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Artificial Intelligence
ROI 2.0—Return on Insight
Reframing AI as cognitive insourcing, not outsourcing.
Updated February 22, 2025 Reviewed by Monica Vilhauer Ph.D.
Key points
- AI shifts us from outsourcing tasks to insourcing thought, amplifying cognition instead of replacing it.
- Return on insight values depth over efficiency, revealing new perspectives rather than just saving time.
- AI doesn’t replace thinking—it refines it, helping us see and shape our own ideas more clearly.
Business thrives on return on investment (ROI)—a metric that reflects the strategic allocation of resources to generate measurable returns. But what if AI is changing the equation? As large language models (LLMs) weave into daily life, we’re no longer just outsourcing work—we’re insourcing thought. Instead of offloading labor to a detached machine, we’re engaging in something more intimate: a process of cognitive amplification.
This shift invites a curious, new perspective: "return on insight." This suggests to me that we consider the redefinition of value that acknowledges depth over output, reflection over efficiency.
AI’s "Closer" Role
Historically, tools have extended human cognition by externalizing it. Writing preserves memory beyond the mind’s fragile limits and calculators spare us arithmetic drudgery. These are classic outsourcing moves—shifting work outward for efficiency. AI, however, operates differently.
LLMs, trained on vast human knowledge, don’t just process. They reflect, synthesize, and respond in ways that mirror human reasoning. Consider a writer querying an AI for a fresh angle on a topic. The response isn’t a mechanical output but a dynamic interplay—ideas that spark further thought. This isn’t delegation in the old sense, it’s a retrieval of insight, a pulling-in of perspective that feels closer to collaboration than offloading.
Insight in Action
This is fertile ground. Cognitive science has long explored how we augment thinking through external scaffolds—consider Vygotsky’s zone of proximal development, where structured support enables growth beyond unaided capacity. AI stretches this idea further.
When we engage with an LLM, we’re not just retrieving data, we’re activating a metacognitive loop—thinking about our thinking, with a partner that mirrors and extends it. The return isn’t measured in time saved or tasks completed, but in clarified thought and expanded understanding.
Consider a practical case. A person faces a career crossroad: stay in a stable role or leap to a riskier venture? She turned to an AI tool, not for a definitive answer, but for structure. The system posed questions—from values to risks—and within moments, she sees her own priorities take shape. The insight didn’t originate with the AI, it surfaced through the exchange. This aligns with the critical role of reflection where external prompts can unlock internal clarity.
Here, the AI acted as a scaffold, not a substitute, yielding a return measured not in efficiency, but in self-awareness.
Partner, Not Threat
This reframing challenges the dominant AI narrative. Popular discourse often casts it as an outsourcing threat—algorithms poised to erode human agency, from factory floors to creative studios. But this view overlooks a subtler dynamic.
Far from pushing cognition outward, AI can draw it inward, aligning with our mental processes in ways that feel uncannily personal. It’s an external system—code and data humming in distant servers—yet its outputs resonate as extensions of our own minds.
This tension echoes deeper philosophical debates about selfhood: Are we diminished by such tools, or are we expanding what “self” can mean?
Insight’s Promise
Of course, there’s a caveat. Over-reliance on AI risks dulling independent reasoning, much like muscle atrophy from disuse and accordingly, balance is key. Used judiciously, AI becomes less a crutch and more a catalyst—akin to a therapist’s probing question or a colleague’s timely nudge. The technology scaffolds insight, but we supply the agency to act on it.
This synergistic approach enhances problem-solving—a finding that reinforces the power of AI as an insourcing tool, rather than an outsourcing mechanism.
Our Return on Insight
What emerges is a new metric for our technological age. Return on investment tracks efficiency; return on insight tracks enrichment. It’s a shift from quantifying output to valuing discovery—a metric not about what AI does for us, but about what it reveals within us. For a society fixated on productivity, this offers a counterpoint. And perhaps the true power of AI isn’t in replacing thought, but in refining it.
As AI reshapes cognition, the question shifts; it's not how much AI can do, but how much deeper it can help us think.
