TL;DR

AI adoption data is not the same as productivity evidence. Licenses assigned, active Copilot users, prompts submitted, tokens consumed, suggestions accepted, and lines of code generated can show that a tool is available and being used. They do not prove that the organization improved revenue, customer experience, end-to-end cycle time, quality, operating margin, or decision speed.

Executives need a measurement chain that separates tool activation, tool consumption, task acceleration, and business productivity. The practical framework is to baseline the complete workflow, measure accepted output rather than raw artifact volume, track rework and error rates, identify the real throughput constraint, calculate net employee capacity released, and include the total cost of the redesigned workflow.

Adoption telemetry belongs in the operating dashboard. Business outcomes belong in the board report.

Introduction

Enterprise AI programs are producing more measurement data than most organizations know how to interpret.

Microsoft 365 can report enabled users, active users, prompts submitted, and usage by application. GitHub can report Copilot adoption, engagement, suggestion acceptance, lines of code, and pull request lifecycle activity. Model gateways can count tokens, requests, latency, and cost. Internal AI platforms can rank teams by agent runs, generated artifacts, or usage growth.

The dashboards look precise. The board slide looks measurable. The conclusion often arrives too quickly: usage is up, therefore productivity is up.

That conclusion is not supported by the telemetry alone.

PwC’s 2026 Global CEO Survey found that 56% of CEOs reported neither higher revenue nor lower costs from AI, while only 12% reported both benefits. That does not mean AI tools are useless. It means broad experimentation and visible activity are not automatically becoming financial results.

The measurement problem is that executives frequently combine four different questions:

• Did employees receive and activate the tool?
• Did they consume the tool?
• Did the tool accelerate a task?
• Did the redesigned workflow improve a business outcome?

Those questions belong to different measurement layers. Treating them as interchangeable creates an AI productivity measurement trap.

The Board Is Often Seeing Telemetry, Not Productivity

Usage telemetry is valuable. It helps IT leaders understand whether licenses are assigned, whether users are engaging, whether training is working, which features are being adopted, and where consumption cost is accumulating.

That is operational evidence about the tool.

Productivity evidence answers a harder question: did the organization produce more accepted value, at the required quality and risk level, with less total time or cost?

A prompt count cannot answer that question. Neither can token consumption, daily active users, suggestion acceptance, or lines of code. These signals can support an investigation, but they cannot complete the causal chain by themselves.

The distinction matters because activity metrics are easy to collect and easy to place on an executive dashboard. Workflow outcomes are harder. They require baseline data, integration across systems, consistent definitions, business ownership, and enough time to observe what happens after the AI-generated output enters the real process.

The easy metric usually arrives first.

The outcome usually arrives later, through a different system, under a different owner.

That gap is where weak AI productivity claims are created.

Four Different Things That Should Never Be Combined

The first control is a shared vocabulary. Every AI metric should be classified before it appears in a scorecard.

Measurement layer	What it answers	Typical metrics	What it does not prove
Tool activation	Was access enabled and tried?	Licenses assigned, enabled users, first use, training completion	Useful work, sustained adoption, productivity
Tool consumption	How much was the tool used?	Active users, prompts, tokens, agent runs, feature actions, model cost	Quality, task completion, customer value, financial return
Task acceleration	Did a bounded task become faster or easier?	Drafting time, coding time, research time, summaries completed, time assisted	End-to-end workflow improvement, capacity realization, business impact
Business productivity	Did the system deliver more accepted value for the total resources consumed?	Cycle time, accepted throughput, rework, error rate, customer outcomes, unit cost, margin	Nothing beyond the defined workflow and measurement period

Tool Activation

Tool activation is a rollout metric.

It tells the CIO whether licenses were assigned, whether users reached the product, whether access controls worked, and whether the enablement campaign produced initial use. Activation is important during deployment because an unused tool cannot create value.

It is still not a productivity metric.

A user can activate a Copilot license, submit one prompt, and never integrate the tool into meaningful work. A team can complete AI training without changing a workflow. A business unit can reach a high license-assignment rate while preserving every old handoff, approval queue, meeting, and manual control around the task.

Activation proves availability and initial contact.

Tool Consumption

Consumption measures interaction with the tool.

