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End2End Innovation | AI-Driven Innovation

Embracing Innovation Automation with AI Assistants in 2024

Paying the right level of attention at the right time to the right topics is what separates innovation leaders from laggards. Top innovators are best in class when it comes to speedily processing all relevant innovation information: be it new trends and technologies, customer pain points, competitor moves, ideas, or innovation increments. However, recent advancements in AI tools and custom GPTs will not only drive innovation proficiency and efficiency but also level the playing field for smaller companies. The competition to be a top innovator will no longer be decided by resource power but by the speed of transferring insights into action and rigorous commitment.

Higher efficiency and universal access to proprietary knowledge

For a long time, there was one truth in business: Size matters. And, size matters because bigger companies have more resources than smaller firms to place bets on riskier activities. They have a bigger workforce, allowing them to process more tasks in less time and to bring in more expertise. The advent of digital business models disrupted this truth more than two decades ago. Still, bigger companies have a size advantage in innovation, but only up until now.

Although smaller firms are praised for their speediness and maneuverability, it is the big companies who innovate at scale. More ideas can be validated simultaneously, the door to co-create and test start-up ideas is wider, and the capabilities to scan market needs are more many-sided. Thus, be it in foresight, idea generation, or value creation and capture; larger companies have a size advantage in every domain of innovation management. For bigger companies, the good news is that with the newest AI tools, they can further increase their efficiency in managing their innovation activities. The bad news for them: the impact on non-innovation leaders is even greater, and AI tools will provide them with access to former proprietary knowledge at no cost and with no specific capabilities needed. Innovation automation, powered by AI tools, is at the forefront of this shift, offering smaller companies a level playing field by automating the access to and analysis of proprietary knowledge.

AI tools are marking a shift in how innovation professionals work…

The source of this pivotal change is generative, conversational, and interactive AI’s effects on knowledge generation and distribution. With 24/7 availability, contextual knowledge processing based on the biggest information carrier available, and engaging human-machine interactions, AI enters a field that was predominantly dominated by consultants, agencies, the Internet, large customer bases, or experts. Bigger companies had easier access to these knowledge carriers and, by that, a lead in innovation.

Remember: Innovation results from a re-combination of knowledge. The easier the access to relevant knowledge, the higher the chances to drive innovation. AI now tears down this barrier, and access to proprietary and curated knowledge is becoming universal. This will drive the efficiency of innovation leaders, yet it puts non-leaders in a position to compete. Plus, the engaging ways of interacting will likely motivate more individuals to research, test, and contribute to innovation - if allowed by corporate policies.

Key Message

"If you know what customers want, what competitors do, and turn this knowledge into unique value, you innovate. AI reduces the efforts of each step, and conversational AI makes it fun and accessible to the broader business landscape. That is a game-changer for innovation practice."

…along all innovation process stages

As knowledge is the key ingredient along all innovation process stages, experts expect that conversational AI systems and specific applications will increase productivity by 60-70 percent. This will manifest in less money spent on external sources, faster time-to-insight, less effort on re-iterations, and - when coupled with an Innovation OS - less double work and more clarity.

How do you use ChatGPT for innovation?

In foresight and research, the main task was always to collect insights and evidence on the topics that will coin a company’s future business landscape. Although AI is no crystal ball, it will accelerate the collection of the most relevant insights. Give it a try and check our AI Trend Analyst GPT to collect the most relevant technologies, trends, customer pain points, or regulations for your specific industry quickly. This will also give you more easily access to dive into topic areas that are further away from your business area, opening a new avenue for transferring insights from other business areas. As the process of collecting insights and evidence will be eased, the main tasks will shift to interpreting the insights for one’s specific company context. The focus will shift to stating more precisely one’s business opportunities and prioritizing the right challenges to commit to.  

In ideation, the main task was to apply the right method to source the best ideas for the challenges at hand - so far. This typically involved crowdsourcing to attract external partners or the start of ideation campaigns. AI now opens an avenue to let the machine generate ideas with concrete details on desirability, feasibility, and viability. At the same time, it can crosscheck ideas against already existing providers that may have already developed a fitting solution. Plus, the idea-generation process itself can be enhanced. Try and check out our Creative Charly, which will help you generate or play through your idea and put it into a convincing concept. As the solution generation process is eased, the focus and task will switch to committing to the right ideas and acting on them as quickly as possible.

In portfolio management, the main tasks will not shift dramatically for now - as long as there are security concerns about the usage of the data. As portfolio management deals with moving projects into implementation, there is hesitation to share such information with third parties and under low controllability. Yet, when companies employ their own models, there will be a direct effect on finding the right synergies, alerting on duplicated efforts and projects not fitting the movement of the business environment anymore. Until then, it can still be beneficial to get a concrete implementation plan and business model for any given project. Test it with our Innovation Architect.

In sum, applying AI in innovation management practices will change the work of innovation practitioners along all innovation process stages. It might not be perfect yet, but it promises significant uplifts of efforts that have been the major tasks for innovation practitioners for a long.

Test the AI assistants automating innovation practices for free

To help you ease your innovation process in the next year, we have compiled the most interesting AI tools to help you leverage the aforementioned effects. Our AI toolbox contains fourteen new tools in total.


Use the ITONICS AI Assistants for free →

These ITONICS tools are conversational bots that put you on a joyful and fast discovery of relevant insights. We have also taken care that you can easily extract the insights so that you can start more quickly on what will become the new decisive factors: contextualizing and commitment.

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