AI ROI in 2026: Statistics, Industry Evidence and Measurement

AI creates financial value when better work changes the economics of a business. Faster output is useful, but an investment pays back only when the benefits exceed implementation, operation and change costs. This report brings together current research, industry evidence and a reproducible calculation model to help leaders make that distinction.

The 2026 evidence points to a gap between individual gains and company-level financial impact. McKinsey’s August survey illustrates that gap below. Its results are self-reported, and the three measures are not stages of one funnel. [1]

Figure 1. Uvik Software visualization of McKinsey 2026 survey findings. EBIT means earnings before interest and taxes.

How to use this report. Use the statistics to understand the market, the industry sections to choose a workflow, and the calculation model to test your own economics. The examples are explicitly hypothetical. They are not Uvik Software client results, vendor prices or promises of performance.

What AI ROI measures

AI return on investment is the net economic benefit attributable to an AI initiative, divided by its total cost over the same period. A useful calculation states its horizon, baseline, cost boundary and attribution method. Without those four elements, two ROI claims may describe very different things.

AI ROI = (realized benefits – total AI costs) ÷ total AI costs × 100. Benefits can include avoided expenditure, additional contribution profit and validated loss reduction. The cost base includes setup and ongoing operation. Label a model “economic ROI” if benefits include capacity value that has not become cash.

Figure 2. The Uvik Software AI ROI Evidence Map 2026 separates financial return from business impact, task productivity and adoption.

Keep three ledgers: cash savings, productive capacity and growth. A released hour belongs in the capacity ledger until someone identifies what happens to it. If it removes overtime, avoids an approved hire or produces profitable additional output, record that conversion with evidence. Do not count the same hour again as both a payroll saving and new revenue.

AI ROI statistics for 2026

The table records what each study actually measures. Publication in 2026 does not mean the underlying observations were collected in 2026. The source register provides samples, timing and links. None of these studies establishes a universal return for every AI project.

ID	Statistic	Population and interpretation
01	37% report positive enterprise EBIT impact [1]	McKinsey 2026 respondents; a financial-impact share, not 37% ROI.
02	80% report improved individual productivity [1]	Self-reported personal benefit; not an observed cash saving.
03	6% qualify as AI high performers [1]	At least 5% EBIT attribution plus significant perceived impact.
04	About 20% face AI operating-cost constraints [1]	Usage constraint, including token costs.
05	22% of CEOs report increased costs due to AI [2]	PwC late-2025 fieldwork; costs can rise during implementation.
06	79% report better public-sector threat identification [10]	A perceived security benefit, not a measured financial return.
07	56% report neither revenue nor cost benefits [2]	PwC global CEO survey; late-2025 fieldwork.
08	30% report increased revenue [2]	Revenue impact before isolating profit or full costs.
09	26% report lower costs [2]	Cost benefit; can overlap with revenue benefit.
10	12% report both revenue and cost benefits [2]	This is not the share with positive net ROI.
11	51% experienced a shift in AI business impact [3]	PwC follow-up panel; changes across impact categories.
12	39% maintained or improved positive impact [3]	Follow-up result; not a directly comparable update to the 12% measure.
13	16% remained negative or saw impact worsen [3]	Different panel classification from net investment return.

Reading rule. Always carry the population and metric with the number. “Some EBIT impact” is a much lower threshold than recovering the cost of a particular implementation. A percentage of respondents is also different from a percentage improvement in their results.