Prompts, tokens, active days, agent runs, feature actions, code suggestions, and model calls help platform teams understand demand, adoption depth, performance, and cost. They can reveal which teams need training, where a feature is useful, where an expensive model is overused, or where an agent is retrying excessively.

Consumption can rise because employees found a valuable workflow.

It can also rise because prompts are poorly designed, context is bloated, agents are looping, users are experimenting, generated work is repeatedly revised, or employees believe visible AI activity will be rewarded.

Recent workplace discussions around tokenmaxxing make the incentive problem concrete. Once token use or AI activity becomes a signal of employee commitment, people will optimize for visible consumption. The organization can end up buying more inference without receiving more value.

Consumption proves workload and cost.

Task Acceleration

Task acceleration is the first layer that begins to resemble productivity.

Controlled studies have shown that generative AI can reduce completion time and improve quality for specific tasks. Research on customer-support agents has also shown higher issues resolved per hour, with the largest gains often concentrated among less-experienced workers.

Those findings matter because they measure a defined task or workflow outcome, not merely tool use.

The limitation is scope. A task can become faster while the full process remains unchanged. Drafting a response in two minutes instead of ten creates no customer benefit if the response waits two days for approval. Generating code faster creates no delivery benefit if review, testing, security remediation, release governance, or operations becomes the constraint.

Task acceleration proves a local improvement under defined conditions.

Business Productivity

Business productivity measures the complete system.

It asks whether the organization increased accepted throughput, reduced elapsed time, improved quality, improved customer or employee outcomes, released usable capacity, lowered unit cost, or improved financial performance.

The word accepted matters. More drafts, summaries, tickets, code, reports, and recommendations are not automatically valuable output. The work must survive review, satisfy quality requirements, reach the customer or operating process, and avoid creating offsetting rework or risk.

Business productivity is not output volume divided by employee count.

A more useful definition is accepted business value divided by the total cost and elapsed time required to produce it.

The Broken Measurement Chain

The diagram below shows where executive reporting usually fails. Adoption metrics are available immediately. Workflow evidence is fragmented. Financial and customer outcomes are several systems away.

The board should not be asked to infer the right side from the left side.

The organization must instrument the middle.

Why More AI Output Can Reduce Productivity

The easiest AI metrics reward volume. Productivity depends on flow.

That difference creates several predictable failure modes.

Output Can Accumulate Before the Constraint

Every workflow has a limiting constraint.

In software delivery, it may be architecture review, test capacity, security review, environment availability, change approval, or production support. In customer operations, it may be identity verification, supervisor approval, specialist availability, or a downstream system.

When AI increases output before the constraint, work-in-progress grows.

The team may produce more code, more draft responses, more analysis, or more recommendations without completing more business transactions. Queues become longer. Review burden increases. Context switching rises. The organization experiences more activity and less flow.

DORA research has described this paradox in software delivery: developers can feel more productive and produce code faster while system-level throughput and stability decline.

The lesson is not that AI coding tools are inherently harmful. The lesson is that local acceleration does not remove the delivery system’s bottleneck.

Generated Artifacts Create Review Debt

AI output still requires acceptance.

A generated document may need fact-checking. A generated customer response may need policy validation. Generated code may need architecture review, tests, security analysis, documentation, and production ownership. An agent recommendation may need source validation and human approval.

If the organization measures only generation time, the review cost disappears from the productivity claim.

A credible calculation includes:

• time spent prompting and supplying context
• time spent reviewing and correcting the output
• rework caused by errors, omissions, or weak assumptions
• downstream coordination and approval time
• exception handling
• operational support after the output is accepted

The AI-assisted step may be faster while the redesigned workflow is more expensive.

Volume Can Hide Quality Degradation

More output can make a dashboard look healthy while increasing defects.

A coding assistant can increase lines added while also increasing change failure, rollback, vulnerability remediation, or operational toil. A service assistant can reduce response time while increasing transfers, repeat contacts, policy exceptions, or customer dissatisfaction. A sales assistant can produce more outreach while reducing response quality or damaging account trust.

Quality must be measured at the acceptance boundary, not at the generation boundary.

Saved Time Is Not Automatically Released Capacity

Hours saved is one of the most common AI value claims.

It is also one of the easiest to overstate.

