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Executives like to say they are “integrating AI.” But most still treat artificial intelligence as a feature, not a foundation: they add a chatbot here, an automated report there, and call it transformation. That’s the same mistake companies made in the early days of the web: building websites as brochures instead of re-thinking their business models around digital interaction.
AI is not a feature. It’s an architectural layer that will reshape every workflow, decision, and product. Those who treat it as decoration will fade, those who treat it as structure will lead.
As product strategist Connor Davis noted, “every great company will soon have an agentic layer, a system that not only automates tasks but also orchestrates them across functions.” The distinction is crucial.
Automation is about efficiency: doing existing tasks faster or cheaper. Agency is about delegation: letting the system make decisions, coordinate actions, and even manage other software on your behalf. Think of it as moving from tools that execute commands to assistants that understand context.
The leap is subtle but profound. When a finance team uses an LLM to summarise quarterly reports, that’s automation. When the same system proactively flags anomalies, adjusts forecasts, and alerts the CFO with recommendations, that’s agency.
Companies that understand this shift are already reorganising around it. They are not adding AI to workflows: they are building workflows around AI.
To be “AI-first” doesn’t mean using the latest model or adding generative features. It means designing products and processes that assume continuous intelligence at their core.
Andrew Bolis captured this well: “AI will become the orchestration layer across every SaaS tool. Instead of humans jumping between apps, agents will execute intent across systems.”
That’s the future of enterprise software. Today’s SaaS stack forces humans to be the middleware: copying data between CRMs, spreadsheets, and dashboards. Tomorrow’s agentic layer will do that work automatically, turning enterprise systems from silos into a single, adaptive “organism”. And here’s no less than a biologist telling you so, and a few years in advance.
This evolution mirrors what happened when APIs transformed the web. At first, companies built isolated web apps: then APIs connected them. Now AI agents will do the connecting… and the deciding too.
From what we’re seeing across industries, AI-first organisations share three foundational traits:
This is not a technical project: it’s a cultural one. Building an AI-first organisation requires leaders to unlearn decades of linear thinking about processes and hierarchy.
The question is no longer how technology can support our employees, but how can employees supervise technology that works alongside them. The manager of the near future won’t just oversee people: they’ll coordinate agents.
Executives who think in terms of software adoption will miss this entirely. The right question isn’t which vendor’s AI tool to buy, but which decisions you’re ready to delegate to a machine.
That shift demands a new kind of governance: clear ethical boundaries, data transparency, and oversight mechanisms that ensure AI recommendations remain auditable and explainable. Companies that fail to define those boundaries early will end up with AI that works… but works for the wrong goals.
The competitive edge in the AI era won’t come from access to the biggest model or most GPUs. It will come from organisational adaptability, or the ability to incorporate AI decision-making without losing accountability.
In every industry, a similar pattern will emerge: the incumbents will integrate AI as a feature, the challengers will rebuild their stack around it. The difference will show up in speed: companies that treat AI as infrastructure will compress decision cycles from weeks to hours. Those that don’t will move at human speed while their competitors move at machine speed.
But don’t confuse velocity with chaos. The best AI-first companies aren’t automating indiscriminately: they’re orchestrating intelligently. They design human-in-the-loop architectures where humans remain the moral and strategic governors, and AI handles execution at scale.
The temptation, of course, is to delegate everything. After all, if agents can optimise marketing spend, supply chains, and code deployment, why not let them? The reason is simple: trust is earned, not automated.
AI agents must be auditable: their decisions explainable and reversible. Without that, an organisation risks the “black box syndrome” that has already plagued large-scale AI deployments. I’ve written before about this risk in Fast Company: when you build on systems you don’t understand, you surrender control.
Agentic systems make that surrender seductive. They don’t crash, they comply. And that’s precisely why they’re dangerous if left unsupervised. Remember the paperclip maximiser…
For leaders beginning their AI-first journey, here’s a roadmap:
AI is no longer the icing on the product: it’s the yeast in the dough. It changes everything from the inside out.
Companies that understand this will design architectures where agents and humans collaborate seamlessly, data flows freely, and decisions happen in real time. Those that don’t will keep bolting AI onto outdated systems and wondering why nothing truly changes.
The agentic future isn’t coming: it’s already here. The only question left is whether your company is ready to stop piloting and start delegating.
ABOUT THE AUTHOR
Enrique Dans has been teaching Innovation at IE Business School since 1990, hacking education as Senior Advisor for Digital Transformation at IE University, and now hacking it even more at Turing Dream. He writes every day on Medium.
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