An AI ROI model is only as useful as the assumptions you can verify. This one makes its cost and value assumptions explicit so you can replace them with your own data as the rollout progresses.
Assume a phased rollout targeting 60% AI adoption or 3,000 active users by month 12. Because adoption ramps over the year, each scenario uses a different average active-user count rather than charging 3,000 seats for all 12 months.
Year 1 cost has three components:
All three figures are illustrative. Replace them with your own quoted seat price, implementation budget, and enablement costs when you have them.
Larridin research found that 85.7% of workers save 10 hours or less per month with AI, while a separate top 6% save 20 or more hours. Those bands don't describe the entire workforce, so the model doesn't treat them as two exhaustive segments. Instead, it uses point estimates of eight hours per month for a typical user and 20 hours for a power user. The power-user share is also a sample input, not Larridin's measured top-6% cohort.
Converting hours into dollars requires a labor-value assumption. This model uses $60 per hour as a fully burdened compensation rate. It isn't drawn from a specific company or survey; replace it with your own blended rate.
Each scenario also applies a productivity-recapture discount because saved time only becomes financial value if the organization redirects it. The base case uses 50%. Forrester's Total Economic Impact study of GitHub Enterprise Cloud uses the same general approach, discounting developer time savings by 50% and another role by 75%. Those rates are specific to that study; this model borrows the method, not its inputs. The 35% and 65% rates are variations around the base case.
Scenario | Avg. Active Users | Power User Share | Recapture Rate | Year 1 Cost | Year 1 Value | Year 1 Net ROI |
|---|---|---|---|---|---|---|
Worst case | 1,200 | 4% | 35% | $934,000 | $2,564,352 | 1.7x |
Base case | 1,750 | 6% | 50% | $1,165,000 | $5,493,600 | 3.7x |
Best case | 2,400 | 10% | 65% | $1,438,000 | $10,333,440 | 6.2x |
Year 1 value = [(typical users × 8 hours) + (power users × 20 hours)] × $60 per hour × 12 × recapture rate.
Net ROI = (Year 1 value - Year 1 cost) ÷ Year 1 cost.
These are sample results. Replace each input with measured data as it becomes available.
The model above turns a set of assumptions into an initial ROI range. Once the rollout begins, the job changes: replace those assumptions with observed data and expand the measurement beyond time savings to proficiency and business outcomes.
Quarter | Measurement Focus | What Happens |
|---|---|---|
Q1 | Spend + baseline | Pilot teams launch. Establish pre-AI task baselines and spend visibility across sanctioned and shadow tools before adoption scales. |
Q2 | Adoption depth | Expand beyond the pilot. Track utilization depth by team and role, not just login counts, as adoption ramps toward the 60% target. |
Q3 | Proficiency | Segment users into beginner through power-user tiers. Target enablement at the teams furthest behind the proficiency curve instead of using a blanket training rollout. |
Q4 | Productivity signal → business outcome | Connect task-level time savings to the cost data from Q1. Present the measured ROI range to the board and use it to inform renewal or expansion. |
The model is useful because its assumptions are visible, not because any one ROI multiple is inherently reliable. If adoption comes in lower than expected or observed time savings fall short, the estimate should change. As the rollout matures, measurement should also expand beyond those pre-launch assumptions to proficiency and business outcomes. The goal is to replace the model with real data, not defend the original number.
It isn't supposed to be exact. A pre-launch model sets a range and shows which assumptions matter most. Replace those assumptions quarterly with real rollout data.
Because hours saved and dollars realized aren't the same thing. Saving two hours doesn't automatically create two hours of additional output. The 50% base-case rate is an illustrative assumption, not a general-workforce benchmark; the model uses the same general discounting principle as the Forrester example above.
Once the rollout is live, look for a platform that replaces the model’s assumptions with observed data where possible and adds signals a pre-launch model can’t provide reliably, including proficiency, productivity, and business outcomes. Larridin is built around that progression: Token Spend & Insights and AI Adoption cover spend and adoption, AI Fluency covers proficiency, and AI Impact connects productivity signals to business outcomes.
See how Larridin connects AI spend, adoption, proficiency, and business outcomes as your rollout scales.