The Measured Path to AI Innovation

The Current Pace of Change

Consider what three hours of jet engine inspection time means to an airline: delayed flights, lost revenue, frustrated passengers. Now GE Aerospace has cut that time in half using AI—not by cutting corners, but by actually improving accuracy. "The experience and successful track record we have had with the AI-enabled inspection tool has given us the confidence to expand across our most advanced commercial engine platforms," explains Nicole Jenkins, GE Aerospace's Chief MRO Engineer.

This example illustrates how AI can meaningfully improve both customer experience and operational efficiency. While we're witnessing a significant transformation in product development, the focus shouldn't be on rushing to implement AI, but rather on understanding how it can enhance value delivery to customers and stakeholders.

Unlike previous technological revolutions that unfolded over decades, AI adoption is expected to accelerate over the next 13-15 years, with increased activity anticipated around 2028-2029. This timeline suggests organizations should start learning about AI's potential now, while maintaining a thoughtful, measured approach to implementation.

The data supports this balanced perspective: Early adopter firms report that increased innovation is their number one benefit from AI implementation, cited by 35% of companies globally. Yet the global adoption rate for AI in new product development remains at 24% as of early 2024. This measured pace of adoption suggests many organizations are taking time to understand how AI can best serve their specific needs rather than rushing to implement it for its own sake.

Information and Value Creation

At its heart, product development is an information challenge. As Cooper frames it, "The new-product process is simply a set of tasks designed to gather information to reduce uncertainty and thereby manage risk." This perspective is particularly relevant in the AI era, where improving how we process and learn from information can enhance decision-making and risk management.

However, it's important to note that while AI has enabled increasing waves of innovation, the technology itself isn't a silver bullet. Success requires thoughtful integration that considers both technical capabilities and organizational readiness. The evidence is encouraging: Projects using AI for ideation and design have three times the success rates of projects where AI isn't used. Companies like Renault have cut development time by 50% while improving product quality through AI simulation. But these successes came from careful implementation focused on specific, well-defined challenges.

Framework for Sustainable AI Innovation

1. Foundation Building: Creating a Learning Culture

The foundation for successful AI adoption starts with creating an environment where learning and experimentation are encouraged.

Three key elements emerge:

• Test and Learn Mindset: Establish processes for controlled experimentation and learning
• Clear Problem Definition: Focus on specific challenges where AI might add value
• Measurement Framework: Define clear metrics for success that align with organizational goals

Netflix exemplifies this approach. Rather than rushing to implement AI everywhere, they've created systems that enable thoughtful experimentation while maintaining operational stability. By treating their audience as fans rather than just viewers, they've developed what their CMO calls "distinctive offerings" that build from and are influenced by careful learning from past initiatives.

2. Human-AI Collaboration: Enhancing Human Capabilities

The most successful implementations of AI in product development don't replace human judgment—they enhance it. Research shows that AI tends to augment rather than replace human capabilities in R&D work. Projects where humans and AI collaborate effectively have three times the success rates of traditional approaches.

This collaboration works best when focused on:

• Supporting Information Analysis: Helping humans process and understand complex data
• Enhancing Decision-Making: Providing additional insights for human judgment
• Augmenting Creativity: Generating options for human refinement and development

As one Mattel designer noted after implementing AI in their design process, they became "better designers, able to look at more options, and be more creative." The goal isn't to automate everything possible but to automate what computers do best while amplifying human capabilities.

3. Organizational Readiness: Building Sustainable Foundations

Technical capability without organizational readiness leads to failed implementation. While 74% of CEOs are "extremely optimistic about their organizations' readiness for generative AI," only 29% of other executives share that confidence. This disconnect highlights the importance of building proper foundations before rushing into implementation.

Four elements are essential for cultural readiness:

• Innovation as Practice: Adopt a methodical, iterative approach to innovation
• Learning Infrastructure: Creating systems to capture and apply insights from AI initiatives
• Clear Vision, Flexible Implementation: Maintain a strong sense of direction while remaining adaptable
• Stakeholder Alignment: Ensuring all levels of the organization understand the goals

Practical Implementation

To move from theory to practice, organizations should follow these steps:

1. Start with Learning
   1. Identify specific problems where AI might add value
   2. Build understanding through small-scale experiments
   3. Create feedback loops for continuous learning
2. Foundation Building
   1. Establish data infrastructure and governance
   2. Develop cross-functional expertise
   3. Create clear success metrics
3. Pilot Selection and Scaling
   1. Focus on areas with clear value potential
   2. Maintain focus on customer and employee experience
   3. Build systems for continuous learning and improvement

Looking Ahead: The Next Wave

As AI adoption continues to accelerate, organizations should focus on building sustainable capabilities that enhance both customer and employee experiences. Success will come not from being first to implement AI, but from thoughtfully integrating it in ways that create lasting value for all stakeholders.

Key areas to watch include:

• Enhanced Customer Experience: Using AI to better understand and serve customers
• Operational Efficiency: Finding ways to improve processes while maintaining quality
• Employee Augmentation: Helping employees work more effectively with AI support

The path forward isn't about rushing to implement AI everywhere, but about thoughtfully learning how this technology can improve experiences and outcomes for all stakeholders. Organizations that take this measured, learning-oriented approach will be better positioned to create sustainable competitive advantage in the future.