If I Took Over A Mid Market SaaS As CPO Here Is My AI Plan For Year One

Last month a founder asked me what I'd do if I joined his Series B company as CPO tomorrow. The board was pushing for "AI features" but nobody had a concrete plan. The engineering team was skeptical. Sales wanted something shiny for demos. And the existing product roadmap was already packed for the next two quarters.

I've been in this exact situation before. When I helped build SimSim AI, we had to figure out how to add generative AI capabilities to an enterprise product that already had paying customers with specific expectations. And at HexaHealth, we shipped a complete platform with AI-powered recommendations in 100 days because we had a clear playbook.

So here's what I'd actually do. Not the consultant-speak version. The real plan I'd execute if I walked into a $10-50M ARR B2B SaaS company on Monday morning.

The Reality Check First

According to Deloitte's Q4 2024 State of Generative AI report, 74% of organizations report their most advanced AI initiatives are meeting or exceeding ROI expectations. That sounds great until you read the fine print: 55-70% of companies need 12+ months to resolve adoption challenges. And 76% say they'll wait at least 12 months before reducing investment if value targets aren't being met.

Translation: this is a long game. Anyone promising AI transformation in 90 days is selling you something.

Microsoft's AI Strategy Roadmap research found that senior leadership vision and support is, by far, the strongest predictor of AI success. Not the technology. Not the data. Leadership buy-in. So before I touch a single line of code, I'm getting alignment at the top.

Quarter 1: Foundation and Quick Win

The first 90 days are about two things: understanding what you're working with and shipping something small that proves the team can execute.

Week 1-2: The Audit

I need to know three things immediately:

1. What data do we actually have? Not what's in the data warehouse documentation. What's actually clean, accessible, and usable. When I did the fraud detection AI project for a financial institution, we spent the first two weeks just figuring out which transaction data was actually reliable. Half of what was supposed to exist didn't.
2. Where are users spending time? I'm looking at product analytics for the workflows where people spend the most minutes. That's where AI assistance has the highest potential impact.
3. What's the engineering team's AI experience? Have they shipped LLM features before? Do they know what RAG is? This determines how much training vs hiring I need to do.

Week 3-4: Pick the Quick Win

I need one AI feature that can ship in 6-8 weeks. Not the big vision. Something small that demonstrates value. The criteria:

Good quick wins: AI-generated summaries of existing content. Smart search over your docs. Auto-categorization of incoming data. Draft suggestions for common tasks.

When we built the enterprise AI platform at SimSim, the first feature we shipped was a simple chatbot that could answer questions about uploaded documents. Nothing fancy. But it proved the pipeline worked and gave us something to demo.

Month 2-3: Hire and Ship

I'm hiring one person in Q1: a senior PM who's shipped AI features before. Not an AI researcher. Not a data scientist. A product manager who knows how to scope AI features, work with uncertainty, and ship iteratively. This person owns the AI roadmap going forward.

While we're hiring, the engineering team ships the quick win. I want it in production before Q1 ends. Doesn't have to be perfect. Has to be real.

Quarter 2: Build the Foundation

Q2 is when the real work starts. We're building the infrastructure that makes AI features sustainable, not just possible.

The Stack Decisions

I'm not building custom infrastructure. We're using managed services wherever possible. The stack for a mid-market SaaS typically looks like:

• Model provider: OpenAI or Anthropic API. Pick one as primary, have a fallback. Don't self-host unless you have a very specific reason.
• Vector database: If you're on Postgres, start with pgvector. It's good enough for most use cases and doesn't add operational complexity.
• Orchestration: Keep it simple. Custom Python code for basic RAG. Only add LangChain if you're building complex chains or agents.
• Observability: Non-negotiable. I want every LLM call logged with prompt, response, latency, cost, and user context.

The Real Feature

Now we build the AI feature that actually matters. This is the one we've been working toward. It should:

• Address a top-3 user pain point identified in the audit
• Have a clear path to monetization (even if we don't charge yet)
• Be defensible through your proprietary data or domain expertise

When I worked on the financial fraud detection system, the "real feature" was the network graph analysis that could trace suspicious transaction patterns across accounts. The quick win had been simple rule-based alerts. The Q2 feature was the AI that could see patterns humans would miss.

