Dr Aruna Dayanatha PhD

Leaders must orchestrate the strengths of both human insight and AI capabilities to achieve better outcomes in the digital era.

Introduction: AI, Humans, and Finding the Gaps

In the age of AI-enabled decision-making, successful leadership is no longer about either human intuition or machine intelligence—it’s about the synergy of both. The most forward-looking executives recognize that AI’s impressive outputs still often miss something. This is where gap-spotting comes in. Gap-spotting is the human ability to identify what’s missing or overlooked in AI-generated analyses, plans, or answers, and it’s fast becoming a critical skill for leaders. In our Orchestration Quotient (OQ) leadership framework—a model we’ve developed to assess a leader’s ability to orchestrate human-machine collaboration—gap-spotting is highlighted as a core competency. It distinguishes those who merely use AI from those who orchestrate AI, ensuring technology’s contributions are complete, context-rich, and strategically aligned with human insight. High-OQ leaders don’t see AI as an infallible oracle; they see it as one voice in the conversation, valuable but incomplete without human context and critical thinking.

In this article, we introduce gap-spotting as an essential leadership skill for the AI era and show how it fits within the OQ model. We’ll explore why AI, for all its pattern-crunching prowess, can produce shallow or partial results. We’ll define what gap-spotting entails and illustrate it with real-world styled scenarios—from business strategy to public policy to innovation management—where a leader’s discerning eye changes the game. Finally, we’ll discuss how leaders and teams can cultivate this skill in practice. The tone here is forward-looking and pragmatic: gap-spotting isn’t about proving AI wrong, but about orchestrating a richer human-machine collaboration. It’s a leadership act of ensuring that the symphony between AI and human insight hits all the right notes.

Why AI Misses Things

AI systems today, especially generative AI and large language models, are astonishing in their ability to generate coherent output from vast data. Yet their very design is also their Achilles’ heel. These models construct answers based on statistical patterns in their training data, not on a true understanding of meaning. In practice, that means an AI might produce text that sounds confident and logical, but it has no genuine grasp of the context or rationale behind it. It predicts the most likely next word or idea, following learned patterns, and therefore often mirrors the average of what’s been said before. The result? If you ask an AI a complex question without careful guidance, you may get an answer that is technically on-topic but superficial or generic. One strategist observed that when they tried letting ChatGPT “come up with a strategy, without any direction or vision, the results were extremely disappointing—soulless platitudes at best, irrelevant answers at worst”. This happens because, without human direction, AI falls back on clichés and the safest, most common conclusions from its training data.

Even when AI’s output is correct on the surface, it can be incomplete. AI lacks the situational awareness to know what hasn’t been said. A striking (and somewhat humorous) illustration comes from researchers who asked an AI to design a railway network with minimal collision risk. The AI’s solution: halt all trains entirely – technically, no crashes, but also no functioning railway. The system did exactly what it was told, not what was meant. This highlights a fundamental limitation: AI doesn’t intuit broader goals or context unless those are explicitly encoded. It tends to follow instructions to the letter, potentially missing the bigger picture or the “why” behind the task.

Moreover, AI-generated content often omits supporting context or counterpoints that a human expert would consider. A Boston Consulting Group study noted that GenAI systems typically produce an answer without showing the evidence or uncertainties behind it. For example, an AI might confidently recommend a strategy because it “sees” data supporting that approach, but it won’t automatically volunteer what it isn’t considering. As BCG experts pointed out, these systems rarely provide counterevidence – they show you why their answer could be right, but not the case for where it could be wrong. This means a human reviewer could easily accept an AI’s plan at face value, missing the fact that a crucial assumption (say, a regulatory factor or a recent market shift) was never accounted for. In short, AI often doesn’t know what it doesn’t know. Its analytical brilliance comes with blind spots: lack of true understanding, lack of context, and lack of judgment about higher-order implications like ethics, long-term impact, or brand values. These are precisely the areas where human leaders must step in.

What Is Gap-Spotting?

