Your AI Strategy Sucks Because It's Not Even Implemented
Why You Need Continuous Delivery for AI: Apply proven principles from software delivery to turn your slide decks into real value.
Incoming Truthbomb
AI Strategy Reality Check
Everyone has an AI strategy now. Just ask their slide decks. But when you peel back the layers, most of these "strategies" are stuck in the planning phase - whiteboard fantasies, endless POCs, or vendor shopping spreadsheets gathering digital dust.
The Missing Link
Here's the truth: if your AI strategy isn't implemented, it isn't a strategy. It's a presentation, maybe even a pretty deck (had you used Gamma). You have a "concept of a plan."
The Solution
So what's the missing link? Execution.
And the best place to learn how to execute fast, iterate safely, and actually deliver value? Continuous Delivery (CD) - a principle software teams have nailed for over a decade, and AI teams need to adopt yesterday. Remember, you can still join the private beta today.
What is Continuous Delivery, really?
Release-Ready Software
Continuous Delivery is the discipline of building software in such a way that it can be released to production at any time.
Short Feedback Loops
It emphasizes short feedback loops, automation, testability, and pushing small changes frequently.
Speed With Safety
It enables organizations to move fast without breaking everything.
Why AI Needs Continuous Delivery
Models Are Just Code (Mostly)
- AI artifacts- models, data pipelines, prompts are just another form of software.
- If you can't version it, test it, deploy it, and monitor it… it's not ready for prime time.
POCs Are the New Graveyards
- Companies waste millions on POCs that never make it to production ($5M-$20M a pop in hard costs, 8 months on average, plus unknown opportunity costs).
- CD kills POC purgatory by forcing your team to think about how to ship from day one.
Feedback Loops Make Models Better
- Continuous deployment = continuous feedback = faster improvement.
- No more "train once, hope it generalizes" mentality.
You're Already Doing MLOps (Poorly)
- Even if you don't call it MLOps, you have a workflow. If it's ad hoc, fragile, and manual - that's your current "AI delivery pipeline."
- CD gives you structure and resilience.
How to Apply CD Principles to Your AI Strategy
Tools That Don't Suck: Open Source Infra for Shipping AI
Good news: you don't need to build all this from scratch. The open source ecosystem has matured a lot in the last couple years. You just need to know what to reach for - and how to run it without pulling your hair out.
KServe – Smart Traffic Splitting for Models
- Run multiple model versions side by side.
- Send 90% of traffic to your production model, 10% to the new one.
- A/B test safely. Roll back instantly.
- Basically, feature flags but for ML.
Langfuse – Observability for LLM Apps
- See what requests are being run, how they're performing, and where they're failing.
- Monitor LLM quality like you monitor the rest of your application's performance.
- Add guardrails, improve prompts, catch regressions.
StarOps – Your Shortcut to Shipping
Here's where it gets spicy. With StarOps (that's us), you can self-host tools like KServe and Langfuse in your own cloud with just a few clicks. No YAML rabbit holes. No infra chaos.
It's DevOps for AI, but without the headaches. Try it for free.
The Continuous Delivery Advantage
Version
Track changes to models, data, and code
Test
Validate performance before deployment
Deploy
Ship small changes frequently and safely
Monitor
Observe real-world performance
Improve
Iterate based on production feedback
The Takeaway
Strategy without implementation
Just a pretty slide deck
Implementation with CD principles
Reliable, testable, shippable AI
Shipping-powered organization
Real AI value in production
Your AI strategy isn't real until it ships. And it can't ship consistently until you treat it like real software - with versioning, automation, observability, and feedback loops baked in.
Want to be an AI-powered org? Cool. Start by being a shipping-powered org.