Operationalizing AI: From Strategy to Measurable Value
- Juan Rodriguez
- Jul 1
- 3 min read
Updated: 3 days ago
Artificial Intelligence has reached a turning point. According to The Economist, citing data from S&P Global, 42% of companies have now abandoned most of their generative AI pilots. A dramatic increase from just 17% the year before. The excitement is shifting from possibilities to one central question. How do we turn AI strategy into lasting business value?
“I’ve spent money… it’s not happening,” one executive told The Economist, capturing the frustration of many leaders navigating stalled AI programs. Source: Welcome to the AI Trough of Disillusionment, The Economist, May 21, 2025. Read the article
The problem isn’t the technology. It’s what happens, or doesn’t happen, after the pilot. Many organizations are realizing that building AI is easy compared to adopting it across teams, integrating it into workflows, and turning it into something the business can actually rely on. In short, the real challenge is operational.
In a recent article, our partner Roberto Argento - Transformation Architect at ValueFlow and SAFe SPCT - makes a compelling case: AI shouldn’t be treated as a project. It needs to become a value stream. His concept of the “AI Factory” captures this shift well. AI, he argues, should be structured and managed like any other long-term strategic capability, with continuous discovery, agile delivery, and strong governance. And it should be connected to real business needs, not just built in a lab.
At Planview, we see this transformation happening every day. Our customers are moving past isolated pilots and building systems to turn AI into repeatable, measurable value. The key is making sure strategy and execution are truly connected. That’s where so many efforts fail. Business leaders often set ambitious goals, but delivery teams lack a clear way to align, prioritize, and measure those goals in action. As a result, the work gets fragmented. Data sits in silos. AI teams chase use cases without a strong link to outcomes. And the value of the work remains unclear.
AI Adoption is a Strategy Problem, Not a Technology Problem The biggest barrier to AI success isn’t the tech, it’s the disconnect between ambition and execution. To scale AI, companies need a shared language between business and delivery teams.
Planview helps close that gap. We provide the tools and frameworks organizations need to move from strategy to execution, with clarity and accountability. By using Lean Portfolio Management and OKR-based planning, companies can align AI investments to business priorities, shift funding as needed, and track impact in real time. Agile delivery processes ensure AI development is iterative and focused on feedback, not perfection. Integrated governance brings oversight and ethics into the picture without slowing things down. And real-time visibility into dependencies, teams, and metrics helps everyone stay aligned.
In short, we give structure to what Roberto calls the AI Factory. This structure matters, especially now, as more companies move past the initial AI hype and into the hard work of scaling. Gartner calls this the “trough of disillusionment,” where big promises meet operational reality. But it’s also where the real winners are built. Those who invest in connecting strategy to action, who treat AI as a long-term capability, and who adopt systems to learn and improve quickly, those are the organizations that will pull ahead.
Think Value Stream, Not Use Case One chatbot or predictive model isn’t a strategy. The AI Factory approach reframes AI as a continuous flow of learning, delivery, and impact. Supported by agile execution and portfolio alignment.
As Roberto notes, the next step in your AI journey might not be a new model. It might be a new way of working. Planview can help you build that bridge, turning AI from disconnected pilots into a value stream that evolves with your business.
If you're ready to explore what that looks like in practice, we’d love to talk.
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