How AI Is Reshaping the Innovation Process: From Brief to Concept
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Summarise with AI
I’ll be discussing some of this on a panel at the Succeet event in Germany in March 2026. You can register for a free pass with the Insight Platforms discount code SUC26-INPL here.
You can also learn more about the issues covered here by watching the replay of this webinar:
Market Logic Software recently collaborated with Ipsos and AlchemyRx to survey 250 CEOs, CMOs and other C-suite leaders about how they think about innovation, and how AI is changing the way innovation work gets done. The results were striking: almost all respondents already have some form of AI in their innovation process, and none said AI would be unimportant.
97% of the C-suite leaders we surveyed already have some form of AI incorporated into their innovation process. Not a single respondent said AI would be unimportant to innovation. The direction of travel is clear. The question is not whether AI will play a role, but how to use it well, and how to avoid the pitfalls that come with using it badly.
The starting point for any honest assessment of AI in innovation is this: AI is an amplifier. If you apply it to a well-structured process with clear thinking behind it, it will amplify the quality of your work. If you apply it to a poor process, it will amplify the problems. The technology does not address the underlying issues in how you approach innovation – it scales whatever approach you already have.
With that in mind, here is how we see AI applying meaningfully across the innovation journey.
Before the Project Starts
One of the most valuable applications of AI sits at the very beginning of the innovation process, before a formal project has even been commissioned. AI can automatically scan existing data, mine research repositories and flag areas that merit further attention. This is not about replacing human judgement, but about surfacing patterns and signals that would be difficult or time-consuming to identify manually, and pointing teams towards the pockets of opportunity worth exploring further.
From my perspective at Market Logic Software, this pre-innovation phase is where much value is left on the table. Many organisations have significant reserves of strategic consumer understanding – demand spaces research, segmentation work, longitudinal tracking. These sit largely dormant because there is no easy way to query or activate them. AI is changing that.
Sharpening the Brief
Once a project is underway, AI can play a useful role in challenging the quality of the brief. One recurring theme in failed innovation projects is that the problem was poorly defined from the start – the brief was not sharp. The challenge was not well understood. AI can act as a sparring partner here, pushing back on assumptions, asking whether the right questions are being asked, and helping teams stress-test their thinking before work begins in earnest.
Surfacing What You Already Know
Innovation teams do not always start from a blank page. Most organisations have years of accumulated research, past concept tests, tracking studies and consumer feedback. The challenge is making that knowledge accessible and usable in real time. AI allows teams to query that body of knowledge across markets, categories, and time, and use it to inform the current project. This builds what I describe as compounding insights: the ability to go back and understand what worked, what did not, and whether it may be time to revisit past ideas.
Generating and Screening Ideas
This is the area that receives the most attention, and for good reason. Traditional ideation processes (workshops, creative sessions, small-group brainstorming) are effective but slow and produce a limited number of ideas. AI can help generate a much larger volume of concepts in a shorter time, which can then be reviewed and selected by human teams.
In the webinar, Adam Brown at Ipsos described how Ipsos has been working with synthetic data and AI-assisted concept testing. The goal, as Adam put it, is not to replace consumer research, but to accelerate the early stages of development, moving faster through the initial ideation and screening phases so that the ideas that go into formal testing are better developed and more genuinely differentiated.
At Market Logic Software, we work with clients to build synthetic personas and panels that can draw on existing consumer understanding assets and make them interactive. Teams can use these tools during the development process to pressure-test ideas and get rapid feedback. This does not replace the ultimate test of actual consumer response in the market, but it is a useful mechanism for better leveraging existing knowledge during development.
Learning From What You Have Done
Adam described this as one of the most underused applications of AI in innovation. Before AI, conducting a proper meta-analysis of your own innovation history was a laborious process: asking which concepts performed well, why some launches succeeded while others failed, and what the common characteristics of your strongest ideas were. Now, with the right data connected to a model, teams can have a genuine conversation with their own history. They can ask what has driven differentiation in the innovations they have launched, and get a meaningful answer.
This connects directly to the broader argument that innovation needs to be managed like a growth engine. The ability to continuously review and refine your process, based on actual data about what has worked, is what separates organisations that improve over time from those that keep making the same mistakes.
The Human in the Loop
There is a risk worth naming directly: cognitive surrender. As AI tools become more capable, they produce outputs that are more detailed, comprehensive and immediately persuasive. The temptation is to accept those outputs without sufficient critical evaluation, clicking through to the next stage because the AI has already done the thinking.
My team has responded to this by deliberately building friction into the process. The goal is not to slow things down for its own sake, but to ensure that human judgement remains genuinely active at each stage. The creative and interpretive work of innovation still requires people. AI can expand the option space, surface what is known, and generate candidates for development, but the human role in guiding, selecting and making decisions remains essential. An end-to-end autonomous innovation process may be technically possible, but the quality of output it would produce is not something any of us would rely on.
The companies that use AI most effectively in innovation are the ones that treat it as part of a back-and-forth. They start with real people and real consumer needs, use AI to help cluster and synthesise, return to human judgement for strategic direction, use AI again to build out options, and return to human review for selection. That iterative approach, rather than handing the process over to the technology, is where the real value lies.
You can also learn more about the issues covered here by watching the replay of this webinar:
