Some expressions appear first in a presentation, then make their way into meetings, and before long every company seems expected to define itself through them.
One of those expressions is AI-first.
We are going to hear it increasingly often: AI-first companies, AI-first products, AI-first teams. The problem is that the phrase can mean so many things that it eventually means nothing.
Is an AI-first company one where everyone uses ChatGPT? One that automates as many tasks as possible? One that sells artificial intelligence? Or are we describing something much deeper?
To me, an AI-first company is an organization that considers the capabilities of artificial intelligence from the moment it designs a product, process, or decision. It does not have to use AI for everything. It has to learn how to combine models, traditional software, data, people, and rules to create a capability that did not exist before.
The distinction looks small, but it changes the entire conversation.
First is an important part of the term
When a company designs a conventional product and adds a chatbot at the end, it is adopting AI. The chatbot may be useful, but the experience has not necessarily changed.
An AI-first approach begins earlier. It asks what would become possible if the product could understand language, generate content, analyze scattered information, learn from examples, or execute part of the work.
The same applies to a process. The goal is not merely to accelerate an isolated task. We need to examine the entire journey: what information enters, which decisions arise, what a system can handle, where human judgment is necessary, and how we verify that the result is good.
At the strategic level, AI stops belonging exclusively to the technology department. It begins to affect product, operations, talent, risk, and the business model.
This resembles ideas such as mobile-first and cloud-first. Designing for mobile did not mean shrinking a page until it fit on a smaller screen. It required rethinking the experience for a person in motion, touching a screen, perhaps with an unreliable connection. Moving to the cloud was not simply relocating servers either; it changed how services were built, deployed, and scaled.
Something similar happens with AI-first. The technology enters the conversation at the beginning, while we can still decide what to build and how to organize the work.
Using AI is not enough to transform a company
Buying assistants for every employee can improve daily work. Automating reports can save hours. A chatbot can answer questions. An internal lab may produce interesting prototypes.
All of that matters. These are signs of adoption, but not necessarily transformation.
The difference appears when a company stops inserting tools into the old process and becomes willing to design a different process.
McKinsey's 2026 global AI survey illustrates the tension. Eight in ten respondents say AI improved their individual productivity, but only 37 percent attribute any EBIT impact to AI at their organization. Personal benefits are becoming visible before company-wide economic results.
Among organizations McKinsey classifies as high performers in AI, nearly three-quarters report fundamentally redesigning workflows. Only one-quarter of other respondents report doing the same.
This is a self-reported survey, so it does not establish causation or provide a universal recipe. It does suggest a direction: distributing licenses is much easier than turning a new technical capability into a new way of operating.
AI-first does not mean AI-only
There is another confusion worth avoiding.
An AI-first company does not need to send every task through a model. A rule, a database query, or a well-designed form may solve a problem faster, at a predictable cost, and with less risk.
Nor does AI-first mean removing people from the equation.
In many cases, the strongest design will be a combination: AI interprets a request, finds relevant history, or prepares a response; conventional software validates precise rules; a person adds context, handles exceptions, or makes a sensitive decision.
The objective is not to maximize AI usage. It is to place each capability where it produces a better outcome.
A clinic, an accounting firm, a factory, or a retailer can follow this logic without training a proprietary model and without showing customers a screen labeled “artificial intelligence.” What matters is whether the organization can solve something it could not solve before, deliver it differently, or serve cases that used to be uneconomical.
Four places to look for change
To distinguish transformation from a collection of tools, I would look in four places.
The first is the product. Does AI enable an experience that was previously impractical? This might be personalization at a scale no human team could provide, an interface capable of handling open-ended requests, or a service accessible to customers who could not afford it before.
The second is process. Did the complete journey change, or only the speed of one task? If we generate ten commercial proposals in the time it once took to prepare two, the bottleneck may simply move to reviewing, approving, or fulfilling them.
The third is decision-making. Does AI turn scattered information into something a person can evaluate? This requires trustworthy sources, context, and traceability. Connecting two contradictory internal documents to a model does not resolve their contradiction. It only makes a deferred decision more visible.
The fourth is the business model. If the way we produce changes, should what we sell and how we charge change too? The customer may no longer want to buy hours or headcount, but rather capacity, speed, deliverables, or outcomes.
That final change is often the most uncomfortable because it stops treating AI as an internal improvement and allows it to reshape how value is captured.
AI Pods and the change of unit
Globant's AI Pods are an interesting example.
