Imagine a world where AI breathes life into new ideas, transforms pitch decks into captivating presentations, and deciphers the intricate preferences of users. What if we told you this world already exists? Let’s explore how companies are leveraging AI’s transformative power in the different facets of the R&D landscape!
In previous blogs, we already discussed that a significant portion of respondents across all company sizes and industries are already using AI tools for innovation in some capacity (check out the blog here). We also took a closer look at how AI impacts the R&D process and what the main challenges are (check out the blog here). Now let’s take a closer look at which level AI is being integrated into specific tasks, starting with idea generation.
Overcoming ideation blues with AI
Let’s start with the most obvious application of AI in the R&D process. Almost two-thirds (64%) of respondents are using AI to some degree to generate new ideas. This responds to nearly 40% of respondents indicating they are experimenting with AI to learn about the use, effectiveness and drawbacks and 24% of the respondents say AI is already to a certain extent integrated into their process. A sizable minority (17%) of all respondents are considering using AI for this task, and 3% are waiting for suitable solutions to get started, both signaling growing adoption rates. Only 16% of respondents have no plans to integrate AI.
AI adoption rate for idea generation per business type ©Verhaert
There are some variations in adoption based on company size. Large corporations have the highest overall adoption (68%), closely followed by start-ups (64%). However, when looking at the level of integration, start-ups are the most mature as 46% of them have already integrated AI into their processes compared to 23% of large corporations. It’s clear that AI is a promising area for innovation generation across different companies. Given the potential benefits of brainstorming and idea generation, it’s likely that adoption rates will
continue to rise.
Enabling next-level pitch deck creation
Looking at the adoption of AI for pitch deck creation, there’s a clear interest across all company sizes. About 39% of all respondents use AI, with 29% experimenting and 10% fully integrating it into their processes. 10% is substantial, but quite a small number compared to those who aren’t using it (yet) (61%). Nevertheless, 26% are considering AI integration in the near or long term, and 9% are waiting for suitable solutions, indicating potential future growth. Adoption rates vary again depending on the company size: SMEs (44%) and start-ups (45%) show higher adoption rates than large companies (35%).
AI adoption rate for pitch deck creation per business type ©Verhaert
Why are companies integrating AI for this task? First and foremost, AI can automate
repetitive tasks in pitch deck creation, such as data representation, visualization and formatting. AI helps identify gaps and uncertainties, saving time and money by automatically generating pitch decks from available information. It can also suggest design elements and tailor content for specific audiences, potentially leading to more compelling presentations. Additionally, AI enables data analysis to enhance information visualization and improve the pitch deck’s persuasiveness.
Overall, while AI in pitch deck creation is not yet a global trend, it shows significant potential. While efficiency benefits drive adoption, the reasons behind its slower integration are not entirely clear.
Unraveling user preferences and motivations
The statistics show the use of AI to understand user preferences and motivations. Overall, there’s a noticeable adoption of AI in user research across all company sizes. More than a third (38%) of respondents are already using AI in some way, with 31% indicating they’re currently experimenting with it and 7% having it integrated into their process. About 31% of respondents are considering doing so in the future or are waiting for suitable solutions.
AI adoption rate for insights on user preferences and motivations per business type ©Verhaert
When comparing company sizes, large corporations (62%) and SMEs (76%) show a lower tendency to adopt AI compared to start-ups (45%). This could be due to the more experimental and agile way of innovation of start-ups compared to the other categories.
These findings underscore AI’s potential to understand user preferences, which is crucial for product development and innovation strategies. Companies are leveraging AI for several reasons:
- Efficiency and scalability: AI automates time-consuming tasks like user research, data interpretation, and report generation, allowing researchers to focus on concept improvement and deeper user understanding.
- Deeper user understanding: AI tools can help analyze vast data, create surrogate personas and digitally prototype study effectiveness. Through digital prototypes, they help identify unclear and hidden preferences and motivations.
Moreover, AI supports increased personalization across various touchpoints, leading to higher user satisfaction and better adoption of innovative outcomes.
Testing the waters of virtual personas with AI
It’s clear from the survey results that AI is rarely adopted to validate assumptions through virtual personas. 38% of respondents indicate they have no plans to use AI at all, and only 3% actually integrate it into their process. Nevertheless, about 43% are considering AI in the future or are waiting for suitable solutions, indicating potential prospective growth. Overall, the data, like in other tasks, suggests potential growth in the future. This highlights the potential of AI in reducing costs, improving accuracy, and scaling user testing efforts.
Start-ups (27%) show a higher tendency to leverage AI for this purpose than large corporations (19%) and SMEs (13%). For large corporations, this could be a result of larger budgets and more defined user research practices than SMEs and start-ups. SMEs may not have a dedicated user research team or less mature methodologies.
AI adoption rate for validating assumptions with virtual personas per business type ©Verhaert
By analyzing demographic, behavioral, and preference data from various sources, AI can define key customer characteristics. This information can then create detailed virtual customer personas to interact with prototypes, competing options, UI/UX designs or product marketing. Through virtual persona interactions, companies can identify (initial) underlying assumptions about user behavior and potential problems more easily. Based on those findings, companies can refine their assumptions, improve their product or service, and conduct further testing with virtual personas.
