Imagine this scenario: Your health plan's appeals team is drowning. They've got 12,000+ cases in backlog, deadlines piling up, denial rates worse than before, and frustrated members calling daily. The culprit? A brand-new AI system that promised to solve everything.
The AI generates recommendations that clinicians don't trust. No one can explain why it makes certain decisions. And the integration with their workflows is complex and ineffective.
Sound familiar? A MIT report on the "State of AI in Business 2025" confirms despite $30–40 billion in enterprise investment into GenAI, "95% of organizations are getting zero return." Ouch!
This isn't an isolated story. It's the pattern I see repeatedly with organizations adopting AI.
The problem isn't the AI itself; it's what's missing underneath it.
The innovation paradox
Here's what I've learned from my research and working with some large organizations: The ones that fail with AI are usually the ones with the most innovative AI models. That sounds backwards, right? But stay with me.
These organizations invest millions in cutting-edge generative AI, rely on brilliant data scientists, and build sophisticated predictive models. Their pilots show incredible promise. Then they fall into MIT's GenAI divide when they move to production and try to scale. Everything falls apart.
Why? Because they skipped a very fundamental part. They didn't build the operational foundation that makes AI work at enterprise scale.
And here's where it gets counterintuitive, this isn't just my observation. In a recent SiliconANGLE article, Pega Founder and CEO, Alan Trefler, explains why most enterprise AI strategies fail, not because the tech is flawed, but because it's applied at the wrong time, in the wrong way. His advice? Use reasoning AI during design time to build operational discipline first and then apply semantic AI at runtime to ensure the predictability and reliability required to scale.
What winners do differently
The organizations succeeding with AI, the ones moving from pilot to production, from few use cases to dozens, have something in common. They didn't start with the flashiest AI. They started by asking and addressing the following questions: Are our workflows AI-ready? Do we have workflows capable of delivering value with AI?
That means:
- Real-time integration with existing systems so AI insights don't sit in a separate dashboard but flow into the work that associates are already doing
- Governed processes where every AI decision can be traced, audited, and explained to regulators
- Explainability built in from day one, not bolted on later when compliance asks questions
- Workflows that survive change because your AI strategy will evolve faster than you think
It won't make headlines. But it's what separates the AI projects that scale from those that stall.
What this actually looks like
Leading global organizations shared at PegaWorld how they've embedded AI across multiple critical areas: financial crime prevention, unified customer experience across channels, clinical documentation, provider network intelligence, appeals processing, and retail order management optimization.
What struck me wasn't the sophistication of their AI models, though those are impressive. It was their operational discipline.
Every AI-generated insight flows into a governed and reimagined workflow. Every recommendation includes an explanation that satisfies both security and regulatory requirements and customer trust. Every integration works within their existing workflow systems, not as a separate entity that lives outside normal operations.
The results? Measurable improvements in turnaround times, 100% compliance with cycle time requirements, reduced handle times, improved customer experience and cost savings that actually show up on the balance sheet.
But more importantly: they scaled. From pilot to production, across multiple use cases, serving millions of customers globally.
You can watch the replays here.
The four questions that matter
If you're evaluating an AI strategy or trying to figure out why your current one isn't scaling, here are the four questions that cut through the hype:
- Are your existing workflows optimized for industry best practices and include the required governance to meet audit compliance?
- Are your AI insights guided by the existing workflows and the rules within them?
- Can you explain every AI decision to a regulator with audit trails that satisfy compliance requirements?
- Can you scale this across a large set of use cases?
If you can't answer "yes" to all four, you don't have an AI problem. You have an operational foundation problem.
Why this matters now
Most industries, if not all, are at an inflection point. As per Gartner recent predictions, more than 40% of agentic AI projects may be cancelled by the end of 2027 as they crumble under "escalating costs, unclear business value, or inadequate risk controls."
The organizations making smart choices about operational foundations today will be the ones leading their industry in the future. The ones chasing the latest AI model without that foundation will still be stuck in pilot purgatory, wondering why their competitors moved faster.
The good news? You don't have to choose between innovation and operational excellence. But you do have to build the operational foundation first. Because the most sophisticated AI in the world is useless and even detrimental if it can't scale, can't be explained, and can't integrate with how work actually gets done.
Ready to get started?
Pega Blueprint™ helps you identify exactly where your operational gaps are. It maps your current workflow state and helps you reimagine the future state with your best operational foundation. You'll discover the fastest path to AI that can be trusted and scale, using reasoning AI to quickly design the most optimal workflows for your business.