By Filippo Frangi
From identifying strategic opportunities to enabling faster experimentation, from co-designing with users to reshaping go-to-market strategies, GenAI is pushing the boundaries of how we define and deliver innovation. GenAI is indeed gaining traction as a key enabler across all stages of innovation processes. This shift demands structure, strategy, and measurable outcomes.
GenAI: a General-Purpose Technology with democratic access
The article draws from the latest research by the Startup Thinking Observatory at the Politecnico of Milan University to outline how companies can move beyond novelty and start integrating Generative AI as a structural component of their innovation systems.
GenAI can assist leadership teams in identifying emerging trends, reshaping innovation strategies, and monitoring entire portfolios of innovation initiatives.
GenAI shares the core traits of General-Purpose Technologies (GPTs, which is not the acronym of ChatGPT), such as electricity and the internet: it is versatile, scalable, and capable of enabling complementary innovations across industries. However, it introduces an additional, unique dimension: accessibility. Thanks to intuitive interfaces and relatively low barriers to entry, non-experts can now leverage GenAI for sophisticated tasks, a phenomenon rarely observed with other transformative technologies.
According to Microsoft’s 2024 Work Trend Index 1, 75 per cent of employees already use AI at work, often informally and before any structured adoption plan. This bottom-up movement highlights the urgency for leadership to harness and regulate this creativity before it scales haphazardly, but often it is already too late!
How GenAI Supports the Innovation Lifecycle in Each Phase
Starting from the four traditional phases that every innovation project must go through, it is easy to understand how GenAI is already delivering tangible value across the innovation lifecycle:
- Exploration: in this phase, GenAI tools analyze vast datasets to identify emerging trends, unmet needs, and strategic foresight scenarios. They can synthesize market signals and competitive landscapes far faster than traditional methods.
- Idea Generation: GenAI systems support divergent thinking by proposing a wide array of creative and unconventional ideas. Controlled experiments have shown that LLMs, for instance, can outperform human brainstorming groups in feasibility and impact, though not necessarily in originality2.
- Experimentation and Prototyping: from UI mockups to working code or product sketches, GenAI enables the rapid development of MVPs. This accelerates the “fail fast, learn faster” approach and reduces the time-to-feedback cycle.
- Execution and Go-to-Market: in this phase, GenAI can assist in personalizing campaigns, automating market segmentation, and generating content tailored to micro-audiences, making market launches more dynamic and responsive.
Beyond the operative stages, the role of GenAI is also becoming relevant at strategy level. GenAI can assist leadership teams in identifying emerging trends, reshaping innovation strategies, and monitoring entire portfolios of innovation initiatives. By turning unstructured data into strategic insights, these tools enable more informed, agile, and forward-looking decision-making processes.
Real-world Applications
Several companies are already demonstrating the practical benefits of GenAI in innovation:
- IKEA used GenAI to design furniture inspired by retro-futuristic aesthetics, challenging its design teams to reimagine product categories.3
- Oreo (Mondelez International) uses AI to accelerate the development of new snack recipes. Their AI tool uses machine learning to generate recipes based on desired characteristics such as flavor, aroma, and appearance.4
- Albert Invent, a chemistry company, is leveraging an AI trained on over 15 million molecular structures to identify effective and safe ingredient combinations quickly, predicting physical, toxicological, and aesthetic properties.5
- Beck’s created a product called “Beck’s Autonomous,” entirely conceptualized by AI, from the recipe to branding and packaging.6
These cases show that GenAI is not only improving internal innovation efficiency but also enabling new forms of user engagement and co-creation. In some instances, companies pursued these projects purely as experimental trials; in others, they addressed more concrete use cases. Either way, these experiences are meaningful examples — often executed with still-maturing tools — that can inspire further applications and strategic refinement.
The Other Side of The Coin: Limits and Risks
To fully leverage GenAI, companies must move from ad-hoc experimentation to structured integration.
Despite the excitement, organizations must remain critical. Several challenges can limit the strategic effectiveness of GenAI. One key concern is the risk of homogenization. GenAI tools trained on existing datasets tend to reinforce dominant patterns, which can inhibit breakthrough originality. This is not only a creative limitation but also a potential driver of bias. By amplifying dominant narratives and patterns found in training data, GenAI can unintentionally reinforce existing prejudices and stereotypes. Another issue relates to the quality of data; outputs are only as good as the inputs, and poor-quality or unrepresentative datasets can lead to misleading or inaccurate results.
Furthermore, there are still numerous ethical and legal ambiguities surrounding GenAI. Questions about the ownership of AI-generated content, the risk of unintentional plagiarism, and the lack of clear regulatory frameworks are pressing challenges that remain unresolved. Finally, even though GenAI can significantly augment creativity, it cannot replace human oversight. The effectiveness of these tools depends heavily on the expertise and critical thinking of users who can steer and validate AI-generated outputs.
Building an Augmented Innovation Model
To fully leverage GenAI, companies must move from ad-hoc experimentation to structured integration. This requires more than tools; it calls for a holistic approach rooted in culture, organization, and collaboration.
First, AI literacy and culture are foundational. Teams need more than access to advanced technologies; they require the right mindset, critical thinking skills, and ongoing learning opportunities. Fostering a culture that encourages experimentation and responsible use is crucial for scaling AI capabilities effectively and ethically.
Second, organizations should establish safe places for experimentations. These environments provide a sandbox for testing GenAI applications in controlled, low-risk settings. Here, teams can explore new ideas, experiment with workflows, and identify best practices that can later be scaled across departments or business units.
Finally, success requires embracing hybrid collaboration. The goal is not to replace human creativity and judgment, but to amplify it. GenAI should be seen as a co-pilot and an intelligent partner that augments human strengths while still requiring strategic direction, ethical oversight, and contextual interpretation from people. Designing systems that integrate human and machine capabilities seamlessly will be a key competitive differentiator in the years to come.
Beyond the Buzzword
Generative AI is not a passing trend. It is a foundational technology that can redefine how organizations innovate, but only if adopted with strategic intent. Companies that move past the hype, invest in capabilities, and embed GenAI into their innovation architecture will move not only faster, but smarter. In a world where change is exponential, the future belongs to those who can innovate with technologies, not just around them.
