How to Triage and Scale Your AI Strategy

The AI era has arrived with immense promise, yet the road to production is littered with abandoned projects. Industry estimates suggest a staggering 70% to 95% of AI pilots fail to move beyond the prototype stage. For many organizations, the excitement of "Pilot Palooza" has turned into "Pilot Purgatory"—a cycle of expensive science experiments that fail to deliver measurable business outcomes.

In the final episode of The AI Advantage, "The CIO’s view on starting, and restarting, AI initiatives," host Barbara Call sat down with Sean Hauver (CIO, Alorica) and Keith Schlosser (multi-time CIO, and industry advisor) to discuss how CIOs can perform an honest audit of their initiatives and pivot toward sustainable success.

1. The "pilot palooza" audit: identifying the warning signs

Before you can fix a failing AI initiative, you have to admit it’s stalling. The panel identified several "red flags" that indicate a project is drifting off course:

• Lack of business “skin in the game”: If the project is viewed solely as an IT initiative, it is destined to fail. Success requires a 50/50 partnership where the business unit defines the value and IT provides the engine.
• The "one and done” fallacy: AI is not a static piece of software. If there is no plan for "Day 2" operations—monitoring data drift and model accuracy—the value will erode immediately after launch.
• Missing success metrics: Many pilots start with a "let's see what happens" mentality. Without clearly defined KPIs and a baseline for ROI, there is no way to justify scaling to production.

2. The triage framework: restarting stalling initiatives

When an audit reveals a project in trouble, leaders must be willing to triage. This doesn't always mean "killing" the project; sometimes, it means a strategic restart.

• Go back to the data bedrock: You cannot build a high-fidelity AI solution on a low-fidelity data foundation. If your data strategy is fragmented, pause the AI build and fix the data pipeline first.
• Simplify the scope: "Scope creep" is a common pilot-killer. Strip the project back to its most basic, high-impact use case. Master that "digestible chunk" before adding complexity.
• Re-align sponsorship: Ensure the business sponsor isn't just a cheerleader but is accountable for the adoption and the resulting business change.

3. The future: augmentation and agentic workflows

The ultimate goal of moving to production isn't just automation—it's human augmentation. The panel emphasized that the most successful AI implementations focus on removing the "minutia" that bogs down employees.

• Scaling efficiency: Whether it's helping developers auto-generate code or providing customer service agents with real-time insights, the focus should be on making the existing workforce more effective.
• Education as an asset: Investing in an "AI University" or internal education programs ensures that everyone from the executive suite to the front line speaks the same AI language.

"This is not about replacing a bunch of humans. This is about making people more effective, giving them the information and the data they need in order to do their jobs better." — Keith Schlosser

Stop wasting budget, start scaling success

Don't let your AI ambitions stall in the lab. Transitioning from a "science experiment" to an enterprise-grade AI operating model requires the right infrastructure and a commitment to business alignment.

Vertesia helps organizations bypass the "pilot graveyard" by providing a high-speed, low-code platform designed to turn unstructured content into production-ready AI agents in weeks, not years.

This concludes our podcast series. You can listen to all five episodes to get perspectives from CEOs, CIOs, CISOs, and leading minds from MIT and beyond on how to bridge the gap between AI potential and production reality.

Visit "The AI Advantage" to catch up on the full series.