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!

The post From ideation to innovation: AI’s magic touch in R&D appeared first on Verhaert Masters in Innovation.

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Who’s mastering AI and why

It’s amazing to see that most respondents are already using AI in their innovation process. So why are some companies already adopting AI and others not?

Companies integrating AI into their R&D processes likely see clear benefits, such as increased efficiency and improved product development. These companies often have a strong innovation culture, embracing new technologies quickly and encouraging experimentation. On the other hand, companies not yet using AI may face obstacles in setting up the necessary AI skillset and infrastructure. This is particularly challenging for start-ups and SMEs with limited budgets. They may lack in-house expertise, sufficient data, or the ability to manage data effectively. The return on investment for AI projects can be hard to quantify without prior experience, making it difficult to justify costs upfront.

AI adoption rate, total and per business type ©Verhaert

When comparing business sizes, start-ups are clearly taking the lead, but SMEs and especially large companies are making headway too. Why is this? Large corporations (49% using AI) often have the financial muscle to invest in AI expertise, infrastructure and experimentation. They also have vast amounts of data from existing operations, a valuable asset for AI. Initial costs can be a significant hurdle for SMEs (42% using AI), even though they are typically more agile and quick to adopt new technologies to remain competitive. Finding and retaining skilled AI talent can be more challenging for smaller companies, too. Many start-ups (85% using AI) operate on cloud-based infrastructure and have a data-centric culture, making it easier to integrate AI solutions. The start-up environment is often more attractive to AI talent, though early-stage start-ups may struggle with access to capital for AI investments.

Navigating value creation in new products and services

In addition to boosting the development process, AI can also add new and extra value to products and services. When looking at the results of the survey, there’s a clear difference in AI usage for this purpose between respondents who aren’t using AI yet in their development process (36%), and those who are (67%). This is not surprising as integrating AI into the development process is often easier and requires less investment thanks to free and easily accessible tools, which can then be a gateway to AI for new features.

Adoption rates in AI for value creation ©Verhaert

There are several possible reasons why respondents might be more likely to activate AI to create new added values through AI in products and services:

• Innovators may have more access to the resources required to implement AI in R&D.
• Companies that have already experimented with AI might be more aware of the potential benefits of AI in their products and services.
• These companies may have a more open and collaborative culture that is more receptive to new technologies like AI.

Exploring AI adoption across the industry spectrum

When you look at the adoption of AI between sectors, there are some big discrepancies. This may be because some sectors more easily see a high potential return on investment in AI and are thus more likely to adopt it. For example, finance and pharmaceuticals could gain significantly and directly from more personalized financial products or efficient drug discovery through AI. Their innovation and R&D processes are costly and complex so the impact on the efficiency of these processes is high. The manufacturing sector could be more cautious about investing in AI for R&D because the perceived benefits are less clear compared to other sectors.

Adoption rates of AI in R&D per industry ©Verhaert

Additionally, sectors like telecommunication generate or have vast amounts of data, making them well-positioned to leverage AI for R&D. For starters, this data could be used to train AI models and extract valuable insights. On the other hand, the construction sector might have a different volume of data compared to finance, limiting the ‘straightforward’ AI application in R&D.

Thirdly, sectors that have a strong embedded culture of innovation are more likely to embrace AI and implement it into their culture and processes. These sectors may have a more risk-tolerant
mindset and can readily allocate resources to explore benefits. The technology sector, for example, is known for being innovation-minded and may adopt AI in product and service development more quickly.

AI in action, examining adoption across R&D stages

From the survey results, we can derive how and where AI is currently already being used in the different phases of the R&D and innovation process.

Let’s start by clarifying those phases and the potential AI usage.

• In the ideation and exploration stage, new ideas are generated. AI can help brainstorm new product ideas, identify existing trends and analyze customer data to understand unmet needs.
• The next stage, feasibility assessment, evaluates the technical feasibility and market viability of a concept. AI can analyze data, assess the concept feasibility and viability and identify potential risks.
• During the concept development stage, promising ideas are turned into more detailed concepts. AI can create simulations, build demonstrators, develop prototypes, and conduct virtual testing.
• During the prototype testing and validation, a detailed version of the final product, physically or digitally, is created to test its functionality and gather user feedback. AI can automate some of the testing processes and analyze the data collected.
• The new product introduction stage focuses on launching the new product into the market. AI can optimize the product design and further investigate user research in the upscaling process.

