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Measure the workflow, not the novelty of the tool. Define one repeatable task, record how the team performs it today, then compare AI-assisted work with a credible control or rollout comparison. Track completion time together with throughput, quality, errors, rework, adoption, customer or business outcomes, and all relevant costs. Convert verified time differences into an estimated labor value, but call them realized savings only when the time is redeployed to useful work or produces a measurable financial or operational result. This approach prevents fast but inaccurate output—or heavily used software—from being mistaken for productivity.
What should you measure first?
Start with a narrowly defined unit of work and a stated outcome. “Use AI to improve productivity” is too broad to evaluate; “minutes per completed support case while maintaining the existing quality standard” is measurable.
Define the task and its outcome
- Workflow: identify the exact process being changed, including intake, drafting, review, approval, handoff and rework.
- Unit of work: choose a consistent unit such as a case, report, customer interaction, briefing note or analysis.
- People and population: specify which roles perform the work and which users will receive the tool.
- Intended outcome: state whether the goal is faster completion, more output, fewer errors, better service, lower overtime or another result.
- Quality floor: define what counts as acceptable accuracy, completeness, compliance and tone before measuring speed.
- Progress signals: select a small set of indicators that can reveal improvement and harm.
The Australian National AI Centre recommends defining the problem, expected outcome and signs of progress before investment. Writing these decisions down prevents the team from changing the definition of success after seeing the first results.
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How do you establish a useful baseline?
A baseline is a measured description of the business-as-usual process before the AI intervention starts. It is the reference point for every later comparison, so capture the process rather than only an average completion time.
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Record the current workflow
- Typical and median time from work starting to an accepted final output.
- Active handling time, waiting time and review time when those components can be separated.
- Volume completed per person, shift or staff-hour.
- Accuracy or quality score from a consistent review rubric.
- Error, exception, escalation and rework rates.
- Customer, user or business outcomes that the task is intended to affect.
- Staffing, tools, review steps, work volume and demand conditions during the measurement period.
Use the same definitions and data sources in the baseline and AI periods. If the process includes complex or subjective work that is difficult to time consistently, prioritize the measures that best answer your stated decision rather than collecting every possible field. GOV.UK evaluation guidance notes that defining a baseline can be challenging when an intervention replaces complex work; a useful, well-documented baseline is better than a large but unreliable dataset.
Choose the measurement window deliberately
Include enough normal work to cover the task mix your team actually handles. Record dates, demand, staffing changes, training, policy changes, model or workflow versions and unusual events. A short period may be appropriate for a tightly controlled pilot, but it can miss learning effects, seasonal demand and rare errors. The official guidance does not set a universal pilot length or sample size; justify your window in relation to the task and decision.
What comparison design shows whether AI caused the change?
The most persuasive practical design compares people with AI access against comparable people continuing the existing workflow, ideally with access or rollout timing assigned at random. If randomization is not feasible, use a documented quasi-experimental or theory-based design and state what it cannot prove.
Randomized access or rollout
Randomly assign eligible users, teams or rollout dates when operationally and ethically appropriate. During the same period, compare outcomes for the treatment group with outcomes for the control group. Random assignment helps balance differences such as experience and workload, but you still need to check whether both groups actually used the assigned workflow and whether the groups remained comparable.
Quasi-experimental options
If a randomized test is impossible, a difference-in-differences design can compare the change over time in an AI group with the change over time in a comparison group. Other options include matched groups, interrupted time series or a theory-based evaluation for a complex service. Document how the comparison was selected and which assumptions make the estimate credible.
Why a simple before-and-after result is weak evidence
Output can improve after a rollout because demand fell, experienced staff joined, a policy changed, people became familiar with the process or a seasonal peak ended. A before-and-after chart can describe what happened, but without a suitable comparison it should not be presented as proof that the AI tool caused the change.
Which metrics belong on an AI productivity scorecard?
Pair speed with quality, useful output and cost. A tool that cuts draft time but increases checking and rework has not shortened the whole workflow.
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|---|---|---|
| Completion or handling time | Median and distribution of time per accepted unit of work | Shows whether the end-to-end task is faster, not merely whether a prompt returns quickly |
| Throughput | Completed acceptable units per person, shift or staff-hour | Shows whether capacity increased without lowering the quality floor |
| Accuracy and quality | Score outputs with a consistent rubric or review standard | Detects plausible but incorrect, incomplete or noncompliant output |
| Errors and exceptions | Count factual errors, policy violations, escalations and failed handoffs | Captures risk that a time metric misses |
| Rework | Share of units requiring correction, regeneration or additional review | Reveals hidden effort after the initial AI output |
| Adoption and repeat use | Eligible users who use the tool, frequency and continued use | Explains reach and implementation, but is not proof of value |
| User or customer experience | Relevant satisfaction, resolution, response or complaint measures | Tests whether internal efficiency translated into a better outcome |
| Redeployment of time | What staff did with released capacity and how much was completed | Separates potential time value from realized productivity |
| Total cost | Usage, licenses, implementation, training, oversight, maintenance and related labor | Allows a net-value calculation rather than a gross saving |
The CDDO framework describes efficiency as achieving the same or better outputs with fewer inputs. It gives median time saved per task and average handling-time reduction as illustrative measures, while also recommending behavioral and real-world outcome data and a mix of quantitative and qualitative methods.
