The Rise of the AI-Advised Executive: Confident Answers, Out-of-Date Data

(©Delmaine Donson - canva.com) Artificial Intelligence (AI) is no longer just a tool used by analysts and engineers. Increasingly, it is influencing the decisions made at the very top of organizations. Confluent's latest Quick Thinking research suggests that 62% of business leaders now use AI to make the majority of their decisions, with many trusting it to weigh in on complex strategic choices. Some are even turning to AI for decisions about hiring and firing. For executives already operating under intense pressure to act quickly, the appeal is obvious. AI can analyze vast volumes of information in seconds, surface patterns that humans might miss, and offer recommendations with persuasive certainty. But this growing reliance reveals an important truth about AI decision-making - the quality of an AI-driven decision is only as good as the data behind it. AI is changing how executives seek advice The shift is not only about how quickly leaders can process information. It is also beginning to reshape who executives turn to when making decisions. The research reveals that 46% of leaders now rely more on AI than on advice from colleagues, while 65% believe AI has made decision-making less collaborative inside their organizations. For an executive role traditionally built on discussion, debate, and experience, that is a striking change. Great leadership has long depended on bringing together diverse perspectives around the table and challenging assumptions before committing to a path forward. AI introduces a different dynamic. Leaders can now prompt it to compare viewpoints, model scenarios, and synthesize information at speed. The output arrives quickly, clearly structured, and often with a strong sense of certainty. Perhaps unsurprisingly, many executives admit this certainty is difficult to challenge. Seven in ten say they now second-guess their own judgment when it conflicts with AI recommendations. None of this necessarily means leaders are surrendering control to machines. But it does suggest the balance of influence in the boardroom is shifting. AI is beginning to shape the confidence leaders place in their own instincts and in the people around them. Why AI decision-making fails without real-time data For all the attention being paid to AI in the boardroom, many executives are still struggling with a far more basic problem - access to reliable, timely data. Despite the explosion of analytics tools and dashboards across the enterprise, decision-makers frequently find themselves working with information that arrives too late to be useful. According to the research, 71% of leaders say data is already out of date by the time it reaches them, while 60% say it is simply too difficult to access when they need it. To me, this is a curious contradiction. AI is increasingly being trusted to guide critical decisions, yet the underlying data feeding those systems often reflects what happened hours, days, or even weeks ago. In other words, many AI systems are still operating on historical snapshots rather than a real-time view of the business. Why real-time data is becoming essential Faced with decisions that need executing faster than ever, many executives say what they actually need is not more automation, but better information at the moment it matters. Nine in ten leaders say they would feel more confident making decisions with access to real-time data, while 80% believe businesses cannot make confident decisions without real-time information. To be clear, ‘real-time’ simply refers to information that is captured, processed and shared across systems as events happen, rather than analyzed later through batch reports. That demand is forcing organizations to rethink how data moves through the enterprise – as well as who moves it. The rise of the data streaming engineer As companies invest in real-time data infrastructure, a new specialist role is emerging - the data streaming engineer. A data streaming engineer is responsible for building the systems that capture events across an organization – such as transactions, customer activity, and operational changes – and then distributing that data instantly to applications, analytics tools, and AI systems. According to the research, 94% of business leaders believe every data-driven organization should employ a data streaming engineer, and 85% intend to hire more in the coming years. Their job is not only to move data faster but to ensure that information flows continuously across the business so that both AI systems and human decision-makers can act on what is happening now. The future boardroom runs on real-time information AI does not eliminate uncertainty. It simply shifts where that uncertainty sits. If the systems guiding decisions are trained on outdated or incomplete data, then even the most sophisticated algorithms risk producing confident answers built on yesterday's reality. This is why the conversation around enterprise AI is beginning to move beyond algorithms and models, and toward the infrastructure that supports them. The future boardroom may not be defined by whether decisions are made by humans or machines. Instead, success will depend on whether both have access to the right information in real time. Explore the full findings of the Confluent Quick Thinking research here.