Adoption rates of AI in R&D per development phase and business size ©Verhaert

But what does the survey say? Respondents who are already integrating AI in the R&D process, do so throughout different phases. Businesses seem to collectively prioritize integrating AI in the ideation and exploration phase, possibly because this is the most straightforward option thanks to tools like ChatGPT, Gemini and Copilot. Feasibility assessment and new product introduction receive comparatively less attention, while concept development, prototype testing and validation fall in between.

When comparing adoption within business types, start-ups appear to be more focused on feasibility assessment and prototype testing than others, possibly reflecting their need to validate ideas with limited resources. Large corporations seem to excel in ideation and new product introduction, leveraging their established resources and experience. SMEs end up in between, showing varying levels of activity across different stages.

Why AI integration varies across sectors

Having covered the differences between types of business, let’s now take a look at the differences between sectors. Some industries don’t yet use AI in all phases, explaining discrepancies with the average adoption rate.

Adoption rates of AI in R&D per development phase and industry ©Verhaert

AI adoption in concept development seems to be higher in machinery, construction, healthcare and pharmaceuticals. This is probably because AI can assist in analyzing vast amounts of data to identify patterns, develop new drugs, personalize services and predict customer churn. For machine builders, AI can optimize designs, making lighter, stronger, or more efficient machines. Additionally, through predictive maintenance, AI can analyze sensor data to predict potential failures and schedule maintenance proactively.

While almost every sector is investing in AI for ideation and exploration, sectors like consumer electronics, finance and economy and telecommunications seem to also emphasize the use of AI in prototyping testing and validation instead of ideation and exploration. There are several possible reasons for this. Prototyping, testing and validation are more scientific aspects of R&D than ideation and exploration. There are already established methods and tools, making it easier for companies to deploy AI in these phases. Prototyping and validation also reduce the risk and cost of failure by identifying issues early and ensuring new products meet customer needs.

Aerospace, manufacturing, healthcare, and pharmaceuticals highly adopt AI in feasibility assessment to manage financial risks, detect fraud, and define investment strategies. In aerospace and manufacturing, AI analyzes simulation data to assess the feasibility and safety of new concepts, mitigating risks.

When it comes to new product introduction, the sectors FMCG, machinery, and transportation and logistics show the highest adoption of AI. In these cases, AI can help design packaging, optimize settings to transition to the production phase or assist in finalizing the design for production upscaling. In addition, AI can help predict consumer demand and forecast the success of newly released products.

In conclusion

In conclusion, the integration of AI in R&D varies significantly across companies and sectors, driven by factors such as financial capacity, data availability, and innovation culture. Start-ups lead in AI adoption due to their agile, data-centric approaches, while large corporations leverage substantial resources and data. Across R&D stages, AI is already predominantly utilized in ideation and exploration, with varied emphasis in other phases depending on business type and sector. This diversity in AI adoption underscores the tailored strategies companies employ to harness AI’s potential.

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How AI is supercharging the innovation process

“AI shows tremendous potential to boost R&D”, you’ve probably heard this before. But what is this potential exactly? In our survey, we asked companies that are already using AI what is the impact is of integrating AI in different stages of the R&D process. In addition, we also compared this to the expected impact of companies who aren’t using AI yet.

In the figure below you can see the average scores on a 5-point scale of how impactful (from 1 – minimal to 5 – transformational impact) respondents considered these opportunities compared to the average usage rates. At first glance, we can see that the estimated impact of the opportunities is in line with the actual usage of them since the dots are relatively close to the baseline. Signaling that people are already investing in AI where they think it’ll have the most impact. Some, however, show some discrepancies.