Gross time saved is the estimated reduction in task effort. Net capacity released subtracts prompting, review, rework, exception handling, coordination, and new operating overhead. Realized capacity is the portion of net capacity that the organization actually redirects into more demand, faster flow, better quality, backlog reduction, avoided hiring, or another measurable outcome.

A worker saving twenty minutes on a task does not automatically create twenty minutes of financial benefit. The time may be fragmented across the day, absorbed by additional demand, spent correcting another AI output, or never converted into usable capacity.

Microsoft’s Copilot impact report demonstrates why this distinction matters. Its assisted-hours metric is explicitly described as an estimate derived from Copilot actions and research-based multipliers, and the documentation notes that the methodology will evolve.

A value calculator can translate estimated hours into estimated currency, but multiplication does not remove the uncertainty in the original assumption.

Assisted hours are a useful planning signal. They are not bankable return until the organization shows what happened to the released capacity.

The measurement chain should therefore distinguish:

Gross task time saved, net capacity released, and capacity economically realized.

A Practical Executive Measurement Framework

The framework starts with the workflow, not the tool.

Define the Outcome Before the Metric

Every use case needs an outcome statement that includes a value target and a guardrail.

Weak objective:

Deploy Copilot to the service desk.

Stronger objective:

Reduce median incident-resolution time by 15% while maintaining customer satisfaction, first-contact resolution, and security-control compliance.

The stronger objective defines what success means and what cannot be sacrificed to obtain it.

Establish a Baseline and Comparison Method

Do not declare improvement without a baseline.

Capture a representative period before the intervention. Segment by work type, complexity, role, volume, seasonality, and business unit when those factors materially affect performance.

Avoid comparing a highly motivated pilot group with an unprepared control group and attributing every difference to the tool.

Useful comparison methods include:

• pre-adoption versus post-adoption analysis with seasonality controls
• matched teams or workflow cohorts
• phased rollout with holdout groups
• task-level randomized trials where appropriate
• time-series analysis across stable process definitions

The measurement method does not need to be academically perfect. It does need to be honest about attribution.

Microsoft’s own Copilot impact documentation warns that collaboration changes observed before and after adoption may be influenced by seasonality, role differences, and other organizational factors.

That is the correct posture.

Correlation is useful evidence, but it is not automatic proof of causation.

Instrument the Complete Workflow

The workflow boundary should begin where demand enters and end where value is accepted.

For a service desk, that may run from ticket creation through resolution confirmation. For software delivery, it may run from approved business change through production validation. For finance, it may run from transaction intake through reconciliation and exception closure.

Measure the AI-assisted task inside that larger path.

Then instrument the handoffs, queues, approvals, exceptions, and downstream effects that determine whether the local time saving becomes system-level value.

Measure Accepted Throughput

Count work that reaches the acceptance boundary.

Examples include:

• incidents resolved and confirmed without repeat contact
• business changes deployed and operating without rollback
• claims processed without later correction
• customer requests completed within policy
• decisions made with required evidence and no reopening
• reports accepted without material revision

Raw artifact volume can remain in the operational dashboard. Accepted throughput belongs in the productivity scorecard.

Include Quality, Rework, and Risk Guardrails

Every speed or throughput metric needs a counter-metric.

If cycle time falls, track whether errors, rework, escalations, complaints, incidents, policy exceptions, or rollback frequency rise. If output volume increases, track acceptance rate and the effort required to accept it. If decision latency falls, track decision reversals and exception rates.

AI productivity should be reported as quality-adjusted throughput, not raw throughput.

Identify the Real Constraint

Measure queue time and wait time, not only touch time.

Touch time is the period in which a person or system actively works on the item. Wait time is the period in which the item sits in a queue, awaits data, waits for approval, or remains blocked by another dependency.

AI commonly reduces touch time first. Many enterprise workflows are dominated by wait time.

If a workflow takes five days end to end and only forty minutes are active work, reducing the active work to twenty minutes does not create a 50% cycle-time improvement.

It creates a twenty-minute local saving inside a five-day system.

The next investment may need to redesign approvals, data access, ownership, or downstream capacity rather than add another AI feature.

Calculate Net Capacity Released

Use a capacity equation that includes the full operating burden.

Net capacity released equals baseline labor time minus AI-assisted labor time, review time, rework time, exception time, and new operational overhead.

