AI in Product: Separating Hype from Reality

Every product roadmap in 2025 seems to have "add AI" as a line item. But here's the question: does AI actually solve a problem your customer has—or are you adding it because everyone else is?

I've seen both. AI that reduces analyst fatigue by 40%: real value. AI that generates a slightly friendlier error message: pointless.

The pressure to "add AI" is immense. Boards ask about it. Investors expect it. Competitors trumpet it. But the worst thing you can do is bolt on AI capabilities without a clear hypothesis about what changes for the user. That path leads to demo-ware that impresses in a slide deck and disappoints in production.

When AI Is the Wrong Answer

Not every problem needs AI. Sometimes the answer is better data. Sometimes it's a simpler rule. Sometimes it's hiring another person. I've seen teams reach for AI when the real problem was data quality—garbage in, garbage out, whether the model is neural or rule-based. Fix the data first.

Similarly, AI can mask process problems. "Let's use AI to triage alerts" sounds great—until you realize the real issue is that you're generating 10x too many alerts. AI might help you cope with the flood, but it doesn't fix the leak. Address root causes before adding complexity.

The Three-Question Test

Before adding any AI capability, ask: (1) What job does this help the user do that they couldn't do before—or do 10x faster? (2) Would a non-AI solution be 80% as good for 20% of the cost? (3) Can we measure the improvement?

If you can't answer all three, pause.

The second question is especially important. Rule-based systems, better UX, or simply hiring another analyst might solve the same problem. AI has to earn its place. It's not inherently better—it's better when scale or pattern complexity makes traditional approaches impractical.

I ran this test on every AI idea we considered. Most failed question two. "AI-powered recommendations" for next actions? We could do 80% with rule-based logic and a good UI. The AI added complexity without proportional gain. "AI that summarizes incident context"? That one passed—the volume of context was too high for humans to process, and the output was measurable (analyst time saved per incident). Ship the latter; kill the former.

Where AI Actually Shines in Security

In cybersecurity, the best AI use cases cluster around scale: triaging alerts, correlating signals across millions of events, finding anomalies in behavior patterns. The worst: replacing human judgment on high-stakes decisions, or adding "smart" features that create more alerts than they reduce.

I've watched teams add AI-powered "threat scoring" that generated so many false positives that analysts turned it off. The model was technically impressive. The outcome was worse than before. Always start with the outcome. The technology is a means, not an end.

Another anti-pattern: AI that generates reports. Sounds useful—until you realize the report doesn't change decisions. If the AI summarizes 50 pages into 2 pages but the analyst still has to read the 50 pages to act, you've added a step, not removed one. The best AI use cases reduce the amount of information the human needs to process before acting—not just repackage it.

AI should amplify your best people, not replace the need for judgment.

Pragmatic Approach

Start with augmentation, not automation. A tool that suggests the next action is safer than one that takes it. Learn where the model helps and where it hallucinates. Then expand.

Build in feedback loops from day one. Can the user correct the AI's suggestion? Does that correction improve the model? Without that, you're flying blind. And in security, flying blind with AI is dangerous. A missed threat because the model was overconfident is worse than a missed threat because you had no tool at all—at least in the latter case you knew you were relying on humans.

We shipped our first AI feature as "suggested action" with a prominent "dismiss" and "correct" button. We tracked when analysts agreed, disagreed, or overrode the suggestion. Within three months we knew exactly where the model helped and where it didn't. That data informed the next iteration. Shipping full automation first would have been reckless—we'd have had no signal on failure modes.

The ROI Conversation

Finally, be ready to defend the ROI. "AI" as a buzzword doesn't close enterprise deals. "This reduces mean time to triage by 30%" does. Instrument your AI features with the same rigor you'd use for any product improvement. If you can't prove value, you shouldn't ship.

One more consideration: model governance. Enterprise security buyers will ask—who trained this? On what data? Can we audit it? AI adds a new dimension to your compliance story. Have answers before the RFP arrives. We built an "AI transparency" doc that addressed these questions. It became a standard attachment in security reviews.

The hype will pass. What remains are products that actually help users. Focus there. The rest is noise.