Demystifying AI First: The Strategic Priority
In recent times, “AI First” has become one of those phrases that appear in corporate presentations, LinkedIn posts, and technology roadmaps. And as often happens with buzzwords, it has become distorted to the point where many repeat it without fully understanding what it means.
Some interpret it as “let’s put AI in everything we do.” Others see it as an isolated, standalone strategy, almost like a separate department living in its own bubble. And a few understand it for what it really is: a prioritization strategy that must coexist with all other organizational strategies.
In this post I want to clarify what AI First really is, how it relates to other “First” strategies we already know, and why the role of the software architect is key to preventing all of this from becoming chaos.
What does “First” really mean?
Before talking about AI First, let’s talk about the suffix “first.” When we say something is “first,” we’re not saying it’s the only thing that matters. We’re saying it’s the first thing to consider when making design and architecture decisions.
“Mobile First” doesn’t mean desktop doesn’t matter. It means we design with mobile in mind first and then adapt. “Cloud First” doesn’t mean everything has to be in the cloud no matter what, but rather that the cloud is the first option to evaluate before considering on-premise alternatives; thinking cloud native first.
So, AI First is a priority, not a declaration of exclusivity. The “first” indicates its position within a ranking of strategies, but it doesn’t imply that the other priorities stop being relevant. In other words, “first before…” and not “instead of…”
The problem of having too many priorities
Here comes the most common trap: if everything is a priority, nothing is a priority. I’ve seen organizations that simultaneously declare themselves AI First, Data First, Customer First, Security First, Cloud First, Mobile First, API First, Privacy First, and more. The result is predictable: when difficult decisions arise where these priorities clash with each other, no one knows what weighs more.
And there’s a phenomenon worth naming here: in practice, stakeholders and developers tend to focus only on the top 5 priorities in the ranking. Not out of bad intention, but because human attention is limited and the ranking works as a natural filter. What’s in the top 5 gets discussed, measured, and demanded; what falls further down gets assumed, forgotten, or postponed.
This doesn’t mean the list should be limited to 5. It means that, without a strong organizational culture or without the active presence of a software architect, priorities that fall outside the top 5 face a real risk of being abandoned. The organization may end up skipping important strategies simply because they weren’t among the first five that the team had in mind.
Organizational level vs. project level
An organization has its set of overall strategies, but each project can have its own order of priorities based on its nature. That said, without deviating from the organizational strategy.
At the organizational level, a ranking might look like this:
- AI First
- Data First
- Customer First
- Security First
- Cloud First
But when we drill down to a specific project, reality changes. Let’s imagine a mobile banking app with AI capabilities. The order might be rearranged like this:
- AI First
- Mobile First
- Data First
- Customer First
- Security First
- Cloud First
Why does “Mobile First” make the list? Because for this particular project, the mobile experience is a design factor that can’t be left for later. If we do, we’ll end up with an app that works “more or less” on mobile, which is exactly the opposite of what the project needs.
Now let’s think about a back-end service:
- AI First
- API First
- Cloud First
- Data First
- Security First
The important thing is that the organizational priority remains the framework, and the project priorities are arranged within that framework without contradicting it.
AI First
Here I want to be very explicit because it’s the most common misunderstanding: AI First is not an isolated strategy. It’s not a separate chapter in the organization’s book. It’s a strategy that must coexist with the others.
When we talk about AI First, we’re saying that artificial intelligence is the first lens through which we evaluate how to solve a problem, design a product, or automate a process. But that lens doesn’t override the others. When we design a solution with AI, we also have to think about the data that feeds it and will support decision-making (Data First), how people will use it and for what purpose (Customer First), how we protect that information and mitigate risks (Security First), and where all of this lives and under what architecture (Cloud First).
If we treat AI First as an island, we’ll build AI solutions without reliable data, without thinking about the end user, without security, and with a poor architecture. And that’s not AI First — that’s an experiment that’s going to fail.
In practice, this translates into a question the team should ask themselves before making important decisions: is there a way that artificial intelligence can add value here? If the answer is yes, that option is evaluated first. If the answer is no, or if the cost doesn’t justify it, it’s discarded and traditional paths are followed. But the question is always asked, not as an afterthought.
Adopting AI First involves several concrete changes:
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At the mindset level, the team stops seeing AI as an “extra” or a special project, and starts considering it as a capability available from the beginning of the design. It’s not something added at the end to score innovation points.
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At the process level, the discovery, design, and architecture phases explicitly include the evaluation of AI components. This requires that teams know enough about model capabilities, their limitations, and their costs to make informed decisions.
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At the infrastructure level, the organization invests in the foundations that make it viable to use AI seriously: reliable data pipelines, platforms for training or consuming models, monitoring and governance mechanisms, and clear policies on how and where it can be applied.
What AI First is not
It’s not using AI in everything just because. It’s not replacing deterministic logic that already works well with a model just to have AI in the solution. It’s not a marketing slogan. And, as we’ve already said, it’s not a strategy that lives isolated from the rest.
The role of the software architect
Here a fundamental piece comes into play: the software architect. It’s not enough to declare priorities in a document and forget about them. Someone has to validate, in every technical decision, that the solution is meeting all the priorities of the project and the organization, not just the ones at the top of the list.
Let’s remember the previous point: teams tend to focus on the top 5 priorities. The architect is precisely the one who prevents the others from getting lost along the way. They are the counterbalance that ensures priority number 7 or 8 is also present in design decisions, even if no one else is mentioning it in meetings.
The architect is responsible for:
- Verifying that the solution is consistent with the AI First strategy without sacrificing the others.
- Ensuring that organizational priorities don’t get lost in the day-to-day decisions of the project, even those that fall outside the top 5.
- Identifying when two priorities conflict and resolving that conflict based on the established order.
- Documenting decisions so the team understands not only the “what” but also the “why.”
This validation work is what differentiates a project that truly fulfills the organizational strategy from one that only fulfills the visible part of the ranking.
Conclusion
AI First is a strategic priority, not a declaration that AI is the only thing that matters. It holds a position in the organization’s strategy ranking and says “we first think about how AI can add value,” but without discarding the other strategies that are also important.
For it to work, we need to maintain coherence between the organizational level and the project level, and keep in mind that teams will naturally tend to focus on the top 5 priorities in the ranking. That’s why we need someone — typically the software architect — validating that every decision meets the complete framework and not just the most visible part.
If your organization is thinking about adopting AI First, don’t treat it as a separate project or as a flag to wave. Treat it for what it is: one more priority in a carefully ranked set of strategies that, together, define how your team builds solutions.
Because in the end, good architecture isn’t about choosing a winning strategy. It’s about making them all coexist in harmony, with a clear order when the moment to decide arrives.
I sincerely hope that this post prevents future senseless discussions on social media like “AI First vs Data First” or “AI First vs Security First.”