LESSONS LEARNT AND BEST PRACTICES FOR THE DESIGN OF AI SERVICES
Written by Alessandro Giulianelli
The world is changing faster than ever before, and in many areas COVID-19 accelerated the rate of this process. To keep up with the fast pace, especially in R&D departments, the challenge is to bring forward innovation and entrepreneurial spirit adopting a lean approach. This means to test one hypothesis as soon as possible and if not confirmed, trash it. Fail fast, fail often. However, when dealing with AI-oriented services, some figures show that there may be the need of a different approach.
AI FOR EVERYONE
According to the Gartner’s Hype Cycle for AI 2020, 47% of artificial intelligence investments were unchanged since the start of the pandemic and 30% of organizations actually plan to increase their investments in artificial intelligence. One out of three CEOs launched AI initiatives in their organizations and they regularly redefine resources, reporting structures and systems to ensure a successful project development. For what concerns the application contexts, AI projects continue to accelerate in healthcare, bioscience, manufacturing, financial services and supply chain sectors despite greater economic & social uncertainty. “AI is starting to deliver on its potential and its benefits for businesses are becoming a reality”, defining one megatrends according which we’re seeing a democratization of Artificial Intelligence, that means that AI is no longer the exclusive subject matter of experts.
This trend is confirmed also by the Futurescape 2021 of IDC that foresees that by 2022, 65% of CIOs will digitally empower and enable front line workers with data, AI, and security to extend their productivity, adaptability, and decision making in the face of rapid changes.
LESSONS LEARNT AND BEST PRACTICES
However, we should not forget another report from Gartner that says only 53% of AI proof of concepts are successfully transitioned to product. Meaning that almost half of AI projects fail, due to the many factors playing a crucial role in this context, as such as the reliability of data, the lack of skills, missing domain understanding and uncertainties in customer relationship.
Based on the experiences developed within the Konica Minolta R&D group, these are some of the most commonly known difficulties that may rise when designing an AI project:
- Business misalignment. The biggest risk with AI-driven technologies appears when we consider them as business steroids, capable of boosting technology to solve any issue. Following the hype around big data, everyone talks about AI, NLP techniques or ML algorithms without understanding the differences and complexities. However, with accurate data-driven insight it is possible to achieve an effective business decision making. To align the expectations of the organisation management with the real potential of AI, we need to define the right assumptions. The developers’ team (the Dev Team) need an exhaustive set of information together with a strong motivation for the action. In few words, we need to focus on the why rather than on the how, and be less task oriented.
- Technology driven approach. When the motivation behind a project is not clarified in the first place, the risk is that the Dev Team is filling the gap with their technological skills, substituting the why with the what. That’s when IT companies create false expectations in teams that commit for delivering a new product or an innovative service starting from the technology. Quite the contrary, clarifying the motivation means focusing on the business value. Following a customer centric approach is at the core of Konica Minolta Global R&D manifesto: with a methodology of Applied R&D, we identify projects that can bring value to a customer rather than developing a project on the basis of its technological content.
- Data strategy. Even though everyone knows data is the oil of the 21st century, many are not aware of the importance of data content, data quality and data governance. The type of data and its quality should be taken into consideration in the very early stage of the project. Then, when dealing with data, it is of utmost importance to avoid silos within the same company: in most cases, siloed departments pose serious obstacles to the understanding of business values and to the modelling the proper domain knowledge. A good coordination of the Dev Team together with business units is essential for the good start of an AI project.
THE ROAD AHEAD
Overall, in the last years, many organisations have learnt a lot about developing projects with artificial intelligence, and nowadays we’re making less mistakes than in the past. Therefore, to keep improving the way we design AI projects and decreasing their failure rate, there is the need to make a switch in the organisational culture engaging the whole company into a real digital transformation. At the basis of this evolution, at Konica Minolta we place the Customer-Centric AI Project Cycle: a process that can be split in five steps to support a robust definition, management and development of a solution based on artificial intelligence.
AI ALONE IS NOT THE ANSWER
Overall, artificial intelligence has a huge potential of impacting our lives in every aspect. However, AI is not the source of innovation as it is only a set of technologies, and we should restrain from thinking about technology as the sole-domain of innovation. We must keep the customer at the core of our approach, resisting the temptation of the powerful lure of AI solutionism. In the end of the day, our customers care for improved processes and better quality of services, and they are rarely interested in the technology that is behind the product that is solving their needs. And it’s within this context that Konica Minolta Global R&D is developing cognitive services that explore the orchestration of AI to effectively manage the growing complexity of every workplace. Get in contact with our researchers to develop together a use case project that focuses on your need.
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