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How do you measure quality and rework consistently?
Use a review standard that is defined before the comparison and applied to both AI-assisted and non-AI work. Speed results are not meaningful if the evaluation quietly accepts lower-quality work.
Create a scoring rubric
Specify the criteria, scale, pass threshold and who reviews a sample. Criteria might include factual correctness, completeness, policy compliance, clarity and required citations. Keep the rubric stable during the measurement period unless a documented change is part of the intervention.
Count downstream effort
Measure time spent checking, correcting, escalating and reproducing an output. Record whether a reviewer had to discard the AI draft and start over. For customer-facing work, include complaints, repeat contacts or unresolved cases where those outcomes are relevant.
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Usage logs and surveys can explain how people work, but they may miss unreported corrections or abandoned attempts. Combine system records with a consistent sample review and interviews or observation. Report the sampling method and its limitations.
How should you convert time saved into money?
First calculate an estimated labor value; then test whether the organization actually realized that value. The basic estimate is:
Estimated labor value = measured time saved per completed unit × number of affected units × staff-time cost per unit of time.
Use the task’s measured time difference, not a vendor’s claimed speed. State whether the cost rate includes salary only or also relevant employment overhead, and keep the rate consistent between scenarios.
Illustrative calculation
Suppose a team completes 1,000 accepted cases per month. Median end-to-end time falls from 30 minutes to 24 minutes, and the fully loaded staff-time rate used for planning is $30 per hour. The estimated labor value is 6 minutes × 1,000 × $30 ÷ 60, or $3,000 for that month, before tool and implementation costs.
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This example is a calculation method, not a benchmark. It assumes the measured time difference applies to the same mix of accepted cases and that quality, rework and volume are comparable.
When does an estimate become realized value?
Time saved becomes realized value when the released capacity produces a measurable result: more accepted work, faster service, improved quality, reduced overtime or contractor spend, avoided hiring, or another documented operational or financial change. If staff simply finish earlier but staffing and output remain unchanged, report potential capacity or estimated labor value rather than cash savings.
The National AI Centre states: “Time saved only delivers value if it’s redirected to useful work, such as serving customers, improving quality or growing the business.” This distinction is central to any credible business case.
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Subtract costs that are caused by the intervention or required to keep it operating. There is no universal cost schedule or ROI threshold; include the categories relevant to your use case and disclose assumptions.
| Cost category | Examples to check |
|---|---|
| Tool and usage | Subscription, seats, consumption, storage or other usage charges |
| Implementation | Configuration, integration, data preparation and security review |
| People | Training, prompt or workflow design, administration and additional review |
| Operations | Monitoring, maintenance, model changes, support and incident handling |
| Residual workflow | Human checking, exception handling, duplicate systems and rework |
| Opportunity cost | Time spent piloting or changing processes instead of other work |
Net value can be reported as realized financial or operational benefit minus these relevant costs. Keep projected benefits separate from observed benefits, and do not treat a favorable estimate as a realized saving until the redeployment or financial effect is evidenced.
How do you compare two AI tools fairly?
Test both tools on the same workflow, task mix, user population, quality standard and evaluation period. If the tools serve different tasks or users, explain those differences instead of ranking raw averages as if the tests were equivalent.
| Comparison axis | Fair-comparison question |
|---|---|
| Task time | Does each tool reduce time per accepted unit after review and rework? |
| Throughput | Does either tool increase acceptable output per person or hour? |
| Quality | Are both evaluated with the same rubric, threshold and reviewer process? |
| Errors and rework | Which tool creates fewer corrections, escalations or failed handoffs? |
| Adoption | Do eligible users adopt and continue using each workflow? |
| Experience | Do users or customers report a meaningful difference? |
| Total cost | What are the recurring and one-time costs at the tested volume? |
| Evidence strength | Was access randomized, matched, time-series based or simply observed before and after? |
Present sample sizes, dates, task mix and quality results beside the averages. A cheaper tool with weaker quality or lower adoption may have less realized value than a more expensive tool that reliably produces acceptable output.
What does the GOV.UK briefing-note example actually show?
GOV.UK guidance gives an illustrative LLM intervention evaluation, not a general productivity benchmark. More than 80% of treatment-group participants reported using the LLM tool for most briefing notes during the test period. Expert-panel scores averaged 80% for treatment-group briefing-note accuracy and quality and 81% for the control group.