More AI return and productivity statistics

ID	Statistic	Scope and limitation
14	66% report productivity or efficiency gains [4]	Deloitte leading-edge organizations; multiple benefits allowed.
15	53% report better insights or decisions [4]	A benefit category, not a profit estimate.
16	40% report cost reduction [4]	Reported achieved benefit.
17	20% report revenue growth [4]	Achieved growth; distinct from future aspirations.
18	$1.49 returned per $1 invested [5][16]	Snowflake/Omdia respondents who quantified ROI; reported 49% ROI.
19	92% report a return on AI investment [5]	Selected vendor-sponsored sample; not all firms globally.
20	60% report storage or compute cost overruns [5]	Budget control signal, not a project-failure rate.
21	88% report a revenue impact [6]	NVIDIA cross-industry respondents; vendor-audience selection.
22	87% report lower annual costs [6]	Separate outcome from revenue; do not infer their intersection.
23	89% report higher revenue in retail and CPG [7]	Reported benefit in a sector survey.
24	95% report lower costs in retail and CPG [7]	Not a 95% cost reduction or net ROI.
25	64% report revenue gains above 5% in finance [8]	Share of respondents crossing a revenue threshold.
26	61% report cost reductions above 5% in finance [8]	Share crossing a cost threshold; different from ROI.
27	85% of healthcare executives report revenue benefit [9]	Executive subset, not all healthcare organizations.
28	80% of healthcare executives report cost benefit [9]	Different denominator from segment use-case results.
29	70% report public-sector productivity gains [10]	Survey of 251 leaders; not a fiscal return calculation.
30	46% of improvers report at least doubled productivity [10]	Conditional subgroup of 176; not 46% of all respondents.
31	15% more support issues resolved per hour [11][20]	Published study of 5,172 agents; task productivity.
32	26.08% more completed developer tasks [12]	Pooled randomized experiments; uncertainty and setting matter.
33	1.4-2× median perceived work-value uplift [14]	Three self-report measures; not experimental or financial ROI.

Why the headline numbers disagree

A CEO survey, an AI-vendor customer audience and a controlled task experiment answer different questions. Mixing them produces a false market average. Uvik Software groups the evidence by what it can support: investment economics, organizational outcomes, task results or adoption.

Figure 3. Uvik Software visualization of PwC CEO responses. Revenue and cost categories overlap.

The January PwC figures describe a broad CEO sample looking back over the previous year. The August Snapshot returns to a smaller panel. Its changing impact categories provide useful current context, but do not form a clean trend line against the full January sample. [2][3]

Four questions to ask before quoting a return

• Who was surveyed: all businesses, AI adopters, executives, developers or a vendor audience?
• What was measured: self-reported benefit, task throughput, cash savings or net return?
• What was included: software fees only, or integration, evaluation, people and maintenance?
• When was it measured: observed operating period, retrospective estimate or future forecast?

Uvik Software interpretation. The strongest decision is rarely “AI works” or “AI does not work.” It is whether a defined workflow has sufficient volume, acceptable quality and a credible path from better output to realized financial value.

The 2026 implementation outlook

Figure 4. Uvik Software visualization of achieved benefits in Deloitte’s 2026 report. These are overlapping survey responses.

Deloitte’s sample comprises organizations already on the leading edge of AI. It should not be treated as the adoption or success rate of every business. The result helps separate efficiency, decision quality and financial outcomes within that selected population. [4]

What changed during 2026

New evidence improved the timing and measurement picture. PwC published a follow-up panel in August; McKinsey fielded its latest survey in May and June. METR’s February update also cautioned that selection effects make its newer developer experiment an unreliable estimate of current productivity uplift. [1][3][13]

The practical response is to shorten the feedback cycle. Evaluate the current model and workflow on representative work, then repeat the check after meaningful changes. A team should know whether quality, acceptance, unit cost or adoption moved. Last year’s experiment cannot settle the economics of a different stack today.

Need to turn a broad AI ambition into an investment case? Explore Uvik Software AI consulting.

AI ROI by industry and business function

There is no defensible universal industry league table in the evidence reviewed here. Studies use different populations, horizons and definitions. The comparison below maps credible evidence to the economic variable that a buyer should measure.