Gap-spotting is the skill of discerning those blind spots and omissions in AI’s output. It’s the ability to read an AI-generated report, recommendation, or answer and ask: “What’s missing here? What hasn’t been addressed?” Rather than taking an AI output as a polished final product, a gap-spotting leader treats it as a draft—often a very good draft—awaiting human refinement. This skill involves a mix of critical thinking, domain expertise, curiosity, and even healthy skepticism. It’s uniquely human in that it requires understanding context, interpreting nuance, and applying values or strategic judgment in ways AI currently can’t. As one marketing strategist put it, *“ChatGPT can easily give you the commonplaces on a subject. And that’s very useful to actually move away from them and go further than dull, self-evident facts.”* In other words, a human expert can use AI to surface the obvious ideas (the common patterns), quickly spot that they are obvious, and then deliberately explore more novel angles that the AI didn’t think to mention. Gap-spotting in action looks like this: AI provides a baseline and the human finds the white space around it.

This skill shines in real-world scenarios. Imagine an AI tool drafts a business expansion plan based on historical market data. It might thoroughly cover cost projections and operational logistics, but perhaps it never mentioned competitor reactions or cultural nuances in a new region. A leader practicing gap-spotting will notice those omissions immediately. They’ll say, “This plan assumes competitors will stand still—an unlikely scenario,” or “We haven’t considered how local consumer behavior in that region might differ from the data.” By identifying these gaps, the leader isn’t throwing out the AI’s work; they are enriching it with deeper insight. In fact, recognizing what the AI missed often becomes the springboard for innovation. The AI might give you 80% of a draft, and gap-spotting helps you supply the critical 20% that makes the strategy robust and original. This could be a missing risk assessment, an unconventional idea, or an ethical consideration that transforms a merely competent plan into an excellent one. Gap-spotting is a human value-add. It’s the modern incarnation of critical thinking for the AI era—part editor, part strategist, ensuring that human creativity and caution fill in where the algorithm left off.

Equally important, gap-spotting is done in the spirit of collaboration, not antagonism. It’s not about catching the AI in a mistake to prove a point; it’s about continually asking, “And what about…?” until the combined human-AI output meets a standard of thoroughness and depth that neither could achieve alone. That’s why this skill is often described as an act of leadership orchestration. The leader is orchestrating insight—using AI for what it does best (speed, data-crunching, pattern recognition) and then layering human judgment on top to address what the AI overlooks. Humans bring the “focus and vision” that even highly advanced tools still need, aligning the results with strategic goals, ethical standards, and real-world practicality. In sum, gap-spotting is the art of seeing the unseen in AI outputs and guiding these outputs to their full potential.

Gap-Spotting as a Dimension of the Orchestration Quotient (OQ)

In our Orchestration Quotient model, gap-spotting is a telltale indicator of a leader’s OQ level. What is OQ? It’s a concept we use to gauge a leader’s ability to conduct the “symphony” of human and AI collaboration. If IQ measures cognitive intelligence and EQ measures emotional intelligence, OQ measures something just as crucial in modern organizations: the intelligence of knowing how to combine human expertise and artificial intelligence into a harmonious, high-performing team. High-OQ leaders resemble great orchestra conductors—they instinctively know when to let the human “musicians” take the lead and when to cue the “AI section” for support. They recognize that humans and AI each have unique strengths: humans contribute creativity, ethics, and contextual understanding, while AI contributes processing power, consistency, and pattern recognition. Leaders with a strong Orchestration Quotient excel at balancing these strengths, ensuring that neither the human nor the AI perspective dominates to the detriment of outcomes.

Gap-spotting plays a critical role in this balancing act. It reflects a leader’s awareness of AI’s limitations and an unwillingness to be lulled into automation bias or false confidence. When a leader regularly practices gap-spotting, they are essentially challenging, enriching, and co-evolving AI outputs in pursuit of better decisions. This behavior demonstrates high OQ because it shows the leader is actively orchestrating the interaction: they treat AI as a collaborator that must be challenged and enriched by human insight. Instead of passively accepting AI outputs, high-OQ leaders probe them—they ask the AI for rationale, question assumptions, and run “what-if” scenarios. For instance, an orchestrator leader might take an AI-generated market analysis and say, “Interesting, but let’s test the opposite scenario” or “Can the AI provide evidence against this recommendation as well?” This aligns with best practices emerging in AI governance: designing systems to give evidence for and against their outputs, so humans can see the full picture. Leaders with high OQ often implement processes or tools that force this kind of dialogue, effectively baking gap-spotting into the collaboration.