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The guidance says the test suggested reduced average task time without lower-quality responses. It also treats whole-service extrapolation as preliminary and calls for additional testing before assuming the result holds at scale. Those figures should therefore be read as an example of how an evaluation can report adoption, time and quality together—not as a forecast for your team.
How should you report the result so decision-makers can trust it?
Make the calculation auditable. A reader should be able to see what changed, for whom, compared with what, and under which assumptions.
- Task definition and intended outcome.
- Population, eligibility and sample size.
- Baseline period and AI measurement period.
- Comparison group or rollout design and its limitations.
- Tool, model, workflow and training versions.
- Time, throughput, quality, error, rework and experience measures.
- Adoption and actual use, including non-use or abandonment.
- Staff-time cost rate and every included cost category.
- Whether each benefit is projected, estimated, observed or realized.
- Unintended effects, differences between user groups and missing data.
Use confidence intervals or uncertainty ranges when your evaluation method supports them. Otherwise, label results as descriptive and preliminary rather than implying precision the design cannot provide.
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What pitfalls can make an AI saving look bigger than it is?
Most misleading results come from measuring only the visible AI step or from treating usage as an outcome.
Measuring prompt latency instead of workflow time
A response that appears instantly may require more editing, verification and approval. Measure from the start of the task to an accepted output.
Counting every hour as cash
Salary expense does not automatically fall when a task takes less time. Show how capacity was redeployed or which financial commitment changed.
Ignoring quality drift
Track errors, rework and downstream complaints throughout the test. A short pilot can miss rare but costly failures.
Using adoption as proof of impact
People may use a tool because it is required, interesting or convenient. Adoption explains reach; it does not establish better outcomes.
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Comparing unlike work
Changes in task mix, staff experience, demand or case complexity can move averages. Stratify results or report those differences rather than combining incompatible work.
Failing to repeat the evaluation
Models, prompts, workflows, training and user behavior change. Re-measure after meaningful changes and examine whether effects differ across roles or other user groups.
What is a practical step-by-step pilot plan?
Use the following sequence when a team needs an evidence-backed go/no-go decision.
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- Map the current process. Record roles, tools, review points, volumes and where time and rework occur.
- Collect baseline observations. Capture completion time, throughput, quality, errors and relevant outcomes using stable definitions.
- Choose the comparison. Prefer randomized access or rollout; otherwise document a matched, time-series, difference-in-differences or theory-based design.
- Prepare the intervention. Freeze the tool and workflow version for the test, train users and record expected usage.
- Run the pilot without changing the scorecard. Log actual use, non-use, exceptions, reviews and redeployed work.
- Analyze the whole workflow. Compare time and throughput alongside quality, errors, rework, experience and adoption.
- Calculate value in layers. Report measured time difference, estimated labor value, relevant costs and realized operational or financial benefit separately.
- Investigate differences. Check whether results vary by role, experience, task complexity or workload.
- Repeat after material changes. Reassess when the model, process, training, scale or population changes.
When should a team stop, redesign or scale the tool?
Scale only when the evidence fits the intended use: acceptable quality is maintained, the comparison supports the claimed effect, adoption reaches the population needed for value, and realized benefits exceed relevant costs. Redesign or stop when quality falls below the agreed floor, rework consumes the apparent time saving, benefits depend on an unrepeatable staffing condition, or the required oversight costs more than the capacity created.
A preliminary result can justify a larger, better-controlled test; it should not be presented as a guaranteed return. Treat evaluation as a continuing operating practice, because the workflow and the intervention will evolve.
Frequently Asked Questions
How much baseline data do we need before testing an AI tool?
There is no universal minimum sample size or pilot duration. Collect enough normal work to represent the task mix and record the conditions that could affect results; then justify the period and sample in relation to the decision you need to make.
Can time saved count as a productivity gain if nobody is laid off?
Yes, but describe it as increased capacity unless the team converts it into additional acceptable output, faster service, better quality, avoided overtime or another measurable result. A lower task time alone does not prove a cash saving.
Should we use employee surveys to measure AI impact?
Surveys are useful for perceived effort, confidence, usability and unintended effects, but they should complement workflow records and quality review. Self-reported time or value can be biased when people know which intervention they received.
How do we handle an AI tool that improves some tasks but harms others?
Report results by task type, role or complexity instead of relying on one blended average. You may need a narrower deployment, different safeguards or separate workflows for the groups with negative effects.
What should we do when a control group is impossible?
Use a documented alternative such as matched historical or concurrent groups, an interrupted time series or a theory-based evaluation. Label the result as less certain and list the factors—seasonality, staffing, demand and process changes—that could also explain the difference.
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