Industry or function	Evidence anchor	What to measure in your project
Customer support	15% more issues resolved per hour in the published support study. [11]	Cost per correctly resolved case; repeat contact; escalation; customer retention.
Software and SaaS	Randomized developer experiments find a positive pooled task effect; newer METR work highlights measurement uncertainty. [12][13]	Accepted production outcomes; review effort; defects; maintenance; delivery bottlenecks.
Retail and CPG	37% of NVIDIA respondents report cost reductions above 10%. [7]	Contribution margin; inventory costs; markdowns; returns; cost per valid catalog update.
Financial services	25% report annual cost reductions above 10% in NVIDIA’s survey. [8]	Net loss avoided; false positives; cost per reviewed document; reconciliation exceptions.
Healthcare	39% of payer/provider respondents identify administration and workflow optimization as a top ROI area. [9]	Administrative minutes; accepted documentation; rework; safety and quality constraints.
Manufacturing and logistics	NVIDIA reports a PepsiCo digital-twin example with 20% initial throughput improvement. [6]	Accepted output at the bottleneck; downtime; scrap; inventory; implementation capital.
Public sector	Survey evidence shows perceived productivity benefits, with a conditional doubling result. [10]	Service backlog; cost per completed case; access; quality; budget impact.

Choose a workflow before choosing an industry benchmark. A document-processing project in a bank may resemble an insurance or logistics workflow more than it resembles a trading system in the same bank. Comparable work, data quality and error costs matter more than the sector label.

A benefit share is not a return magnitude. A high share of companies reporting some improvement does not establish which industry has the highest net ROI. Use external research to choose a test, then use your own controlled evidence to make the investment decision.

AI ROI in software development

Software teams can generate more code while delivering little additional customer value. The relevant output is a working change that passes review, meets acceptance criteria and stays reliable in production. This makes engineering a useful test of the difference between task acceleration and business return.

What the evidence supports

The Microsoft Research summary of three field experiments reports 26.08% more completed tasks across 4,867 developers, with a standard error of 10.3 percentage points. The result is pooled across settings and does not measure net software-business ROI. [12]

METR’s early-2025 experiment found tasks took 19% longer with AI. Its February 2026 update reported later estimates pointing toward speedup, while warning that participant and task selection prevent a reliable estimate of the current effect. Treat the old slowdown as a result for that setting and period, not a permanent property of AI coding. [13]

A separate 2026 METR survey found perceived work-value gains that differed from perceived speed gains. The authors caution against treating those self-reports as ground truth. [14]

Build a quality-adjusted engineering business case

• Define the unit: a shipped and accepted feature, resolved defect or completed migration, rather than lines of code.
• Include the full delivery path: specification, coding, review, testing, deployment and support.
• Track whether review queues or integration work become the bottleneck after code generation speeds up.
• Use a matched work sample and record complexity, developer experience and repository familiarity.
• Count maintenance, security remediation and rollback work within the evaluation horizon.

For an existing team, faster coding is usually released capacity first. It becomes economic benefit when the team ships additional valuable work, removes contractor spend, avoids an approved hire or reduces a measurable operational loss. Additional output needs a customer or operational use; a larger backlog of unneeded features is not a return.

A build-versus-buy decision should include lifecycle costs. A small internal tool may be cheap to generate and expensive to own. Estimate testing, permissions, data access, monitoring, incident response, upgrades and the opportunity cost of the people maintaining it. Compare that total with the complete subscription alternative.

Google Cloud’s 2026 DORA-oriented guidance also frames ROI within organizational systems and technical strategy. For an engineering leader, the useful operating question is where AI changes the delivery system’s actual constraint. [15]

AI ROI in customer support and commerce

Customer support and conversational AI

The published customer-support study measured issues resolved per hour, not the net financial return on a chatbot. Its staggered deployment and worker-level data make it useful evidence for a productivity mechanism. It does not provide a universal automation rate for your support queue. [11][20]

Start with a defined intent group such as order status, product information or password recovery. Measure accurate resolution, repeated contact within an agreed window, escalation rate and customer satisfaction. A conversation marked “contained” can still fail if the customer abandons the channel or has to contact the company again.

An assisted-agent deployment and a fully autonomous chatbot have different economics. The assistant may improve quality while retaining the human handling cost. Autonomous resolution may remove more effort, but adds exposure to wrong actions, refunds, policy errors and customer recovery work. Separate these cohorts in reporting.

