How to add AI to your product without breaking it
As a founder, you’re probably feeling pressure to add AI to your product.
But ship those AI features too fast, and your codebase could turn into chaos. Move too slowly, and you may miss the moment.
Here's how to add AI the right way, whether you’re building your product for users or processes for your team.
Step 1: Choose where AI belongs (product and process)
AI isn't needed in every part of your product. In fact, some parts should never have it.
First, protect the parts of your product that can’t fail: billing, permissions, database writes, transactions. No AI there — ever.
Once you've done that, look for ways AI can help you safely:
- For customers: personalized recommendations, faster answers, smarter search.
- For your team: summarizing PRs, flagging bugs, prioritizing tickets.
Clear boundaries make everything else easier.
Step 2: Build in layers, not hacks (product only)
Think of your product as three layers:
Layer 1 — The deterministic core
This is the part of your app that cannot break.
- Pricing logic
- Permissions
- Database writes
- Transactions
This layer must always be predictable. No AI here. Ever.
Layer 2 — Context via helper functions
Your AI is only as useful as the data it has access to.
That’s where helper functions come in — small bits of code that fetch the exact context the AI needs to do its job.
Examples:
get_user(email): { plan, usage }
lookup_policy(slug): { title, body }
search_docs(query): [{ snippet, url }]
get_owner(table): "[@oncall](/oncall)"
These helpers give you control over what the AI sees. And they make debugging way easier.
Layer 3 — AI as an assistant
Once your foundation is solid and your functions are clean, AI becomes useful.
It can help with things like:
- Drafting support replies
- Summarizing pull requests
- Flagging weird behavior
- Suggesting next actions
Golden rule:
- Automate only low-risk, reversible tasks: summarizing, tagging, prioritizing.
- Keep humans in control of high-risk, irreversible tasks: billing, refunds, destructive actions.
Just remember: AI proposes. Your code enforces. You decide. That’s how you keep things stable — and trustworthy.
Step 3: Start with one painful workflow (process only)
Don’t try to “AI-ify” everything at once. Pick one workflow and start small.
- If you’re building a user-facing feature, pick something that improves the customer experience. For example: smarter search, personalized recommendations, or AI-assisted form filling.
- If you’re improving internal tools, choose a workflow that slows your team down. For example: summarizing PRs, tagging support tickets, or flagging anomalies in logs.
Pick one workflow and set a clear metric for success. For example: “Reduce first-response time from 9 hours to 2 hours.”
Win one workflow first. Then move to the next.
Step 4: Put AI where it matters (product only)
If you’re building AI features for users, don’t hide them behind extra clicks or separate dashboards.
Put AI where work already happens: inside inboxes, dashboards, and search bars.
When AI shows up naturally, users are much more likely to trust it — and actually use it.
Step 5: Treat AI like an unreliable intern (product and process)
AI can be powerful, but it makes mistakes. Design around that reality.
- For internal tools, always keep a human in the loop.
- For user-facing features, never let AI perform irreversible actions automatically.
It also helps to show sources, hide low-confidence results, and default to drafts.
That’s how you move fast without breaking trust.
Step 6: Do retrieval first, skip fine-tuning (product and process)
You probably don’t need a custom model yet.
Start by making sure your AI can find the right information first:
- Gather your FAQs
- Docs
- Policies
- Past tickets
…and make them searchable.
This approach works for both customer-facing assistants and internal tools, and it gets you most of the value without months of model training.
Step 7: Log everything (product and process)
AI failures are sneaky. If you’re not logging, you’re flying blind.
Log:
- Inputs
- Data fetched
- AI outputs
- Final user actions
Then review three metrics weekly:
- Success rate: How often users accept AI outputs
- Fallback rate: How often they escalate instead
- Latency: Keep interactive features <3 seconds
If you can measure it, you can fix it. If you can’t, you’ll ship blind.
Step 8: Run a weekly “reliability loop” (product and process)
Pick one hour every week to make AI better:
- Review 10 failed cases from your logs.
- Tag the root cause — missing data, broken tools, unclear prompts, bad reasoning.
- Fix the top two issues.
- Retest those cases next week.
This small ritual compounds into huge quality gains.
Step 9: Scale deliberately (product and process)
Once your first AI feature works:
-
Move to a second workflow only after the KPI improves.
-
Reuse your retrieval layer and tools wherever possible.
-
Keep your deterministic core clean. AI always stays on top, never inside.
This is how you avoid creating a fragile, unmaintainable mess.
