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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAI can help organizations analyze data, build models and make data easier for more people to query. Its strategic value, however, is not established by adoption alone: organizations need a defined business decision to improve, suitable and governed data, and a way to measure outcomes against a baseline.
What AI contributes to data analytics
AI is a set of capabilities that can support different parts of an analytics workflow. It can help teams analyze large amounts of information, build predictive or other models, and make data more accessible through natural-language questions. These uses can support decisions; they do not, by themselves, show that a decision improved or that AI caused a business result.
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Reported use is already broad, though the figures describe different populations. In a 2026 ISACA poll of more than 3,400 digital trust professionals, 49% said their organization used AI to analyze large amounts of data. In the UK Department for Science, Innovation and Technology’s 2026 survey, 41% of 4,090 UK businesses handling digitised data reported using AI technologies in 2025 to 2026. The measures are not identical: one asks about a particular use among professionals, the other about AI use among UK businesses handling digitised data. ISACA’s 2026 findings and the UK Business Data Survey 2026 provide the respective details.
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Analyzing data and building models
AI can be used to examine large datasets, identify patterns, and support the creation of models that inform forecasts or decisions. The appropriate role depends on the question being asked, the data available, and how the resulting analysis will be checked and used. UK business-size figures suggest adoption is uneven: 32% of large businesses reported using AI to analyse data or build models, compared with 15% of medium businesses, 13% of small businesses, 8% of micro businesses and 6% of sole traders. These are reported use rates in the UK government’s 2026 survey, not evidence that the larger organizations achieved better outcomes.
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Making data questions more approachable
Natural-language interfaces can let a person ask a question in everyday language rather than first constructing a query or navigating a dashboard. This may lower a usability barrier, but a fluent answer is not necessarily a correct or well-grounded one. Organizations still need to establish which data the system can access, how metrics are defined, and how users can verify a result before acting on it.
In a Salesforce survey fielded from June 27 to August 13, 2025, 93% of surveyed business leaders said they would perform better if they could ask data questions in natural language. Salesforce’s 2026 report describes responses from 3,800 analytics and IT decision-makers and 3,852 line-of-business leaders across 18 countries. This is a vendor-published self-reported survey finding, not a measured improvement in performance. Salesforce’s 2026 report gives the survey context.
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Supporting information work around analytics
AI may also assist with research and summarization tasks that help teams work with information surrounding a decision. These tasks can make information easier to review, but they are not substitutes for defining the decision, validating relevant facts, or applying appropriate human judgment.
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Why adoption figures do not establish strategic return
Use, perceived adoption, and realized business impact are different things. In the 2026 ISACA poll, 90% of respondents believed employees were using AI in their organization; that is respondents’ perception, not an independently audited adoption rate. In the same poll, 22% said AI return on investment met or exceeded expectations. The latter is a reported assessment, not a causal estimate of what AI contributed.
Measurement remains a challenge in broader data and analytics work as well. Gartner reported that 30% of surveyed chief data and analytics officers identified inability to measure the impact of data, analytics and AI on business outcomes as a top challenge. Its survey was conducted from September to November 2024 among 504 global data and analytics executive leaders. Gartner also found that 22% of surveyed organizations had defined, tracked and communicated business-impact metrics for the bulk of their data and analytics use cases. These figures describe distinct survey questions and populations; neither should be treated as a direct comparison with ISACA’s ROI result. See Gartner’s 2025 survey announcement.
Together, the findings point to a practical distinction: deploying AI is an activity, while strategic value is an outcome that must be defined and measured. A useful evaluation connects a specific AI-supported task to a decision and an agreed business measure, then checks whether the result changed under comparable conditions.
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How to evaluate an AI analytics use case
- Start with a decision, not a tool. State who will make which decision, what information they need, and what current process or limitation the proposed use addresses.
- Check the data. Confirm that relevant data exists, is sufficiently reliable and current, and may be used for the intended purpose. Document important gaps or biases that could affect the result.
- Fit the method to the workflow. Determine how the AI capability will connect to existing data systems and how its output reaches the people making the decision. Natural-language access, for example, is useful only if the underlying definitions and permissions are dependable.
- Set a baseline and outcome measure. Record how the decision or process performs before deployment. Choose a measure tied to the intended business outcome, define how it will be calculated, and decide when it will be reviewed.
- Establish governance and oversight. Define access, privacy and security controls; assign responsibility for reviewing outputs; and specify when a person must verify, approve or override an AI-assisted result.
- Review results and unintended effects. Compare observed performance with the baseline, account for other changes that may have influenced it, and look for errors or harms. Continue, adjust or stop the use case based on that evidence rather than adoption alone.
Governance is part of the strategy
Responsible use depends on stewardship of the data and decisions involved, not just the model. The UK survey found that among UK businesses using AI, 17% reported having a policy or guidelines regarding AI use or development, while 5% reported a formal written policy. These are separate reported categories from the 2026 survey; they indicate that documented rules were not universal among AI-using businesses.
In Salesforce’s 2025 survey data published in its 2026 report, 88% of surveyed data and analytics leaders agreed that AI demands new approaches to governance and security. This is an attributed survey finding, not a universal consensus measure. In practice, governance should clarify permitted data use, privacy and security safeguards, accountability for outputs, and human review appropriate to the consequences of a decision.
Best Value
What a sound strategy looks like
- A business question and decision are specific enough to guide the work.
- The data is relevant, reliable, permitted for use and appropriately protected.
- The AI capability fits existing systems and the users’ actual workflow.
- Success is defined with a baseline and a business-impact measure before rollout.
- People remain accountable for consequential decisions, with a clear process to check outputs.
- Results are reviewed over time, including failures, costs and effects beyond the intended metric.
AI’s strategic role in analytics is therefore conditional: it can expand how organizations examine information and interact with data, but the value comes from applying it to a meaningful decision and governing and measuring the work around it. The available surveys document adoption, expectations and measurement gaps; they do not establish that AI itself caused better organizational performance.
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