Furthermore, gap-spotting enables what we call the co-evolution of AI and human outputs. When a leader spots a gap and feeds that insight back into the process, the next iteration of the AI’s output can be adjusted or re-prompted to account for it. Over time, the AI can “learn” (through updated prompts, fine-tuning, or simply through the human steering it better) to produce more context-aware suggestions. In other words, the human isn’t just editing one AI output; they’re gradually raising the quality of all future outputs by setting new expectations. This iterative improvement is a dance of give-and-take—AI suggests, human refines, AI adapts, and so on. It’s a hallmark of high OQ leadership to facilitate this dance. These leaders create a culture where AI is a starting point, and human wisdom provides the finale. They ensure that AI adoption doesn’t lead to a de-skilling of human teams, but rather to an up-skilling: teams learn to ask sharper questions of AI and interpret its answers more insightfully. The outcome is far richer than either party alone could achieve. As one commentator aptly noted, *“The future belongs not to those who resist technological change nor to those who blindly embrace it, but to those who can conduct the symphony of human-AI collaboration with wisdom, vision, and skill.”* Gap-spotting is one of those wise and skillful practices that elevates a leader from merely using AI to truly orchestrating with AI.

Case Examples: Gap-Spotting in Action

To see how gap-spotting changes outcomes, let’s consider a few scenarios inspired by real-world challenges in business and policy. In each case, an AI system provides a solid starting point, but it’s the leader’s gap-spotting that steers the effort from good to great (or from potential failure to success):

• Business Strategy – The Missed Competitor: A multinational retail company uses a generative AI tool to analyze sales data and consumer trends, and the AI recommends an aggressive expansion of the company’s online marketplace in Category X (where demand is surging). The AI’s recommendation is data-driven and logical, emphasizing growth where the numbers look promising. However, a senior executive reviewing the plan notices a gap: the analysis is entirely inward- and customer-focused, and it hasn’t accounted for a new competitor about to enter that very category. The competitor isn’t prominent in the historical data (which is why the AI overlooked it), but industry news and the executive’s network knowledge indicate a big launch is coming. By spotting this omission, the executive adjusts the strategy—perhaps slowing the expansion in Category X until the competitor’s move is clear, and simultaneously bolstering differentiation and customer loyalty efforts. The result is a more resilient strategy. Without gap-spotting, the company might have over-invested in a battleground about to become far more contested, potentially eroding their ROI. The leader’s orchestration of human market insight with AI’s data analysis prevented a one-dimensional plan and replaced it with a nuanced strategy accounting for competitive dynamics.
• Policy Design – Beyond the Algorithm’s Optimization: A city government employs an AI system to help draft a new urban transportation policy. The AI sifts through traffic data, commuter patterns, and budget constraints, then proposes optimizing traffic flow by adjusting signal timings and adding lanes to major arteries. On paper, commute times would drop and costs are within budget—it looks like a win. But a policy advisor in the review meeting raises a critical question: “This addresses traffic efficiency, but what about the neighborhoods that will be affected by those changes?” On inspecting the AI’s work, they find it didn’t consider the side effects on pedestrian safety in residential areas where traffic might increase, nor did it factor in community feedback about public transport needs. Recognizing these gaps, the team broadens the policy. They incorporate additional measures for pedestrian crossings and decide to invest in a bus rapid transit line—sacrificing a bit of vehicle throughput in favor of more equitable transit access. The enriched policy strikes a better balance between efficiency and community well-being. In this scenario, gap-spotting by human officials ensured the AI’s narrow optimization didn’t lead to unintended social consequences. The policy became not just data-informed, but also context-informed and values-informed.
• Innovation & Product Development – Uncovering Unspoken Needs: A product innovation team at a tech company is using an AI assistant to mine customer feedback and usage data, hoping to identify the next big feature for their software platform. The AI, scanning thousands of support tickets and reviews, suggests building Feature A, a modest improvement on a current tool that many users have requested. It’s a safe, obvious choice supported by the data. The product manager, however, pauses. Through gap-spotting, she asks, “Is the AI only picking up what users explicitly say? What about needs the customers aren’t voicing outright?” She recalls that during a few recent customer interviews (qualitative insights the AI could not fully parse), some power-users hinted at a different problem — one that could be solved by a more radical Feature B, which no one has directly requested because they don’t know it’s possible. This insight is nowhere in the AI’s output because it’s an inferred need rather than a stated one. The manager decides to prototype Feature B alongside the safer Feature A. In testing, Feature B wows the users and opens a new market segment for the platform, far exceeding the impact of the incremental Feature A. Here, the human leader spotted the gap between what data was telling and what users actually needed at a deeper level. By doing so, she orchestrated the innovation process to not just iterate on the past, but to leap toward a novel solution—something AI alone wouldn’t have proposed.

