Innovation is back on the agenda: Turbocharge it using AI tools
If you’re one of the 80%+ of enterprises preparing to start, reboot or turbocharge your innovation approach, then you need to understand how AI is massively changing the innovation game itself. From filling blank pages to MVP testing, AI tools are essential for innovation teams in 2025.
Innovation is back as a 2025 strategic imperative
Innovation has been mostly absent from the agenda of UK enterprise organisations over the last few years. Faced with soaring fuel costs, talent shortages, and the culture shock of post-COVID remote working, innovation has struggled to climb far up the priority list, as the prevailing mantra became “survive ’til 25.”
A BCG report from June 2024 highlighted the global gap between the 83% of companies prioritising innovation this year, and the meagre 3% which are innovation ready – down from 20% two years ago.
These stats explain and corroborate my personal experience: since 2022 I’ve had dozens of conversations with talented colleagues leaving downsized, “refocused” and straight-up shuttered innovation teams.
For those companies that did survive, it’s basically 2025 already, and there are signs that innovation is zooming back into focus, and once again becoming a strategic imperative. The overwhelming drivers to innovate are coming from internal and external competitive pressure to adopt AI and automation at speed, in turn to achieve cost savings, mitigate talent shortages and gain time to market.
The game has changed
If you’ve been out of action in innovation for the last two years, then the game has changed beyond recognition. Innovation itself is currently undergoing a drastic rewiring from AI itself and poised to transform again radically in the next year.
As a traditionally human-intensive process, innovation lends itself to the current and near future wave of generative and cognitive AI tools. Miro, the default home of the post-Covid, digital native innovation consultant, introduced AI tools earlier this year, enabling practitioners to accelerate and eliminate everything from basic de-duplicating and clustering Post-it notes to automating product strategy creation. In addition, an impressive event by Board of Innovation back in June showcased the depth and breadth of tools emerging to automate all aspects of the process.
As an orientation, we’ll touch on just three problems which AI is currently addressing within innovation:
The Blank Page Problem
The blank page problem can be solved by generative AI tools
The challenge of starting from scratch and the fear of beginning can be intimidating, often leading to analysis paralysis or creative block.
AI-driven brainstorming tools can suggest a variety of perspectives and solutions which might not have been considered by those in the room, and also have the potential to free this phase from the constraints, prejudices, and biases of a small group of innovation consultants.
As practitioners are taught to build on, rather than criticise ideas at the ideation stage, AI concept generation can lead to a surfeit of starting points which the human brains can pick up and elaborate on (and in certain instances rival humans with the capacity to create concepts within parameters).
By turning the blank page into a canvas with initial concepts, AI reduces the cognitive load on innovators, enabling them to focus on refining and implementing their ideas.
The Experimentation Problem
Once an idea has developed, the most important thing is to get early validation of potential market demand, ideally through contact with the buying public.
Teams have traditionally gauged potential demand by rapidly coding up prototypes, landing pages and smoke tests, A/B testing messaging and journeys. This is where innovation legends are born – it’s traditionally been a gruelling process run by small, passionate but underfunded teams burning the midnight oil, and getting it right here can enable teams to either be first to market or uncover winning insights.
With AI able to generate code, change buying journeys on the fly, generate analytics codes and buy search terms in seconds, much of the grunt work can be removed, enabling teams to run multiple sequential and parallel market tests, and get to better quality insights in days rather than weeks.
And if your value hypothesis proves to be unfounded or just plain wrong, there are models to generate, reframe and test multiple hypotheses in real time.
The (MVP) Testing Problem
As you begin to approach the development of a minimum viable product, even the most raw and unfinished MVP will need to undergo testing and just how “minimum” the product is will depend on the audience and innovation team’s appetite for letting them get their hands on it.
AI can streamline this process by automating and optimising testing procedures, as mainstream product and software teams are already discovering. As most test books reuse common elements (for example, preconditions, multiple variants on similar success cases), AI can automatically generate test scripts that cover a broad range of user scenarios, enhancing the reliability of the final product, and allowing teams to focus their energy on more thorny issues.
Use AI tools to speed up MVP creation and testing
3 Key Takeaways
We’re just at the start of the AI revolution: Alphabet’s Eric Schmidt hinted at his excitement for upcoming developments within the year (very large context windows, agents and text-to-action), all of which will have a disproportionate impact on knowledge and intellect based work, not least innovation.
Here are just three things you need to remember if you are planning to re-enter the innovation space:
The idea of a completely autonomous innovation unit is still some way off. But as companies turn to embrace AI at speed, the way they innovate must also be enhanced and powered by AI itself.
Feel free to contact me or DWG’s strategy, leadership and innovation team should you wish to know more.