder for us to do X, which is one of the reasons we’re looking for a new tool.” That’s a very different conversation that helps you decide whether the software actually does what you need it to do.

No champion (or one that’s too quiet) 

Almost every successful onboarding has a champion. This person doesn’t need to be the CEO. Often they’re a team lead or a mid-level leader with a bird’s-eye view. When that champion is strong, onboarding has a spine: they define the first workflow, they are willing to set expectations about updating the system, they can say “this is the source of truth” and mean it, they can translate the why to the rest of the team in language that lands – and so on. 

When that champion is absent, or even too quiet, onboarding becomes fragile. There is no one who narrows scope, enforces criteria, or calls out drift early. The process looks the same, but it lacks the gravity of someone pushing it forwards.

Great implementation…  without any behaviour change 

You can roll out a new system perfectly and still fail if the way people work doesn’t change with it. 

An example: most teams don’t struggle to understand what structured data is. They struggle with internalising that producing it is part of their job. Scientists are typically evaluated on running experiments, not on making those experiments legible to others later. So a quiet assumption creeps in: “my job is to run the assay, not capture it cleanly.”

That assumption holds... until a team tries to scale. When a leader needs to review dozens of experiments, or when a question appears months later - have we already tested this compound against this cell line? – the cost becomes obvious. The work exists, but can’t be reliably found..

In our onboardings, we reinforce a strong rule: if work isn’t captured in structured form, it might as well not exist for decision-making. Seen this way, capturing work isn’t admin, and an experiment isn’t finished until the organization can actually use review, query and build on it. 

That is why onboarding can’t stop at teaching people which buttons to click. Effective onboarding means explaining a shift in what “done” means. Without that behavioural change, implementation might succeed but adoption will remain capped.

Frequency is not always a good proxy for value in R&D tooling

One of the more counterintuitive points we’ve learned: it's tempting to assume that high-value usage looks like frequent usage. In R&D, it often doesn’t.

Certain workflows are naturally sticky – task management, status updates, comments, coordination. Those require frequent touches, so analytics looks healthy. Other workflows are deeply valuable but inherently infrequent: review meetings, milestone readiness, cross-program visibility, pulling together an answer that used to take days. Those moments don’t happen every day. They happen on a monthly or quarterly basis, or when the stakes are high. 

A system can massively change the speed and quality of decision-making and still look quiet sometimes based on analytics alone. 

All in all, onboarding is a mirror

Onboarding is a true reflection of the organization as a whole (and whether they’ll be a good customer to partner with long term). Just a few things we’ve seen onboarding reveal: 

• whether decisions can be made without dissolving into consensus
• whether someone is willing to own the process
• whether structured capture is treated as craft or as clerical work
• whether the organization is prepared to trust a new system

Most of this becomes visible pretty early in the process. That’s why, for our team, onboarding isn’t a phase to get through. It’s a time to pay close attention to whether an organization actually wants the operating system it says it wants – and where both us and our customers get an early signal of the kind of partner we’re each going to be working with long-term.

Kaleidoscope is a software platform for Life Science teams to robustly manage their R&D operations. With Kaleidoscope, teams can plan, monitor, and de-risk their programs with confidence, ensuring that they hit key milestones on time and on budget. By connecting teams, projects, decisions, and underlying data in one spot, Kaleidoscope enables R&D teams to save months each year in their path to market.