Thanks to AI, Self-Service Business Intelligence (SSBI) has emerged as a way for non-technical users to meaningfully interact with data and extract insights without relying on dedicated data teams. The next step in that evolution is decision intelligence, where AI doesn’t just make insights more accessible, but also drives action.
But is AI in its current form capable of making those decisions on its own?
“AI is incredible at processing data, spotting patterns, and making predictions based on these findings. But it doesn’t have intuition, nor does it understand the nuances of your market or the complexities of your company’s strategy. That will always be left to the humans,” explains Omri Kohl, CEO of Pyramid Analytics, a leading decision intelligence platform that integrates AI-driven insights with human context to support better business decisions.
“AI could never replace people who are curious, excited, and engaged,” he continues. “These people will be asking the non-trivial questions and bringing their own perspectives into the mix, while AI will be there to provide the information they need and walk them through the journey.”
The distinction between insight and plan of action is very important. Many businesses are eager to move faster and reduce decision bottlenecks, but without a clear understanding of AI’s limitations, they risk introducing new forms of error or bias into critical processes.
In the article, we will explore where AI delivers the most value in decision intelligence, where it falls short, and why keeping humans in the loop remains essential.
AI’s Strengths and Limitations
Perhaps the biggest advantage that AI brings to decision intelligence is in predictive analytics. It can analyze vast volumes of data much quicker than a human, identifying patterns and making predictions with a high degree of accuracy.
Organizations that aren’t yet data-driven because of budget and human capital constraints can now very affordably access powerful analytical capabilities that were typically reserved for large enterprises.
But making predictions is only part of the equation. To turn insights into action, organizations also rely on prescriptive analytics, and this is where things get a little more complicated. This type of analytics is where insights transform into recommendations or even actions. And while AI can certainly generate options based on data, it often lacks the contextual awareness, strategic judgment, and ethical considerations needed to choose the best course of action.
Even advanced models tend to produce recommendations that sound good and logical on paper, but that might not actually work in reality.
Why Human-in-the-Loop Still Matters
Despite these limitations, the temptation to remove human involvement is growing. With generative and agentic AI, organizations have all the tools to fully automate decision-making workflows and even execution. But is that the smart thing to do?
What might appear as an optimal decision to an algorithm could, in reality, conflict with basic business principles or priorities. The problem is that AI optimizes for what the data shows, not necessarily for what’s right for the business or its stakeholders. Often, nuanced contextual factors that are extremely important to a decision simply don’t appear in the data itself.
What’s more, data reflects the past, but business decisions shape the future. Organizations that implement fully automated decision-making risk becoming reactive and locked into decisions that reflect historical patterns rather than current realities or future aspirations.
That’s why a human-in-the-loop (HITL) approach remains the best way to make the most of AI initiatives. Organizations should still absolutely take advantage of the speed, scale, and analytical power of AI. But maintaining the ability to review and adjust AI recommendations ensures that decision intelligence stays fully aligned with business strategy.
Balancing Automation With Human Judgement
The question then becomes: when should we trust AI to act on its own, and when should a human step in to make or validate the decision?
One way to do so is to categorize tasks by risk and complexity. Low-risk or routine tasks like generating reports or surfacing anomalies can usually be automated entirely. Humans can get involved with higher-stakes decisions, such as when launching new products, making changes in pricing strategy, or navigating sensitive brand and compliance-related issues.
Between these two extremes – where low-risk tasks are handled entirely by AI while high-stakes decisions are reserved for humans – is a growing category of collaborative use cases. For example, AI might draft up initial forecasts and recommend actions, while human experts review the suggestions, apply strategic context, and make the final call.
In these situations, AI serves as a co-pilot to make the decision-making process faster, more informed, and data-driven. Ultimately, the goal is to ensure that automation enhances human decision making without undermining it.
AI Is a Partner, Not a Replacement
Professionals across industries are worried that AI is coming for their jobs. But while AI has earned its place in the decision-making process, the technology has yet to earn a seat at the head of the table. For now, and likely in the foreseeable future, humans will remain the final decision makers.
The future of decision intelligence lies in creating collaborative environments where machines handle the complexity of data, and humans bring the wisdom and perspective needed to turn insight into impact.
AI is a powerful analyst, not a strategist. It can point you toward what might happen, but not always what should happen – and that’s a critical distinction in decision intelligence.