Fast-tracking patent analysis and documentation
Next, let’s explore AI adoption in patent analysis and documentation. Almost half of respondents are already using AI in some way, whether experimentation (28%) or integration (16%). In addition, 35% are considering using AI in this field in the future, indicating there’s still quite some potential growth.
AI adoption rate for accelerating patent analysis and documentation per business type ©Verhaert
Comparing the differences between company types in the figure above, large corporations (55%) show a higher tendency to leverage AI in these tasks than SMEs (31%) and start-ups (27%). Due to larger budgets and a higher need to analyze documents and generate design documentation, the ROI will be more impactful, whereas SMEs and start-ups often deploy a lean R&D process, and don’t have as much documentation and patenting work.
What are the potential benefits for companies?
- Improved efficiency in patenting: AI automates patent research and analysis, allowing engineers and scientists to focus on strategic work. It helps identify relevant patents and trends, keeping companies competitive and innovative. AI also detects potentially infringing patents, helping to avoid legal issues.
- Standardized and automated documentation: AI can automate the creation of documentation, ensuring consistency and reducing the risk of errors. Additionally, AI-powered platforms can facilitate collaboration between engineers and designers by providing a centralized repository for all design information.
Exploring the new normal of coding with AI
When it comes to testing and validating code, there’s clearly a high adoption of AI tools already across all company sizes. About 63% of respondents are already using AI in some capacity, with 36% experimenting and 27% integrating it into their process. In addition, 23% are considering using AI for these tasks in the future or are waiting for a suitable solution. Only 14% of respondents have no plans to do so currently.
AI adoption rate for testing and developing code per business type ©Verhaert
Diving deeper into the different business types, SMEs (94%) and start-ups (73%) have the highest adoption rates, whereas large enterprises show a much lower rate (45%). This could be due to the more complex software product portfolio and the more stringent requirements of their development processes and code quality requirements.
These results aren’t surprising. AI can automate repetitive testing tasks and optimize software performance, freeing up developers to focus on more complex issues, and the tools to do so are already quite on point. AI can also be used to identify potential bugs and vulnerabilities in code, improving code quality and automatically generating test cases, which will improve test coverage. As AI technology continues to advance and companies are becoming more aware of its benefits, we expect the uptake will only increase.
Cooking up better products with AI-enhanced simulations
The statistics below show a relatively low adoption of AI to enhance simulation work. Only 22% are using AI in some form, with 14% in the experimentation and 8% in the integration phase. 40% of respondents have no plans currently to integrate it, and 38% are considering this in the future, suggesting like in many other development tasks that there’s growth potential. When comparing business sizes, large corporations have the highest adoption rate, potentially due to factors like budget or the complexity of a company’s development tasks.
AI adoption rate for surrogate simulations per business type ©Verhaert
For this topic more than others, companies might not yet know of the potential benefits of AI. There might also be a perception that AI for surrogate simulations is too complex or expensive to implement. Regardless, AI can create simulations that mimic real-world conditions, helping to test products or processes before physical prototypes are built and can optimize designs based on simulation results. By using surrogate simulations, companies could reduce the time and cost associated with traditional development processes, as well as improve the technical predictability of product performances.
Harnessing the future of self-learning solutions
Lastly, let’s take a look at the adoption of AI in facilitating adaptive, self-learning products and services powered by advanced algorithms. Overall, 38% of respondents are using AI for this task, 36% are considering it and 26% don’t. Despite the seemingly moderate uptake, like before, these results again show promising outcomes for the future.
AI adoption rate for enabling new product and service features per business type ©Verhaert
Looking at the differences between company sizes, 35% of large corporations have already deployed AI tools, with nearly 20% already integrating AI into their processes. Of SMEs, on the other hand, 44% of respondents have no intent to use AI. When looking at the start-ups, they seem to be the most enthusiastic about AI, with only about 20% not currently using it, an equal amount using it experimentally and almost 40% already having it integrated into their process. It’s clear that start-ups are leading in the development of disruption and highly innovative products using AI, and we see large corporations starting to pick up on it as well.
Why companies choose to develop their own AI
Finally, let’s take a look at which tools companies would prefer to integrate AI into their R&D processes. Free LLMs show the highest adoption rate, suggesting that companies might prefer to experiment with free tools before making big investments. Nevertheless, it’s clear that a lot of companies show interest in developing their own AI models. This tells us that the uptake of those solutions will continue to grow. There are several reasons for this:
- Retaining control over data and algorithms to avoid sharing sensitive information
- Getting a competitive edge with unique insights or processes
- Investing in long-term, cost-effective, specialized solutions
Desired AI solutions ©Verhaert
In conclusion
it’s clear that AI is more than a fleeting trend—it’s a game-changer. From start-ups to large corporations, businesses are harnessing AI to boost efficiency, uncover hidden insights, and create cutting-edge products. While some are just testing the waters, others are already riding the wave of AI-driven innovation. Want to learn more about the tools and features that are already out there? Make sure to check out our perspective on ‘Leading GenAI tools for innovation’! The future is bright and brimming with possibilities, so stay tuned as AI continues to reshape the world of research and development, one smart solution at a time!