Adoption of AI vs. estimated impact per development task ©Verhaert

The data reveals several key insights:

• Top activities like ‘test and develop code’ and ‘generate new innovation ideas’ are heavily utilized, leveraging AI’s ability to streamline repetitive tasks and spark creativity.
• Tasks such as ‘validate through surrogate simulations’ and ‘validate assumptions with virtual personas’ are valued but underused, indicating potential barriers to integration into workflows.
• Activities like ‘create pitch decks automatically’ and ‘validate assumptions with virtual personas’ are deemed less impactful, possibly due to the need for human creativity, leading to limited AI adoption.
• ‘Test and develop code’ and ‘validate through surrogate simulations’ stand out for their high impact potential, highlighting AI’s capacity to accelerate R&D by optimizing processes.

Overall, researchers see AI as beneficial for automating tasks and fostering innovation, yet skepticism persists regarding its effectiveness in creative endeavors.

Overcoming roadblocks in AI for R&D

No technology comes without its challenges. That’s why we also asked our respondents what their roadblocks to integrating AI into their R&D processes are. We also compared the input from companies that are already using AI to those who haven’t yet.

1. Challenges in AI adoption of AI users

First, let’s take a look at the results of the respondents who are already using AI. From the figure below, it’s clear that the biggest challenges are the lack of expertise and the lack of the right toolchains and methods.

Challenges in AI adoption rates for AI users per business size ©Verhaert

Looking more in-depth at the results, the challenges in AI adoption vary across different company types. Large corporations and start-ups cite a lack of expertise as the primary barrier, while SMEs struggle with limited access to toolchains and established methods for AI deployment.
The challenge of finding the appropriate expertise could indicate a need for more training and education on AI as well as a shortage of AI engineers and data scientists on the market. In the meantime, integrating existing AI talent, whether sourced internally or externally, into innovation departments could provide immediate solutions.

Corporate strategy gaps are particularly notable among larger firms. This suggests that some larger companies may not have a concrete plan for how AI will be integrated into their R&D processes.

Although less common, access to toolchains and methods appears to be a challenge, especially for SMEs. This could be due to resource constraints and less structured tools and processes. In addition, the absence of concrete projects hinders effective AI applications. Defined projects are crucial for identifying AI’s efficacy and establishing measurable KPIs.

2. Challenges in AI adoption of non-AI users

Next, let’s analyze the insights from the respondents who are not (yet) using AI. Looking at the total average, the main issue to overcome again seems to be finding the right expertise, followed by the lack of corporate strategy and toolchain and methods. These perceived or expected challenges could signal why they’re not yet using AI and be a growth opportunity for AI tools. Addressing the elephant in the room, there’s a clear difference in the answers of the start-up. Start-ups that aren’t using AI see the lack of a specific project and a corporate strategy as the most important causes. With the latter twice as important.

Challenges in AI adoption rates for non-AI users per business size ©Verhaert

3. Challenges in AI adoption AI users vs. no AI users

To conclude, let’s compare the results of AI users compared to non-users and evaluate the key similarities and differences.

Challenges in AI adoption AI users vs. no AI users ©Verhaert

For both groups, the lack of expertise (43% and 44% ) is the biggest challenge. This suggests that a lack of knowledge and skills is a major barrier to AI adoption in R&D in general.

Lack of corporate strategy (20% and 23%) is the second-biggest for both groups and shows a slightly bigger difference between AI users and non-users. Nevertheless, the difference between this challenge and the previous one is significant. This suggests that both those who are using AI and those who aren’t may struggle to get buy-in from senior management for AI initiatives.

Lack of toolchain and methods (20% and 21%) shows comparable results for both AI users and non-users, but rate it considerably less than challenges in expertise. This suggests that companies don’t struggle as much in finding the right tools and methods, but that even companies that are interested in using AI may encounter these challenges after they start the integration.

Lastly, the lack of concrete projects (16% and 12%) is the least common challenge and again shows a rather small difference between AI users and non-users. This suggests that most companies have concrete applications in mind when considering AI and that the biggest challenges lie in the actual application of it.

In conclusion

The data speaks for itself, the respondents agree that AI can have a clear positive impact on their R&D process, even those that are not yet using it. AI is poised to revolutionize the way we innovate. Nevertheless, to guarantee success, there’s a clear need for more training, education and for incorporating AI personnel into R&D departments, as well as corporate strategies for how AI will be integrated into their R&D.

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Let’s dive into the diverse avenues of progress in the coffee industry!