Use cost per correctly resolved issue as the primary unit metric. Include retrieval, model calls, channel charges, review, handoff and follow-up. If a bot resolves easy cases first, compare it with the historical cost of those same easy cases rather than the average cost of the entire queue.

Retail and ecommerce

NVIDIA’s retail survey reports both revenue and cost benefits, but those are respondents’ assessments. They are not controlled estimates of incremental gross profit. The sector evidence is a reason to test specific commercial workflows, not a basis for assigning the same ROI to every retailer. [7]

For recommendations and personalization, randomize eligible traffic where practical. Measure contribution profit after discounts, fulfillment, returns and support. Conversion improvements can be economically weak if they mostly move demand between channels or encourage low-margin purchases.

For catalog enrichment, count accepted product records and the downstream outcomes they support. Establish human-review thresholds for technical specifications, compatibility and regulated claims. A lower cost per description has limited value if inaccurate content increases returns.

Inventory forecasting has a different value equation: lower stockouts and working capital must be balanced against inventory carrying costs, markdowns and service levels. Use comparable products and account for promotions, seasonality and supplier disruption before attributing change to AI.

The most useful initial test

Choose a repeatable workflow with observable demand and an inexpensive quality check. Record a baseline before deployment. Make adoption and exception handling visible from day one. This creates evidence that both the commercial team and finance can use, even if the first model underperforms.

AI ROI in finance operations and complex industries

Financial services and treasury operations

NVIDIA’s financial-services survey covers more than 800 professionals and reports cost and revenue benefits at specified thresholds. It does not establish a causal net return for a particular bank, treasury team or fraud system. [8]

For invoice matching, reconciliations and document processing, use cost per correctly completed case, exception rate and time to final approval. For forecasting or financial planning, connect model quality to a decision: a smaller error has value only if it improves cash allocation, inventory, funding or another economic outcome.

Fraud detection needs a net-benefit measure. Prevented losses must be reduced by false-positive friction, lost legitimate business, analyst time and investigation costs. Keep human approval for high-impact actions until reliability and controls justify a broader scope. Include control operation in the cost base, not as an afterthought.

Manufacturing and supply chains

The PepsiCo example summarized by NVIDIA concerns an initial digital-twin deployment. It is an attributed company example, not a cross-industry average or a general promise for generative AI. [6]

A plant gains from higher throughput only if the improved step is a constraint and there is profitable demand for the additional output. If another station, warehouse or sales channel limits volume, local machine speed can increase work in progress rather than profit. Track accepted units, scrap, downtime and the full constraint path.

Separate predictive maintenance, forecasting, simulation and language-model applications. Their data, implementation costs and failure modes differ. A manufacturing AI business case may need sensor integration and capital expenditure that a document assistant does not.

Healthcare administration

Healthcare survey findings cover executives and different industry segments. Administration and workflow optimization appear among reported ROI areas, but clinical results require their own evidence and validation. [9]

For an administrative pilot, track accepted documentation, scheduling effort, coding rework and backlog. Do not monetize every minute of professional time as payroll savings. Establish how time returns to patient service, reduces overtime or changes external expenditure, and preserve quality and safety constraints.

Public services and regulated operations

Google Cloud’s public-sector study reports perceived productivity improvement. Its doubling statistic applies only to respondents who already reported a gain. It is not an estimate that half of all agencies doubled output. [10]

Public-sector value can include shorter waiting times, improved access and fewer errors. Report those outcomes alongside budget impact instead of forcing every benefit into a cash ROI percentage. The same principle applies to compliance and resilience work in commercial organizations.

How to calculate AI implementation ROI

Use one horizon and one accounting boundary. The model below uses first-year economic benefits and cash costs, with setup expensed for the comparison. A finance team may also need discounted cash flow, tax treatment or capitalization schedules. Keep those analyses separate and reconciled.

