Two forms of intelligence have mushroomed on planet earth: “artificial” and “actual.” The former came later, to a great deal of fanfare. The latter, human intelligence, matured slowly, gave birth to the former, and some say may be eclipsed by it. Kalifa Oliver, Executive Advisor & Author, begs to differ. Where decision-making is concerned, she asserts that artificial intelligence is best used as a supportive apparatus to the intelligence that created it. Many AI-driven initiatives fail because they treat human oversight as a binary toggle: either the machine does everything, or the human does everything. When you look at where high-stakes decisions actually get made, in boardrooms, operating rooms, and trading floors, pure automation breaks down. So how should the labor of decisionmaking be divided between man and machine?
Oliver argues the best outcomes occur not when we look to AI to make decisions for us, but when we invite the AI to provide “decision intelligence” (all the support, context, and action recommendations), so that a decision can be made by the “other AI,” actual intelligence, a.k.a you.
“When you're positioning decision intelligence, you're not talking about AI, you're talking about the human in the loop. The outcome should be the person, not the tool. The way I frame it is that there are two AIs in this situation: my AI tool provides the decision intelligence, all the support and the action recommendations, so that a decision can be made by the other AI: actual intelligence. When the AI tool informs my AI, we get good decisions” - Kalifa Oliver, Executive Advisor & Author
When you use AI to inform actual intelligence, you don’t just get faster output. You get fundamentally better decisions than AI or humans working alone. Here, we’ll explore the benefits of this decisionmaking model and tips for making it work.
Most enterprise software pitches rely on a single, tired metric: time saved.
They promise that by automating tasks, your team will regain five hours a week. While efficiency matters, measuring decisions purely by speed misses the point. The true cost of a bad decision vastly outweighs the labor cost of the hours spent making it.
A 'decision intelligence’ model shifts the primary ROI metric from time reduction to impact.
For example, the true ROI of enhancing the human-in-the-loop with AI might be:
If an AI tool takes two hours to guide a leader toward a decision that saves the company millions, the tool did not just save two hours. It secured a valuable outcome.
An algorithm can analyze millions of data points in seconds, but a human brain can only digest a fraction of that information before experiencing cognitive overload. If your AI tool dumps raw data or opaque confidence scores onto a user, it hasn't solved a problem. It has shifted the burden.
Building for Actual Intelligence requires cognitive ergonomics: architecting information in a way that respects how humans process risk and make choices.
“There are two AIs today: artificial intelligence and actual intelligence. When the first AI informs my AI, we get good decisions. The outcome should be the person, not the tool.” Kalifa Oliver, Executive Advisor & Author
To help your AI tool provide the best decision intelligence, add these considerations to your prompt:
By implementing these, organizations avoid two dangerous extremes: automation bias (blindly trusting a flawed model) and analysis paralysis (ignoring machine insights due to lack of trust).
Even the best decision intelligence tool will fail if the person using it doesn't trust it. When organizations introduce AI recommendations, leaders usually swing between two extremes: blind trust (accepting a flawed model just because it has a slick dashboard) or total skepticism (ignoring machine insights entirely because it “must’ve missed something”).
To bridge AI trust issues, teams need to know that the data it used to form a recommendation was structurally sound. To that end, an AI tool won’t generate credible recommendations if the engine behind it is running on fragmented, unmodeled, or inaccurate data. True confidence starts with an unshakeable, enterprise-grade data foundation.
Enterprise data is notoriously messy, scattered across dozens of disconnected platforms and legacy systems. When AI tries to draw conclusions from unaligned or siloed data, it will produce skewed recommendations. A governed data foundation tool (like One Model) acts as a central engine that cleans, unifies, and harmonizes disparate data before any AI model touches them. When decision-makers know the underlying information is accurate and standardized, skeptical pushback decreases.
AI-assisted decision-making in your workforce introduces an entirely different level of stakes. There, you aren't managing units of inventory; you’re dealing with people’s lives, livelihoods, and career outcomes. Furthermore, workforce data involves some of the most sensitive, private information an enterprise holds. In these high-stakes human scenarios, there is even less margin for error, hallucinations, or opaque reasoning.
A governed data foundation builds strict role-based access, privacy protections, and compliance rules directly into the data layer. This ensures the AI respects sensitive boundaries while giving human leaders the safe, transparent environment they need to test assumptions, weigh trade-offs, and make the final call.
Building trust in AI isn't just about changing company culture. It's an infrastructure strategy. When you pair clear-headed human judgment with a governed, enterprise-ready data foundation, AI becomes what it was always meant to be: a reliable, transparent partner to the actual intelligence making the call.
Ultimately, the future belongs to systems that elevate human thinkers, not systems that replace them. When you design, position, and deploy artificial intelligence as an engine to power actual intelligence, you stop selling software and start delivering better outcomes.
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