1. Intelligent home roasting

Redefining coffee quality and sustainability

Bunafr‘s participation in CES 2024 introduces an intelligent home coffee roaster that transforms the roasting experience. Beyond being a machine, it offers a fully automated, smoke-free process with advanced technologies. The innovation focuses on personalized roasting, sustainability, and direct connections with coffee farmers, promising an unparalleled coffee experience.

Coffee machines: ©Bunafr (left) – ©Midea (right)

2. AI-guided personalization

The future of smart coffee systems

Midea presents the ‘Barista Brew’ coffee brewer/grinder system, a smart coffee solution that leverages AI-guided personalization. With one-touch favorites, BrewIQ AI suggestions and easy tracking, this innovation aims to make coffee brewing accessible to all, regardless of expertise. It sets a new standard for smart coffee systems, emphasizing simplicity and technological advancement.

3. Premium espresso with technological precision

Bosch’s fully automatic machines

Bosch introduces a new line of fully automatic espresso machines, emphasizing premium-quality coffee and technological precision. The Aroma Max System, Active Select Display, and integration with Home Connect® redefine the home coffee experience. This innovation aims to deliver consistent coffee quality, personalization and over 35 beverage options through advanced technology.

Coffee machines: ©FrazyBot (left) – ©Bosch (right)

4. Robotic beverage crafting

The FrazyBot revolution

FrazyBot, a modular beverage brewing robot, redefines at-home drink preparation. Designed to minimize manual interventions and clean-up, the FrazyBot promises precision and professionalism, comparable to café or bar drinks. The Ingredient-Sensing Robotic Technology (InSeRT) ensures an effortless, customizable and high-quality beverage experience at the press of a button.

5. Eco-friendly espresso

Lavazza’s Tiny Eco revolution

Lavazza‘s Tiny Eco emerges as a compact and sustainable espresso machine made from recycled plastic and compostable capsules. With a focus on sustainability and design, this innovation offers an authentic Italian espresso experience. Its easy maintenance, dishwasher-safe components and compatibility with Lavazza capsules contribute to an eco-friendly and responsible coffee-making approach.

Coffee machine ©Lavazza

Comparing the strategies behind the devices

How do these devices and innovation strategies compare to one another? We made the following assessment based on people’s opinions on a coffee lover’s user perception. We used criteria such as emotional evocation, premium feel, personalization, and the social responsibility of the device from a user’s perception.

Notes:

• Emotion addresses: Reflects how the innovation addresses the emotional aspect of the coffee lover.
• Premium feel: Represents the perceived luxury or premium feel associated with the innovation.
• More personalized feeling: Indicates the degree to which the innovation provides a personalized and tailored experience.
• Social responsibility: Reflects the level of commitment to social and environmental responsibility in the innovation.

People however don’t switch their current coffee maker easily, you need a new compelling reason. When looking for innovative features you need to investigate the importance and the satisfaction level of each of the features. That’s why we also added an assessment relative to the willingness to pay.

Notes:

• Satisfaction rating (1-5): Reflects the current satisfaction level with the existing coffee machine.
• Eagerness to Upgrade (1-5): Indicates how interested the potential buyer is in upgrading the coffee machine based on the discussed factors.
• Willingness to Pay Rating (Low-Med-High): Represents the potential buyer’s willingness to pay for an upgraded coffee machine, considering the perceived value of the innovations.

In conclusion

In conclusion, these innovations highlight diverse avenues of innovative progress for the coffee industry, ranging from intelligent roasting and AI-guided personalization to hands-free brewing and eco-friendly espresso solutions. Each emphasizes the unique innovation direction chosen by these respective brands, providing consumers with a glimpse into the transformative landscape of home coffee experiences. However, only 3 concepts stand out in giving enough reasons to switch from their older device to a new coffee maker. This brings me to my final thought: Would a device combining the 3 outstanding strategies (premiumized coffee, robotics and sustainability) be the holy grail of innovation? Or would it more likely fit in the contradiction of less is more?

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How does the IR&D organization change?

Outcome-based innovation requires redesigning the IR&D organization to allow for interdisciplinary teams and methodologies, focusing more effectively on experience, and business model outcomes by creating a service. That means you need to expand the engineering team’s scope of activity and modernize the methods, processes and team structure. At the same time, you need to integrate multiple parallel workstreams and upgrade technical workstreams with business, service design, user interaction, and as such develop an effective culture of multidisciplinarity. Success requires cross-functional integration of proposition and pricing model design.

