How AI Can Refocus Companies on Their True Value Proposition

The most expensive mistake a company can make with artificial intelligence is not failing to adopt it. It is deploying it everywhere. AI generates value only when positioned surgically on a specific friction point, in service of an identified professional competence. Spread indiscriminately across an entire process, it produces costs, errors, and a loss of professional identity that ultimately outweighs the problem it was supposed to solve.

Every profession has what philosophy calls a "social virtue": an expected quality, an implicit standard that defines professional excellence. The virtue of an accountant is precision. The virtue of a salesperson is relational intuition. The virtue of a physician is diagnostic listening. The strategic question is not "how can AI replace these qualities?" but "how can AI amplify them?" This shift in perspective transforms AI from a destabilizing force into a tool for refocusing.

Social Virtues: The Forgotten Concept That Explains Everything

In the eighteenth century, philosopher David Hume theorized a concept too rarely discussed in the business world: social virtues. Unlike the philosophical tradition that searched for a universal human virtue (charity, wisdom, justice), Hume discovered that each social group develops its own specific virtues, tied to its trade and function. We expect a mechanic to be reliable and honest. We expect a teacher to be pedagogical and understanding. We expect a trader to be rational and composed. These expectations are not interchangeable. Asking a priest to act like a trader does not work.

Hume identified a powerful mechanism: whoever fails to respect the virtues of their social group faces economic sanction. This sanction is not legal. It is relational and reputational. A baker who smiles but cannot produce a decent croissant loses customers. A technically brilliant doctor who lacks empathy loses patients. This is the foundational morality of the liberal economic system: rules are not imposed from above; they emerge from social expectation.

This is precisely where AI becomes constructive. The real question for every professional is not "will AI replace me?" but "how can AI make me even better at what people expect from me?" How can the accountant become the ultimate Swiss clock by removing the administrative friction that slows the work down? How can salespeople spend more time on client relationships by automating file preparation? How can physicians be freed from paperwork to spend more time with patients?

Viewed from this angle, AI is not there to pull professionals away from their core mission. It is there to drive them back to it, forcefully, by stripping away everything that kept them from it.

The Safety Net, Not the Autopilot

A metaphor perfectly illustrates the trap of generalized AI deployment. On Réunion Island, there was a cliff road exposed to rockfalls. To secure it, authorities installed a protective net across the entire cliff face. The cost was astronomical. Maintenance was endless. The road became one of the most expensive per kilometer in the world.

This is exactly what happens when a company deploys AI across all its processes without prior targeting. Costs explode. Errors multiply. Return on investment evaporates. According to the McKinsey State of AI 2025 report, only 6% of companies see real EBIT impact despite widespread AI adoption. The difference between companies that succeed and those that do not comes down to a simple principle: the former redesigned workflows around AI rather than layering AI onto existing processes.

The right approach involves identifying specific friction points where AI can serve as a safety net. Not an autopilot. A net. A mechanism that reduces errors, verifies compliance, and lightens cognitive load on low-value tasks. Billing startups that integrated AI into receipt scanning understood this well: the problem was not the entire process, but the error rate at a specific step. By targeting that step, they reduced errors without destabilizing the rest.

For decision makers, the strategic question is therefore not "how do we deploy AI on our process?" but "what specific problem will AI solve, and where will it reduce our error rate while strengthening our social virtue?" This surgical precision is what separates profitable investments from wasted spending.

Reducing Operational Friction to Unlock Human Value

Operational friction is the silent enemy of both customer experience and employee satisfaction. It surfaces everywhere: redundant forms, unnecessarily complex administrative procedures, time wasted manually cross-referencing data that should be connected automatically. Every consumer wonders why tax authorities, employers, and social agencies do not already share their data. This frustration with unnecessary complexity is universal.

AI excels at reducing this kind of friction. A telecom operator dealing with an angry customer whose subscription cannot be modified for a month could, with properly configured AI, instantly search contractual terms for a regulatory exception. Information retrieval is faster. Situation management is more effective. The customer is retained. The human agent, freed from document research, can focus on what they do best: the relationship.

This friction reduction logic also applies internally. Employees who spend their days on repetitive spreadsheet tasks are the first candidates for burnout. They say it themselves: "I can't find meaning in my work anymore." AI can absorb these tasks and return time for core work. A physician who spends less time on administration and more with patients rediscovers the very reason for choosing the profession. This is not a luxury. It is a lever for retention, performance, and organizational health.

Intelligent feedback centralization illustrates this principle in customer experience. Rather than asking CX teams to manually compile customer feedback scattered across multiple tools, AI centralizes, analyzes, and surfaces weak signals in real time. The professional can then focus on interpretation and action, not collection.

Toward a Deontology of AI in Business

Deontology is a philosophical concept formalized by Kant: acting in such a way that any agent, in the same situation, would act identically. In medicine, deontology defines what a doctor can and cannot do, regardless of what is technically possible. In journalism, it sets limits on what can be published. In cosmetic surgery, it prevents procedures that are technically feasible but ethically questionable.

AI needs an equivalent deontology. Just because you can generate manipulated images does not mean you should. Just because you can automate an entire decision process does not mean you should. The principle is simple: "just because you can doesn't mean you should." This is the baseline of any applied deontology.

In organizations, this deontology translates into clear rules around AI usage. Which tasks require AI? Which explicitly do not? Where does human intervention remain mandatory in the decision chain? These questions are not barriers to innovation. They are the condition for its longevity. Medical deontology did not prevent surgical progress. It made that progress socially acceptable and sustainably integrated.

A Harvard Business Review article warns that AI can destroy an organization's individual DNA by smoothing everything toward a generic standard. Organizations become more automated but less adaptive, more data-driven but less wise, more efficient but less legitimate in the eyes of employees and customers. AI deontology is precisely what protects against this drift.

The AI License: Train, Frame, Empower

The driving license analogy may be the most productive lens for thinking about AI governance in organizations. In the early days of automobiles, there were no licenses, no seatbelts, no speed limits. Accidents multiplied. Eventually, a traffic code was established, not to restrict drivers, but to make freedom of movement effective for all. Law, as Hegel reminds us, is a condition of possibility for human action. For free wills to coexist, that coexistence must take a regulated form.

AI deserves comparable governance. New drivers do not get access to any vehicle: power is limited, progression is supervised. France even has a chainsaw license, applicable under local planning regulations. When the tool is powerful, training for its use is no longer optional.

In organizations, this means acting at three levels. At the individual level, each team member must understand what AI does and does not do, know its limits, and recognize when a generated result is compelling but wrong. At the team level, operating rules must define which tasks are eligible for AI and which must remain purely human. At the organizational level, creating a dedicated role (an "AI operations lead," a governance officer) allows oversight of the whole system and ensures alignment between technological ambition and operational reality.

The goal is not to slow adoption. It is to make it sustainable. Companies that integrate AI without a deontological framework, without tailored training, and without precise targeting of friction points end up accumulating technical and organizational debt that cancels initial gains. Those that establish a clear framework, train their teams to think before they prompt, and deploy AI as a safety net rather than an autopilot unlock considerable potential. They make their employees more competent, their customers better served, and their organization more faithful to what it is meant to be.

AI does not transform companies. It reveals what they truly are. The question is not "should we adopt AI?" The question is "do we still know what we do best, and are we ready to do it even better?"