Since 2024, nearly every engineering team has been under pressure to “do something with AI.”
Some teams are already shipping LLM-powered search, recommendations, or copilots. Others are experimenting in hackathons, running pilots, or just trying to stay ahead of the board’s expectations.
But here’s a question that rarely gets asked up front:
Is your system even ready to support AI?
Adding AI to a product isn’t as simple as plugging in an API or using GitHub Copilot. It takes a different kind of system readiness, something most modern engineering teams haven’t set up yet.
In this post, I’ll walk through five signs your stack is (or isn’t) AI-ready, what engineering leaders often miss, and how to move forward even if you’re not “there” yet.
Most Systems Weren’t Built With AI in Mind
Let’s say you want to experiment with OpenAI’s API, embed an LLM-powered assistant, or run a basic model fine-tuned on your internal documentation.
Here’s what happens at many companies:
- Data is messy or inaccessible
- APIs aren’t structured or documented enough to integrate with
- There’s no infrastructure for lightweight experimentation
- No clear observability to monitor model performance or user behavior
- The team doesn’t have the time, tools, or fluency to move fast
In short, even simple AI features stall out because the underlying system isn’t designed to support them.
So, what does an AI-ready stack look like?
The 5 Pillars of AI-Readiness
You don’t need a state-of-the-art ML platform or a team of PhDs to be AI-ready. But you do need some critical building blocks.
Here’s a simplified framework I’ve seen work across companies experimenting with AI responsibly:
1. Data Architecture
If your data is messy, scattered across systems, or hard to access, your AI efforts will likely stall. Even the best models rely on clean, consistent input. To get there, your product, user, and content data need to be organized, accessible, and ready for use.
Ask:
- Is our product, user, and content data normalized and queryable?
- Can we feed relevant data into an AI model without a giant ETL project?
- Are we capturing events and metadata that matter?
Many teams rush into AI before taking a close look at their data foundation. If your inputs are unreliable or poorly structured, your outputs will reflect that.
Example: Intercom’s AI chatbot uses internal support tickets and documentation, but only because they spent years structuring that data to be ingestible and reliable.
2. API Surface Area
AI needs to talk to other systems to be useful. That means your APIs play a key role. If they’re poorly documented, inconsistent, or tightly coupled to other services, building anything AI-related becomes a slow, painful process.
A flexible, well-designed API layer makes it easier to connect your models to the rest of your product. It also allows for safer testing and faster iteration.
Ask:
- Do our APIs return clean, consistent, and predictable data?
- Is our service architecture modular enough to support small experiments?
- Can we safely expose internal tools or functions to models without risking system stability?
Without a stable and accessible API layer, even simple AI features can take weeks to build. And when teams are stuck just trying to wire things together, there's little room for creative problem-solving.
Example: Shopify’s AI tools rely heavily on internal APIs that provide access to pricing, inventory levels, and user behavior. These APIs weren’t originally built with AI in mind, so they had to be refactored to work safely with their internal model systems. That investment paid off by giving their teams the flexibility to build new features quickly and with less risk.
3. Experimentation Infrastructure
If your team can’t quickly test ideas, your AI roadmap will stay stuck in planning. You don’t need a fully polished platform from day one, but you do need a way to build and break things without risking production.
A good experimentation setup gives engineers the freedom to explore, learn, and fail safely. Without it, every prototype turns into a heavy lift, requiring coordination across multiple teams and slowing everything down.
Ask:
- Can a single engineer spin up an experiment without getting blocked by infrastructure?
- Do we have a safe way to deploy internal-only LLM features for testing?
- Is there a testbed or sandbox environment where things can go wrong without consequences?
- Can we track and compare different model versions or approaches easily?
Many companies want to move fast with AI, but don’t give their teams the tools to explore. The ability to run low-stakes trials is often the difference between an idea that ships and one that never gets off the ground.
Example: Netflix has built internal platforms like Pensive and Metaflow to support fast, safe experimentation. These tools let teams try out machine learning and AI ideas at a small scale, without long setup times or the need to coordinate with several other departments. That freedom to explore is what helps them stay ahead.