How does outcome-based innovation integrate customer trends?

Amidst the evolving landscape of innovation, there’s a growing need not just to build products but also to create services to allow for a circular economy and digital add-ons that extend the functionalities of the physical product. Outcome-based innovation will become a critical source of such a competitive advantage. It will become the foundation for entirely new business models where products aren’t just offered through a different sales model, but business model requirements are aligned with user service requirements. In turn, they then also have to be aligned with the soft- and hardware requirements of the digital and physical product subsystems. You can expect outcome-based solutions to become a significant share of the business, expanding into new areas.

So there’s a role to play for digital?

Companies are embedding novel features in their soft- and hardware products based on artificial intelligence, machine learning, cloud connectivity, and edge computing. Such features can transform unconnected products into smart devices. Digital technologies allow companies to push or upsell upgraded function packages over the air. The aim is to delight customers on a regular basis with new experiences, to ensure they keep addicted to the solution, and hence allow the company to produce an ongoing stream of revenue as opposed to a one-time product sale. Outcome-based innovation requires big data and analytics capabilities, the ability to create insights over multiple value chains, and multiple product instances in the market.

Are outcome-based systems a stepstone toward circularity?

Leaders are designing goods for a circular economy by reusing materials, recycling, and remanufacturing products to extend their lifespan. Yet for many, it remains a challenge to create new added value for their business and transform the product into a highly successful outcome-based service solution. A great circular design strategy involves the use of modularity, in which subsystems and components are designed to be easy to replace, upgrade, change, or offer a longer lifespan. Even extending the product with remote monitoring services helps companies to better understand the value in the field and its potential for refurbishment.

Outcome-based strategies help you look for ways to create added value on one or multiple parameters. In addition, it embraces digital and circular strategy design to deliver the utmost value creation for customers or end users, whether it be by added functionalities, increased performances, convenience or experience up to the disrupting possibility of the solution. However, outcome-based innovation requires overhauling the current IR&D organization to allow for a truly multidisciplinary team. Excited to join the outcome-based innovation journey? Get your spot at our Innovation Day on Tuesday November 21st at our innovation center in Kruibeke.

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Outcome-based innovation value canvas

If you haven’t read our previous blog yet where we look into 5 examples of outcome-based innovation, make sure to do so first. When we map these examples on the following parameters: added functionalities, performance optimization, convenience, experience, market disruption potential on the characteristics of the services added, they each show a particular profile or value canvas (see figure below).

Maturity-like categorization

When trying to evaluate ideas and derive strategic insights on how to create more value, a maturity-like categorization comes in very useful. We used 5 different strategies each graded in 3 levels in ascending order as a methodology. This resulted in the following structure.

Enhanced functionality services

• L1 – Services that focus on adding functionality to the product, enhancing its capabilities beyond the baseline features of the product.
• L2 – Monitoring & control: services that offer control over product functions and performance.
• L3 – Insights: more advanced services that provide actionable insights for the user based on performance data.

Performance optimization services

• L1 – Services that aim to optimize the product’s performance, ensuring it operates at its highest potential, resulting in improved functionality and reliability. They hence can be sent as services that offer minor improvements in product performance.
• L2 – Operational enhancement: services that enhance the overall operational efficiency of the product.
• L3 – Performance optimization: advanced services that significantly improve product performance, availability, lifespan, or even deliver prescriptive advice

Convenience services

• L1 – Standard convenience: services that offer basic convenience enhancements.
• L2 – Efficiency enhancement: services that focus on improving the efficiency of product-related processes.
• L3 – Seamless convenience: high-value services that create a seamless, effortless user experience taking the hassle out of the operator’s hands.

Experience services

• L1 – Enhanced experience: services that add some level of enhancement to the user experience.
• L2 – Immersive experience: services that create immersive and enjoyable user interactions.
• L3 – Delightful experience: high-value services that aim to delight users through unique experiences often multisensory.

