Consultants and tech firms often define human roles by their most basic function (e.g., a doorman just opens doors). Automating this function saves costs but destroys the immense, unquantified value created through tacit human skills like security, guest recognition, and providing status.
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The best barometer for AI's enterprise value is not replacing the bottom 5% of workers. A better goal is empowering most employees to become 10x more productive. This reframes the AI conversation from a cost-cutting tool to a massive value-creation engine through human-AI partnership.
The common fear of AI eliminating jobs is misguided. In practice, AI automates specific, often administrative, tasks within a role. This allows human workers to offload minutiae and focus on uniquely human skills like relationship building and strategic thinking, ultimately increasing their leverage and value.
Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").
As AI automates technical and mundane tasks, the economic value of those skills will decrease. The most critical roles will be leaders with high emotional intelligence whose function is to foster culture and manage the human teams that leverage AI. 'Human skills' will become the new premium in the workforce.
AI models lack access to the rich, contextual signals from physical, real-world interactions. Humans will remain essential because their job is to participate in this world, gather unique context from experiences like customer conversations, and feed it into AI systems, which cannot glean it on their own.
Using the historical parallel of ATMs, CEO Sim Shabalala argues that AI won't eliminate human roles but will automate routine tasks. This frees humans for higher-order work involving empathy, complex problem-solving, and valuable client interaction.
A tangible way to implement a "more human" AI strategy is to use automation to free up employee time from repetitive tasks. This saved time should then be deliberately reallocated to high-value, human-centric activities, such as providing personalized customer consultations, that technology cannot replicate.
Previously, building 'just a feature' was a flawed strategy. Now, an AI feature that replaces a human role (e.g., a receptionist) can command a high enough price to be a viable company wedge, even before it becomes a full product.
As AI makes it incredibly easy to build products, the market will be flooded with options. The critical, differentiating skill will no longer be technical execution but human judgment: deciding *what* should exist, which features matter, and the right distribution strategy. Synthesizing these elements is where future value lies.
In a world where AI makes software cheap or free, the primary value shifts to specialized human expertise. Companies can monetize by using their software as a low-cost distribution channel to sell high-margin, high-ticket services that customers cannot easily replicate, like specialized security analysis.
