Everyone’s Going “AI First.” Here’s Why That’s a Huge Mistake.
What your company really needs before touching AI — and why skipping this step will cost you.
Everyone’s “AI first” now. But hold on a minute, what the hell does AI First even mean?
“An ‘AI-first’ company is one that prioritises artificial intelligence at the center of its value proposition, infrastructure, and strategy...”
Source: CB Insights - What is an AI-First Company? (2021)
“You have to believe that AI can help you. That’s the foundation of the AI First mindset... You first think of AI for anything you want to do—video editing, writing a speech, analyzing a contract, looking for insights in numbers—all of those things.”
Duolingo’s CEO said it. Shopify’s CEO sent a memo about it. Box is doing it too. Across tech, leaders are declaring their companies will prioritise artificial intelligence in everything from product strategy to hiring decisions.
But here’s the thing no one says out loud:
Most teams declaring they’re “AI first” are still operating with broken processes, undocumented workflows, and scattered internal knowledge. And AI isn’t going to fix that. It’s going to amplify it.
This isn’t an anti-AI post. I’m excited about the potential, I use these tools every day. But I’ve also worked with enough teams and clients to see the reality: AI can’t help you if your foundations are shaky.
In this piece, I want to break down why ‘AI First’ isn’t all it is made out to be and what you need to be aware of first before diving head first into integrating AI into your workforce:
What does AI First Even Mean?
Stop Chasing Tools, Start Solving Real Problems
Why AI Fails Without Structure
Documentation ≠ Alignment: Here’s What Matters
A 6-Step Playbook to Prep Your Org for AI
What Does AI-First Even Mean?
It sounds bold. Futuristic. Strategic.
But “AI First” has quickly become a slogan in search of a shared definition. Companies are using it to mean everything from “we’ve launched a co-pilot” to “we’re rethinking our entire operating model.” And right now, it’s hard to tell which ones are serious and which are just trend-chasing.
Let’s look at Shopify. In April 2025, CEO Tobi Lütke sent a memo to the entire company stating:
“Reflexive AI usage is now a baseline expectation at Shopify.”
This wasn’t a PR stunt. It was a top-down directive to rethink hiring, workflows, and even performance reviews through the lens of AI. Every employee, not just engineers, is expected to find ways to integrate AI into their work.
Duolingo and Box have made similar declarations. In each case, the phrase “AI first” signals a culture shift, not just a technical implementation. These companies want AI embedded into how people work, not just what products they ship.
But here’s the skeptic’s take: while the internal message may be about efficiency and innovation, the external optics suggest something else entirely - cost-cutting. Labor is the biggest expense for most tech companies, and memos about AI-first strategies inevitably land well with shareholders when they hint at doing more with fewer people.
Before we rush to call ourselves AI-first, maybe we should ask: AI-first at what? For whom? And built on what foundation?
Stop Chasing Tools, Start Solving Real Problems
Before you can talk about AI, you need to drop the AI-first mindset entirely.
Why? Because AI isn’t a vision, it’s a response. A response to friction, to repetition, to wasted time. If you don’t know what problems exist inside your org, you’re just guessing where to apply the shiny stuff.
Curiosity Is a Superpower
A strong leader doesn’t walk into a room and say, “I have a great idea — let’s implement it.”
A strong leader walks in thinking, “I don’t know enough yet. Let me understand how my people work before I suggest anything.”
That requires curiosity.
Deep, consistent, open-ended curiosity about how your organisation is held together.
You need to be relentlessly curious about:
What people actually do all day
What feels clunky, manual, or repetitive
What information they chase down over and over
What decisions keep stalling, and why
That curiosity is where the real work starts.
Why AI Fails Without Structure
Before you get excited about being “AI-first,” ask this: Are the fundamentals even in place? Because AI’s going to amplify your org—good or bad.
What Needs to Be Under the Hood
Documented Processes (Visual Mapping FTW)
Start by diagramming your core workflows, literally map the flow in Miro, FigJam, or a whiteboard. You want to know what happens if someone answers “yes,” “no,” or hits a roadblock. Where do they go? Who notifies whom? Without that structure, AI has nothing consistent to follow, like trying to teach someone a play without ever writing down the script.
Accessible, or Even Missing, Data Sources
It’s not just about fragmentation. The real issue is absence. Many teams don’t even have basic SOPs, internal wikis, or documented guidance. You need enough material FAQs, policies, product specs in retrievable form. Deloitte says it best: “Without a robust, repeatable value‑chain, AI simply cannot scale across an organization.” That means structural clarity, not just messy aggregation.
Decision-Making Flow (Part of the Process, or Its Own Beast?)
Where does this live? Who reviews? Who signs off? Decision logic is the glue between steps. If it’s unclear, you’ll end up with AI hallucinations or plausible but pointless outputs. This part could sit under documented processes, but call it out if sign-offs and escalation paths are inconsistently handled in your team.
