Is Artificial Intelligence finally making mass customization possible?
Based on how important decision-makers think digitally enabled product and service development is, as well as how they expect AI, advanced data analytics and digital twins to evolve, it is possible to outline the future trajectory for how these enterprises can enter the era of mass customization.
As mentioned in the second report of this study, Digitally enabled product and service development is both the highest-ranked use-case and the one with the highest average current investment (expressed as share of IS/IT budget). This indicates the importance of new ways of innovating and developing products.
In fact, 100 years after Henry Ford changed the manufacturing story, we are finally on the verge of another shift - the birth of a new era and a new epoch of manufacturing: mass customization. While it has long been discussed, is Artificial Intelligence finally making mass customization possible now?
Many upsides to mass customization
There are many signals that enterprises today are increasingly embracing the mass customization era. Mckinsey research found that companies that excel at customization generate 40 percent more revenue from those activities than average players. Further, their research shows that 71 percent of consumers expect companies to deliver personalized interactions, recognize them as individuals and know their interests. Moreover, Deloitte research shows that consumers are willing to pay more for customized products and that they want to be actively involved in the process. According to Deloitte, 22 percent of consumers said they expect the businesses they buy from to recognize them as individuals and know their interests. These findings show businesses need to add customization to their offerings to not jeopardize revenues and customer loyalty.
Today there are many examples of mass customization, where even major producers like Nike and Adidas offer services such as specialized web portals that allow customers to design sneakers personalized to their own aesthetic and functional needs. For an added premium, customers can even 3-D print a sole custom-suited for their own feet.
When we talk about AI and fashion, it's really about increasing the efficiency of the consumer experience, reducing waste, and creating more sophisticated just-in-time inventory systems that take into account consumers' needs and preferences when they're selecting an item.
Leveraging AI, cloud and cellular technology in product and service development
There may, however, be a potential contrast between providing customizations and delivering high volumes. The move from mass production to mass customization can, if not done correctly, have big cost implications. As a result, the spotlight has shifted towards solutions such as cloud, AI, 3D printing, and cellular technology, which enable organizations to adapt to the demands of this new era in a cost-efficient way. The ability to leverage AI technologies in digital product and service development is already a reality with many companies creating tailored offerings that meet the unique needs of individual customers.
Digital service development meaning the use of tools such as apps for collaborative innovation, Generative AI and digital twins is undeniably on the rise among enterprises. The proportion of product and service development done digitally is expected to rise from 74% today to a stunning 82% in a 7-10 years' timeframe. Especially the use of AI is at the center of interest and today, 53% of decision-makers in our study state that AI technologies are being extensively or fully used at their companies. Further, the usage of Generative AI applications for text, image, and video creation and processing is expected to rise from 47% to 78% in a 7-10 years’ timeframe, indicating a significant shift towards AI-driven content creation and digital product development.
Figure 5: Percent of decision-makers saying they are enhancing digital product development with each tool today, or expect to do so in the next 7-10 years
This indicates a significant reliance on AI to not only drive digital transformation and innovation, but also to become the foundation for mass customization.
As can be seen in figure 6, in the coming ten years, 78% of decision-makers expect to have implemented data-driven customized offerings based on machine learning of customer behavior data. When we look specifically at the manufacturing industry 57% of decision-makers think designs, volumes and supply are adjusted in real time based on predicted buying patterns by the year 2030. Additionally, 55% of the same decision-makers think the battleground for successful production/manufacturing is about having the best algorithms for deep-learning and decision-making in autonomous operations by the year 2030.
In summary this suggests a growing reliance on AI and data analytics to drive personalized offerings and cater to individual customer needs. However, when we look at the employees, we can see slightly lower numbers, indicating that not all employees still have recognized the benefits in using these technologies.
Figure 6: Percent of decision-makers and employees that say they are already selling/using data-driven customized offerings based on machine learning of customer behavioural data or plan to in the future
Additionally, there are other emerging technologies that contribute to the ability to mass customize products and services. For example, 51% of decision-makers state that Digital Twin solutions are extensively or fully being used at their companies today. The usage of Digital Twin tools, where new products can be designed and evaluated, is expected to increase from 44% today to 77% in a 7-10 years timeframe, highlighting the growing importance of digital simulation and prototyping tools in customized product development. The introduction of digital twins on a broad scale will take mass customization to a new level. With virtual twins of ourselves, we will see applications such as virtual fitting experiences, personalized medications, and food suggestions. As an example, a digital twin of ourselves gives the possibility to test medical interventions and medicine at a minimal risk but with greater benefit. Consumers will no longer need to try and fail when testing new products, which in turn, can reduce waste and extensive transport, and lead to more precision in delivery.
Reshaping Industries and Consumer Experiences
While the benefits, as outlined earlier are significant, there are also aspects that needs to be considered to ensure a successful continued evolution towards mass-customization. Security is such an aspect, with 63% of decision-makers expressing the belief that digital product and service development solutions are not yet secure enough.
Another such aspect, also closely linked to security, is the handling of personal data. In the era of mass-customization, brands and the AI models will need much more data: precise traceability details, specific human body measurements, color analysis, shape analysis, style analysis, and of course, visual analysis and potential forecasts. It therefore becomes tremendously important to handle this personal data in the most secure and trustworthy fashion.
Offering mass customization requires a reevaluation of business operations including manufacturing, distribution, marketing, and customer service. This will also require a data governance model that gives consumers control over how their data is used in this process. As the usage of AI and data analytics continues to expand, the need on organizations to address these concerns to ensure the responsible use of customer data in order to maintain consumer trust will increase.
Future Implications of Mass Customization
In conclusion, the mass customization era, powered by data analytics and AI, is driving significant changes in industries. The extensive use of AI, digital twins and data analytics, coupled with innovative simulation and testing tools, is reshaping consumer experiences and presenting new challenges and opportunities for organizations and society. Mass customization in fashion, retail, healthcare, and education may provide means for reducing natural resources waste, while enhancing delivery precision, leading to lower costs and increased customer satisfaction. This can in turn reduces the need for storage and fulfillment centers, a leaner product development cycle and less transport in society. There are, of course, also risks where an increased reliance on personalized AI-driven recommendations may lead to a reduction in serendipitous discovery and limit individuals' exposure to diverse perspectives and ideas. This can, in turn, reduce the need for storage and fulfillment centers, streamline the product development cycle, and decrease transportation needs in society.
