AI investment focused on priorities that justified the commitment.
A restaurant franchise group wanted to reduce food waste and make better staffing decisions. We helped corporate and franchisee leaders test competing AI proposals, agree on priorities, and make continued funding depend on operating evidence.
AI proposals assessed to priorities funded
Why it matters
Leaders directed funding toward reducing food waste and improving staffing, and avoided tying up capital in a voice-ordering rollout with a projected payback beyond six years.
The starting point
The proposals were competing for funding without a shared decision standard.
Reducing food waste and improving staffing offered ways to strengthen restaurant profitability. Corporate leaders and franchisees brought sixteen AI proposals to the table, but they had no consistent way to compare the outcomes, costs, and operating requirements behind them.
A prior content pilot lacked an owner, baseline, and definition of success. A voice-ordering vendor presented an attractive savings forecast that still needed testing against the group’s own operating data.
What the business needed
Choose the work worth funding, establish who would own it, and define the evidence needed to keep investing.
What we changed
We brought business evidence and shared accountability into the investment decision.
Test the case for each proposal
We scored each idea against the single business outcome it claimed to change and required a representative baseline. Testing vendor forecasts against data from ten stores exposed a projected voice-ordering payback beyond six years.
Give corporate and franchisee leaders a shared decision
A portfolio group gave corporate and franchisee representatives equal participation. A tied vote meant no funding.
Sequence the work and make funding conditional
Food-waste forecasting came first. Scheduling depended on improvements to timekeeping data, so that prerequisite shaped the sequence. Funding required a named owner, representative baseline, and 90-day review gate.
What the client could use next
- A prioritized AI portfolio
- Sixteen proposals assessed against their intended outcomes, two funded priorities, and a sequence that accounted for data dependencies.
- An assessment of vendor claims
- Operating-data checks that gave leaders a basis for accepting or challenging vendor forecasts before committing to rollout.
- A repeatable funding process
- Shared corporate and franchisee decision rules, named owners, representative baselines, and 90-day review gates.
What the evidence showed
Food waste was lower at stores using the model.
Waste as a share of sales
- Stores using the forecasting model
- 3.5%
- Measurement-only comparison stores
- 4.2%
These figures compare different groups of stores. They are not a before-and-after baseline and do not establish how much of the difference the model caused.
In the latest quarter documented in the engagement record, waste stood at 3.7% of sales across 60 stores.
Two priorities received funding
Leaders narrowed sixteen competing proposals to two active priorities at budget season, with franchisee representatives supporting the decision.
The voice-ordering proposal stopped
Testing the business case against operating data showed a projected payback beyond six years. Leaders declined to commit to rollout.
A failed review gate changed the allocation
The content initiative failed its 90-day gate and stopped. Its unspent budget moved to scheduling-data work.
Different measures answer different questions.
The waste figures describe operating performance. The proposal count describes funding decisions. The voice-ordering payback was a projection used to assess the investment, not a realized saving.
The decisions had a review rhythm
Monthly gate reviews continued with advisory support, giving leaders a recurring point to examine evidence and decide what deserved further funding.
Imajin Labs gave us a clear basis for deciding where AI deserved our investment. Corporate and franchisee leaders could agree on what to fund, what needed to wait, and what to stop. We also had a practical way to hold the funded work accountable.