In all these cases, AI provided significant value. The AI was not “wrong” per se; it performed exactly as instructed—analyzing data, optimizing for stated goals, and presenting plausible recommendations. But leadership isn’t just about taking the output and running with it. It’s about seeing where the output lacks breadth, foresight, or humanity, and then making deliberate interventions. A leader skilled in gap-spotting looks at an AI’s contribution and sees a part of the puzzle, not the whole picture. They complete the puzzle by adding the missing pieces—be it awareness of external factors, ethical considerations, or creative leaps of imagination. The outcomes are demonstrably better. Strategies become more robust, policies more inclusive, and innovations more groundbreaking. This is the power of gap-spotting in action: it turns AI from a mere efficiency tool into a true strategic partner, with the leader orchestrating the partnership.

Building the Gap-Spotting Skill

The good news is that gap-spotting is a trainable discipline, not an innate talent reserved for the few. To cultivate this skill, leaders and their teams should focus on a few practical habits and organizational practices:

• Foster a Culture of Questioning: Encourage teams to view AI outputs with a curious and critical eye. Make it standard practice to ask “What are we not seeing here?” whenever an AI presents a conclusion. This can be as simple as reserving five minutes in every AI-assisted meeting for the group to identify one or two potential gaps or alternative explanations. Leaders can model this by gently probing AI outputs in meetings themselves. For example, after an AI-driven presentation, a leader might say, “Thanks for the analysis. Now, let’s play devil’s advocate—what might this be missing?” By normalizing this behavior, gap-spotting becomes a shared responsibility rather than a personal quirk of one skeptical manager.
• Develop Structured Oversight Processes: Relying on intuition alone to catch AI’s misses can be risky. Instead, create checklists or rubrics for reviewing AI-generated results. Think of it as quality control for hybrid work. What should a reviewer look for? Possible checklist items: Data sources used/omitted? Assumptions the AI made? Stakeholders or factors not considered? BCG experts argue that having guidelines beats relying on “vibes” when evaluating AI outputs. For instance, an organization might implement a rule that any AI-generated report on strategy must include a section on risks and counterpoints—and if the AI doesn’t generate it, the human reviewer must add it. Some companies even design their AI systems to output supporting evidence and counter-evidence side by side, essentially automating a bit of the gap-spotting process by revealing what would strengthen or weaken the AI’s case. Even if your tools don’t do this automatically, the team can do it manually: run a prompt asking the AI, “What could make this recommendation fail?” The answers can be illuminating and point directly to gaps.
• Train in Critical Thinking and Domain Knowledge: Gap-spotting sits at the intersection of general critical thinking and specific domain expertise. Invest in training programs or workshops that sharpen skills like problem reframing, assumption testing, and scenario planning. One insightful piece of advice is to *“scale human judgment as you scale AI”*. This means upskilling your people in parallel with deploying new AI capabilities. For domain-specific leadership (be it finance, healthcare, supply chain, etc.), ensure the team maintains strong subject-matter expertise and stays updated on contextual factors in that field. It’s often that deep contextual knowledge that enables someone to say, “Wait a minute, the AI’s outcome doesn’t account for X which I know is important.” Knowledge and continuous learning are the fuel for spotting gaps.
• Use AI to Assist Gap-Spotting: In a twist, you can even use AI itself to help find gaps—if you prompt it correctly. Sophisticated users are learning techniques to interrogate AI outputs by asking the AI to critique or question its own answer. For example, after getting an initial output, a leader might ask the AI: “List some counterarguments to your recommendation,” or “What factors could invalidate your conclusion?” Similarly, one can increase an AI model’s “creativity” settings or ask it for out-of-the-box ideas, essentially forcing it to venture beyond the most probable (and possibly shallow) answer. Another novel approach is running multiple AI models or instances in parallel and comparing results—differences between them can highlight uncertainties or overlooked angles. While AI will never fully replace human critical thinking, these methods can surface a list of potential gaps faster, which the human can then review and consider. Think of it as using AI to stress-test AI: a high-OQ leader orchestrates different tools against each other to ensure nothing slips through unchecked.
• Reward and Recognize Gap-Spotting Behavior: Finally, bake gap-spotting into your leadership development and performance evaluations. When team members demonstrate the courage to question an AI-derived plan (especially in cultures that may be enamored with technology or data), recognize and reward it. Share success stories internally where a well-spotted gap averted a mistake or led to an innovation. Over time, these stories build the narrative that “around here, we value the human touch that makes our AI usage truly effective.” This positive reinforcement will encourage others to speak up and contribute their insight, closing the loop on human-machine collaboration.