Component	Include	Common omission
Setup	Discovery, data preparation, integration, migration, evaluation, security work and launch training.	Internal staff time and systems integration.
Recurring operation	Models, tools, hosting, retrieval, monitoring, support, review and exception handling.	Retries, long context, idle capacity and human correction.
Change and upkeep	Workflow redesign, adoption support, retraining, re-evaluation and version upgrades.	Maintenance after the pilot team leaves.
Economic benefit	Avoided spend, incremental contribution profit and validated loss reduction.	A documented path from saved time to realized value.
Transition exposure	Ramp-up, dual running, service interruptions and replacement costs.	Assuming full utilization immediately.

A reproducible benefit calculation

Annual capacity value = eligible annual volume × adoption × net time saved per unit ÷ 60 × loaded hourly cost. “Net time saved” must already account for additional review and correction time, unless those are separately costed. Do not subtract the same work twice.

Realized capacity benefit = annual capacity value × realization rate. The realization rate is the share that becomes verified avoided expenditure or valued additional productive work. It is an explicit assumption until an owner proves the conversion.

Incremental contribution profit = attributable incremental revenue × contribution margin. Use a margin after variable costs relevant to the project. Revenue alone is not the return. Model revenue cannibalization and displaced human work where applicable.

First-year net value = realized benefits – setup costs – operating costs. First-year ROI = net value ÷ total first-year costs. Simple payback = setup cost ÷ recurring monthly net benefit, but only when that monthly benefit is positive and stable.

If the business case depends on connecting AI to production workflows, review Uvik Software AI integration services.

The Uvik Software AI ROI calculation example

Consider a support operations team evaluating AI assistance for a repetitive workflow. All values below are assumptions chosen to expose the drivers of return. They are not prices quoted by Uvik Software or observations from a customer.

Input	Assumption	How to validate
Eligible monthly volume	20,000 cases	Remove ineligible intents, duplicate contacts and low-quality input.
Adoption of eligible work	50%	Measure actual workflow use, not purchased seats.
Net time released	3 minutes per adopted case	Include review and correction when timing the workflow.
Loaded hourly cost	$40	Use an agreed cost basis; distinguish loaded cost from cash avoidability.
Benefit realization	40%	Identify avoided overtime, approved hiring or additional useful output.
One-time setup	$60,000	Include internal time, integration and evaluation.
Recurring operation	$6,000 per month	All-in incremental cost, with no duplicate review charges.
Revenue and risk upside	$0 in the base case	Add only after separate attribution and measurement.

The arithmetic

20,000 × 50% × 3 ÷ 60 = 500 hours released per month. At $40 per hour, this is $20,000 of monthly capacity value, or $240,000 annually. A 40% realization rate converts that to $96,000 of annual economic benefit.

The first-year cost is $60,000 + (12 × $6,000) = $132,000. Net value is $96,000 – $132,000 = -$36,000. First-year ROI is -$36,000 ÷ $132,000 = -27.3%.

This model counts only the realized part of released capacity. If the organization keeps its payroll and cannot deploy the extra capacity productively, finance may recognize less benefit. If it avoids an approved cost or creates profitable additional output, recognition may be higher.

Why time savings can still lose money

Figure 5. Uvik Software illustrative ROI waterfall. The $96,000 benefit subtotal is not an additional benefit; it shows the result after the realization adjustment.

The attractive headline is $240,000 of annual capacity. The investment decision depends on how much of that value is captured and on the full cost of making the system work. In this example, only $96,000 becomes an economic benefit.

Economic return and cash return

Suppose the team uses the released time to improve service while headcount remains unchanged. That may be valuable, but it is not automatically a cash saving. Record the service improvement as a separate outcome until its financial effect is demonstrated.

If a hiring plan is avoided, record the approved counterfactual and the hiring date. If the team reduces overtime, use actual expenditure. If throughput increases, prove that the additional output was needed and produced value. A generic hourly cost multiplied by every saved minute is not sufficient evidence of cash return.

A disciplined investment decision

The base case can still be acceptable if the organization has a longer horizon and credible persistence. At steady state, annual net benefit is $96,000 – $72,000 = $24,000. With immediate steady-state operation, simple payback is 30 months. Ramp-up, disruption and discounting make the real investment analysis more demanding.

