Playbook: AI Value Ledger: Hours Saved Is Not EBIT
Playbook helps separate soft benefits, useful productivity, exit-rate value, and bankable impact. AI programs often count activity, usage, or claimed time savings long before the business can bank value. The missing discipline is a value ledger that links AI activity to an actual business mechanism.
AI Value Ledger: Hours Saved Is Not EBIT
Use this playbook to separate soft benefits, useful productivity, exit-rate value, and bankable impact.
In plain English
AI programs often count activity, usage, or claimed time savings long before the business can bank value.
AI costs can rise quickly as usage grows, especially when agents take multi-step actions. At the same time, leaders want ROI. The missing discipline is a value ledger that links AI activity to an actual business mechanism.
How this connects to the sequence
Autonomy decides what AI is allowed to do. The Value Ledger asks what that activity is worth and whether it is worth the cost, risk, and adoption effort.
Signal linked to this playbook
More AI usage is not the same as more value
This Signal links usage growth and token costs to the Operator question: what value did the work actually create?
The value types
- Soft signal: people like it or use it, but value is not yet proven.
- Productivity signal: time or effort is reduced, but not yet monetised.
- Operational value: cycle time, quality, rework, backlog, or throughput improves.
- Financial exit-rate: the improvement is running, but not fully banked this year.
- Bankable EBIT / value: finance can tie the improvement to cost, revenue, margin, capacity, or working capital.
Use this when
- An AI project claims huge savings from hours saved.
- Usage is rising but nobody can explain value.
- A vendor tool says it found savings but the business cannot realise them.
- Compute or token costs are increasing.
- Leadership asks which AI initiatives should scale.
The ledger questions
- What is the baseline?
- What changed in the workflow?
- Who owns the metric?
- What is the financial mechanism?
- What is the implementation cost?
- What is the ongoing run cost?
- What adoption level is needed?
- What risk or control cost offsets the value?
- What is bankable now vs later?
What good looks like
- Each claim has a baseline.
- Hours saved are not counted as EBIT unless there is a mechanism.
- Costs include software, tokens, support, training, and redesign.
- Finance and the business owner agree what counts.
The first move
Take the five loudest AI value claims. Put each into one category: soft signal, productivity signal, operational value, exit-rate, or bankable. Anything without a baseline stays out of the bankable column.
Human work signal
Value discipline protects people too. It stops organisations from turning vague time-saved claims into unrealistic workforce or budget promises.
What to capture in the worksheet
| # | Field | Why it matters |
|---|---|---|
| 1 | AI initiative | Identifies the initiative being evaluated. |
| 2 | Claimed benefit | States the expected business outcome. |
| 3 | Baseline | Provides a starting point for measurement. |
| 4 | Metric owner | Assigns accountability for tracking value. |
| 5 | Value type | Separates perceived value from bankable value. |
| 6 | Cost to run | Captures the ongoing cost of delivery. |
| 7 | Adoption requirement | Defines the usage needed to realise value. |
| 8 | Financial mechanism | Explains how benefits translate into financial impact. |
| 9 | Evidence | Validates claims with measurable results. |
| 10 | Decision | Records whether the initiative should scale. |
Get the lightweight workbook
The public playbook gives you the method. The member workbook gives you the simple working sheet across various Playbooks.
