Addressing AI FOMO: Moving beyond the strategic façade

The age of AI sparked a global frenzy—and a massive case of FOMO. As tools like ChatGPT and Gemini have moved past the “shiny and new” phase, many organizations fear they are getting left behind. However, the rush toward AI adoption has revealed a stark gap between ambition and readiness.

Slalom’s 2026 AI Research highlights that even among organizations actively pursuing AI transformations, true enterprise maturity remains an anomaly. With enterprise-level use cases hovering at 21% across industries and a dismal 2% in the public sector, it is clear that most organizations are unable to move past the piloting phase. And often, these pilots act as a strategic facade—a way to project progress while masking the structural instability that cannot support an enterprise-wide transformation. They tell a story of leaders trapped between a pressing demand to appear AI-forward and the sobering reality of not being AI-ready.

As a technology strategist, I’ve sat across from CIOs and CTOs who feel the weight of this paradox. They are being pushed to build a skyscraper with AI while their foundation is still shaky with outdated, legacy systems and a lack of clean, usable data. If this sounds like your organization, take a breath. You aren’t too late; you’re just in time to do it right.

The expensive experiment

Almost every organization I partner with has some form of AI pilot in flight. However, studies show that ROI remains elusive for most. An MIT study found that 95% of generative AI pilots fail to deliver a measurable financial return, and Gartner predicts that by 2026, organizations will abandon 60% of AI projects. These failures rarely stem from bad technology; instead, they are the result of structural flaws like the absence of a reliable data foundation.

To join the 5% who succeed, organizations must establish the structural readiness required to drive value for both the business and the customer. Without it, what looks like a low-stakes experiment is often a costly risk. Especially for smaller, resource-constrained organizations, sinking time and capital into AI pilots without a clear ROI is an expensive diversion from the core work that truly moves the needle.

The strategic pivot to readiness

Success in the AI race belongs to those who realize that structural readiness is speed. Even if your AI pilots are already in flight, you must address the gaps in your foundation to unlock AI’s full potential. I advise a dual-track approach: continue high-value pilots to maintain momentum, while simultaneously remediating the data, technology, and strategic bottlenecks that will eventually stall your progress. Since most organizations can’t fix everything at once, the question then becomes: where should you start to balance innovation with long-term stability?

At Slalom, we approach AI transformations through a high-tech, high-touch lens, recognizing that the most advanced AI is only as effective as the people who use it. We use a comprehensive, proven framework that balances technical implementation with the organizational change required for long-term scale. However, when time and budget are tight, I advise my clients to start with the following three high-impact areas that reinforce the core foundation of their organization.

Foundations over flash: A pragmatic checklist

1. Strategic alignment: Tie every initiative to a business goal

Every investment—whether money, time, or people—must tie back to a measurable objective. Ask yourself: Will this increase customer reach? Will it optimize costs or drive efficiency? The same applies to AI. Even exploratory pilots need a clear North Star.

Instead of a generic goal like “explore AI for productivity,” challenge your team to solve for a specific pain point. For instance, if your North Star is operational efficiency, ask your team to identify three use cases where AI reduces the time spent consolidating data manually in their current reporting process. This shift increases the likelihood that the time and effort invested in these pilots translate into measurable business value.

Pro tip: To achieve these outcomes faster, start with what you already own. The native AI capabilities of your existing software-as-a-service (SaaS) tools are often the fastest path to value realization.

2. The tech stack audit: Streamline now to scale later

You can’t run high-compute AI on a fractured, legacy stack. In my experience leading large-scale transformations, I often see organizations operating on autopilot, carrying forward outdated systems that don’t talk to each other and paying for multiple tools that do the same thing.

Consider this surprisingly common scenario: an organization that considers itself a “Microsoft shop” uses Box for its data storage. Teams quickly discover they can’t fully leverage Copilot because the integrations are clunky at best—a reminder that technical friction will eventually cap even the most ambitious AI potential.

To move toward true AI readiness, you must spend some time to critically assess and rationalize your application inventory. Determine what to sunset, sustain, or scale by asking:

• Is this application mission-critical?
• Does another existing tool perform this function better?
• Where are the biggest friction points?

Consolidating your technical ecosystem and clearing the “structural debris” does more than reduce maintenance and licensing costs. It removes hidden constraints that slow modernization efforts and clarifies where your organization is truly ready to scale, including with AI.

3. Data hygiene: Address your data problems (they won’t go away)

If you don’t trust your data now, you won’t trust it later. I often see a wide gap between awareness (“we know having clean data is important”) and management (“we don’t know what to do with all our data, so we’re going to hoard everything in the archives for now”). The lack of data hygiene results in a high-cost, low-trust environment and also poses a significant risk. When AI models retrieve incomplete or inconsistent data, they produce hallucinations that lead to poor decisions, or worse, security vulnerabilities.

To be clear, “AI-ready” doesn’t mean perfect or require a multi-year, enterprise-wide “spring cleaning.” I advise my clients to focus on cleaning specific data sources your AI will retrieve from. For example, if your goal is to use AI to drive personalized guest experiences, start by cleaning your guest profile data. If a single guest has multiple profiles across your loyalty, booking, and on-property systems, your AI will fail to provide a cohesive experience.

Regardless of the approach, one thing is clear: developing a data management strategy to ensure your data is clean, usable, and trustworthy will provide continuous returns down the road.

Beyond the foundation: The path to scale

Perhaps your organization has already checked these boxes. If so, you’re ahead of the curve. However, the journey doesn’t end here. While a stable foundation is key to unlocking AI’s full potential, a true enterprise transformation requires far more: securing infrastructure through robust cybersecurity, mitigating data privacy and compliance risks, and establishing a strategic operating model that aligns incentives with new ways of working, among other things. These components reinforce the structural integrity needed to begin scaling value across the enterprise.

In conclusion

AI hasn’t rewritten the rules of business; it has simply raised the stakes for those who ignore the basics. While the disruption brought forth by AI is significant, the path to success remains familiar. Is your organization building a skyscraper on shaky ground? How are you balancing the shiny with the stable? If your organization is also experiencing the “fear of missing out” in this AI era, just remember: The most successful leaders aren’t the ones who move the fastest, but the ones who build the most stable foundations for growth.