You Can Ignore AI — But Your Organization Won’t

For a while, the conversation about how organizations use AI took place in the comfort zone of aspirational verbs: “encourage,” “explore,” “experiment,” “test.” Not for much longer. An article in the Wall Street Journal, “Tech firms aren’t just encouraging their workers to use AI. They’re enforcing it”, describes how in technology companies, AI skills are no longer an extra, but a monitorable, assessable and, in practice, enforceable requirement. “AI competence” is scored, becomes a performance variable, is observed on panels, integrated into promotions and filtered into hiring. AI is not a tool: it is a cultural norm that, when mature, becomes political.

This isn’t just happening in productivity-driven startups, where the CEO can openly use a carrot-and-stick strategy, assigning a score, and even cash bonuses for employees based on their use of AI. Established, large companies are doing the same: Amazon Web Services with usage dashboards for engineering, Google incorporating the use of AI into some evaluations, Meta quantifying code written with the help of AI, Microsoft asking for justification and quantification of how it’s used, Salesforce measuring “adoption” and assuming that employees who don’t use AI are probably “underperforming.”

When an organization invests heavily in building AI tools (or buying and deploying them), adoption is no longer a “nice to have” and becomes the way to demonstrate return. The WSJ article explains how this works with unsettling clarity: there comes a turning point, “there are already enough people who use a tool, and the mandate to use it arrives”, and suddenly even internal processes such as requesting holidays are necessarily channeled through an AI agent. The next step is the phrase that should set alarm bells ringing: “they probably won’t survive in the long term”, applied to those who don’t get on the program.

And if this is the new normal in business, it should come as no surprise if a parallel phenomenon takes place in higher education. Because the university, although it sometimes pretends to live in a capsule of exceptionality, is not on the margins of general-purpose technologies: it absorbs them, discusses them, regulates them… and, finally, it institutionalizes them. At IE University, where I work, this institutionalization is not taking place behind the scenes, but explicitly and with a desire for governance. The Institutional Statement on Artificial Intelligence outlines it as part of the institution’s DNA and as an opportunity to improve pedagogy and productivity, also insisting on the role of human mediation, critical thinking and a humanistic approach to innovation. And, most importantly for what we are discussing here: it makes it clear that there are academic and pedagogical teams adapting programs and methodologies, designing content and materials, and developing specific programs for students, teachers, and staff.

All this has practical, far-reaching consequences. At IE University, teachers are expected to use it, we are trained to do so, and the portfolio of tools is continuously expanded and refined. Not as a fad, but as an infrastructure. And it is done, moreover, with in a way that completely changes the debate: radical transparency. There is no question of “hiding” the use of AI or “disguising it” as our own work; there is nothing to be embarrassed about. Because when the use is transparent and evolves from “I ask a question then I copy and paste it” to more reasonable models with added value, the focus shifts: we’re no longer talking about possible cheating, but whether the design of the activity, the criteria and the evaluation are well constructed.

Here’s the part that interests me most: the result is not measured by time saved, but by supervised quality. AI works well when it is subject to control, review, traceability and continuous improvement. This is why IE University’s manifesto insists on the need for an ethical approach, academic policies for classroom use and a human-centered perspective.

And then the uncomfortable question arises that many would prefer not to ask: can you teach at IE University if you don’t want to use AI? Of course you can, because academic freedom is not a slogan: it is embedded in our culture. But it would be naïve to ignore the organizational culture, which provides training, tools, accompaniment, and because the quality differential, when done well, becomes evident. And not least because our students ask for it, because the market demands it, because research accelerates it, and because, quite simply, the alternative begins to seem more and more anachronistic. In short, when everyone around you is raising their productivity and their capacity to teach with AI, refusal is hardly an option.

The real discussion about AI centers on what kind of organization you want: one that ignores change or one that defines clear rules, invests in training, demands transparency, and monitors results. UNESCO, in its guide on using AI in education and research, insists on the need to develop human capacity, policies, and a human-centered vision so that these technologies do not become an irresponsible shortcut. In turn, the OECD proposes safeguards for an effective and equitable use of AI in education.

In the corporate world, we are already seeing the demand formalize: Accenture, for example, links promotion to the regular use of AI. In a university, explicit coercion would certainly be a cultural mistake, but normalization will probably end up being just as powerful. The difference is that, if we do our job well, the university can be the place where normalization doesn’t lead to blind obedience, but critical competence: using artificial intelligence because it improves results, while understanding it, auditing it, discussing it and teaching it honestly.

And here’s the irony: for years, business schools studied companies so as to “model innovation.” Today, organizations are teaching us something else: how a technology becomes a requirement without the need for grandiloquent decrees. It is enough to measure it, train it, incorporate it into the workflow, add feedback, improve it, and reward its results. At IE University, the challenge and the opportunity is to demonstrate that this transition can be made without losing our academic soul: with freedom, yes, but also with responsibility. AI must always be supervised and used with total transparency. For the veterans like me, who lived through the arrival of the internet and its incorporation into everything, it’s fascinating to relive something similar with AI. And if that’s the new contract, we’d better write it before it’s written, literally, by a machine.

(En español, aquí)

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This post was previously published on MEDIUM.COM.

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