The  Harvard Business Review article “Match Your AI Strategy to Your Organization’s Reality” — co-authored by Christopher J. Wright, Chief Invention Officer at Iprova, Julian Nolan, Iprova’s Founder and CEO, and Professor Cyril Bouquet of IMD Business School — highlights a critical challenge facing companies investing in AI-driven innovation: breakthrough ideas alone do not guarantee breakthrough impact

The authors explain that AI success depends less on the sophistication of algorithms and more on the fit between a company’s ambitions and its organizational reality. Too often, AI initiatives fail not because the ideas are weak, but because the operating models required to transform those ideas into market-ready innovation are not yet fully in place.
To help leaders understand this risk, the article introduces a two-dimension framework:

• Value-chain control — influence over the full journey from idea to market
• Technological breadth — how well the systems needed to support innovation are integrated

Organizations strong in both dimensions are far more likely to turn human-AI created concepts into real-world products.

One example highlighted is Procter & Gamble, which leverages its tightly integrated R&D, manufacturing, and go-to-market structure to embed AI deeply into operational decision-making and delivery — giving the company a powerful foundation to scale innovation.

What This Means for Innovation Leaders & Iprova’s Role
At first glance, companies like P&G may look like the ideal blueprint for AI adoption.
But the broader insight — echoed by strategists and reinforced by the HBR analysis — is this:

• AI alone is not a strategy.
• AI becomes an advantage only when the systems around it are ready.

And that is where many businesses struggle.

Fragmented value chains, siloed data, slow or rigid R&D processes — these bottlenecks prevent AI-driven inventions from becoming tangible competitive advantage.

This is where Iprova provides critical value.

Iprova enables companies to:

• Translate strategic objectives into targeted invention programs
• Use human-AI collaboration to engineer and create and patent-ready inventions — not just conceptual ideas
• Scale invention output — moving from pilots to a repeatable and scalable invention model
• Accelerate breakthrough creation across multiple technologies, product lines, and markets

In the decade ahead, the companies that win in innovation will not be those running the most AI experiments — but those who build the systems to turn ambition into outcomes.

Contact us at Hello@Iprova.com