AI improves productivity when it raises useful, sustainable output per unit of input—and when leaders redesign the work around it. An AI tool by itself can generate text, summarize documents or classify cases. It does not set priorities, repair a broken process, verify quality, protect confidential data or decide who is accountable. Those organizational disciplines determine whether apparent efficiency becomes durable value or simply transfers work and risk to employees, customers and future budgets.
For managers allocating capital and labor, “doing more with less” should therefore mean improving the output-input relationship without quietly lowering quality, resilience or workforce capacity.
Productivity is an output-to-input relationship
McKinsey partner Charles Atkins defines productivity as “a measure of output relative to some set of inputs,” with labor productivity expressed as output per worker for each hour worked. The useful unit is not the number of activities completed; it is valuable output compared with the labor, capital, time, energy and other resources required to produce it.
That definition changes how an AI project should be judged. A support team that closes more tickets but creates more rework may be less productive. A factory that reduces maintenance spending while increasing downtime has not necessarily created an efficiency gain. A finance department that accelerates reporting but introduces material errors may have increased activity while reducing economic value.
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- Output: the service, decision, product or outcome the customer or stakeholder actually needs.
- Quality: accuracy, reliability, safety, compliance and user experience at the target output level.
- Inputs: worker-hours, technology, data, management attention, capital, energy and external services.
- Sustainability: whether the organization can maintain the result without exhaustion, deferred maintenance or unacceptable risk.
Olivia White, a McKinsey senior partner, summarizes the aspiration as “doing more with less.” The phrase is meaningful only when the “more” is useful and the “less” does not conceal costs that surface later.
Why AI needs an operating model, not just a license
AI can lower the time required for a task, expand the number of cases a specialist can review or make previously expensive analysis feasible. The gains are usually conditional on complementary changes in how the organization works.
Start with a strategic outcome
Define the business result before choosing a model: shorter payment-cycle time, fewer preventable errors, faster resolution of a defined customer issue or better forecast accuracy. A vague goal such as “use AI everywhere” makes it impossible to distinguish productivity from increased experimentation.
Redesign the process around the technology
Map the current workflow, including handoffs, approvals, exceptions and rework. Decide which steps AI performs, which remain human, and where a person must review or override an output. Automating one step while leaving the surrounding bottlenecks intact often produces little net improvement.
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Make accountability explicit
Name the owner of the outcome, not merely the owner of the software. That person should be able to stop deployment, change the workflow and act on quality or risk signals. A vendor’s accuracy claim cannot replace internal responsibility for a consequential decision.
Invest in intangible capability
McKinsey’s 2023 analysis of productive companies points to investment in research and development, intellectual property, workforce capabilities and other intangible assets. It also describes leading firms setting technology-enabled goals, reconfiguring operations and holding teams accountable for results. These complements take management time and money; they are inputs to productivity, not overhead that can be ignored.
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The same analysis reported that companies typically capture only about 25% to 30% of expected value from digital transformations. That figure is McKinsey’s reported finding and interpretation, not a universal rate for every AI program. It illustrates why buying technology without changing strategy and the operating model can leave most of the anticipated value unrealized.
What the historical evidence can—and cannot—show
The available figures are bounded in time. US productivity growth averaged 1.4% annually over the 15 years discussed by McKinsey Global Institute in its February 16, 2023 article, compared with 2.2% annually since 1948. McKinsey estimated that returning to the longer-run trend could add $10 trillion to cumulative US GDP by 2030. That was a conditional estimate, not an observed result or a guarantee, and neither figure is a current 2026 measurement.
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McKinsey’s Global AI Survey, published November 22, 2019, collected 2,360 online responses between March 26 and April 5 of that year. Respondents described results in the business areas where their organizations used AI:
| 2019 survey measure | Reported result | How to interpret it |
|---|---|---|
| Respondents reporting cost reductions in AI-using areas | 44% | Perception reported by respondents; not a causal estimate or current benchmark. |
| Respondents reporting revenue increases in AI-using areas | 63% | Perception reported by respondents; revenue movement cannot by itself prove AI caused the increase. |
| Respondents at AI high-performing companies reporting alignment between AI and corporate strategy | 72%, versus 29% at other AI-using companies | An association using the survey’s definition of high performers, not proof that alignment alone produced the outcome. |
| Respondents at AI-using companies expecting at least some workers to be retrained within three years | 83% | A 2019 expectation, not a later observed result or a statement of what employers expect now. |
The survey also found that fewer than half of respondents said their organizations comprehensively identified and prioritized AI risks. Taken together, the results are a historical signal: strategy, risk management and skills appeared more common among self-described high performers, but the survey cannot establish that any one practice caused better performance. Leaders should use the findings to frame questions, not to promise a return.
A disciplined method for evaluating an AI initiative
1. Establish the baseline
Record current volume, cycle time, error and rework rates, worker-hours, direct cost and service outcomes. Include the cost of supervision, data preparation, maintenance and exceptions. Without a baseline, a faster process can look successful even if it creates hidden work elsewhere.
2. Specify the quality floor
Set non-negotiable thresholds for accuracy, safety, privacy, security, explainability and customer experience. A productivity gain below the quality floor is a failure, not a trade-off to be buried in an average.
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3. Choose the right level of automation
Use full automation only where errors are detectable, consequences are limited and recovery is practical. Use human review or decision support where context, judgment or accountability matters. Document the cases that must be escalated.