Then track how that capacity is used.

Capacity can become:

• more demand completed
• backlog reduced
• service hours expanded
• higher-quality review
• faster decisions
• avoided overtime
• avoided hiring
• reassignment to higher-value work

Without a destination, capacity release remains an estimate.

Include the Total Cost of the Redesigned Workflow

The cost model must extend beyond the license.

Include:

• software licenses and subscriptions
• token, credit, request, or agent consumption
• model and infrastructure cost
• data preparation and retrieval services
• integration and platform engineering
• security, governance, and compliance controls
• training and change management
• human review and exception handling
• evaluation, observability, and support
• rework, failure, and rollback cost

The relevant unit is not cost per prompt.

It is cost per accepted business outcome.

The Executive AI Productivity Scorecard

A useful scorecard connects operational telemetry to workflow and outcome evidence without pretending that every metric has equal weight.

Dimension	Executive question	Example measure	Required evidence	Typical owner
Activation	Can the intended workforce access and use the capability?	Active eligible users, training completion	License and usage telemetry	CIO or platform owner
Consumption	What demand and cost is the AI platform carrying?	Tokens, requests, agent runs, cost by workflow	Model gateway and billing data	AI platform and FinOps
Task acceleration	Did the bounded task become faster?	Median touch-time reduction	Task timestamps and sampled studies	Workflow owner
End-to-end flow	Did the complete process become faster?	Request-to-acceptance cycle time	Source and target system events	Business process owner
Accepted throughput	Did more useful work reach completion?	Quality-approved transactions per period	Acceptance and closure records	Business owner
Quality and rework	Did speed create downstream correction?	Error, reopen, rollback, escalation, revision rate	Quality and incident systems	Quality, operations, or risk
Customer outcome	Did the experience improve?	Customer effort, satisfaction, retention, repeat contact	Customer and service data	Customer or service leader
Decision latency	Did the organization make sound decisions faster?	Time to decision plus reversal rate	Workflow and decision records	Executive process owner
Capacity released	Was saved effort converted into usable capacity?	Net hours released and disposition	Time study, volume, staffing, backlog	Business and finance
Unit economics	Did cost per accepted outcome improve?	Total workflow cost per accepted result	Finance, platform, labor, and operations data	CFO and outcome owner
Risk	Did the control posture remain acceptable?	Policy exceptions, data incidents, unauthorized actions	Governance and security evidence	Risk, security, and compliance

The board does not need every underlying telemetry field.

It needs the measurement chain, the business outcome, the guardrails, the cost, the confidence level, and the decision that follows.

A Worked Example: AI-Assisted Service Desk Triage

Consider an enterprise service desk that deploys an AI assistant for ticket summarization, classification, priority recommendations, and draft resolution notes.

After ninety days, the adoption dashboard shows:

• 82% of licensed analysts are active
• prompts per user increased steadily
• AI-generated summaries are used on most eligible tickets
• token consumption doubled
• analysts report that initial triage feels faster

Those are positive adoption signals.

They do not yet prove productivity.

The workflow analysis finds that median analyst touch time fell from twelve minutes to seven minutes. However, ticket reassignment increased because classification quality varied across complex incidents. Security-related tickets required additional review. The largest queue remained specialist assignment, which averaged eleven hours. Customer satisfaction stayed flat, and repeat-contact rate increased slightly.

The correct conclusion is not that the tool failed.

The correct conclusion is that AI accelerated the initial triage task, released some analyst capacity, and exposed the next workflow constraints. The next design work should improve classification evaluation, route complex tickets differently, strengthen knowledge grounding, and reduce the specialist-assignment queue.

A board report should say:

• task acceleration: confirmed
• end-to-end cycle-time improvement: limited
• accepted throughput improvement: not yet established
• quality guardrail: requires correction
• customer outcome: unchanged
• unit cost: under review because consumption increased
• next decision: redesign routing before expanding the rollout

That is a more useful AI value story than reporting prompt growth.

Software Delivery Needs Quality-Adjusted Metrics

Software engineering makes the measurement trap especially visible because AI can generate large amounts of code quickly.

GitHub correctly describes lines-of-code telemetry as directional. The metric can show how much code Copilot suggested, added, or deleted. It can help platform teams understand adoption and workflow patterns.

