A recent survey conducted by Teradata and NewtonX found that while 61% of executives trust the reliability and validity of their AI outputs, 40% doubt their company’s data readiness for accurate outcomes. The most critical factors for trust in AI include reliable and validated outcomes, consistency or repeatability of results, and the reputation of the company that built their AI. Despite the potential for productivity gains, executives believe that there must be human supervision over AI-generated recommendations, especially in high-stakes industries like insurance where algorithmic bias can have severe repercussions.
Experts warn against blind reliance on AI solutions, citing documented instances of errors and biased results. Trust in AI outputs remains limited, with concerns about contextual misunderstandings, biased results, and hallucinations. Building greater trust in AI systems depends on the governance of data used and the level of risk involved. Human oversight is deemed critical, with the importance of explicit human approval before advancing any AI recommended actions into action highlighted as crucial for ensuring transparency and accountability.
To build trust in AI, companies must be clear and open about their use of AI in decision-making processes. AI-generated recommended actions should include a complete evidence package that explains the basis for the recommendation and require explicit human approval before advancement. By adopting a “machine suggested, human verified” approach, businesses can ensure that AI is a supportive tool rather than an infallible authority. Human oversight must be maintained throughout the AI process, with mechanisms for review and override by individuals directly involved in the AI transaction.
The need for human-guided interactions with AI is exemplified by experiences with self-driving cars, where the driver retains the ability to take control when necessary. Similarly, business users must be able to review how AI affects their financial books, email, or business processes before actions are taken. The AI engine should propose plans in natural language to facilitate human reviews and transparency. Scalable and sustainable AI processes require clear monitoring roles, transparency in AI models, regular audits, feedback mechanisms, and direct human authority to revise or stop AI transactions.
As organizations seek to leverage AI for efficiency and productivity gains, it remains essential to maintain human oversight and intervention in the decision-making process. Human experts, subject matter specialists, and individuals with authority must be involved in reviewing and approving AI recommendations to minimize errors, biases, and potential risks. By striking a balance between human supervision and AI capabilities, businesses can build trust in AI systems while ensuring accountability, transparency, and compliance with regulations. Ultimately, the tagline for AI-enhanced processes should be “machine suggested, human verified,” emphasizing the importance of human involvement in AI decision-making.