(Autonomous) disruptive services:

• L1 – Increased autonomy: services that introduce autonomy to the product so it can operate without an operator or user, yet doesn’t disrupt the market.
• L2 – Transformation: services that bring about a significant market transformation through automation.
• L3 – Disruption: high-value services that disrupt existing markets and create new ones through advanced automation.

A practical framework to drive outcome-based innovation

The framework presented is not just a theoretical concept but a practical tool that can drive change. It empowers innovators and R&D professionals to make strategic decisions that align with customer needs and market dynamics to become better at innovation. By categorizing ideas into levels and offering the ability to rank these by increasing added value, you can pave the way for more successful and impactful innovations.

The pursuit of new outcome-based values is a dynamic and evolving process. Your customers and end users are looking for solutions that not only meet their needs but also exceed their current expectations, whether it’s in terms of performance, convenience, experiences, or disruption. To harness the potential of this framework, we encourage you to explore our Innovation Day.

Innovative solutions reshape industries, enhance user experiences, and create lasting value. We look forward to being a part of your innovation journey and helping you achieve success through outcome-based innovation. Don’t hesitate to reach out, ask questions, and connect with our experts. Together, we can unlock a world of possibilities and drive meaningful change in the realm of product and service innovation.

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Shifting the focus towards outcome-based innovation

In the past, the primary focus of innovation was on making products better and cheaper, integrating technologies updates and looking for new functions and better use cases. Recently though, products are getting equipped with features like performance monitoring and predictive maintenance. The start of ‘servitized products’, services on top of the product integrated into the baseline, as a competitor differentiation strategy, or as an option activated upon ordering.

Over time, this trend has been proving its efficiency in creating added value. Services that fit better with the job-to-be-done are the reason why customers assign a higher value perception compared to the remaining initial product part. Services allow shifting the functionalities and performances of the product from must-haves to rivaling and outperforming appreciation.

Creating added value through different features

So what are the typical features in outcome-based innovation that create added value? What differences are there? And does one type of feature create more added value than others? Let’s analyze some examples.

IoT home draft ~ Innovate to create new user features

How to convince beer lovers to buy an in-home beer-pouring connected appliance? With a next-level user experience. Features like real-time volume and expiration updates, personalized temperature scheduling, and a waste-reducing loyalty system create a premium experience and encourage loyalty. Especially when it’s then also linked to a points system that helps you collect free kegs. Nevertheless, these services cannot be executed through software only. It requires IoT-enabled hardware that offers the user a better deal and outcome compared to other, older products.

Clinical study IoT application ~ Innovate with convenience in mind

Regular check-ups and follow-ups at the hospital take up a lot of time for both the patient and physician. On top of that, they don’t always give accurate data but merely a sparse set of data that isn’t representative of patients’ real lives. How can you boost convenience and accuracy while taking the pressure of healthcare personnel? A mobile application that helps to conduct remote tests and questionnaires, and can be used for real-time study data, clinical tests and quality control. This is a great example of how a digital set of functions allows you to rethink the reigning use case and take it to the next level.

Stationary tilt bike innovation ~Innovate with motivational nudges

Stationary bicycles to train from home have been around for quite some time. However, doing the same training every time quickly becomes boring. How can you motivate users to stay loyal to your product? How can you pinpoint the desired customer outcomes and create more lifelike experiences? If you put the user experience first and redefine your use cases around their needs and wants, you’ll create solutions like offering endless training combinations. Effectively beating your competitors and cultivating loyal customers that never get bored.

Light-as-a-service ~ Innovate with new business models

Businesses like ETAP have already switched from selling products to selling products-as-a-service. They did a complete overhaul of their company: what they sell, how they earn money, and how products are conceived so they’re easily serviced. On top of that, it pushes them to products with lifetimes beyond traditional electronic standards, and capitalize on their portfolio. These kinds of business models require modularity not merely on the product level with serviceable and swappable parts, but also on the design level with easy-to-exchange submodules and future components.

Autonomous robots ~ Innovate to disrupt

Robotics used to stumble on the complexity of the desired outcome. Thanks to recent advances in algorithms, machine learning, and AI technologies, robots can do more than navigate and interact with their environment. They allow people to take over many repetitive, difficult, and dangerous tasks. Resulting in completely new business models, not just limited to autonomous mobility or home cleaning robots, but applications that disrupt and transform your entire business.

