Every AI pitch deck I see leads with efficiency. Save time. Cut costs. Do more with less. But efficiency is a ceiling, not a floor. The work redesign literature shows that the most consequential innovations do not optimize existing tasks. They create entirely new categories of work. So what happens when we stop asking what AI makes cheaper and start asking what AI makes newly possible?
The hypothesis: organizations that frame AI primarily as an efficiency tool risk capturing only incremental value, because the efficiency frame anchors organizational imagination to existing workflows and forecloses the design space where transformative applications emerge.
Three Takeaways
First, the efficiency frame anchors organizational imagination to the status quo. Tversky and Kahneman's (1974) work on anchoring demonstrates that initial reference points powerfully constrain subsequent judgments. When a leadership team begins with "how can AI make us more efficient," the entire design process becomes anchored to existing tasks and workflows. The frame determines what questions get asked, which determines what solutions get designed. Efficiency is not a neutral starting point. It is a constraint that forecloses the most valuable design space before the conversation begins.
Second, the history of transformative technologies shows that the most valuable applications were not anticipated by the efficiency frame. When electrification reached factories in the late 19th century, the initial use case was direct replacement: electric motors replaced steam engines in the same factory layout. David (1990) documented that it took decades before manufacturers redesigned factories around the new capabilities electricity enabled. The transformative value was not in the motor. It was in the redesign. Organizations that measured electrification's value by how much it reduced power costs missed the structural revolution that redefined manufacturing. The same pattern is emerging with AI: the efficiency gains are real but modest compared to what becomes possible when the work itself is redesigned.
Third, efficiency narratives create a ceiling on workforce engagement by appealing to none of the psychological needs that drive sustained motivation. Deci and Ryan's (1985) Self-Determination Theory identifies autonomy, competence, and relatedness as core psychological needs. "Your job will be faster" appeals to none of these. "You will be able to do things that were previously impossible" appeals to competence and autonomy. When both the human and the AI system are expanding what they can do through interaction, the work itself becomes more engaging, not just more efficient. The efficiency frame treats people as inputs to be optimized. The capability frame treats them as agents whose potential is being expanded.
The Longer View
Schumpeter's (1942) concept of creative destruction provides the macro-level frame. Efficiency improvements are sustaining innovations: they make existing processes cheaper but do not alter the competitive structure of an industry. The economic dynamism that reshapes markets comes from structural novelty: new products, new processes, new organizational forms that render existing approaches obsolete. Applied to AI, the efficiency frame produces sustaining value. The capability frame produces the conditions for structural novelty.
Art history offers a counterintuitive parallel. The invention of the camera in the mid-19th century initially threatened to make portrait painting obsolete. The efficiency story would have predicted the death of painting: photography could reproduce reality faster and cheaper. Instead, the new technology liberated painting. Freed from the obligation to represent reality accurately, painters explored impressionism, expressionism, and abstraction. The camera did not kill art. It expanded what art could be. This is the pattern the efficiency frame consistently misses: when a technology automates an existing function, it simultaneously opens design space for work that was previously impossible or impractical.
Self-Determination Theory (Deci & Ryan, 1985) grounds the argument in what actually sustains human performance. Organizations that frame AI as an efficiency tool inadvertently signal to their workforce that the goal is to need them less. Organizations that frame AI as a capability expander signal that the goal is to enable them to do more. The difference in framing produces measurably different engagement outcomes because the frames appeal to different psychological needs. Co-evolution, where humans and AI systems develop new capabilities through interaction, requires intrinsic motivation that the efficiency frame undermines.
My Two Cents
I find myself in too many rooms where the AI conversation begins and ends with efficiency. And I understand the appeal. Efficiency is measurable, justifiable, and safe. But the organizations that will define the next era of work are the ones asking the harder question: what can we do now that we could not do before? That question requires organizational imagination, and organizational imagination requires the kind of psychological safety and structural support that most efficiency-focused cultures have systematically stripped away.
The irony is that the efficiency frame often prevents organizations from capturing even the efficiency gains they are seeking. When the frame narrows attention to doing the same thing faster, it suppresses the experiments that would reveal where AI creates the most value. The organizations I see making genuine progress are the ones where someone had the courage to reframe the conversation entirely: from "how do we save money" to "what does this make possible that was not possible before."
In your next AI strategy conversation, try removing the word efficiency entirely. Ask instead: what does this technology make newly possible for our teams, our customers, our industry? Track how the conversation changes when the anchor shifts.
Read to Learn More
Academic: David, P. A. (1990). The dynamo and the computer: An historical perspective on the modern productivity paradox. American Economic Review, 80(2), 355–361.
Industry: Deloitte. (2025). State of AI in the enterprise (7th ed.). Deloitte Insights.
References
David, P. A. (1990). The dynamo and the computer: An historical perspective on the modern productivity paradox. American Economic Review, 80(2), 355–361.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press.
Deloitte. (2025). State of AI in the enterprise (7th ed.). Deloitte Insights.
Schumpeter, J. A. (1942). Capitalism, socialism, and democracy. Harper & Brothers.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.