The company introduced the model in 2025 for services including software engineering, product definition, design, and testing. It combines agents with human specialists who supervise alignment, quality, and traceability.
The interesting part is not only which tools are used. It is the unit offered to the customer.
For many years, much of the technology-services industry sold profiles, people, and hours: I need five developers for six months. Globant instead proposes a monthly subscription with capacity measured in tokens and aligned with outcomes.
In its current AI Pods offering, the company describes deliverables ranging from product definition to development, testing, and system modernization. Agents execute parts of the workflow while experts supervise the output.
These claims should be read for what they are: Globant's commercial proposition, not independent proof that this will become the industry's definitive model. Still, the case clearly shows the difference between using AI inside a business and allowing AI to change the shape of the business.
If a different combination of people and systems produces the work, selling human effort alone may stop representing what the client receives.
Transformation takes place across several layers
Installing an application is quick. Building an organizational capability is not.
First comes the value proposition. Which customer problem can we solve better? Can we personalize something that used to be standard, shorten the distance between a need and an answer, or serve a segment that was previously unprofitable?
Then comes the actual work. Every acceleration moves the bottleneck. If a system drafts proposals much faster, somebody still needs to confirm that they are correct, that the company can fulfill them, and that unusual cases do not slip through.
There is also data and knowledge. The system needs to know which information is current, who can see it, and what to do when it is missing. A disorganized knowledge base does not become reliable merely because it is connected to a more capable model.
The fourth layer is responsibility. The organization must define what the system may execute, what requires approval, and who responds when something fails. Human oversight is real only if that person has the context, time, and authority to intervene. An approval button at the end of a hundred decisions does not guarantee control.
Finally, there is culture. Teams need permission to experiment, but also ways to share what they learn. Shopify, for example, made habitual AI use a workplace expectation and paired it with access to models, editors, and internal spaces for publishing prototypes. Its account of that process is a corporate narrative rather than an independent profitability study, but it shows something important: asking people to adopt a technology without creating the conditions to learn usually remains a slogan.
When these layers advance together, AI stops being a tool used by a few individuals and becomes an organizational capability.
How I would start without transforming everything at once
Being AI-first can sound like an enormous reorganization. I would begin with one concrete journey.
First, choose a business intention: resolve complaints faster, prepare more accurate proposals, detect failures earlier, or shorten product launch time. Before discussing models, we need to know what we want to improve.
Then map one process with a beginning, an end, and an accountable owner. What happens between receiving a request and delivering a solution? Where does it stall? What information must be found? Where do errors and exceptions appear?
Record a baseline: time, cost, quality, errors, or satisfaction. Without that reference, a demonstration may look impressive while telling us nothing about whether the outcome improved.
Only then would I design the new workflow:
- what AI will do;
- what traditional software will continue to handle;
- what people will decide;
- which cases must be escalated;
- what information and permissions each part requires;
- how the system will record what happened.
The test should involve real work, including uncomfortable cases: ambiguous requests, incomplete data, and exceptions. If it works, expand it. If it does not, adjust or discard it. A pilot also creates value when it shows early that an idea was not suitable.
The difficult leap comes next: integrating the solution, assigning ownership, monitoring quality and cost, training the team, and maintaining everything as models and the business change.
Moving from an experiment to a repeatable practice is what turns a demonstration into a capability.
There is no AI-first certificate
I do not imagine a day when a company crosses a line and declares its transformation complete.
AI-first is more useful as a direction and as a way of asking questions:
- For a product: what can we design now that this capability exists?
- For a process: are we accelerating a task or improving the whole outcome?
- For a decision: what can the system prepare, and what must a person judge?
- For the business: if production changed, should what we sell change too?
- For automation: how do we preserve accountability, control, and trust?
Not every company will or should follow the same path. A regulated organization will have different boundaries from a startup. A reversible process supports more experimentation than a medical or financial decision.
Maturity does not mean using the newest model in every box. It means knowing where AI creates an advantage, where it introduces risk, and who is accountable for both.
So when a company says it wants to become AI-first, I would not begin by asking how many licenses it purchased.
I would ask what it can now offer that was impossible before. Which customer journey changed. How people and systems divide the work. What the organization learned from its experiments. And whether this new capability is already transforming how it creates and captures value.
AI stops being decoration when it purposefully changes how a company thinks, decides, and delivers results.
If you want to explore this idea further, you can watch the original video on YouTube and tell me where your organization stands today: individual tools, isolated experiments, or processes that have already been redesigned.