Every vendor claims to be AI-first, but what does it actually mean? Here are four definitions, the diagnostic questions that tell them apart, and the one that actually matters most for the next decade.

Walk into almost any technology vendor pitch this year, and you'll hear the phrase "AI-first" within the first ten minutes. It's on the homepage, in the sales deck, and even on the lanyard. But ask any three vendors what "AI-first" actually means, and you'll probably get three very different answers (sometimes four).

For a Chief Digital Officer (CDO) or Head of Enterprise Architecture trying to set long-term technology direction, that ambiguity around what AI-first means is more than an annoyance. It's a procurement problem. Picking the wrong interpretation of "AI-first" can lead to a six-figure investment in something that doesn't actually move the business forward, and you won't realise the mismatch until the project is already underway.

So before the term gets any more diluted, it's worth pulling it apart.

4 Things Vendors Might Mean When They Say “AI-First”

1. AI-first as product strategy

Some would say this was the original sense of the phrase. When Google CEO Sundar Pichai announced in 2017 that they were shifting from "mobile-first to AI-first," he meant that AI and machine learning would no longer be features bolted onto existing products. They would become the core capability the products were designed around.

The test for this type of AI-first is simple: If you removed the AI capability, would the product still exist? For products like Google Photos search, modern chatbots, or voice assistants, the answer is no. The AI isn't an enhancement; it's the reason the product works at all.

In an enterprise context, this is what a vendor means when they say their software is AI-first. The application's value proposition depends on the model. Strip it out, and there's nothing left to sell.

2. AI-first as engineering practice

This is a very different claim. Here, "AI-first" describes how software gets built, not what the software does.

An AI-first engineering practice means developers use AI tooling as a primary part of their workflow: AI-assisted coding, AI-driven code review and AI in the testing and deployment pipeline. The end product might be entirely conventional. What makes it AI-first is the way the team builds software: faster, with smaller teams and a different cost structure than traditional development.

This is the meaning that matters most when you're evaluating a development partner. A vendor who's AI-first in this sense can deliver more, faster, for less, without compromising on the architectural rigour that keeps systems stable. A vendor who claims to be AI-first but can’t explain how it changes the way their teams actually work is probably using the term as a buzzword.

3. AI-first as operating model

A third interpretation is when the entire business runs on AI. Not just engineering, but support, sales, operations, finance and internal tooling. Shopify CEO Tobi Lütke's widely shared 2025 AI memo captured this version well, requiring teams to demonstrate why AI couldn't do the work before requesting additional headcount.

Examples include the original OpenClaw and UK founder Matthew Gallagher, who built a $1.8bn MedTech company using AI agents to automate large parts of the operation.

This is an organisational design choice more than a technology one. It changes hiring, workflow and decision rights. When a consultancy says they're "AI-first" in this sense, they're talking about themselves as an organisation, not what they'll build for you.

It's worth knowing this version exists, because it's often how a vendor's marketing department uses the term, while their delivery team means something else entirely.

4. AI-first as an architecture posture

This one is the most important, yet the least talked about.

Architecture-level “AI-first” means designing systems with the assumption that AI tools and agents may interact with them, not just human users. This usually means cleaner APIs, better-structured data, and systems that are easier for both people and AI to navigate and automate.

For most of the last thirty years, enterprise software has been designed around a screen that a human looks at and interacts with. Architecture-level AI-first asks a different question: What does this system look like if the primary user is an agent acting on a human's behalf?

Surprisingly, the answer is often: back to text. Text-based interfaces (TUIs) and command-line interfaces (CLIs) are inherently easier for AI to interact with than complex graphical interfaces. In some ways, it's a return to the days of the green screen, but for entirely different reasons.

AI also changes how you think about security and permissions. Traditional API integrations often receive broad access because they're deterministic. AI agents shouldn't. Their permissions need to be tightly scoped to well-defined functions, with clear guardrails around what they can and can't do.

For an executive setting long-term technology direction, this is the version of AI-first that has the biggest implications. It's also the one most vendors aren't yet equipped to deliver.

How to tell which one your vendor means

The four meanings aren't mutually exclusive, but they're not interchangeable. These three questions usually reveal which definition they're using:

1. "Are you describing the product, the process, the organisation or the architecture?" Many will pause here because they haven't made the distinction themselves. The answer tells you which conversation you're actually in.
   
   
2. "What changes for me if I work with an AI-first partner versus a traditional one?" A credible answer should involve deliverables, timelines or architectural decisions.
   
   
3. "Can you show me how an AI-first approach changed something concrete on a recent project?" This is the hardest one to fake. Either there's a specific example or there isn't.

The one bet that's hardest to get wrong

For a Chief Digital Officer or Head of Architecture, the challenge isn't choosing the AI trend that's most exciting today. It's choosing an approach that will still make sense five years from now.

Product features will change. AI development tools will improve. Every company will eventually use AI in some way. Those advantages won't stay unique for long.

What lasts much longer is the architecture underneath your systems.

If your software is designed so that both people and AI agents can interact with it through well-designed APIs, structured data, and clear permission boundaries, you're in a much stronger position. As AI evolves, those systems become easier to extend rather than replace.

If your architecture assumes that every interaction happens through a human clicking buttons on a screen, adding AI later becomes far more difficult. Many organisations will find themselves rebuilding those systems within a few years.

That's why we believe architecture-level AI-first is the most important definition of the four. It isn't about putting an LLM into every workflow. It's about building systems that are ready for AI where it adds value, while keeping humans involved wherever judgment, accountability, or oversight are needed.

When evaluating a development partner, ask a simple question:

"What changes in the architecture when AI agents become users of the system, not just people?"

A good answer should include things like API design, data structures, security, permissions, and observability. If the answer is mostly marketing language, you've probably found a vendor using AI-first as a slogan rather than an engineering principle.

Choosing the Right AI-First Partner

The key is understanding how AI supports your long-term technology roadmap, not just your next project.

This is where the right development partner makes a difference. A team that can clearly explain where and how AI fits in their context, show how it changes the software they ship and connect that to architectural decisions that hold up for the next decade, not just the next sprint.

If your organisation is evaluating partners for a transformation initiative and wants to pressure-test what "AI-first" actually looks like in delivery, Kohde works with transformation leaders to cut through the noise and design systems that hold up beyond the current hype cycle.

If you're evaluating what AI-first should mean for your organisation, speak to the team.