As organisations in Nigeria explore increasingly autonomous AI to drive efficiency, innovation and faster decision-making, new research from SAS, a global leader in Data & AI software, and with insights provided by IDC, highlights a critical challenge: the foundations needed to make AI trustworthy are not keeping pace with its growing autonomy.
   
The 2026 Data and AI Impact Report examined what organisations need to deploy increasingly autonomous AI responsibly, confidently and at scale.
  
The research also measures the trustworthiness of AI across five dimensions: data quality and governance; model governance and oversight; explainability and fairness; responsible AI policy and audit and accountability. Together, these dimensions form the Trustworthiness Index. Organisations scoring 80 or higher are classified as trustworthiness leaders.
  
As identified in the second yearly ‘Data and AI Impact Report: The New Economics of Trust’, organisations with the strongest governance, data quality and auditability practices, a comparatively small market segment, consistently outperformed peers, reporting at least double the ROI from AI deployments. Fewer than one in 20 trustworthy AI ‘laggard’ organisations reported the same.
  
CTO at SAS, Bryan Harris, said: “When AI works, it’s incredibly impactful. However, it is well documented that state-of-the-art agents can have error rates that exceed 25 per cent 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.”
   
Vice President at IDC, Chris Marshall, said: “As AI becomes more autonomous, organisations face a new challenge: maintaining confidence in systems people don’t fully understand. Our findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully.”
   
Organisations are entering a new phase of AI adoption, and that changes the trust dynamic. As systems evolve from generating outputs to taking action, the requirements for trust are changing as well. Many of the results in this report – from override rates and explainability challenges to governance investments and ROI performance – reflect organisations adapting to this new reality.
   
The report’s findings span three themes. They are: trust drives outcomes and drops the moment AI starts to act. Deployment is outpacing trust. Agentic AI is trusted by 66 per cent of respondents, trailing generative AI by 10 points as AI moves from generating insights to taking action. Roughly 89 per cent of respondents said their AI agents now play a role in decision-making, from informing human-approved decisions to acting autonomously, yet trust in agentic AI remains 10 points lower than GenAI.

Organisations with trustworthy AI practices are 15 times more likely to report strong or high ROI (62 per cent vs four per cent).