4. Observability & Monitoring
AI systems rarely break in obvious ways. More often, they start producing weaker results over time. Without the right visibility, these issues slip through unnoticed, and by the time they’re caught, user trust may already be lost.
Good observability helps your team understand how a model performs in the real world. It gives you the ability to track outcomes, catch unusual behavior early, and keep your systems improving instead of slowly degrading.
Ask:
- Can we monitor how a model or external API is performing in production?
- Can we compare AI and non-AI experiences across users or cohorts?
- Are we collecting feedback or edge cases that help with retraining or tuning?
Many teams forget that AI features need constant care. Unlike traditional systems, models evolve, user behavior shifts, and what worked yesterday might fail tomorrow. If you can’t see what’s happening, you can’t make things better.
Example: Teams working with RAG (retrieval-augmented generation) often discover how fragile model quality can be. Without detailed feedback and usage data, performance drops quickly. The teams that manage to keep their systems useful are the ones that treat observability as part of the core product, not an afterthought.
5. Team Fluency
Even with a strong technical foundation, teams still need to understand how to work with AI. If they aren’t familiar with the basic concepts, tools, and limitations, progress slows. Features either don’t get shipped, don’t work as expected, or never reach the quality needed to scale.
AI introduces new ways of thinking. Engineers need to be comfortable with ideas like prompt design, token usage, embeddings, and model constraints. Product managers need to understand what’s possible and what’s risky. Designers and QA teams need to know how AI can behave in unpredictable ways.
Ask:
- Can our engineers think in terms of prompts, embeddings, and token limits?
- Do our PMs understand what AI can and can’t do at this stage?
- Is there a shared language across teams around risk, hallucinations, latency, and model behavior?
Without fluency, teams waste time chasing unrealistic ideas or treating AI as a black box. When everyone has a basic working knowledge, collaboration becomes easier and ideas get turned into features faster.
Example: Atlassian now includes prompt engineering in their developer documentation. This reflects how seriously they take team fluency. When everyone involved in the product understands how to design and debug prompts, they’re better equipped to build AI features that are useful, realistic, and reliable.
Where Most Teams Get Stuck
In my experience, teams that struggle to adopt AI tend to:
- Treat it as an isolated feature instead of a system-wide evolution
- Underestimate the architectural and process shifts required
- Skip internal enablement and hope "smart people will figure it out"
Meanwhile, teams that move faster:
- Build a sandbox to experiment in
- Assign a small cross-functional team (PM + engineer + design)
- Start with internal AI tooling (code search, support automation, doc lookup)
- Treat delivery velocity as the real measure of AI readiness
What You Can Do Next
If you’re a VP or CTO figuring out how to move your team toward AI, you don’t need to start with a big launch or a full AI strategy deck. Start small, stay practical, and build momentum with focused steps.
Here’s a simple 3-step approach that works:
Run a fast audit
Look at the five areas outlined above and map them to your current systems. Where are you solid, and where are you blocked? Focus first on the gaps that are preventing any kind of AI progress. The rest can wait.
Pick one internal use case
Build something low-risk and internal, like an AI-powered tool for support, documentation search, or code review. Don’t worry about perfection. The goal is to surface real constraints and start fixing them. You’ll learn more from this than from weeks of planning.
Upskill one team
Choose one motivated team and give them the resources to explore. That might mean a few lunch-and-learns, access to tools, simple templates, or time to experiment. Once that team builds something useful, their knowledge will spread faster than any formal training program.
If your team is facing bandwidth or expertise gaps, bringing in outside support can help you move faster without slowing down core development. Adeva is a network of senior engineers who’ve worked on preparing infrastructure, cleaning up APIs, and building systems that are ready for AI. They’re brought in by teams that need focused help without the overhead of a long hiring process.
Final Thoughts
Adding AI to your product takes more than hiring an ML engineer or connecting to an API. The real question is whether your systems, tools, and team are set up to support the kind of fast, messy, feedback-heavy work that AI depends on.
If the answer is no, that’s okay. But it’s better to figure that out now, before you’re under pressure to deliver something your stack can’t handle yet.
