What a pilot is for
A demonstration shows how a product behaves in the hands of someone who knows it well, using data chosen to look good. A trial or pilot shows how it behaves with your people, your information and your habits. That difference is why pilots catch problems that nothing else will: confusing screens, slow searches, awkward approvals, imports that fail on real-world data.
A pilot is not a free first phase of the rollout, and it is not a way to confirm a choice already made. It exists to produce evidence that could change your mind. If the outcome is already decided, say so and treat the exercise as an early implementation instead.
Decide what you want to learn
Write down two or three questions the pilot must answer. Examples are whether the team can complete its main tasks faster or with fewer errors, whether the product copes with your data, and whether users will actually adopt it. These flow from your written requirements, which is why requirements gathering comes first. Without them, pilot feedback becomes a collection of opinions.
Keep the scope narrow enough to finish. A pilot that tries to test everything usually tests nothing thoroughly.
Choose participants who represent real use
The people in a pilot determine what you learn.
- Include a mix of heavy and occasional users, and at least one person who is sceptical. Enthusiasts only will give you a flattering picture.
- Cover the different roles who will use the tool, including administrators.
- Avoid selecting only the people who are most comfortable with technology unless that reflects the future user base.
- Make time available. Asking busy people to test on top of a full workload tends to produce thin feedback.
For a larger rollout, a pilot group can also become a set of local advocates later, an idea related to change champions.
Use real tasks and real data
Prepare scenarios drawn from actual work: a typical request from start to finish, an unusual case, a task done under time pressure, a task that involves another system. Give every product under comparison the same scenarios, so results can be compared fairly.
Use a sample of your own data, handled according to your data protection obligations. Where real data is sensitive, ask your security and privacy colleagues to decide what is appropriate, perhaps using masked or synthetic records. The test should also include import and export, and the exchange of data with your other systems, as discussed under system interoperability.
Agree success criteria before starting
Success criteria turn opinions into evidence. For each question, define in advance what counts as a good enough result. A criterion should be observable, such as “all participants can finish the standard request without help” or “the import completes without losing records”. Be wary of criteria that are only a feeling, like “people like it”.
Where you can, record a performance baseline before the pilot, such as how long a task currently takes, so you have something to compare against. Setting a baseline is explored under performance baseline work. Without it, an apparent improvement cannot be shown.
Decide, too, who will judge the results and how disagreements are settled.
Keep the comparison fair
When trialling several products, treat them equally.
- Give each the same length of trial, the same scenarios and the same support access.
- Avoid letting one vendor configure everything for you while another leaves you alone, unless you want to learn about their support, in which case measure it on purpose.
- Rotate the order in which people try the products, since the first tool used often seems harder or easier simply because of novelty.
- Record problems consistently using a common template: what was attempted, what happened, how severe it was.
Capture evidence while it is fresh
Short, structured feedback beats a long survey at the end. Ask participants to note issues as they occur, and hold a brief check-in partway through. At the end, collect scores against the agreed criteria alongside comments. A weighted scoring matrix is one way to bring these results together, provided you treat it as a prompt for discussion.
Also track items beyond features: how long onboarding took, how responsive support was when something went wrong, and how many questions needed outside help. These indicate what ownership will feel like.
Know the limits of a pilot
A pilot is a small sample under special conditions. People are paying more attention than they will later. Volumes are lower than at full scale, so performance problems may not show. Short trials cannot reveal what happens at month-end, year-end or during an upgrade. Participants may also be gentler than they would be in real use, or harsher if the trial caused disruption.
State these limitations in the summary so decision-makers do not overread the results. If the product will be critical, consider extending the trial or phasing the rollout.
Close the pilot cleanly
At the end, write a short summary: what you set out to learn, what you found against each criterion, unresolved risks, and a recommendation. Include a plan for the data entered during the pilot, whether it will be kept, migrated or deleted, and confirm access for trial accounts is removed. Share findings with participants, because people who see their feedback used are more likely to support the eventual change.
If the evidence is mixed, say so. A pilot that reveals reasons not to proceed has done its job.