An implementation team should not improve the slide by silently raising realization. The next step is a test: validate whether the organization can capture more value, reduce operating cost or change the scope enough to meet its hurdle rate.

AI ROI sensitivity and payback

Figure 6. Uvik Software first-year ROI sensitivity. Adoption and realization are independent inputs in this hypothetical scenario; no market probabilities are assigned.

At full adoption, the model releases $480,000 of annual capacity value before realization. First-year break-even therefore requires adoption × realization ≥ 27.5% because $132,000 ÷ $480,000 = 27.5%.

At 50% adoption, realization must reach 55% for first-year break-even. At 75% adoption, it must reach approximately 36.7%. These are algebraic thresholds, not forecasts of what a deployment will achieve.

Scenario	Annual benefit	Year-one ROI	Steady-state payback
Base: 50% adoption, 40% realization	$96,000	-27.3%	30 months
More capture: 50%, 60%	$144,000	9.1%	10 months
Broader use: 75%, 60%	$216,000	63.6%	5 months

Payback starts from setup expenditure and assumes immediate stable monthly economics. A production rollout needs a monthly cash-flow schedule: implementation period, adoption ramp, maintenance and expected lifetime. If recurring net benefit is zero or negative, simple payback is not reached under those assumptions.

Agentic AI ROI and cost per accepted outcome

An agent can call several models, retrieve data, use paid tools and retry failed steps before completing one task. A low token price is therefore an incomplete cost measure. Uvik Software uses cost per accepted outcome to compare complete workflows.

Figure 7. Illustrative Uvik Software comparison. Workflow A costs $4.30 per attempted case at 80% acceptance; B costs $2.50 at 95%. Accepted outcomes are assumed to meet the same quality standard.

A costs ($0.80 models and tools + $1.50 review + $2.00 exceptions) ÷ 80% = $5.38 per accepted outcome. B costs ($1.20 + $0.80 + $0.50) ÷ 95% = $2.63. B uses more expensive model/tool execution but has better complete-workflow economics. The figures are assumptions, not current model prices.

Make the denominator difficult to game

Define acceptance before deployment. Count unresolved cases, retries and abandoned attempts in total cost. Measure latency, reliability and error severity alongside cost. If a failed case is eventually completed by a human, either include that final accepted outcome and its full cost or report a separate escalation cohort consistently.

Use budgets at the workflow and customer level. Constrain unnecessary loops, log paid tool calls, cache reusable context and route work to the least costly system that meets the acceptance standard. Re-evaluate after model changes because cheaper inference can increase total usage or shift work toward supervision.

For a production agent with measurable acceptance and cost controls, explore Uvik Software AI agent development.

How to prove AI ROI in a 90 day pilot

A 90-day pilot can establish a decision-quality signal. It does not guarantee 90-day payback. The objective is to learn whether the workflow has a credible path to return, what blocks it and which assumptions deserve more investment.

Period and owner	Work	Evidence required to continue
Days 1-14 Business owner and finance	Choose one workflow, define the baseline, approve the cost boundary and specify a financial conversion mechanism.	Enough volume to test; an accountable benefit owner; an observable outcome.
Days 15-30 Engineering and operations	Prepare representative evaluation cases, integration paths, permissions, review and fallback.	Quality and reliability meet the predefined threshold on realistic cases.
Days 31-60 Operations and analytics	Run a controlled or matched pilot; collect all attempts, costs, time, quality and adoption.	Improvement survives rework and case-mix adjustment; no unacceptable failures.
Days 61-90 Finance and sponsor	Reconcile benefits, update the full cost model and decide whether to scale, redesign or stop.	A credible return under the agreed horizon and downside scenario.

Use a credible counterfactual

Random assignment is useful when feasible. Otherwise use a comparable control group, stagger deployment or a well-defined pre/post design with adjustment for work mix. Document concurrent staffing changes, seasonality, product launches and process changes that could explain the outcome. An uncontrolled improvement after launch is weaker evidence of causation.

