Every innovation team relies on software, but relatively few use software designed specifically to support innovation activities.
When we last surveyed our members on the topic in 2020, fewer than half of the 200 respondents were using innovation-specific software platforms. And our community tends to shade toward the “early adopter” side of things, so general organizational adoption is likely even lower.
Why is that the status quo — especially in organizations that have high expectations for their innovation teams?
Every business professional is accustomed to using mainstream productivity apps for managing email, scheduling appointments, organizing complex projects, and building spreadsheets or presentations. These arrows were already in your quiver the moment you were given responsibility for innovation, or that you joined an innovation team.
For more than two decades, a category of more specialized business application has taken shape: innovation management software. These tools – like those from Brightidea, edison365, IdeaScale, Planbox, Qmarkets, Sopheon and Wazoku – are designed to help innovation leaders and teams work more efficiently and effectively. They’re designed with the needs of innovation leaders and teams in mind.
The challenge for these innovation management tool providers has always been clear. Busy innovators are already able to use the free applications already provided (and supported) by their organizations, and may have limited incentive to educate their leaders and colleagues on the value of innovation software; convince their organizations to budget for such software; convince their IT organizations to deploy such software; or train their colleagues on how to get value from the software.
Even though the most successful innovation software companies have gotten very good at helping leaders accomplish those tasks, it can still be a challenging journey to embark on.
And that journey, in 2024, is becoming even more complicated.
Generative AI Platforms Arrive on the Scene
Generative AI-based applications are, by definition, great at combining existing data with user inputs to create new data like text, software code, images, video and audio. Generative AI-based chatbots like ChatGPT, Google Bard, Microsoft Copilot, Perplexity, Jasper, and YouChat provide user-friendly interfaces that allow anyone to produce such new data.
It is easy to use a generative AI-based chatbot to support innovation activities. As we have written about here and here, you can leverage it to address:
Strategy-level context and questions like:
- “Help me develop a growth strategy for a large healthcare company. What steps should I take to start?”
Opportunity-level context and questions like:
- “My target customer segment is parents in their 40s with two children between 5 and 10 years old who would like their children to be active on rainy, cold days. I am a retailer that sells outdoor gear. What are some new experiences I could create that would excite my target customer segment?”
Product-level context and questions like:
- “We are a continuous glucose monitor manufacturer. Our monitor takes glucose-level data from the person wearing the monitor and shares with them recommendations for how to improve their lifestyle to minimize spikes and dips in their glucose levels. One of the concerns of our customers is that they are becoming anxious after meals because they expect the monitor to alert them of changes in their glucose levels. How might we mitigate these concerns?”
From there, you can have a “conversation” with the chatbot that will generate, at a minimum, fantastic thought-starter ideas that you can work to validate rather than having to create the ideas yourself from scratch.
If you are careful to use an enterprise solution like Microsoft Copilot for Office 365 (versus Copilot Pro for consumers), you can also upload all sorts of internal company documents containing things like research notes, survey data, product specifications, customer support records, marketing collateral, and competitive analysis results and ask the chatbot to help you extract insights, make recommendations, build presentations for different internal audiences and even generate code for minimum viable product (MVP) applications or websites to test new product demand. (Not to mention email or social marketing copy.)
Given the expansive capability of generative AI to help professionals do their jobs, it’s no surprise that it is quickly finding its way into both the mainstream productivity applications, and also the innovation management software platforms mentioned above.
There are also new tools emerging – some developed by new software companies and others by innovation consultancies – that make it easier for corporate innovators to leverage generative AI by reducing the need to write detailed prompts to accomplish their tasks. (You might consider these a “native gen AI” subset of the innovation management category.)
Raising the Bar
In 2023 and 2024, we’re seeing mainstream productivity applications become even more helpful when it comes to completing innovation tasks. That raises the bar for innovation management software companies trying to persuade leaders to adopt their platforms. Those companies will need to move quickly to integrate generative AI capabilities — which are becoming table stakes — into their platforms.
And the advent of those new “native gen AI” innovation tools also threaten both the traditional innovation management software market — while also having the potential to truly disrupt the market for innovation consulting.
If you’re an innovation leader reading this, you’re already using mainstream productivity applications to help you do your job, and it is definitely worth exploring how generative AI features are already making those applications more helpful to you.
If you or your organization are already using an innovation management platform – or if you are beginning to build the case for adopting such a platform – it’s similarly worth exploring how generative AI is already beginning to reshape that market. And, to stay ahead of the curve, it’s worth keeping tabs on the maturation of those new “native gen AI” offerings since they may provide good support for some stages of your process — and be worth jumping through the hoops required to introduce a new software tool in your organization.
Ultimately, several important tasks will remain human-centric for the foreseeable future: identifying the right tool for the job, making the case for its adoption, engaging colleagues throughout the process, positioning the organization to use the tool effectively, and doing the best job possible communicating the value it creates.
Although, come to think of it, you can now use generative AI to support you on several of those tasks…
Alex Slawsby is InnoLead’s Chief Growth Officer. He has previously worked as a Director of Innovation at Embraer; as a technology analyst at IDC; and for the consulting firm Innosight. This article is part of a 2024 research initiative that will explore how corporate leaders use software as part of their innovation, transformation, and R&D activities.
