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Generative AI can improve specific tasks inside an enterprise, but the value a company actually realizes depends on the task, the workers doing it, how the tool is deployed, and the risk controls around it. The most rigorous evidence so far comes from one customer-support deployment, which found a 14% average gain in issues resolved per hour. That gain was concentrated among less experienced agents. Workplace surveys show that adoption is already widespread and that workers report time savings, but survey responses are not the same as independently measured firm-level productivity. No current source establishes a productivity gain that holds across all sectors.
What the strongest benefit evidence shows
A measured gain in customer support
The clearest causal evidence comes from a field study by Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, published as NBER Working Paper 31161, “Generative AI at Work” (issued April 2023, revised November 2023; NBER lists a published version in 2025). The authors studied the staggered introduction of a generative-AI conversational assistant to 5,179 customer-support agents at one company. Because access rolled out in stages, the authors could compare agents with and without access over time.
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Access to the assistant increased issues resolved per hour by 14% on average. The estimated improvement was 34% for novice and lower-skilled workers, with minimal impact on experienced and highly skilled workers. The authors also describe improved customer sentiment, higher employee retention, and possible learning effects for workers. These findings describe one company and one support workflow. They are not a forecast for other functions or organizations.
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The most useful lesson in the support study is not the average. The assistant appeared to act as a kind of expertise transfer: it helped less experienced agents close the gap with stronger peers, while adding little for those who already performed well. An enterprise that deploys a similar tool to a mixed team should expect different results by role and skill level, and should measure those groups separately rather than relying on a single company-wide number.
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Adoption and reported time savings
A second study, NBER Working Paper 32966 by Alexander Bick, Adam Blandin, and David J. Deming, “The Rapid Adoption of Generative AI” (issued September 2024, revised February 2025), uses a nationally representative U.S. survey. As of late 2024, 23% of employed respondents said they had used generative AI for work at least once in the prior week, and 9% used it every work day.
Respondents reported that generative AI assisted with 1% to 5% of their work hours and that the time it saved equaled 1.4% of total work hours. The authors read these figures as evidence that substantial productivity gains are possible. They are self-reported survey measures, not a direct measurement of output or of productivity across firms. The most common uses were writing, searching for information, and obtaining detailed instructions.
That distinction matters for planning. Usage shows that employees have found practical tasks for the tools. Reported time savings suggest where value might be. Neither proves that a company’s costs fell, that output rose, or that quality held steady.
What the evidence does not establish
- A universal productivity gain across industries. The field study covers one customer-support operation, and the survey covers self-reported use in the U.S. workforce.
- Causal effects on quality, cost, or employment at the enterprise level. Neither source measures those outcomes at the firm level.
- Risk-adjusted value. A time saving that comes with more errors, compliance exposure, or rework may produce little net benefit, and the sources do not quantify that trade-off.
- Superiority of a particular vendor, model, or deployment pattern. No head-to-head enterprise product assessment was identified in the material reviewed for this article.
Benefit and evidence types at a glance
| Evidence type | Source | What it measures | What it does not show |
|---|---|---|---|
| Field experiment in customer support | Brynjolfsson, Li, and Raymond (NBER w31161, 2023; revised 2023) | Issues resolved per hour for 5,179 agents, with staggered access | Results outside customer support or outside that company |
| Nationally representative U.S. survey | Bick, Blandin, and Deming (NBER w32966, 2024; revised 2025) | Self-reported use frequency, share of hours assisted, and reported time saved (late 2024) | Measured output, firm-wide productivity, or quality |
| Risk-management guidance | NIST Generative AI Profile (2024) | Categories of generative-AI risk and suggested actions | Measured risk levels or required controls for any specific organization |
Risks enterprises need to manage
NIST’s July 2024 announcement identifies a set of risks that generative AI introduces or intensifies. It gives examples rather than a complete list, and the three below are the most frequently relevant for enterprise deployments.
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Cybersecurity misuse
NIST names lowered barriers to cybersecurity attacks as a generative-AI risk. A tool that makes it easier to write convincing phishing messages, generate malicious code, or probe systems can increase the threat faced by a company even if the company never deploys the tool itself. Enterprises should treat AI-assisted attacks as part of their existing threat model rather than as a separate category.
Misinformation and harmful content
NIST also lists the production of misinformation and harmful content. In an enterprise setting this shows up when generated text is published externally, used in customer communications, or fed into decisions without review. The risk grows with the audience and with the consequences of a wrong or offensive output.
Hallucinated or confabulated output
NIST notes that generative systems can confabulate, sometimes described as “hallucinating,” and produce output that looks authoritative but is false. This is the risk most directly tied to the workflows in which the benefits above appear. Writing, search, and instruction-following tasks all depend on the accuracy of what the model returns, so each workflow needs a defined way to verify output before it is acted on.
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Using the NIST Generative AI Profile for governance
What the profile is
NIST’s AI Risk Management Framework is the parent resource. The Generative AI Profile is a cross-sectoral companion to AI RMF 1.0. It defines risks that are novel to or made worse by generative AI and suggests actions for organizations to govern, map, measure, and manage them. NIST describes the framework as voluntary and presents the profile as a way to align risk management with an organization’s goals and priorities.
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Scale of the guidance
The profile centers on 12 risks and just over 200 suggested actions. A company does not need to adopt every action. The useful approach is to select the actions that match its specific use cases, its risk tolerance, and the requirements that apply to it.
Why the framing matters
In April 2024, Laurie E. Locascio, then Under Secretary of Commerce for Standards and Technology and NIST Director, said: “For all its potentially transformative benefits, generative AI also brings risks that are significantly different from those we see with traditional software.” The statement appears in a NIST announcement dated April 29, 2024. The point for enterprises is that generative AI cannot be governed simply by extending existing software controls.
How to evaluate a workflow before deploying
Because value depends on the task and the people, the evaluation should happen workflow by workflow. The steps below follow the distinctions the evidence supports.
Quick Recap
- Define the task and its baseline. Record how long the task takes now, what quality looks like, and what errors cost. Without a baseline, reported time savings cannot be interpreted.
- Segment workers by experience. The customer-support study found that gains were concentrated among novice and lower-skilled workers. Measure outcomes separately for each group.
- Measure outcomes, not just usage. Track issues resolved, error rates, rework, and customer outcomes. Usage frequency and self-reported savings are useful context but do not confirm value.
- Assess the consequence of an error. A wrong internal draft and a wrong customer-facing statement carry very different costs. Set review requirements to match.
- Map risks against NIST’s categories. Use the profile to check cybersecurity exposure, harmful-content risk, and confabulation for that specific workflow, and select actions that fit the organization’s risk tolerance and obligations.
- Compare options on the same criteria. Judge each model or tool by the same task outcomes, worker experience, and error consequences. The available evidence does not support naming one model or vendor as best for enterprises in general.
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