iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
AI can help organizations analyze large datasets, build models and make data easier to query—but adoption alone does not show that it is improving business results. Its strategic role is to support better-informed decisions when the use case is clear, the data is fit and responsibly governed, and outcomes are measured against a baseline.
How AI is used in data analytics
AI can assist with work across the analytics process: finding patterns in large volumes of data, developing predictive models, summarizing information, and helping people ask questions of data in everyday language. These uses can make analysis more accessible and help teams bring evidence into decisions. They do not, by themselves, establish that a decision is correct or that a business outcome improved.
Reported use varies by survey and population. 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. The same poll found that 90% believed employees were using AI at their organization; this is respondents’ perception, not an independently audited adoption rate. ISACA’s 2026 findings describe these professionals’ responses, not all organizations.
A separate UK government survey found that 41% of UK businesses handling digitised data reported using AI technologies in 2025 to 2026. Its base was 4,090 businesses. Use for analysing data or building models rose with business size: 32% of large businesses reported it, compared with 15% of medium, 13% of small, 8% of micro businesses and 6% of sole traders. These figures describe UK businesses and should not be generalized to other countries or treated as directly comparable with ISACA’s professional poll. See the UK Business Data Survey 2026.
#1 Best Overall
Where AI can support business decisions
Analysis and model building
AI tools can help teams examine large datasets and build models that inform choices such as where to focus resources or which patterns merit investigation. The business question should lead the work: define the decision the analysis is meant to support before choosing a model or tool. A model output is evidence to evaluate, not a decision mandate.
More approachable access to data
Natural-language interfaces let people pose data questions in ordinary language rather than writing queries or navigating complex dashboards. That can lower a usability barrier for decision-makers, but useful answers still depend on which data is connected, how it is defined and whether the system interprets the question correctly.
Rank #2
- Wiley
- Language: english
- Book - storytelling with data: a data visualization guide for business professionals
In survey data published in Salesforce’s 2026 report, 93% of surveyed business leaders said they would perform better if they could ask data questions in natural language. Salesforce collected responses from analytics and IT decision-makers and line-of-business leaders across 18 countries between June 27 and August 13, 2025. This is a vendor-published survey finding about respondents’ views, not proof that natural-language analytics improves performance. Salesforce’s report also says 88% of surveyed data and analytics leaders agreed that AI demands new approaches to governance and security.
Free tools Windows power users keep installed
One-click scans. No signup required.
Information research and summarization
AI is also used for broader information tasks, including research and summarization. These capabilities can help staff navigate material, but summaries and generated answers need appropriate checking before they inform consequential decisions. Organizations should account for source quality, permitted use and the cost of errors as well as convenience.
Why adoption does not prove strategic value
Usage statistics show that organizations are trying AI; they do not show that it caused higher revenue, lower costs, faster decisions or better outcomes. In ISACA’s 2026 poll, 22% of respondents said AI return on investment met or exceeded expectations. That is a reported assessment, not a controlled estimate of AI’s causal effect.
Measurement is a separate organizational challenge. Gartner reported in 2025 that 30% of surveyed chief data and analytics officers named inability to measure the impact of data, analytics and AI on business outcomes as a top challenge. The survey included 504 global data and analytics executive leaders and was conducted from September through November 2024. 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. This is a different population and measure from ISACA’s ROI finding; the percentages should not be compared as if they describe the same respondents or outcome. See Gartner’s survey announcement.
Rank #4
How to evaluate an AI analytics use case
A practical evaluation starts with the decision and the outcome, not the availability of a new AI feature. Before deployment, teams can work through these checks:
Recommended Free Tools
- Name the decision. Specify who will use the analysis, what choice it informs and what action may follow.
- Check the data. Confirm that relevant data exists, is sufficiently reliable and current, and may be used for this purpose. Identify gaps, inconsistent definitions and access constraints.
- Fit the tool into the workflow. Determine how it connects to existing data systems and how users will review, challenge or act on its output.
- Set a baseline and success measure. Record the current outcome before implementation, then choose a measure tied to the business objective. Define how and when it will be tracked and communicated.
- Set oversight and safeguards. Decide who checks outputs, how errors or unexpected effects are handled, and what privacy, security and policy controls apply.
- Review results before expanding. Compare observed results with the baseline and account for other changes that could explain them. Expand only when the evidence and governance arrangements support doing so.
This process distinguishes a useful pilot from a successful business intervention. A tool may be usable and technically functional while failing to move the outcome that justified it.
Best Value
Governance is part of the strategy
AI analytics depends on data that organizations can use responsibly, with clear stewardship over access, quality, privacy and security. Governance is not merely a compliance step after deployment: it affects what data can be analyzed, who can see the results and whether decision-makers can rely on them.
The UK Business Data Survey 2026 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 figures indicate that reported policy coverage was limited in that surveyed group; they do not establish the quality or effectiveness of any policy. ISACA likewise identifies data and privacy governance as foundational to trustworthy AI use. The need for new governance and security approaches was also recognized by 88% of data and analytics leaders surveyed for Salesforce’s report, as noted above.
What the evidence can—and cannot—establish
The cited surveys document reported adoption, views about potential, governance practices and measurement challenges. They do not establish that AI itself caused better organizational performance, nor do they show that AI replaces analysts or guarantees faster, more accurate or more profitable decisions. The strategic case is therefore conditional: AI can be valuable when it supports a defined decision, works with appropriate data and is evaluated against a meaningful business measure under responsible governance.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
