Can AI redefine the rules of innovation?
Generative AI is becoming part of the daily operations of large companies: more than 60% have already integrated it into their processes, according to a Google Cloud study reported by Forbes. Adoption has been rapid and often profitable — 74% are seeing significant gains, from cost reduction to operational optimisation. The real question now is whether this technology can go further: contribute meaningfully to a field where uncertainty is the norm, and where tools must illuminate without oversimplifying — innovation management. When used properly, AI does not replace expertise or judgment; it enhances teams’ ability to structure, iterate, and arbitrate. At the intersection of intuition and systems, it opens a new space for dialogue between humans and machines.
Innovation management: from experimentation to co-construction
Artificial intelligence has grown at an unprecedented pace, and its global market could reach 1.847 trillion dollars by 2030. AI integration in companies has already demonstrated its potential impact across many domains, from customer relations to production, marketing, and procurement. In every function, it enables significant productivity gains. Innovation management, however, still has a long way to go. According to a study conducted in November 2024 by the Paris Innovation Directors Club, AI remains in an experimental phase in this field. Only 28% of respondents say they use AI “regularly” for innovation management (67% “to a limited extent” and 5% “not at all”). For now, data search/synthesis and the identification of technical solutions are the most common uses, while other applications — such as ideation — remain under-explored. Nonetheless, adoption should accelerate steadily, judging by the study’s positive feedback on AI’s usefulness for product/service design, fostering a shared innovation culture, and simplifying benchmarks.
Reducing analysis time to support decision-making
For managers and project leaders, AI greatly expands possibilities: data collection and analysis, synthesis, reformulation, machine learning, performance indicators… AI becomes a valuable source of information to identify market trends and assess risks associated with each project. In other words, AI becomes an associate in the project, ideal for reducing analysis time and enabling faster, more informed decisions. It increases data-processing capacity and broadens the field of exploration for building innovative and more robust business cases. Predictive analysis also offers valuable support for identifying risks — delays, budget overruns, quality issues. A genuine toolbox for project managers, who can apply corrective actions early to reduce risks and increase the chances of success. Before integrating AI into project management, however, several questions must be asked: How will the project team use AI effectively? Are they able to adopt these tools quickly without jeopardising timelines? What governance model is best suited to overseeing the entire chain?
Adoption and cultural shift: tools that serve people for effective project management
Misunderstandings, lack of a shared language, and divergent visions can bring any innovation project to a halt. This makes it essential to rely on solid tools to consolidate information, iterate efficiently, and encourage communication and collaboration among stakeholders. Many AI applications now support information sharing, data circulation, and task coordination. And results speak for themselves: according to the Google Cloud study, 45% of large companies using generative AI have seen their teams’ productivity double. Generative AI enables faster structuring, finer steering, and more serene decision-making. Upstream, it accelerates hypothesis formulation and sharpens objective-setting. During execution, it automates indicator tracking, offers continuous visibility on progress, and detects weak signals early. On the coordination side, it streamlines exchanges, facilitates alignment across stakeholders, and supports the diffusion of a shared project culture. By reducing repetitive tasks — planning, reminders, minutes — it frees up valuable time (estimated at five hours per week according to BCG) for more strategic engagement.
AI also enables personalised interactions within organisations through behavioural data analysis. Sentiment analysis, based on NLP, machine learning, and deep learning, interprets opinions and feelings expressed in exchanges. It identifies trends and helps adjust internal strategies and methods accordingly. Cross-functional collaboration, organisational agility, a reinforced culture… Properly calibrated, AI strengthens teams and makes projects more secure.
AI integration: knowing the limits to move beyond them
In 2024, French companies invested an average of €1.78 million in AI solutions — a dramatic increase compared to 2022 (€18,760). And 42% of European companies now use AI “systematically”. But should AI be adopted without precautions or a clear governance model? Project teams cannot rely solely on automated decision-making without ensuring the integrity of the underlying data. Humans remain fully accountable for AI-generated actions and content. Decisions based solely on AI-generated data — without context — carry significant risks: misjudgment, misalignment with the project or the targeted market. Outdated or incomplete data can be extremely costly.
Resistance to change must also be anticipated. Training on tools and prompting practices significantly reduces this friction. And this resistance appears to be decreasing: according to a HubSpot study shared by Orange Pro, 71% of employees believe AI will become an essential work tool in the future.
As Thomas Herlin, Head of Sales at Vianeo, points out:
“Artificial intelligence can only be a lever for innovation if it is placed at the service of an already expert mindset. It is neither a decision-maker nor an autonomous source of innovation — it amplifies what we already master. Delegating innovation to AI without understanding the foundations of what we explore is like entrusting a project to a tool without vision.”
Hybrid AI-team approaches: keeping humans at the centre
Integrating generative AI requires building new internal capabilities, often referred to as “fusion skills”: intelligent questioning, reciprocal learning, and judgement integration. These capabilities ensure that humans remain at the heart of the system, able to understand both the strengths and limitations of AI-generated data.
Economists at BCG, Martin Reeves and Jay Barney, warn against the risk of uniformisation: companies using the same prompts may generate identical outputs. Differentiation then disappears. Again, the human factor becomes the true source of uniqueness and competitive advantage. AI should remain an “assistant”, not a regulator of innovation. It must adapt to teams — not the other way around.
A conversational AI to challenge project leaders
At Vianeo, conversational AI takes shape in NEO, a virtual assistant designed to intervene where everything begins: the formulation of a project. In this foundational phase, often unclear or fragmented, NEO acts as a revealer of intention and structure. It helps innovators clarify their vision, connect intuition to market realities, and lay the groundwork for a coherent action plan. Based on the proprietary Strategic Business Design method — founded on 17 years of field practice and scientifically validated — NEO does not provide ready-made solutions. It stimulates, challenges, and structures. A real cognitive sparring partner, it helps build projects step by step by uncovering blind spots, strengthening decisions, and clarifying intent.
As Séverine Herlin, founder of Vianeo, explains:
“Innovation emerges from the dialogue between human intelligence and artificial intelligence. AI should not merely deliver answers — it should push teams to reflect, challenge ideas, and validate assumptions. No innovative project is born from a business plan generated in seconds: true innovation requires confronting reality and learning through action.”
AI will not replace strategic intuition or field expertise, but it is already reshaping collective action. For companies, the real challenge is not whether to adopt AI, but how to integrate it into a clear framework aligned with their innovation logic. The question is no longer “Should we use it?” but “Under which rules, with what governance, and serving which vision?”