This is the central challenge for 2024/2025: moving from 'AI as a feature' to 'AI as the core user experience' without introducing brittleness.
The most effective pattern I've seen for this is Agentic Workflows, not just chatbots. Instead of one monolithic AI trying to do everything, you break the user's problem into a sequence of specialized, smaller tasks.
For example, don't build an "AI that writes reports." Build a system where:
This modular approach means a failure in one part doesn't break the whole system. You can use a tool like n8n or LangGraph to orchestrate this. The user gets a single, magical outcome, but under the hood, it's a robust, debuggable assembly line of AI steps.
The key is to start by AI-enabling a single, high-value workflow end-to-end, rather than adding AI 'sprinkles' everywhere. This delivers tangible value immediately and gives you a playbook for scaling AI to the rest of your product."
Really appreciate how clearly you broke this down, Aytekin 🙌
The “AI proposes, your code enforces” line hit hard — that’s exactly the mindset I’ve been following while building an AI-based tool that reviews freelancer contracts for risky clauses.
Totally agree that boundaries and logging are what make AI trustworthy in real-world use. Great post.
This is one of the few AI “playbooks” that actually respects engineering reality.
Too many teams treat AI as a layer of glitter — bolted onto fragile systems that were never designed to reason probabilistically. What you’re describing is the opposite: a layered, permissioned, bounded architecture.
The part that resonated most with me is “AI proposes, your code enforces.” That line should be printed on every founder’s desk. It captures the essence of responsible AI integration — human judgment framed by deterministic systems.
In my own product, we started by adding AI to the lowest-risk workflow: summarizing user interactions and tagging document insights. No billing, no database writes — just context-rich assistance. Once that proved stable, we scaled the same retrieval and helper layers into other processes.
The result wasn’t just “AI features.” It was observability — a deeper understanding of how our system behaves and where human input actually matters.
This essay isn’t about adding AI. It’s about designing for fallibility — and that’s exactly how innovation survives contact with the real world.
Love this topic — so many products try to bolt on AI features and end up making the UX worse instead of smarter.
In our experience, the key is to integrate AI around user behavior, not in place of it. For example, instead of auto-generating everything, start with small assistive actions — like intelligent suggestions or summarization — that still let the user stay in control.
Curious — which use cases do you think are the hardest to integrate AI into without hurting the core experience?
We’ve been experimenting with AI-UX blends for a few startup MVPs lately, and the difference between AI that delights and AI that annoys is often just design timing.
thanks , very usefull.
Great title — and a solid breakdown. I’m exploring how small teams can add AI thoughtfully, focusing on trust and simplicity rather than hype. Curious what principles guide your balance between speed and reliability?
I may have found the most solid AI implementation guide yet. I particularly enjoyed the phrase "treat AI like an unreliable intern," which is spot-on. So many teams forget that humans still need to decide.
saving this for later. As I begin incorporating AI into my own side project, I probably will use it as a mental checklist.
This might be the most grounded AI implementation guide I’ve seen so far.
Especially liked the “treat AI like an unreliable intern” part — hits the nail on the head. So many teams forget that humans still need to decide.
Bookmarking this. Will probably use it as a sanity checklist as I start integrating AI into my own side project.
Yes....ai should amplify workflows, not invade them. clean, structured advice.
This is one of the clearest frameworks I’ve seen for adding AI without turning a product into a fragile experiment. The “AI = unreliable intern” mindset is so true — it keeps expectations realistic and protects user trust.
I especially agree with starting with retrieval before fine-tuning. Many founders jump straight into custom models when 80% of value comes from giving AI the right context first.
The layered approach makes this super actionable — thank you for breaking it down so well. 🙌
nice information
This is one of the clearest, most practical takes on adding AI I’ve seen. The idea of protecting the core logic while layering AI on top is solid... too many teams skip that and end up with fragile systems. The “unreliable intern” comparison nails the reality of current AI tools: helpful, but never fully trustworthy.
I especially liked the reminder to focus on retrieval before jumping into fine-tuning. Clean data and good context get you most of the value without the chaos. Simple, structured, and grounded advice that actually translates to better products.
Great write up. Maybe on the sidelines of the core product, I'd add AI-based FAQs for users
Fantastic practical guide! The layered approach really resonates - especially protecting the deterministic core while letting AI enhance the experience. Love the emphasis on starting small with one workflow rather than trying to AI-ify everything at once. This is exactly the kind of measured, strategic thinking we need more of in the AI rush!
we need more leaders to demystify how they're actually incorporating AI into their processes. thanks again