By systematically building these habits, leaders will find that gap-spotting becomes second nature. Teams will grow more confident in their role as critical interpreters of AI output rather than passive consumers. And rather than slowing things down, this practice will save time and resources in the long run—heading off blind alley investments, preventing costly oversights, and unveiling opportunities that a superficial analysis would have left on the table. As one analysis on AI strategy noted, *“Do not just scale AI. Scale the thinking that directs it.”* Gap-spotting is exactly that kind of thinking: a compass to ensure AI’s horsepower is always pointed in the right direction.

Conclusion: Orchestrating the Future with Human Insight

AI is here to stay in the realm of decision-making and strategy, but it doesn’t render human leadership obsolete—far from it. In fact, the rise of AI makes distinctly human skills more vital than ever. Gap-spotting exemplifies the value humans bring to AI-enabled work. It’s not a critique of AI; it’s a commitment to excellence. It’s the leader saying, “Together, my team and our AI will produce something better than either of us could alone.” This mindset transforms the human-AI relationship from one of tool user and tool, into one of true collaborators co-creating solutions.

Framed within the Orchestration Quotient leadership model, gap-spotting is a linchpin capability. It elevates a leader’s role to that of an orchestrator—someone who can harness AI’s strengths while covering its weaknesses, ensuring that the end result is comprehensive, context-aware, and strategically sound. Leaders with high OQ don’t passively delegate thinking to machines, nor do they dismiss the machines outright; instead, they deftly weave the two together. They challenge AI outputs and then refine them, much like a conductor nudges a section of the orchestra to modulate their volume or tempo for the sake of the whole piece. The music is sweetest when every part is in harmony.

As you lead your organization through the transformative age of AI, remember that embracing AI is only step one. Step two is mastering how to question and augment AI’s contributions. That is the essence of gap-spotting. It’s asking the next question, seeking the hidden dimension, and never losing sight of the broader purpose and principles at stake. This skill will keep you and your organization not just effective and risk-aware, but truly innovative. After all, innovation often lies in the gaps—in the things that were missing until someone had the insight to spot them.

In conclusion, gap-spotting is the kind of leadership practice that turns AI from a merely efficient tool into a springboard for transformative outcomes. It ensures that human creativity, ethics, and strategic thinking remain at the heart of decision-making, where they belong. Leaders who cultivate this skill position themselves and their companies to not only navigate the AI era, but to orchestrate it. And in that orchestration, we find the future of leadership—one where human and machine together achieve what neither could alone, hitting all the right notes from vision to execution.

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