Beta Program

By end of Q2, we're in beta with 10-20 customers. Not a waitlist. Actual usage. These customers need to be:

• Power users who will actually try it
• Honest enough to tell us when it sucks
• Patient enough to stick with us through bugs

Quarter 3: Scale and Monetize

Q3 is about proving the business case. We're taking the beta feature general and figuring out how to charge for it.

General Availability

Before GA, we need:

• Reliability: 99.5%+ uptime for AI features. Graceful degradation when the LLM is slow or unavailable.
• Performance: Response times under 3 seconds for interactive features. Users won't wait longer.
• Guardrails: Content filtering, rate limiting, abuse prevention. You don't want to be on the wrong end of a viral tweet about your AI saying something inappropriate.

At HexaHealth, we learned this the hard way. Our recommendation engine was accurate but slow. Users would click, wait 5 seconds, assume it was broken, and click again. We had to completely rearchitect the caching layer before it was usable. Build for speed from the start.

Pricing Strategy

This is where most teams get stuck. Here's the decision framework I use:

My default for mid-market SaaS: include basic AI in all tiers, charge for advanced features or higher usage limits. This gets adoption while still capturing value from power users.

Second Feature

While scaling the first feature, we start building the second. The pattern repeats: identify high-value use case, build quick prototype, test with beta users, iterate, ship.

By end of Q3, you should have 2 AI features in production. One at scale, one in early access.

Quarter 4: Optimize and Plan

Q4 is about proving ROI and setting up Year 2.

Measure What Matters

By now you should be able to answer:

• What's the AI feature adoption rate?
• How does AI usage correlate with retention?
• What's the cost per AI operation?
• What revenue is directly attributable to AI features?

The Deloitte research shows that the most advanced AI initiatives target IT (28%), operations (11%), marketing (10%), and customer service (8%). But here's the interesting part: beyond IT, organizations focus their deepest deployments on functions uniquely critical to success in their industries. For a SaaS product, that's usually the core workflow your customers use daily.

Cost Optimization

LLM costs add up. Q4 is when we get serious about optimization:

• Prompt engineering: Shorter prompts cost less. Review and trim.
• Model routing: Use smaller models for simple tasks. Save the expensive models for complex queries.
• Caching: If users ask similar questions, cache the responses.
• Batching: For non-interactive tasks, batch requests during off-peak times.

Year 2 Planning

Based on what we've learned, the Year 2 plan should address:

• Which AI features get expanded?
• What new capabilities do we need?
• Do we need more specialized AI talent?
• What's the investment ask for the board?

The Team You Need

I get asked about team structure a lot. For a mid-market SaaS, here's what I'd build over Year 1:

Notice what's not on this list: a Chief AI Officer, a massive data science team, or ML researchers. For Year 1, you need execution capacity, not research capacity. Build the research team in Year 2 if you're seeing differentiation through AI.

What Can Go Wrong

After shipping 50+ products, I've seen most of the failure modes. Here are the ones specific to AI:

The demo trap. You build an impressive demo that wows the board but doesn't work reliably in production. I've seen companies spend 6 months on demos before anyone asks "but does it actually help users?"

The accuracy obsession. You keep tuning the model to get from 92% to 94% accuracy when users would be happy with 85% if it was faster and more reliable.

The platform trap. You try to build an "AI platform" instead of specific AI features. Platforms are for Year 3, not Year 1.

The privacy surprise. You ship an AI feature and then discover your enterprise customers can't use it because of data residency or compliance issues. Check this in Q1.

The Bottom Line

Year 1 is not about becoming an AI company. It's about proving that AI can add value to your existing product. Ship 2-3 features. Prove ROI on at least one. Build the infrastructure and team to do more in Year 2.

The companies winning at AI right now aren't the ones with the fanciest models or the biggest data science teams. They're the ones who shipped something useful, learned from it, and iterated. That's the playbook.

Deloitte found that organizations with scaled AI initiatives are focusing on competitive differentiation through functions critical to their industry. For a SaaS company, that means AI features that make your core workflow meaningfully better. Everything else is noise.

Your Year 1 AI strategy should fit on one page. If it doesn't, you're overthinking it. Ship something, measure it, improve it. Repeat.— Nasr Khan