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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 problemsMany organizations report returns from AI investments, yet struggle to put the business rules and operational knowledge AI needs into real workflows. In Alteryx’s 2026 IT Leader Research, 69% of respondents said their organizations saw moderate or significant returns, while 53% said they struggled to translate business context into AI systems and workflows. The findings come from a survey commissioned by Alteryx, not an audit of returns, and do not show that adding context alone causes higher ROI.
What “business context” means in an AI workflow
A model can process general information, but applying it usefully in a company often depends on local rules and decision criteria. Business context includes definitions, exceptions, assumptions, and operational knowledge that determine how work is actually done.
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For example, a finance workflow may need the assumptions behind a forecast, not just the latest figures. A tax process may need rules for exceptions, while a supply-chain decision may depend on inventory thresholds and timing. Alteryx’s release says such knowledge can reside in spreadsheets, macros, documents, email, or employees’ expertise. The challenge reported is making relevant context available and usable within systems and workflows—not an inherent inability of AI to use company information.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →In the Alteryx survey, 77% of respondents considered business context critical to accurate and relevant AI outputs, while 53% said their organizations struggled to incorporate it. Coleman Parkes conducted the survey in April and May 2026 among 1,400 technology leaders—1,000 IT leaders and 400 automation specialists—across North America, Europe, the Middle East, and APAC. The figures describe respondents’ views, not all organizations. Alteryx’s 2026 IT Leader Research release was republished by TMCnet; it is not the full underlying report.
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Why context can be difficult to operationalize
Expertise and technical delivery sit in different places
Business experts understand the decisions, edge cases, and consequences in a workflow. Technical teams build and maintain the systems that use data and models. When those groups do not jointly define requirements and validate results, important rules can remain implicit or be translated incorrectly. In Alteryx’s survey, 71% of technology leaders believed AI initiatives are most successful when IT and business teams collaborate closely. That is a reported belief, not proof that collaboration by itself produces better outcomes.
Access to data is not the same as useful access
Only 18% of organizations in the Alteryx survey reported fully self-service access to cloud data for business users. That is one indicator of access, not a complete measure of data readiness. Even when data is available, teams still need appropriate permissions, clear definitions, and a way for business users to check whether a workflow reflects how the process works.
Knowledge must be maintained as processes change
Rules, thresholds, and exceptions can change. A workflow that once reflected a policy may become unreliable if its assumptions are not owned, reviewed, and updated. Embedding context therefore involves more than documenting requirements at launch: teams need responsibility for validating and maintaining the logic used in production.
Rising investment does not settle the ROI question
Investment, perceived returns, measured payback, and enterprise-wide impact are different things. Alteryx reported that 80% of surveyed organizations expected AI spending to increase over the following two years, and 69% reported moderate or significant ROI. These results can coexist with implementation difficulty: organizations may see value in some uses while struggling to scale others or to attribute their effects.
Other surveys point to measurement and time-horizon problems, but their populations and definitions differ. Deloitte surveyed 1,854 senior executives in 14 European and Middle Eastern countries from August 15 to September 5, 2025. It found that 85% said their organizations had increased AI investment in the prior 12 months and 91% planned another increase in the following 12 months. Only 6% reported AI payback in under a year; respondents typically reported satisfactory ROI on an AI use case within two to four years. Deloitte’s AI ROI Performance Index defines performance using direct financial return, AI-driven revenue growth, operating cost savings, and the speed at which results were achieved. It classified around one in five organizations as AI ROI Leaders.
In a separate 2026 study of more than 1,000 senior executives at large U.S. companies, Infosys found that two-thirds said their organizations had difficulty measuring AI ROI. Nearly three-quarters said fewer than 25% of pilots had scaled enterprise-wide while delivering intended ROI. Respondents also identified speed to market, operational efficiency, and cost reduction as sources of value that can be missed if organizations measure revenue alone. These are findings from Infosys’s survey, not universal rates. Infosys’s 2026 CEO insights describes the study.
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A 2025 Wharton Human-AI Research and GBK Collective report found that three out of four executives saw positive returns on GenAI investments. That is another survey result, not a directly comparable estimate: the studies ask different questions of different groups. Their percentages should not be combined into a single trend or treated as independent audits. Wharton’s report page also discusses training and ROI measurement.
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How to make an AI ROI case more credible
The following steps translate the studies’ emphasis on outcomes, measurement, collaboration, and governance into a practical review process. They are guidance, not a tested formula or guarantee of returns.
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- Name the business outcome. Specify what should improve—such as operating cost, process quality, speed to market, revenue, or decision consistency—and define how the organization will recognize success.
- Record a baseline. Capture the process’s current performance before deployment, including relevant costs, time, quality, and volume. Without a baseline, a later change is difficult to attribute to the AI workflow.
- Map the context and its owners. Ask business experts to identify definitions, assumptions, exceptions, thresholds, and decision criteria. Assign someone to approve changes and keep that logic current.
- Design and validate across teams. Involve business users, IT, and other relevant process owners in requirements, testing, and review. Check outputs against representative cases, including exceptions, before relying on them in live decisions.
- Provide governed data access. Give the people responsible for building or validating a workflow access to the data they need, with permissions and safeguards appropriate to its use.
- Measure costs and benefits over a suitable period. Track implementation and operating costs alongside the chosen outcomes. Set a review horizon that reflects the process and expected payback rather than assuming every use case should return value within a year.
- Review scale and workforce readiness. Monitor whether the workflow is used as intended and whether it delivers its intended result beyond a pilot. Include training, oversight, governance, and clear escalation paths in the rollout.
What the survey evidence can—and cannot—establish
Across these surveys, the consistent practical signal is that investment and reported positive returns do not remove the work of integrating expertise, data, people, and measurement. Deloitte’s study was regional, although Deloitte describes its insights as directionally applicable globally. The Alteryx figures are from vendor-commissioned research, and the available release does not provide the full underlying report. Infosys’s cited feature does not state the survey field dates. None of these findings establishes that context integration alone raises ROI, or ranks a particular product or implementation approach.
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