Most FMCG manufacturers have more AI ideas than they have time, data or people to test, so choosing the wrong pilots wastes months and damages trust. Good pilot selection scores each idea on value, data readiness, complexity and the chance of success. A simple scoring model makes the shortlist transparent and easier to defend to leaders and plant teams. The result is fewer pilots with a higher chance of reaching production. To score your own ideas, you can work through a pilot scoring session with our team.
AI foundation and pilot planning
Pick the AI Pilots Most Likely to Succeed and Scale
iFactory AI helps FMCG manufacturers score ideas on value, data, complexity and probability of success, so the shortlist is based on evidence.
Illustrative pilot scores
Why selection mattersA Pilot That Cannot Scale Is an Expensive Experiment
Pilots consume scarce time from operators, IT and quality. Weak choices burn goodwill.
Many ideas
Every function suggests something
Loudest voice wins
Pilots chosen by influence, not evidence
Pilot stalls
Data is missing or the value is small
Trust falls
Teams doubt the next proposal
A transparent scoring method moves the discussion from opinion to evidence.
Four criteriaValue, Data Readiness, Complexity and Success Probability
Value size
How large is the likely benefit if the pilot succeeds and scales? Include cost, quality, speed and risk.
Data readiness
Is the data available, accurate, time-stamped and accessible? Poor data is the most common pilot killer.
Complexity
How many systems, teams and process changes are involved? More complexity means more delay.
Success probability
How likely is it that the pilot reaches production, given sponsorship, skills and track record?
Weights can differ by company. Many teams start with equal weights and adjust after the first round.
The scoreA Simple Weighted Score You Can Explain
Pilot score = value x weight + data readiness x weight + ease x weight + success probability x weight
Rate each criterion from one to five and treat complexity as ease, where five means simple. Multiply by the agreed weights and add them up. The example below is illustrative.
| Pilot idea | Value | Data | Ease | Success | Weighted score |
|---|
| Visual inspection on one line | 4 | 4 | 4 | 4 | 82 |
| Predictive maintenance on key machines | 5 | 3 | 3 | 3 | 71 |
| Demand forecasting for one category | 4 | 3 | 2 | 3 | 55 |
| Energy optimisation across all plants | 3 | 2 | 1 | 2 | 38 |
Score Your AI Ideas Before You Commit
Share your list of AI ideas and see how iFactory AI would score and rank them for your plants.
Value and easePlot Ideas on a Simple Grid
A grid shows quickly which ideas deserve action first.
Idea position
Easy
Hard
High value
Do first
Quick wins that build trust
Plan carefully
Valuable but needs preparation
Lower value
Fill gaps
Small wins if there is spare capacity
Drop or park
Hard and low value, so avoid
Start with a mix. One or two quick wins fund credibility, while one larger project builds the pipeline.
Checking data readinessFive Questions to Ask Before You Commit
1
Does the data exist?
Check that the measure is actually recorded.
2
Is it accurate and time-stamped?
Poor clocks and gaps spoil models.
3
Can we access it?
Check security, ownership and system links.
4
Is there enough history?
Models need examples of both normal and abnormal conditions.
5
Do labels exist?
For defects and failures, known outcomes are needed to learn from.
A short data check often changes the ranking. To run one, ask our team for a data readiness checklist.
Decision bandsTurn Scores Into Clear Actions
Go
High scores with a named sponsor and plan.
Prepare
Medium scores. Fix data or scope, then re-score.
Stop or park
Low scores. Record the reason and revisit later.
Write down why each idea was stopped. This avoids repeated debates and shows teams the process is fair.
Pilot lifecycleFour Stages From Shortlist to Decision
1
Define
Agree the problem, measures and success criteria.
2
Build
Create the smallest version that can prove value.
3
Test
Run beside current practice and compare results.
4
Decide
Scale, adjust or stop based on the agreed measures.
Set a time limit for each pilot. Open-ended pilots tend to drift and lose sponsorship.
Common trapsMistakes That Sink Pilots
Common traps
Choosing the most exciting idea
Ignoring data quality until late
No named business owner
No plan for scaling after success
Better habits
Choosing by score and evidence
Checking data before committing
A named sponsor and an operations owner
A scaling plan written in advance
Making it routineRun Selection as a Regular Cycle and Use This Checklist
1
Collect ideas
Gather suggestions from every plant and function.
2
Score
Rate each idea with the agreed criteria.
3
Review
A small group checks scores and adds context.
4
Select
Choose a few pilots for the next period.
5
Learn
Record results and update weights and criteria.
Criteria and weights agreed
Idea list collected from all plants
Data readiness checked
Business owner named for each pilot
Success measures and time limit set
Scaling plan outlined
Stop decisions recorded with reasons
Selection repeated every quarter
Expert reviewWhat a Transformation Lead Would Check First
Expert review
Pilot choice is a portfolio decision. A balanced shortlist of quick wins and one or two bigger bets protects both credibility and long-term value.
VerdictStrong when scoring is simple, visible and tied to named owners.
Watch forScores adjusted to favour pet projects.
Do nextScore your top ten ideas and pilot the top two in different plants.
Frequently asked questionsWhat Leaders Ask About AI Pilot Selection
How many pilots should we run at once?
Fewer than most companies expect. Two to four well-supported pilots usually outperform a long list, because each gets enough attention from operations and IT. Increase the number only when capacity and results support it.
See a sample pilot portfolio.
What weights should we use for scoring?
Start with equal weights and adjust after reviewing early results. Companies with weak data often give data readiness more weight, while those with large budgets may weigh value more heavily. Write the weights down and keep them stable within a cycle.
Ask about weighting.
How do we measure success probability?
Look at sponsorship, team skills, past results with similar projects and how well the problem is defined. A named business owner and clear success criteria raise the probability. Be honest about gaps.
Discuss success factors.
What if a high-value idea scores poorly on data?
Treat data work as its own small project, then re-score. Improving data often unlocks several ideas at once, which makes it a valuable investment. Do not start the pilot until the data basics are in place.
Ask about data improvement steps.
When should we stop a pilot?
Stop when agreed criteria are not met by the deadline, when data problems prove unfixable or when the sponsor withdraws. Record what was learned so the next idea benefits. Stopping early is a sign of discipline.
Plan stop criteria.
Fewer pilots, better results
Shortlist AI Pilots With Confidence
Book a session with iFactory AI to score your ideas, check data readiness and choose the pilots most likely to reach production.