4. Pilot the complete workflow
Test the model, the data pipeline, approvals, user interface, exception handling and monitoring together. Compare results with the baseline using a defined period and a representative workload. A demonstration on easy cases says little about production performance.
5. Assign benefits and costs to an owner
The accountable owner should track realized output, quality, total operating cost and workforce effects. Include implementation, integration, licensing, training, audit and ongoing model-management costs rather than counting only the subscription.
6. Scale only after review
Set explicit conditions for expansion, redesign or shutdown. Review whether the pilot moved work to another team, increased unpaid overtime, weakened controls or created a dependency that will be expensive to maintain.
Resource cuts can destroy the capacity that produces output
Brookings contributor and University of Michigan professor John Leslie King cautioned that “Lower inputs mean less will be done; doing more with less is wishful thinking.” His argument is not that efficiency is impossible. Removing genuine waste or improving a process can deliver more output with fewer inputs. The warning is against reducing resources while leaving expectations unchanged.
Before treating an AI-enabled headcount or budget reduction as a productivity gain, ask:
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- What work will stop, and is it truly low value?
- Who absorbs exceptions, quality checks, maintenance and customer complaints?
- What leading indicator would reveal a quality or service decline before financial results do?
- Which skills or relationships will be difficult to rebuild if removed?
- Are maintenance, security updates, documentation or training being deferred?
- Which customers, suppliers, employees or communities bear the downside?
Compare output, quality and stakeholder impact alongside cost. A lower expense line is evidence of lower spending; it is not, by itself, evidence of higher productivity.
Workforce capability and sustainable performance
AI changes job content even when it does not eliminate a job. Employees may need to check model outputs, handle unusual cases, manage data, explain decisions or learn a new operating procedure. Training, time to practice and clear escalation rights are therefore productive inputs.
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- Identify which tasks are augmented, automated or newly created.
- Teach people how to verify outputs and recognize failure modes.
- Give affected workers time and support to learn the redesigned process.
- Measure whether training reduces errors and rework, not merely whether attendance was recorded.
Jennifer Moss argues that burnout must be addressed through upstream systems, policies and workload, rather than relying only on wellness technology, gym subsidies, yoga or breathing exercises. Her point is directly relevant to AI economics: if a tool raises nominal throughput by intensifying pace, extending availability or shifting monitoring work onto employees, the apparent gain may be unsustainable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Governance is part of the productivity calculation
Controls protect the value that an AI system is supposed to create. They also consume resources, so they should be designed into the workflow rather than added after deployment.
Risk identification
List foreseeable privacy, security, discrimination, intellectual-property, reliability and explainability risks. Identify who could be harmed and how the organization would detect the harm.
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Data and access controls
Limit the data and permissions each system needs. Define retention, logging and vendor-access rules, and prevent confidential information from entering tools that are not approved for it.
Human review and appeal
Specify when a qualified person must review an output, how disagreements are resolved and how an affected customer or employee can seek correction.
Monitoring and incident response
Track drift, error patterns, override rates, complaints and unusual outcomes. Establish a process to pause the system, investigate and restore service when controls fail.
These safeguards are not proof that an AI project will succeed. They make failures visible and limit the damage when assumptions are wrong.
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| Strategy | Potential value | Inputs and risks to examine | Discipline required |
|---|---|---|---|
| Automate a repeatable, low-consequence task | Lower handling time and more consistent throughput | Data quality, exception volume, integration and loss of human context | Define quality thresholds, fallback steps and periodic sampling. |
| Augment specialists on complex work | More analysis or decisions per specialist-hour | Verification time, overreliance, training and accountability for final decisions | Keep human ownership and measure decision quality, not drafts produced. |
| Use AI to redesign an end-to-end process | Removal of bottlenecks and fewer handoffs | Change-management effort, dependencies and disruption during transition | Assign an outcome owner and test the complete workflow against a baseline. |
| Cut resources without redesigning work | Immediate expense reduction | Deferred maintenance, burnout, service decline and lost capability | Require evidence that output and quality remain at the promised level. |
A management dashboard that keeps “less” honest
Review a small set of measures together rather than optimizing one number:
- Useful output per worker-hour: completed outcomes that meet the quality standard divided by labor hours.
- First-pass quality: the share accepted without correction, rework or customer remediation.
- Total cost per acceptable outcome: labor, technology, data, oversight, training and maintenance included.
- Cycle time and backlog: speed alongside the amount of unfinished work.
- Exception and override rates: where automation is failing or human judgment is routinely required.
- Workforce sustainability: overtime, absence, turnover, reported workload and time available for training.
- Risk indicators: incidents, near misses, privacy events, complaints and unresolved model issues.
Set the review cadence and decision rights before launch. If a measure deteriorates, the owner should be able to adjust the workflow or stop the system rather than explain away the result.
The economic test
An AI investment passes a disciplined test when it produces more valuable, quality-compliant and sustainable output for the total resources consumed. That may mean doing more with fewer inputs, doing more with the same inputs—as McKinsey’s discussion also suggests—or doing the same essential work with lower risk and better resilience.
The central choice is not AI versus people, or growth versus cuts. It is whether leaders will pair technology with strategy, process redesign, accountability, capability building and risk control. Without those complements, “less” can simply mean less capacity to deliver the outcomes the organization promised.
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