News & Analysis

Team Diversity Often Defines Success on AI-based Projects

With increasing instances of enterprises adopting artificial intelligence to transform businesses, having the right team in place is becoming more critical

As more and more enterprises across industry verticals begin using artificial intelligence (AI) to transform business processes, a crucial aspect of success is coming down to getting the right set of resources to handle big data and the technology around it. 

If there is one crucial aspect that goes a long way towards creating a crack AI team, it would be diversity of skills and expertise. Besides the regular data scientists and programmers, the team requires to rely extensively on business process experts and overall operations. What this means is that such teams need to have a dotted line to someone on the C-suite. 

Why so? Because, the executive leadership not only needs to have a buy-in before the project gets off the ground, the person also needs to perform the task of a project sponsor. Which means, they are responsible for ensuring that the project actually comes to fruition, both by way of making funds available and ensuring an enterprise-level buy-in. 

Of course, there are other roles that form the core of the project team that does the actual grunt work, but maintaining a constant connection between the process owners and this project team is what makes the sponsor’s role even more crucial. For example, if an AI team has to liaise with a sales team, a marketing team and the supply chain in a retail business, the sponsor would have their task cut out in managing knowledge gaps amongst the division heads. 

Let’s take a brief look at the sort of experts that one would want on a team working on AI-backed solutions in an enterprise…

  • The AI Designer is the architect of the project who communicates the real use cases to the developers and is critical in developing prototypes based on the intended user experience. They also have a trust-building role between the users and the AI system over how the module can continuously improve based on user feedback. 
  • Domain Experts use their exposure to a particular industry vertical to sift through available data and communicate the value of the AI project in real business cases. Their contributions often form the deciding factor between success and failure as the rest of the team seldom possesses the actual domain knowledge to tie the solutions to the problems. 
  • Data Stewards & Data Engineers work closely with each other, with one overseeing the maintenance of quality of data and its continuous flow while the other builds and maintains the systems around which the company’s data infrastructure is constructed. The data engineers build the pipelines to collect and organize data, while the stewards ensure that this data is used consistently across the enterprise in the wake of changing data rules. 
  • Machine Learning Engineers may not build the ML models, but they’re the ones that implement them. Which means they require considerable server configuration skills and an understanding of back-end programming. Expertise in understanding modules, their continuous integration and delivery deployment becomes crucial to the entire exercise. 
  • Data Scientists form the core of any AI team as they’re the ones that process and analyze data before creating machine learning (ML) models and constantly enhancing them. The typical expert would be one who adds product and business analytics to her skillset, in addition to some basic understanding of machine learning. 

In addition to these roles, companies also hire product managers and an AI strategist to give it a semblance of a hierarchy in this largely round structure. While the product manager collates customer needs and leads the development and marketing efforts, the AI strategists bring in their operational expertise at the corporate level to coordinate with the entire team.

In some cases, if the Project Sponsor is too busy with her C-suite functions, a Chief AI Officer takes over the responsibility of managing the process flow within this team. It becomes their role to manage both the human and technological complexities of the project and updating the leadership of possible issues, challenges and solutions. 

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