How To Develop AI Solutions Through Design And Applied Research Methodologies

Design thinking applications linked to the world of AI represent, in the international context, an expanding frontier; the role of research is strategic for experimenting and innovating following an interdisciplinary perspective, which combines technological aspects with creative and managerial ones.

How can design practices improve and accelerate the development of new AI solutions? What advantages and concrete benefits can this methodology bring to creating and applying innovation within companies? To answer these questions, it is essential to understand the increasingly central and strategic role applied research can play in the innovation and digitalization of companies.

In the current production context, where AI services are spreading within every industry and field of application, we can genuinely argue that AI has “the potential to transform industries and societies “. In this scenario, Design takes on crucial importance: an ever-increasing number of companies in the sector are resorting to the discipline of service design to manage the creation of AI solutions. What is it about? Design thinking methodologies allow you to create practical solutions through the direct involvement of end users (customers) and the development team.

In this way, we achieve a result more aligned with expectations, fueling company innovation. At the same time, introducing Design thinking guarantees a faster and more effective project development cycle. Design thinking applications linked to the world of AI represent, in the international context, an expanding frontier; research’s role becomes strategic to experiment and innovate following an interdisciplinary perspective, which combines technological aspects with creative and managerial ones.

We can define service design as “an approach to design that deals with defining how the relationship takes place between a person or a group of people, the users of the service, and an organization, or supplier, that provides the service, with the ultimate goal of generating a quality experience for both parties involved.”

How can this approach contribute to innovation? True innovation comes when a company applies cutting-edge technology to its production context, also through a change in processes. From this perspective, the redefinition of technology-enabled business processes becomes indispensable: it can only occur within a well-structured initial design phase.

The Design, as underlined in the Steve Jobs quote, embodies the very functioning of the solution. The quality of Design, therefore, becomes fundamental within new projects and services to generate real innovation. Among the most widespread disciplines in this field is Design thinking, an operational framework born in the design world that can offer critical new resources for developing artificial intelligence solutions.

Also Read: Artificial Intelligence For Companies: All The Advantages For The Future Of Companies

Improving Development With Design Thinking

The discipline of Design thinking (DT) was born and developed in recent decades as a modus operandi within design agencies across a disparate range of industrial sectors. Over the last decade, more and more companies and start-ups in “software development and digital products” have started to include DT practices within their production processes.

In practice, DT is envisioned as a set of visual frameworks to propose solutions starting from Design. In this way, by placing the user experience (the customer) at the center, a “typical” design paradigm is reversed, which starts from technical development and arrives at formulating UX specifications, adapting them (sometimes with difficulty) to an already implemented technology.

Applying DT in AI means translating artificial intelligence into products and services that customers understand and use. If, on the one hand, the usability of services is an essential component in the value creation process (the customer must be able to interact with AI models, integrating them to support complex and irreplaceable processes), on the other hand, understanding the service is fundamentally offered, which determines a double benefit: it eliminates misunderstandings.

It guides development clearly and quickly right from the start. Therefore, it becomes necessary to know how to translate a still abstract idea relating to the potential of AI ( brief ) into a clear and applicable concept ( concept ), which can be accepted by the customer and can guide the commercial offer, i.e. the development of the solution, in a clear and defined way.

In turn, the DT brings together various practices within it that allow the entire design process to be developed:

Design Sprint

A set of group practices, conceived in 2016 by Jake Knapp, Google Venture designer, and consists of focusing 5 full-time days (the sprint) to build a testable prototype of a product, starting from an idea through 5 phases:

• Map – process mapping
• Sketch – drawing of the proposed solutions
• Decide – choosing a unique solution
• Prototype – construction of a “realistic facade” of the solution
• Test – testing the prototype with customers

Discovery Workshop

A path that leads to the discovery of possible use cases of digital technologies (AI) for one’s work processes is divided into several phases, alternatively, divergent and convergent, which quickly lead to the formulation of multiple research questions, guided formulations, which enhance potential critical issues in the process and orient them towards a possible solution.

Digital Transformation Assessment

A tool that can be activated in parallel or following the Discovery session consists of an initial technical analysis of the potential of the data sources provided by the customer, which allows you to extrapolate the development potential of the projects and prefigure a direction.

MOTS Sprint

A tool designed internally defines the custom elements of semi-standardized solutions (Modified-Off-The-Shelf ). Through a series of interactive sessions, the user experience details, the data necessary for the solution to function and the micro-objectives are defined, directing the development phase.

Advantages Of Applied Design

These design methodologies, particularly Design thinking, are becoming a precious resource for all those development teams that work closely with end users. But not only. DT is a practice that can be applied transversally within companies, bringing benefits beyond improving mere project management.

It is necessary to recognize that one of the most significant strategic advantages of these practices consists in the possibility of bringing customers more quickly closer to the potential of AI projects from all possible points of view (be they communicative, operational or economic), giving priority to the most decisive: the perception of the value of AI. In particular, thanks to methodologies such as the Design sprint and the Discovery workshop, it is possible to:

• Improve dialogue and relationship with the customer
• Recover precious development time and time dedicated to iterations in the work in progress
• Maintain focus on the ultimate objectives and the results to be achieved
• Define the evolution of the project in a structured way
• Increase satisfaction with the Design and usability of the solution.

Applied Research And Businesses: The Energy Of Data

It is the companies that make the research go around. According to the AI ​​Index 2023, created by Stanford University, until 2014, most significant machine learning models were released by academia, but the industry has since taken over. In 2022, there will be 32 machine learning models produced by industry, compared to only 3 produced in academia.

Building cutting-edge AI systems requires increasingly large amounts of data and investments, resources that companies in the sector possess more significantly than non-profit organizations and the academic world. The creation of research centers specialized in experimenting and promoting solutions with a highly innovative impact; therefore, they take on an increasingly strategic role in the growth and development of the territory, starting from the productive fabric.

An example in this sense is the “The Energy of Data” Research Center, designed to promote the integration between artificial intelligence, design methodologies and creative thinking. Born in 2021, at the behest of the Emilia-Romagna Region and the AI ​​company Ammagamma, which is based in Modena, the Center is an open space aimed at individuals, businesses, institutions and other research bodies interested in experimenting with the potential of AI and to generate knowledge through data.

The services provided by the “The Energy of Data” Center consist of actual innovation accompaniment paths, from training programs to the technical prototyping of AI products, and can be consulted on the web page.

Conclusions

We have highlighted how the complexity of the AI ​​design phase requires tools capable of managing it through creative, collaborative and management methods. Design thinking disciplines are increasingly emerging as methodologies capable of addressing the challenges posed therein; the market shows us how the world of sector companies is moving in this direction, also through virtuous interaction with the world of research and experimentation with new methodologies and techniques.

Also Read: Conditions For The Sustainable Use Of AI In Business And Society