Keep quality and adoption visible

Track the percentage of eligible work that actually uses the system, then distinguish low adoption caused by training from low adoption caused by poor utility. Do not remove difficult cases from the denominator after seeing the results. Review acceptance criteria with the people responsible for service quality.

Make the stop decision possible

Set the threshold before launching. A pilot should stop or change if the cost per accepted outcome is worse than the baseline, critical failures remain unresolved, adoption is persistently too low or there is no credible way to realize the benefit. Strategic learning may justify a limited experiment, but label that budget as learning rather than proven ROI.

The final review should fit on one page: baseline, intervention, sample, result, all-in cost, uncertainty, observed failure modes and the next investment decision. Preserve the supporting data so finance can reproduce the calculation.

What to watch through the end of 2026 and into 2027

The following scenarios are Uvik Software analysis, not market forecasts. They describe what must be true for different outcomes and what evidence would change an investment decision.

Scenario	What must be true	Signal to watch
Returns broaden	Organizations redesign work and capture released capacity; integrations and controls become repeatable.	More accepted outcomes per total dollar, followed by verified cash or profit effects.
Productivity stays local	Individuals work faster, but bottlenecks, weak demand or review queues absorb the gains.	Higher tool usage without lower unit cost or more valuable output.
Cost erodes the benefit	Agent loops, review, failures or infrastructure consume the apparent efficiency gain.	Rising spend per accepted outcome despite falling unit model prices.

Choose flexibility without hiding the cost

Model choice can preserve options, but maintaining several providers adds engineering and evaluation work. Open-weight deployment can create control and cost advantages at the right scale, while dedicated infrastructure, operations and upgrades change the denominator. Compare complete service levels and workloads rather than model labels.

The next useful research question

The most valuable future benchmark is a comparable, longitudinal view of complete workflows: what was attempted, what was accepted, what it cost, how it changed revenue or spending and whether the result persisted. A series of snapshots can suggest movement, but a consistent measurement method is needed to establish a trend.

For a buyer, the immediate priority is a repeatable operating record. Review the financial and quality result after each material change in model, prompt, retrieval data, workflow or user population. A system that passed a pilot still needs to earn its operating budget.

Ready to identify a workflow worth funding? Discuss its baseline, constraints and implementation options with Uvik Software’s AI consulting team.

Methodology and how to cite this report

Scope. This is a Uvik Software synthesis of public primary research and an original set of explanatory calculation models. It is not a new survey or a meta-analysis. Research was checked through 17 October 2026. Earlier studies are retained when their methods add useful evidence and their date is explicit.

Inclusion. Numeric findings are attributed to the original research publisher or author institution. Each statistic retains its population, measure and meaningful condition. We distinguish experimental or quasi-experimental task results from survey responses and separate observed outcomes from expectations.

Comparability. We do not average unrelated ROI claims, rank industries using incompatible surveys or equate revenue growth with profit. We do not convert a percentage of respondents into an effect size. Figures based on overlapping responses are labeled accordingly.

Original analysis. The evidence map, worked calculation, waterfall, sensitivity grid and agent economics comparison are Uvik Software analytical contributions. The calculations use disclosed assumptions and do not represent client results. External research remains credited to its original authors.

Limits. Surveys can be affected by selection, recall and incentive effects. Experiments may have limited applicability beyond their tasks and participants. The evidence does not justify a single global AI ROI percentage. The Google Cloud 2026 landing page is listed for update tracking; unretrieved gated findings are not included in the statistics. [17]

Suggested attribution

Uvik Software, AI ROI in 2026: Statistics, Industry Evidence and Measurement, 2026 edition. For an original Uvik Software calculation or graphic, cite the report and retain its assumptions. For an external statistic, cite the original study as well as this report when using its synthesis or visualization.

Reusable finding from the original model

In Uvik Software’s illustrative AI ROI model, $240,000 of annual capacity value produces -27.3% first-year ROI when 40% of that capacity becomes economic benefit and total first-year costs are $132,000. At 50% adoption, first-year break-even requires 55% benefit realization.

