AI now generates insights in seconds, but determining which outputs to trust still requires human judgment. This challenge was explored with Intel CEO Lip-Bu Tan during a conversation at Vista’s 2026 Annual General Meeting (AGM) about deploying AI at scale.
The discussion reflects a broader shift in the decision-making environment. AI can generate analysis almost immediately, but its outputs may differ or conflict. The focus is now on deciding which information deserves confidence.
For investors and business leaders, trusting outputs can make the difference between capturing a real competitive advantage or falling behind. AI can provide a range of perspectives when evaluating an opportunity, but it cannot decide which assumptions are credible or how much weight an output should carry. Those decisions still require human judgment.
The conversation reinforced a central principle: AI should strengthen the decisions people make without displacing the judgment behind them.
When analysis is easily accessible, human judgment becomes more valuable. That is the shift investors and business leaders are navigating today.
How AI Tools Can Augment Executive Decision-Making
While AI tools can help decision makers process information quickly and present perspectives that may have been overlooked, an AI response does not necessarily settle a question. It may be incomplete or based on assumptions that do not fit the specifics or nuances of an issue at hand. Investors and business leaders must determine whether the information is credible and fits the context, and think through how it should affect their choices.
The value of AI thus lies in helping people examine information, while human judgment determines what the decision should be.
Using Human Judgment to Evaluate AI Outputs
When models produce conflicting outputs, leaders must sift through the information they have and determine how they can apply to the decision at hand.
Several characteristics distinguish AI-assisted decision-making from traditional approaches:
- Speed: AI tools shorten the time required to gather and process information, helping teams make informed decisions sooner.
- Prediction: AI tools identify patterns in historical data and support forward-looking analysis. Leaders remain responsible for evaluating those projections within the appropriate business context.
- Scope: AI tools widen the range of information available to decision-makers. This broader view increases the need to assess which inputs are relevant and where gaps may remain.
Ultimately, AI tools can support executive decision-making by synthesizing and analyzing large amounts of information. Leaders must still evaluate AI outputs before making business decisions.
Comparing Outputs From Multiple AI Models
At Vista’s AGM, Tan explained why he consults multiple AI models when evaluating information.
“You can’t just use one; you need to use two or three,” Tan said.
Comparing outputs helps show where models agree and where their conclusions differ. Agreement does not guarantee accuracy, but conflicting answers can identify areas that require further review.
To make the comparison useful, ask multiple models the same question with the same context. Consider where the responses overlap and diverge, along with what evidence may support their claims. Any meaningful discrepancies can then be verified against trusted sources or examined more closely.
The same discipline may help investors and business leaders avoid treating an AI-generated answer as a final conclusion. Comparing outputs can clarify where human judgment is still needed.
What AI Readiness Can Tell Investors About a Company
For investors, the way leaders oversee AI-generated information can offer an early indication of how thoughtfully the technology is being adopted throughout the organization, before its impact even appears in financial results. This includes whether AI outputs receive appropriate human review and whether employees are prepared to evaluate and apply them effectively.
That level of readiness matters because access to AI alone does not create business value. The results depend on how effectively an organization incorporates the technology into its decision-making and operations.
Taken together, these indicators may offer insight into whether an organization’s technology spending is likely to translate into durable value, alongside traditional measures of leadership and execution.
How Talent Development Supports AI Readiness
Organizations need employees who can evaluate AI-generated information within the context of their work. Technical access alone does not ensure that an output will be understood or applied effectively. Critical thinking therefore remains central to AI adoption.
Combining human judgment with skills such as data fluency and AI literacy can strengthen decision-making throughout an organization.
As Agentic AI changes how work moves through an organization, talent development and education should be part of its change management strategy. Employees need clear guidance about where AI supports a workflow and where human review remains necessary.
The Role of Leadership in Evaluating AI Outputs
In a video about Vista’s 2026 outlook for Agentic AI and enterprise software, Robert F. Smith addressed the challenge of moving from AI innovation to enterprise adoption.
“While you can innovate and create products, actually embedding them into the enterprise is a whole different matter,” Smith said.
That distinction makes leadership and workforce readiness central to AI adoption.
AI can expand the amount of information available to a decision-maker, but it cannot assume responsibility for the outcome. As AI plays a greater role in business and investment decisions, human judgment becomes more valuable. More analysis does not eliminate the need to decide which information matters.
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