It should not become an employee productivity score.

Lines of code reward verbosity, duplication, generated scaffolding, and churn. They do not reveal whether the change solved the right problem, reduced complexity, passed review, improved reliability, or created long-term maintenance cost.

A quality-adjusted AI software-delivery scorecard should connect Copilot telemetry to:

• lead time for an accepted business change
• review time and review burden
• automated test pass rate and coverage changes
• escaped defects
• change failure and rollback frequency
• security findings and remediation effort
• production incidents linked to the change
• operational toil after release
• cost per accepted business change

The unit of value is not code generated.

It is a reliable business change accepted into production.

Do Not Turn AI Usage Into Employee Surveillance

The most dangerous version of the measurement trap appears when tool telemetry becomes a performance-management signal.

Prompt counts, token volume, active days, acceptance rates, agent runs, and generated code can be useful for platform operations, cost governance, training, and adoption analysis. They are weak measures of individual contribution because they do not control for role, task complexity, work quality, collaboration, judgment, risk, or the value of the outcome.

They also create predictable gaming behavior. Employees learn that visible usage is rewarded, so they generate more visible usage. Managers then receive a clean dashboard that reflects the incentive system they created, not the productivity they intended to measure.

A safer governance posture is to:

• measure adoption at team or workflow level whenever possible
• use individual telemetry for support, enablement, security, or cost investigation under defined policy
• prohibit ranking employees by prompt count, token consumption, or lines of code
• separate AI fluency assessment from raw usage volume
• document the purpose, access, retention, and permitted use of workforce telemetry
• require HR, legal, privacy, security, and employee-relations review before performance use
• evaluate business outcomes through the workflow, not through the visible activity of one person

AI measurement should improve the operating system of work.

It should not reward people for feeding the meter.

A Measurement Contract for Every AI Workflow

A production AI use case should carry a machine-readable measurement contract alongside its architecture, policy, and operating model.

The example below is intentionally platform-neutral. It forces the team to define the baseline, outcome, guardrails, cost, attribution method, and reporting boundary before the board sees a productivity claim.

ai_productivity_measurement:
  workflow_id: service-desk-triage
  accountable_owner: service-operations
  measurement_owner: enterprise-analytics
  review_cadence: monthly

  outcome:
    primary: reduce_end_to_end_resolution_time
    target_percent: 15
    acceptance_boundary: ticket_closed_and_confirmed

  baseline:
    period_days: 90
    segment_by:
      - ticket_type
      - priority
      - complexity
      - support_group
    required_measures:
      - touch_time
      - wait_time
      - reopen_rate
      - transfer_rate
      - customer_satisfaction
      - total_workflow_cost

  adoption_signals:
    diagnostic_only: true
    measures:
      - eligible_users
      - active_users
      - prompts
      - ai_actions
      - token_cost

  workflow_measures:
    - median_touch_time
    - median_end_to_end_cycle_time
    - accepted_throughput
    - specialist_queue_time
    - decision_latency

  quality_guardrails:
    - reopen_rate_not_increased
    - transfer_rate_not_increased
    - security_exception_rate_not_increased
    - customer_satisfaction_not_reduced

  capacity:
    gross_time_saved: measured
    review_and_rework_time: measured
    net_capacity_released: calculated
    capacity_disposition: required

  economics:
    include:
      - licenses
      - model_consumption
      - platform_operations
      - integration
      - human_review
      - rework
      - governance
    unit_metric: cost_per_confirmed_resolution

  attribution:
    method: phased_rollout_with_matched_cohorts
    confidence_rating: required
    known_confounders:
      - seasonality
      - staffing_changes
      - ticket_mix
      - policy_changes

  reporting:
    board_view:
      - outcome_progress
      - quality_guardrails
      - unit_cost
      - net_capacity_released
      - risk_status
      - confidence_rating
    exclude_from_employee_ranking:
      - prompts
      - tokens
      - active_days
      - generated_artifacts
      - lines_of_code

This contract does not make attribution perfect. It makes the claim reviewable.

It also prevents the measurement model from being invented after the pilot produces attractive activity data.

What the Board Should Ask

Boards do not need to become experts in token accounting or Copilot telemetry. They do need to challenge the evidence chain.