Sources and evidence notes

Every numbered citation links to its source. Online sources were checked on 17 October 2026. Publication dates and observation periods are kept separate.

1 McKinsey

The state of AI in 2026 On the road to ROI | 25 August 2026. Open original source

1,719 participants in 97 nations; fieldwork 4 May-8 June 2026; GDP weighting. Self-reported enterprise and individual outcomes.

2 PwC

29th Global CEO Survey | 19 January 2026. Open original source

4,454 CEOs in 95 countries and territories; fieldwork 30 September-10 November 2025. Global results weighted by GDP.

3 PwC

CEO Survey Snapshot August 2026 | 4 August 2026. Open original source

Follow-up panel of 351 CEOs in 59 countries and 27 sectors; fieldwork 15 May-22 June 2026. Do not compare its percentages directly with the full January sample.

4 Deloitte

State of AI in the Enterprise 2026 | 2026 edition. Open original source

3,235 leaders in 24 countries; fieldwork August-September 2025; equal IT and business representation; organizations on the leading edge of AI.

5 Snowflake and Omdia

The ROI of Gen AI and Agents | 10 March 2026. Open original source

Vendor-sponsored study of 2,050 business and IT leaders in 10 countries. The $1.49 return applies to respondents who quantified ROI, as specified on the report landing page.

6 NVIDIA

State of AI cross-industry findings | 9 March 2026. Open original source

Over 3,200 responses; August-December 2025 fieldwork. Opt-in NVIDIA audiences, social media and regional agency recruitment. Selection toward AI interest matters.

7 NVIDIA

State of AI in Retail and CPG | 7 January 2026. Open original source

Hundreds of responses; vendor survey. Revenue and cost-impact shares are not net ROI percentages.

Industry and experimental sources

8 NVIDIA

State of AI in Financial Services | 22 January 2026. Open original source

More than 800 industry professionals. Use thresholds and denominators as reported; no industry-average net ROI is established.

9 NVIDIA

State of AI in Healthcare and Life Sciences | 24 February 2026. Open original source

Vendor survey spanning multiple healthcare segments. Executive-only findings and segment-specific findings have different denominators.

10 Google Cloud and National Research Group

ROI of AI in the public sector | 3 February 2026. Open original source

251 senior public-sector leaders. Productivity doubling is conditional on reported improvement, a subgroup of 176 respondents.

11 Brynjolfsson Li and Raymond

Generative AI at Work | QJE 2025; earlier working paper 2023. Open original source

Published version: 5,172 customer-support agents; staggered rollout; 15% average increase in issues resolved per hour. Earlier working-paper versions used different estimates.

12 Cui and coauthors

Three field experiments with software developers | June 2025. Open original source

Randomized access across 4,867 developers at Microsoft, Accenture and a Fortune 100 company. Pooled task estimate has standard error 10.3 percentage points.

13 METR

We are Changing our Developer Productivity Experiment Design | 24 February 2026. Open original source

Update covers the 2025 slowdown and later experiment. Selection effects and concurrent-agent time measurement prevent a reliable current uplift estimate.

14 METR

Self-reported impact of early 2026 AI on technical workers | 11 May 2026. Open original source

349 technical workers surveyed February-April 2026. Perceived value and speed are separate measures, not audited financial returns.

Methods and publication references

15 Google Cloud

How to unlock true ROI in software development | June 2026. Open original source

DORA-oriented measurement guidance. Editorial methodological context, not an industry ROI benchmark.

16 Snowflake

The ROI of Gen AI and Agents report landing page | 2026 edition. Open original source

Confirms that the $1.49 figure is conditional on respondents who quantified their ROI.

17 Google Cloud

ROI of AI 2026 report landing page | 2026 edition. Open original source

Public landing page describes more than 2,400 executives. No unretrieved gated results are used as numeric evidence.

20 Stanford Digital Economy Lab

Generative AI at Work publication summary | Accessed 17 October 2026. Open original source

Author-institution summary corroborates the 5,172-agent sample and 15% productivity estimate.