-Hey High Stakers,-

Good morning and welcome to the 7th issue of High Stakes!

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AI Made Your Product Cooler.

Now It’s Killing Your Margin.

🔍 1-Min Briefing

This is a live and tricky situation I am dealing with as I write this. Apparently this is quite a common thing with quite a few B2B SaaS companies that have embedded in AI features into their SaaS offering.

Over the last 12 months, they are starting to notice something strange:

• Renewals are flat.

• Expansion is lagging.

• Infrastructure costs are creeping up.

Welcome to the hidden cost of becoming a SaaS firm with embedded AI features. The product got smarter. But the business got harder to manage.

This edition zooms in on one such company. Fictionalised here, but very real. And goes to show how the GTM layer needs rewiring, and the execution loop needs reinforcement.

🪩 Meet Finlytic: When Smart Product Meets Stalling Business

"We shipped all the right features. AI is in the product. It works well. Customers say nice things. But it’s not expanding accounts. It’s not creating upsell energy. And the infra bills are starting to feel scary. It’s that gnawing panic, when you’ve made something new and good, but it isn’t translating into growth. I have no idea what I am missing."

That was the rant from Jim, founder-CEO of Finlytic (name changed), a Series C SaaS company that helps finance teams automate workflows, reconcile payables, and detect issues before auditors do.

In the last year, they launched three major AI features:

• An invoice summarizer (LLM-powered)

• A duplicate detection engine (ML + rules-based)

• A smart alert assistant for anomalies

All three were marketed hard. Sales loved them. Customers said all the right things.

But six months in:

• Expansion deals are stuck.

• Usage is uneven.

• Infra bills have doubled.

The team has tried adding usage-based SKUs. They've gated features. They even created a new pricing tier.

Still stuck.

And that’s when Jim called me.

✉️ What I Told Him: Let’s Go Back to First Principles

When GTM starts wobbling after an AI launch, it usually means three things:

1. The value isn't clear.

   • What does this feature actually save the customer?

   • Has to be one of: Is it reducing time, cost, risk, or churn? Or improving revenues?

2. The cost isn't tracked.

   • What’s the unit economics of this AI feature?

   • Can we attribute infra costs to specific use?

3. The pricing doesn’t match value or cost.

   • Is this a premium add-on?

   • Or a usage-metered enhancement?

   • Or something that should never be free?

We ran a quick GTM audit:

• Mapped AI features to workflows

• Identified lag between feature use and CS visibility

• Found OpenAI usage patterns that made one SKU wildly unprofitable

That’s when the lightbulb went off.

This wasn’t a product problem. Or a sales problem.

It was a GTM alignment issue.

So I told Jim: let's break it into two tracks.

🔗 Track One: Strategic GTM Fix (What I Do)

1. Reframe the product story

   • From: "We have AI."

   • To: "We reduce month-end cycle time by 38%."

2. Segment pricing by use case, not feature

   • Bundle AI features into use-case tiers: compliance, speed, accuracy

   • Stop offering AI in base plans without activation telemetry

3. Introduce real ROI proof

   • Tie QBRs to AI-powered outcomes

   • Arm Sales and CS with success data, not just demo checklists

We’re not fixing the pricing sheet. We’re fixing the narrative, the monetization logic, and the ‘why now’.

⚖️ Track Two: Delivery and Execution (How to Get It Done)

Once the GTM strategy is aligned, the real work begins.

You could build this muscle with your own internal team, or use a partner team with the right mix of capabilities, to get the GTM execution loop firing.

Here's the team composition you'd need:

• Usage telemetry: Engineers who can tag and track how each AI feature is used: by role, frequency, and impact

• Infra mapping: Cloud/FinOps engineers who can map OpenAI/LLM costs back to specific features and SKUs

• SKU planning dashboards: Product ops specialists who can simulate pricing tiers and margin scenarios

• Onboarding flows: UX and workflow designers who turn AI features into usable, valuable experiences

• Customer outcome tagging: CS enablement pros who turn backend usage into proof-of-value stories for renewals

You could assemble this yourself. Or use a ready-to-go execution partner who’s done it across multiple SaaS stacks, like one of my portfolio companies, Stack Digital.

This is how features turn into renewals. And how margin becomes a managed asset, not a mystery. And how margin gets protected.

🚀 What Comes Next

In the Paid Edition, I’ll walk through:

• The three most common traps SaaS companies fall into post-AI feature launch

• A live telemetry model you can adapt to monitor AI value and risk

• A playbook to reprice AI without triggering churn

• Outcome-based onboarding flows you can launch in two weeks

• And a checklist that shows which of your AI features are quietly failing

Upgrade to Paid Edition to read the full playbook and get access instantly.

If your product got smarter, but your GTM feels dumber—you’re not alone. Let’s fix it.

- Srini Founder, High Stakes

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