AI in Enterprise Software: Separating Hype from Value

Personalizing at Scale

The clearest AI wins are automating work that's time-consuming and rules-based but not quite automatable with traditional logic. Classifying support tickets. Extracting data from documents. Suggesting next actions based on patterns. These applications save time without requiring users to trust AI with high-stakes decisions.

Large datasets contain patterns that humans can't see. Anomaly detection. Correlation discovery. Predictive indicators. AI that surfaces these patterns—then lets humans decide what to do—adds genuine intelligence to workflows.

Personalization that would be impossible to configure manually becomes feasible with AI. Recommendations, priority rankings, and tailored interfaces can adapt to individual users across millions of interactions.

Explainability Gaps

AI is bad at decisions that require context, ethics, or accountability. When a system makes a recommendation, who's responsible if it's wrong? Enterprises need clear answers. "The AI decided" isn't acceptable.

AI requires training data. New problem domains, rare events, or situations without historical patterns don't have the data AI needs. Promising AI solutions for these areas is premature.

In regulated industries, you often need to explain why a decision was made. Black-box AI that can't articulate its reasoning creates compliance problems. Sometimes simpler, explainable approaches win over sophisticated but opaque ones.

Measure What Matters

AI capabilities require thoughtful product management:

Help stakeholders understand what AI can and can't do. Counter hype with honesty. A clear-eyed view of AI's capabilities builds more trust than overpromising.

AI makes mistakes. Products need to handle those gracefully—giving users visibility into AI confidence, allowing overrides, providing feedback loops. The experience when AI fails matters as much as when it succeeds.

AI projects often optimize for model metrics (accuracy, precision) that don't directly map to business outcomes. Focus on end-to-end impact: time saved, errors prevented, revenue generated.

The Path Forward

AI capabilities are improving rapidly. Things that don't work today might work next year. The product manager's job is to stay current enough to spot genuine opportunities while being skeptical enough to avoid hype-driven investments.

The Takeaway

AI is a tool, not magic. It creates real value in specific situations and overpromises in others. The enterprises that succeed with AI will be those who deploy it thoughtfully—solving real problems, setting honest expectations, and measuring actual impact.

The hype will fade. The value will remain.