Are you struggling to get your AI initiative through the early stages? Consider these lessons from teams that have successfully implemented and scaled AI strategies.
Artificial intelligence (AI) is increasingly seen as an indispensable technology that enables companies to be agile and innovate on a large scale. IDC predicts that global spending on artificial intelligence (AI) systems will increase from $ 50 billion in 2020 to $ 110 billion in 2024. Gartner Research estimates that 50 percent of AI implementations struggle to surpass the proof-of-concept phase and get implemented on a large scale. The reasons range from exaggerated expectations and a lack of vision to inadequate data infrastructure and a lack of qualified resources.
Another important factor is the team that works on the AI programs. While AI teams may have the tools and technology they need, many other key functions are missing, like mining for the right use cases and streamlining decision making, which are essential to success. Successful AI teams who work at the company level have the following characteristics:
1. They frame the problem well
Teams need to be able to analyze the complexity of the situation to pinpoint the crux of the problem before arriving at the right solution. This means playing the role of the translator and bridging the gap between the technology and the business case. Go deep into the data to make unexpected connections and come up with ideas that shed better light on the problem. In addition to understanding data and algorithms, successful teams also show empathy for customers and other users and thus help to solve problems holistically. You are creative and curious; they look at the world from an exploratory perspective and are not afraid to question the status quo. These characteristics allow them to constantly consider how their work is affecting the business for which they are innovating.
2. They think enterprise-scale right from the start
In most cases, AI pilots show promising results, but then they don’t get upscaled. Surveys from Accenture show that 84% of Csuite executives recognize that scaling AI is important for future growth, but a whopping 76% also admit they struggle with it. The only way to realize the full potential of AI is to scale it across the enterprise. Unfortunately, some AI teams only think of running a workable prototype to create a proof of concept or at best to transform a department or function. The design phase can be successfully transferred from pilot production to corporate scale. They often build and work on MLOps platforms to standardize the ML lifecycle and build a factory line for data preparation, cataloging, model management, AI validation, and more.
3. They democratize AI and are diverse
Artificial intelligence technologies require enormous processing and storage capacities, which often only large and highly developed organizations can afford. Because resources are limited, access to artificial intelligence is privileged in most organizations, which affects performance, as fewer minds mean fewer ideas, fewer problems identified, and fewer innovations. The more diverse the team, the better they can identify problems and establish data connections.
At Infosys, we addressed this by using an AI cloud as a strategic platform to scale compute resources and share knowledge to make AI accessible to all. We also added different roles and skills within the AI team, not just technical like data scientists , Data engineers and machine learning experts, but also those with business areas, product management, user interface design and software engineering, to involve more of our workforce in our programs for artificial intelligence and to develop more business-critical applications for artificial intelligence. Ultimately, the democratization of artificial intelligence leads to better project results.
