AI though revolutionary in itself is an enabler that needs to be used effectively to achieve business objectives. This requires that the executive leader of an AI team should have a deep understanding of core business, carry significant influence and also have knowledge of the AI domain.
“AI teams need to be smart and require to be on their toes all the time. To meet their business needs a good blend of business skills and result oriented individuals act as a major requirement. CIOs assembling AI teams look out for subject matter experts, software engineering skills, and the ability to translate learning algorithms into actual business value. Along with this, a mindset to accommodate into a centralized team can work cross-functionally with all business leaders to develop specific AI applications completes the structure to call itself a good AI team,” said Kishan Sundar, Senior Vice President, Digital Business Unit, Maveric Systems.
Domain and technical experts
AI projects require two major skill sets, that of a domain expertise and a technical expertise. The technical expert has the required computational and statistical background to approach and solve the problem. The domain expert on the other hand, helps generate relevant hypothesis, interpret and present industry level insights from the data and tell whether the application of AI has helped improve the business outcome.
Additionally, the key skills would include – ability to understand critical business pain points / problems that could potentially be solved by AI, a deep understanding of analytics concepts, ability to work with variety and volume of data and AI architect to integrate AI to current infrastructure & govern the AI solution.
“To successfully operationalize and scale AI initiatives, organizations need to build diverse AI roles and skills. However, access to the right talent is a major issue in this new age of AI. The demand is especially intense for technical skills that don’t widely exist. Lack of AI skills is one of the top-three concerns that early AI adopters have today. The limitations of technical skills may be more prominent as organizations launch more AI solutions, and as those solutions increase in complexity and scale,” said Saurabh Kumar, Partner, Deloitte India.
AI is being used in almost every sector, such as banking, healthcare, logistics, retail, ecommerce etc. AI teams are huge and for every unique aspect of the business it expects a specific skill set. An AI team must have a Domain Expert, Data Engineer, AI Data Analyst, Business Intelligence Developer, Data Scientist, Product Designers, Research Scientist to keep the product going.
Freelancers
As businesses have realized the importance and power of AI in transforming their business, the demand for AI talent has increased exponentially but the growth in availability of AI talent has been much slower as people require time to reskill/upskill in new technology. This demand-supply gap has made hiring a talented AI workforce a very expensive proposition. Only companies with the deepest pockets likely have the resources to keep industry-leading AI talent on the payroll on a permanent basis.
In such a scenario freelancing has emerged as a new resourcing alternative. Freelancers bring specialized expertise needed for a particular project thus allowing better quality work delivered. Additionally, companies save money by employing project specific short-term hires.
“But freelancing also exposes the organizations to additional risks. The organizations need to be prudent and need to mitigate the risks of employing freelancers by ensuring that knowledge is properly documented, internal staff is cross trained and firms’ IP is protected. Freelancers should not be employed for all AI projects; the organization needs to have a robust framework to evaluate the need of employing freelancers so that the exposure is limited and controlled,” Kumar added.
Key skills and roles
The skill set keeps changing from time to time as the AI industry goes through a massive upgradation quite frequently. A few basic skills that the industry has been following in the AI field are Programming languages (Python, R, Java are the most necessary) Linear algebra and statistics, Signal processing techniques, Neural network architectures and SQL.
“In a typical AI COE (center of excellence) we see roles of domain SME, Statisticians/AI researchers (typically Ph.D.), Technical architects in Deep Learning/Machine learning techniques, Data scientists, NLP (natural language programming) experts, data modelers, and scrum/Agile based roles like product manager, scrum master, etc. apart from technical developers. We are seeing interesting roles like CAIO (Chief AI officer), Chief Analytics/Data officers emerge and this shows the kind of importance companies around the world are giving to this AI revolution,” said Ramesh Alluri Reddy, Director- Managed Services & Professional Services, The Adecco Group India.
