Trustworthy AI Is Becoming A Business Imperative, Report Shows

As organisations move from experimenting with artificial intelligence to embedding it in business operations, the question is increasingly shifting from whether they should use AI to whether they can trust the systems they are putting into production.

New research from SAS, with insights from IDC, suggests that the answer could have a significant bearing on the financial returns organisations achieve from their AI investments. The second annual Data and AI Impact Report: The New Economics of Trust found that organisations applying trustworthy AI practices were 15 times more likely to report strong or high returns on investment from their AI projects.

The finding points to a broader issue facing technology leaders. AI adoption is no longer simply about acquiring models or deploying new applications. Organisations also need the governance, data quality, oversight and accountability structures required to ensure that those systems can be relied upon.

The report, based on a survey of 2,699 decision-makers across 28 countries and four industries, found a substantial gap between organisations that have established trustworthy AI practices and those that have not. Organisations investing in trustworthy AI measures were 15 times more likely to report strong or high ROI, at 62% compared with 4% among those with weaker practices. That difference suggests that trust is not necessarily a constraint on AI adoption. Instead, the research indicates that the processes organisations put around AI can influence how effectively they turn deployments into business value.

“When AI works, it’s incredibly impactful,” said Bryan Harris, CTO at SAS. “However, it is well documented that state-of-the-art agents can have error rates that exceed 25% on complex tasks – which is unacceptable in high-stakes decision-making. In order to achieve accuracy and repeatability, organisations must embed domain expertise into agentic workflows, while keeping people at the centre of governance and oversight. Organisations that do this successfully will close the trust gap and gain a competitive advantage in the market with AI.”

Trust becomes harder as AI becomes more autonomous

The challenge becomes more pronounced as organisations move towards agentic AI, where systems can perform increasingly complex tasks with less direct human intervention. According to the report, trust in AI falls from 76% for generative AI to 66% for agentic AI. At the same time, 97.2% of users globally override AI-generated recommendations in at least some cases. The leading reason for those overrides is a lack of explanation for how the AI arrived at its decision.

This creates a practical problem for enterprises. An AI system may produce an apparently useful recommendation, but if employees cannot understand why the recommendation was made, they may be reluctant to act on it. Repeatedly overriding AI also undermines one of the principal reasons organisations deploy the technology in the first place: improving productivity and decision-making at scale. The report notes that manual corrections resulting from a lack of trust can consume time, reduce productivity and affect profitability.

For technology leaders, explainability therefore becomes more than a technical feature. It becomes part of the operational relationship between employees and AI systems.

“As AI becomes more autonomous, organisations face a new challenge: maintaining confidence in systems people don’t fully understand,” said Chris Marshall, Vice President at IDC. “Our findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully.”

The data underneath AI matters just as much

The report also highlights a less visible part of the AI trust problem: the quality and maturity of the data infrastructure supporting these systems. Only 17.5% of enterprises surveyed have fully optimised data infrastructure considered mature enough for the demands of agentic AI. Organisations with an optimised data foundation were four times more likely to expect strong ROI from AI projects and six times more likely to mandate the data quality and explainability controls required to build trust.

This reinforces a familiar challenge for CIOs. AI systems can only operate within the constraints of the data, processes and governance surrounding them. Deploying increasingly sophisticated models on fragmented, outdated or poorly governed data can therefore create a mismatch between the sophistication of the technology and the reliability of the decisions it produces.

Trustworthy AI consequently begins well before a model reaches production. It involves establishing confidence in the underlying data, defining who is accountable for AI-driven decisions and putting controls in place to monitor how systems behave.

The report defines trustworthy AI across five dimensions: data quality and governance; model governance and oversight; explainability and fairness; responsible AI policy; and audit and accountability. Organisations classified as trustworthy AI leaders achieved an average score of at least 80 out of 100 across these dimensions.

South Africa shows progress, but uneven adoption remains

The South African findings provide a regional perspective on the challenge. The country’s Trustworthiness Index increased by 23.9 points to 67.9 in 2026, while the trust gap narrowed from 35.6 points to 8.1. All five trustworthiness dimensions were above their respective global benchmarks.

However, the findings also point to differences in maturity between organisations. Data Quality & Governance emerged as the leading reliability priority, increasing from 16.2% in 2025 to 71.2% in 2026. The report notes that leading banks and JSE-listed organisations remain ahead of much of the mid-market.

“South Africa has made real progress in building the governance foundation needed for AI, particularly in banking and among larger listed organisations,” said Joy Naidoo, Country Leader SAS South Africa. “The challenge is spreading that discipline more widely. For organisations moving AI into production, the quality of the data and the way systems are governed have to keep pace with adoption.”

The country’s experience illustrates an important point for organisations elsewhere in Africa: AI maturity cannot be measured only by the number of pilots, applications or models an organisation has deployed. The supporting governance infrastructure is equally important.

Governance could determine the next phase of AI adoption

The report’s findings also show that trustworthy AI is increasingly being treated as an investment rather than simply a risk-management exercise. Organisations with the strongest trustworthy AI practices reported 1.85 times greater gains across 13 business outcomes, including revenue growth, cost savings and customer experience. Meanwhile, 85% of these AI leaders planned to increase their investment in trustworthy AI by more than 10% during the year.

The trend extends across industries. Among banking organisations identified as AI leaders, 85% had established governance frameworks, compared with 29% of laggards. In the public sector, 41% of leaders planned to increase trustworthy AI investment by more than 20% in the year ahead, while 23% of life sciences organisations had already scaled AI across the company. For CIOs and other technology decision-makers, the implication is that AI strategy increasingly needs to incorporate trust from the beginning rather than treating it as a layer to be added after deployment.

The commercial case is becoming clearer. An AI system that cannot be explained, audited or trusted by its users can create friction even when its underlying technology is capable. Conversely, stronger data foundations, human oversight and clear accountability can make it easier for organisations to move AI from experimentation into repeatable business processes.

As AI becomes more autonomous, the value of trust may ultimately be measured not by how little human involvement remains, but by whether organisations have created the right structures for humans to understand, challenge and govern the decisions these systems make.