The most useful questions are:

• Which business workflow changed, not merely which tool was deployed?
• What was the baseline before AI was introduced?
• Where is the acceptance boundary for useful output?
• Did end-to-end cycle time improve, or only one task?
• Did rework, error, escalation, rollback, or review burden change?
• What capacity was released, and where was it redeployed?
• What is the total cost per accepted outcome?
• What customer, employee, risk, or financial outcome changed?
• What other factors could explain the result?
• What decision will management make if the outcome does not improve?

The final question matters.

Measurement should change investment, workflow design, licensing, model selection, training, or governance. A dashboard with no decision attached is reporting theater.

A Practical Ninety-Day Implementation Path

The measurement framework does not require an enterprise-wide analytics transformation before it can begin.

Start with three workflows where demand, ownership, source systems, and acceptance criteria are clear.

During the first thirty days, define the workflow boundary, outcome owner, baseline, acceptance event, quality guardrails, cost model, and telemetry sources. Remove prompt count, token volume, and artifact volume from any employee-performance rubric.

During days thirty-one through sixty, instrument touch time, wait time, accepted throughput, rework, exceptions, customer outcomes, and total workflow cost. Use adoption telemetry to identify training or access problems, not to claim productivity.

During days sixty-one through ninety, compare the AI-assisted cohort with the baseline or a matched group. Calculate net capacity released, document confounding factors, assign a confidence rating, and decide whether to scale, redesign, constrain, or stop the use case.

The board report should remain short:

• outcome achieved or not achieved
• quality and risk guardrails
• unit economics
• capacity released and disposition
• confidence in attribution
• next management decision

The detailed adoption dashboard can stay with the operating team.

Conclusion

AI productivity is not measured by how loudly the meter spins.

Tool activation shows whether access exists. Consumption shows whether people and agents are using the capability. Task acceleration shows whether a bounded activity became faster. Business productivity shows whether the complete workflow delivered more accepted value, at the required quality and risk level, for less total time or cost.

The board should not be asked to infer revenue, margin, customer experience, or workforce productivity from prompts, tokens, active users, Copilot actions, generated artifacts, or lines of code. Those signals are useful inputs, but they are not the outcome.

The practical path is to instrument the middle of the measurement chain. Establish the baseline. Define the acceptance boundary. Measure touch time and wait time. Track rework, errors, quality, risk, and customer outcomes. Identify the real constraint. Calculate net capacity released. Include the full cost of the redesigned workflow. State how confident the organization is that AI caused the observed change.

AI can create real productivity gains. The evidence becomes credible when the organization stops measuring the tool as the result and starts measuring the workflow as the system that creates value.

External References

• PwC: PwC 2026 Global CEO Survey
  Canonical URL: https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html
• Microsoft Learn: Microsoft 365 Copilot usage report
  Canonical URL: https://learn.microsoft.com/en-us/microsoft-365/admin/activity-reports/microsoft-365-copilot-usage?view=o365-worldwide
• Microsoft Learn: Microsoft 365 Copilot impact report
  Canonical URL: https://learn.microsoft.com/en-us/viva/insights/advanced/analyst/templates/microsoft-365-copilot-impact
• Microsoft WorkLab: 2026 Work Trend Index report: Agents, human agency, and opportunity
  Canonical URL: https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization
• GitHub Docs: GitHub Copilot usage metrics
  Canonical URL: https://docs.github.com/en/copilot/concepts/copilot-usage-metrics/copilot-metrics
• GitHub Docs: Lines of Code metrics
  Canonical URL: https://docs.github.com/en/copilot/reference/copilot-usage-metrics/lines-of-code-metrics
• Google Cloud: Unlock Software Delivery Excellence and Quality with Gemini Code Assist Agents
  Canonical URL: https://cloud.google.com/blog/topics/developers-practitioners/read-doras-latest-research-on-software-excellence
• National Bureau of Economic Research: Generative AI at Work
  Canonical URL: https://www.nber.org/papers/w31161
• Science: Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence
  Canonical URL: https://www.science.org/doi/10.1126/science.adh2586
• SHRM: ‘Tokenmaxxing’ Offers HR a Valuable Lesson About Metrics
  Canonical URL: https://www.shrm.org/mena/topics-tools/news/technology/tokenmaxxing-hr-performance-metrics

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