Although Artificial intelligence is set to transform the enterprise landscape in the coming decades, many organizations still struggle to get their AI initiatives past the early stages.
As per many industry experts, artificial intelligence (AI) is a must-have technology that can empower organizations to become agile and innovate at a rapid scale. In the past few years, many enterprises increased their AI budget, and others are increasingly jumping on the bandwagon, so as to not get left behind in the AI race. As per IDC’s World Artificial Intelligence Spending Guide, the global spending on AI projects will increase from US $50 billion in 2020 to US $110 billion in 2024. However, many enterprises are still struggling to get their AI projects up and running.
According to Gartner’s 2020 “How to Staff Your AI Team” research report, 50% of AI initiatives struggle to get past the proof-of-concept stage and be implemented at scale. Even though there are many factors in play, such as overhyped expectations, lack of vision and inadequate data infrastructure in place, not having skilled professionals top the list. While AI teams may have the necessary tools and technologies in place, many of them lack key capabilities like mining for the correct use cases and optimizing decision-making that successfully drives the success of projects. Thus, CIOs should reassess the capabilities of their AI staff and take steps that can help them drive the success of their AI projects.
Here are Three traits of successful AI teams working at enterprise scale:
Understanding the issue
To identify and frame the problem of an AI project accurately, successful AI teams need to understand how to sift through the complexities of the situation. Meaning, they should interpret and bridge the gap between technology and the business case. Along with the in-depth knowledge of understanding data and algorithms, successful AI teams showcase empathy for customers and users, enabling them to solve problems holistically. Looking at the world from an exploratory perspective, successful AI teams are unafraid to challenge the status quo. Such traits allow them to continuously assess how their work impacts the business they are trying to innovate for.
Thinking enterprise-scale from the beginning
In most cases, AI pilot programs show promising results but then fail to scale. Even though many C-suite executives admit that scaling AI is important to drive growth, many fail to do so. This is the result of AI teams only thinking about executing a workable prototype to establish proof-of-concept or, at best, to transform a particular department or function. But, successful AI teams think enterprise-scale at the design stage itself and can effortlessly go from pilot to enterprise-scale production. They can build and work on ML-Ops platforms to standardize the ML lifecycle and build a factory line for data preparation, model management, AI assurance and more.
Appreciating the ethics of AI
Finding the right use cases and building AI systems at enterprise scale is just half the battle. Managing the ethical side of AI implementations is just as important and it demands CIOs take input from regulators and policymakers. Successful AI teams know this well and take steps to work within the framework of regulatory compliance. They strive to implement strong and auditable risk management practices throughout AI development, validation and monitoring. This enables them to build interpretable, unbiased, replicable, and accountable AI that deliver business outcomes that are fair and transparent.