Stay tuned on outcome-based solutions

As you may have sensed yourself, these categories can be ranked somehow in increasing added value, creating new business models and even disrupting businesses. Curious to know how these five distinctive types of servitization compare? Stay tuned for our next blog to find out how they’re ranked through combined examples.

Want to experience firsthand how these solutions came about? Excited to build your own outcome-based solutions? Join our Innovation Day on November 21st!

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Rapidly-evolving innovation landscape

As technology continues to evolve, customer preferences shift, and market demands change, staying ahead of the competition has become more complex than ever. And while many companies are already embracing the efficiency gains through AI, like ChatGPT and LLMs, it won’t give you a true competitive edge. You have to find growth avenues beyond productivity that genuinely revolutionize your products and services. So how do you push the boundaries of innovation with digital solutions?

AI beyond productivity gains

While increasing efficiency is important, AI’s capabilities extend to various other domains and present exciting opportunities for transformative change. From autonomous systems and personalization to energy optimization and product improvements. By leveraging AI’s capabilities, you can foster continuous improvement and innovation to stay at the forefront of your industry. During the ‘AI-infused innovation’ seminar, you’ll gain insights from Jeroen Baert, imec, BNP Paribas Fortis and Verhaert Digital innovation on how to successfully revolutionize your business with AI:

• 9h00 – Event introduction
• 9h30 – Navigating the AI playground (Jeroen Baert)
• 10h00 – Cracking the AI code (Verhaert Digital Innovation)
• 10h30 – Advancing AI horizons (imec)
• 11h00 – Maximizing the AI potential (BNP Paribas Fortis)
• 11h30 – Walking lunch networking

Interested in the AI-infused innovation seminar on September 12th in Westerlo? Visit the event page to register or find more information about the sessions.

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Difficulties in minimally-invasive surgery

Although minimally-invasive spine surgery offers many benefits, current (manual) needle and instrument navigation relies on an iterative, rigid and assumed constant spine position based on X-ray imaging. “Because of surgical dynamicity and patient movements, this kind of navigation is unreliable and imprecise”, explains Michiel Twisk, Program Coordinator Life Sciences at Verhaert, “It also causes unwanted radiation exposure for everyone in the operating room. In-patient navigation remains a challenging task, especially combined with complex instrumentation dexterity. Ultimately this simply affects the patient outcome as mistakes are made.”

Guaranteeing high accuracy and safety

The innovative robotic technology platform taps into the power of multi-modal sensing and digital twin technology, combining pre-operative MRI or CT scans, intraoperative 3D ultrasound, optical and magnetic tracking. With REISS, pre-operative planning and effective execution become a reality. An optimal approach toward the herniated disk is determined by the surgeon which is then supported or facilitated by the REISS robot during surgery, eliminating trial-and-error navigation. This is done by compensating for vertebrae disc movement, a re-positioned patient and patient breathing. It contributes to patient safety, outcome and reduced X-ray exposure, as well as reducing effective operation time and allowing the surgeon to treat more patients on a daily basis.

Revolutionizing patient care worldwide

By enabling new surgeries and expanding the range of applications, these advancements are revolutionizing patient care, increasing surgical precision and reducing potential risks. “We’re committed to disrupting the status quo in healthcare and engineering breakthrough technologies to improve people’s lives”, continues Kaat Dhondt, Manager Innovation Acceleration Life Sciences at Verhaert. “The rapid evolution of technology opens up many opportunities for healthcare companies to reduce procedural time, shorten hospital stays, and minimize infection risks, ultimately benefiting countless patients worldwide.”

Want to know more about how this project is pushing the boundaries of medical innovation? Check out our perspective!

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Room for improvement

Although minimally-invasive surgery offers many benefits, there’s still quite some room for improvement. To start, the current procedure requires X-ray imaging for localization inside the patient. This not only leads to frequent radiation exposure for everyone in the operating room, it also provides limited 2D visibility. On top of that, it also means the slightest movement, whether through patient breathing, body movement or repositioning by the surgeon, makes the images outdated. In short, radiation exposure, precision, visibility and real-time validity could all be improved.

Or check out the key features in the video below!

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