Why 80% of AI Efforts Crash or Stall
RAND Corporation interviewed data scientists and engineers across industries and found that over 80% of AI initiatives fail, which is double the failure rate of non‑AI IT projects . The root cause? They found five repeatable blockers:
Miscommunication or misunderstanding of the actual problem
Lack of usable data to train systems
A focus on shiny tech instead of solving real problems
Poor infrastructure for deployment and governance
Trying to solve tasks that are just too hard for AI today
RAND recommends that winning teams:
“Focus on the problem, not the technology… invest in infrastructure… and ensure everyone understands the project purpose and domain context” .
Unless your team respects these fundamentals, AI won’t scale, it will stall or sink.
The report also mentions:
“97 percent of business leaders reported that the urgency to deploy AI-powered technologies has increased. Despite this, the same survey found that only 14 percent of organisations responded that they were fully ready to integrate AI into their businesses.”
Only 14% are ready? Yes the innovation of AI has grown exponentially but the fact of the matter remains, most companies are understaffed and don’t have the time or the resources to focus on building out there data sources, processes and decision making flows.
Documentation ≠ Alignment: Here’s What Matters
So you’ve documented your processes, written up your SOPs, maybe even started diagramming how work flows between teams.
But here’s the catch:
Just because something is documented doesn’t mean it’s trusted, used, or aligned on.
In smaller orgs, documentation efforts might be centralised, collaborative, and understood by everyone, you’ve got fewer people, more shared context, and less distance between teams. But even then, tool usage and terminology can quietly fragment.
In larger orgs, or even in mid-sized ones where teams are a bit siloed, you run into a much bigger problem:
Everyone documents things differently. Everyone thinks they’re following the same process, but they’re not!
Team A writes their own doc in Confluence.
Team B logs tasks in Notion.
Team C says, “just ping me on Teams.”
Nobody uses the same language. Nobody follows the same review process. And nobody agrees on what “done” means.
Misalignment Is the Real Killer
Even if you’ve got good documentation, it’s useless unless:
People agree on it.
It reflects how the team actually works.
It’s kept alive (not left to die in a Sharepoint folder).
It’s the classic product doc problem: you write a well-structured PRD that no one reads, then spend two weeks re-explaining everything that was already in the doc. AI won’t fix that, it will just hallucinate answers based on whatever version of truth it’s been given.
Misunderstandings and miscommunications about the intent and purpose of the project are the most common reasons for AI project failure
A 6-Step Playbook to Prep Your Org for AI
By now, you’ve hopefully reframed your mindset: you’re not chasing AI, you’re chasing clarity.
So here’s a practical starting point, a playbook to help you uncover the real problems, align your teams, and build the organisational scaffolding that makes AI worth using later.
1. Run a Discovery Workshop
Start here. This is your first move toward becoming problem-obsessed.
Bring teams into a room - product, ops, support, engineering, whoever. You can lead the session, or better yet, just sit back and ask great questions.
Let people walk you through how things actually work. Don’t bring solutions. Just observe, document, and get curious.
2. Diagram the End-to-End Process
After the workshop, map out the full flow.
Where are the decision points? What are the handoffs? Where are the blockers, delays, and repetitive tasks?
You should walk away with a clear picture of what work actually looks like in your org not what lives in a slide deck.
3. Trace and Document the Data Sources
Now that you understand the process, the next question is: Where does the information live that supports this work?
Maybe it’s an SOP in Confluence. Maybe it’s a checklist in an Excel. Maybe it’s tribal knowledge passed around on Teams. That’s fine, just get visibility. You need to know what’s missing and what’s actually useful.
4. Define a Shared Language
You can’t have a coherent system if everyone defines “done” or “reviewed” differently.
Agree on terms, tools, stages, and responsibilities. This is where you start translating tribal knowledge into something scalable.
5. Assign Knowledge Owners
Every major process should have an owner. This isn’t just to keep documents fresh, it’s about accountability.
Organisations change. Processes evolve. Someone needs to be in charge of making sure the way you work stays documented and accurate.
6. Only Then Explore AI Use Cases
Once you’ve done the hard work, mapped the pain, understood the process, identified the data, then you can ask:
“Can AI help here?”
You might spot automation opportunities or obvious places for copilots. But if you jump straight to this step without the five above, you’ll just be layering technology on top of chaos.
(Stay tuned — I’ll share a separate post soon on how to layer AI into a well-structured system.)
Conclusion
Let’s wrap this up.
Understand → Align → Act → Automate.
If your org is unclear, misaligned, and undocumented, AI won’t save you. It’ll just amplify the mess. The teams that make AI work aren’t magical, they’re just organised.
AI is like a supplement: it’ll speed up a strong system, but it won’t build one for you.
You still have to eat right, train well, and build core strength. Only then does the extra boost actually deliver impact.
You don’t need to be AI first. You need to be problem first, process honest & team aligned
That’s the real work. This whole thing was never about AI. It’s about organizational structure. AI just forces you to get honest about it.
If your team’s struggling to figure out how to actually use AI, or you suspect your workflows aren’t ready for it, let’s talk.
At Hibernia Venture Labs, we run structured product discovery sessions that help you:
Map real-world processes (not just what's on paper)
Identify data gaps and misalignment
Spot where AI can actually make an impact
Drop me a message or comment below. We’d love to help you build clarity before you build anything else.