AI in SaaS: Why Spending Doesn’t Always Equal Value

High-visibility moment: SaaS leader Confluent recently faced a steep stock drop—despite beating quarterly estimates—because their cloud subscription growth slipped below 30% and revenue momentum flagged. The warning? Strong AI messaging alone didn’t sell a long-term growth narrative.

This story isn’t unique. An AlixPartners study found over 100 publicly traded mid-market software companies are being squeezed as AI shifts crash through traditional SaaS models, causing retention rates to dip even as feature velocity climbs Business Insider.

Why So Many AI Initiatives Underperform

It’s tempting: glitzy AI interfaces, dashboards, assistants. But without strategy, those initiatives become feature projects—not value drivers.

• Example Feature Hypothesis: “Add AI summarization.”

• Without Validation: We don’t know if customers need it—or if it moves behavior.

• Outcome Blind: AI lands, but customer engagement doesn’t budge.

  
  

Backlog prioritization and grooming often feed these projects their oxygen—but the strategic thinking happens far upstream. If grooming dominates PM time, decisions are execution-driven: small, tactical, but disconnected from strategic outcomes.

How CEOs Can Avoid the AI Vanity Trap

1. Track impact, not output Success isn't shipping dashboard updates—it’s boosting retention, expansion, and customer lifetime value.

2. Fund Strategic Learning Don’t just budget feature work. Budget discovery: interviews, prototypes, experiments, failure. Encourage quick failures over slow feature churn.

3. Empower “Go/No-Go” Decisions Allow PMs to kill features fast if they don’t deliver outcomes—and celebrate it. This means redirecting funds to bets that show early traction.

4. Declare Strategic Health Metrics Separate metrics reviews from sprint reviews. Ask:

• What customer problem are we solving next?

• How will we know we delivered value?

• What’s the risk if it fails?

  
  

Realigning AI Spending With Impact

When analysts downgraded Confluent, it wasn't because AI failed—it was because AI wasn’t enough. Their features didn’t convert into meaningful growth. And according to AlixPartners, many SaaS companies face the same fate: spending big on AI, but seeing flat or declining customer stickiness.

In short: lagging retention and sluggish revenue after AI launches often reveals deeper issues—like disconnected strategy, poor prioritization, or no real feedback loops.

Bottom Line

As a CEO, demand clarity: Is the initiative solving a validated user problem? Has it shown early impact? Do we know when—and how—to shut it down?