How Is AI Really Changing Innovation Management?

Why should executives rethink their assumptions about AI and innovation?

Most boardroom narratives position AI as a “super-brainstorming assistant” that magically solves front-end innovation challenges. The Technovation article “Artificial intelligence and innovation management: Charting the evolving landscape” challenges this narrow view with empirical data from a representative survey of US innovation managers, complemented by in-depth interviews across sectors like IT services, financial services, automotive, and manufacturing.

Firstly, AI use is already high and widespread in innovation-active firms; more than half of their projects leverage some form of AI. Secondly, managers plan to further increase usage of both AI and generative AI in the coming years, despite ongoing skepticism about media hype. Finally, the study shows that AI is not just an R&D toy or a digital-product phenomenon—it cuts across multiple industries and innovation types. For senior leaders, the message is clear: AI in innovation is no longer experimental; it is becoming infrastructural.

Where in the innovation process does AI create the most value today?

Executives often imagine AI as a front-end creativity engine, yet the data shows its center of gravity sits elsewhere. In the study, managers reported using AI across three stages—idea generation, development, and commercialization—but adoption was highest in the development stage of the innovation process. Mean usage scores were 4.48 for development versus 4.28 for idea generation and 4.34 for commercialization, on a scale where higher values indicate more frequent use.

This pattern makes sense: development phases are rich in tasks that AI excels at—simulation, optimization, virtual prototyping, and digital twins. AI helps reduce time and cost by testing designs virtually, refining specifications, and optimizing processes before committing to physical prototypes. It also supports technology scanning and integration, where firms acquire and exploit new technologies. In contrast, ideation still relies heavily on human tacit knowledge, intuition, and “visceral feel,” which managers perceive as less automatable, even if AI can support data-driven inspiration.

How are AI and generative AI actually used at each stage of innovation?

At the idea stage, AI and generative AI are used to analyze large volumes of data: online reviews, social media, customer feedback, competitor moves, and market trends. Natural language processing and machine learning help detect emerging needs, anomalies, and weak signals, which can then feed human creativity and portfolio prioritization.

In the development stage, AI shows its greatest impact. Companies use it for code generation, design optimization, simulation, and testing. Managers in sectors like IT services and automotive described heavy use of generative AI to create software components, design options, and non-physical prototypes.

In the commercialization stage, AI supports pricing analytics, personalization, recommendation engines, and dynamic content creation—but current adoption is still lower than in development. The study suggests firms may be underusing AI for go-to-market experimentation and performance optimization, leaving value on the table in a costly and risky phase.

How does generative AI differ from “traditional” AI in innovation management?

Traditional AI focuses on pattern recognition, prediction, and optimization—essentially “discriminative” tasks such as forecasting demand, clustering customers, or detecting anomalies. Generative AI goes a step further by creating new content—text, code, images, or designs—based on training data.

The study reveals two important contrasts. Firstly, generative AI adoption is currently lowerthan that of traditional AI across all stages of the innovation process. Secondly, managers expect generative AI to grow and attribute distinct advantages to it: they believe it is more likely to make employees’ jobs more fulfilling and to support more radical forms of innovation, rather than incremental improvements alone.

However, interviews also highlight skepticism: some managers see generative AI as overhyped and emphasize the need for “humans in the loop,” especially for idea generation and strategic decisions that carry ethical and reputational risks. In practice, generative AI is emerging not as an autonomous innovator but as a creative partner embedded in human-centered workflows.

What are the key managerial implications for designing AI-enabled innovation systems?

The paper suggests several implications that can be translated into concrete design choices for innovation systems:

1. Treat AI as an end-to-end capability, not a front-end gadget.
   Rather than limiting AI experiments to hackathons or idea contests, firms should map where AI already delivers value (often in development) and then orchestrate its integration across the full funnel—from opportunity      discovery to commercialization.

2. Build human–AI collaboration patterns for each stage.
   In ideation, AI can provide data-driven stimulus while humans apply intuition and contextual judgment. In development, AI can accelerate iteration while engineers focus on system-level trade-offs. In      commercialization, AI can personalize offerings while marketers safeguard brand and ethics.

3. Use generative AI selectively for creativity and job enrichment.
   Managers expect generative AI to make work more fulfilling by automating drudgery and expanding creative scope. This only happens if workflows are deliberately redesigned so that humans move “up” the value chain instead of being squeezed by productivity pressure.

4. Institutionalize governance and ethics, especially for generative AI.
   Concerns about legal, ethical, and reputational risks explain why managers insist on human oversight for critical decisions. Robust governance—covering data quality, bias, transparency, and accountability—is now part of innovation management, not an external constraint.

How can companies move from pilots to a scalable AI-in-innovation strategy?

To escape the “pilot graveyard,” companies need to anchor AI in their innovation strategy and key performance indicators. The survey indicates that greater use of AI in innovation is associated with better innovation performance, but this relationship is mediated by how well AI is embedded in processes and capabilities, not just by technology spending.

A practical roadmap might include:

• Mapping current AI usage across idea, development, and commercialization stages to identify gaps and duplications.

• Creating cross-functional teams that combine innovation, data, IT, and business units to own AI-enabled experimentation.

• Prioritizing high-leverage use cases in the development and commercialization stages where time-to-market, cost, and risk reductions can be measured.

• Setting learning metrics, not just ROI—such as cycle time reduction, prototype iterations, or percentage of projects using AI.

Coupling these steps with targeted training for managers and intrapreneurs helps shift AI from “interesting tool” to “core innovation infrastructure.”

Key Takeaways

• AI is already used in more than half of innovation projects in innovation-active firms, with usage expected to increase further in the near future.

• Contrary to popular narratives, AI is used most intensively in the development stage of innovation, not in ideation.

• Generative AI adoption lags behind traditional AI but is expected to grow; managers see it as a driver of more fulfilling jobs and more radical innovation.

• Human–AI collaboration, not full automation, is the dominant pattern—especially in idea generation and strategic decision-making.

• To capture value, companies must redesign their innovation processes, governance, and capability-building around AI, rather than running isolated pilots.

Mini-FAQ

1. Is generative AI already transforming innovation more than traditional AI?
Not yet. Traditional AI remains more widely used across the innovation process. Generative AI is emerging, with lower current adoption but strong future expectations, particularly for creativity and job enrichment.

2. Should we focus AI investments on idea generation tools?
No. The evidence suggests you should prioritize development and commercialization use cases—simulation, optimization, personalization, and experimentation—while still using AI to support data-driven ideation.

3. What is the biggest risk in scaling AI for innovation?
The main risk is adopting AI driven by hype, without governance, clear roles, or redesigned workflows. This can create ethical, legal, and reputational exposure, and fails to realize the promised performance gains.

References

• Roberts, D. L., & Candi, M. (2024). Artificial intelligence and innovation management: Charting the evolving landscape. Technovation, 136, 103081.

• Mariani, M. M., Machado, I., & Magrelli, V. (2023). Artificial Intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623.

• Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

• McKinsey Global Institute. (2023). The economic potential of generative AI.

• Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based      theorizing. Academy of Management Discoveries, 4(4), 404–409.