Businesses across the world are leveraging the power of data to build tech-enabled solutions. But this is not something which can be achieved overnight. To become a company fully capitalize on AI and ML, it must first assemble a strong team \u2014 A-Team. \n\nSo, how to build a team that knows the nuts and bolts of AI and ML; a team that has the capability to solve some of the biggest problems; a team which is the A-Team?\n\nChallenges While Building A Dedicated AI Team\n\nArtificial Intelligence and Machine Learning require tremendous knowledge to become an expert. And when you are on the go to establish a dedicated department for AI and ML, it is imperative to focus on some of the certain roles and skill sets. Therefore it is always considered to be good practice to take a strategic approach and focus on the key roles and areas where you want your AI team to work on.\n\nHowever, there is a challenge \u2014 talent scarcity. People spend many years to master the sorcery of these two techs. So, when it comes to building a team, finding the right talent is something that takes time. \n\n\u201cThe biggest challenge companies face while hiring is finding candidates that is a good fit for their organisation. How does one identify if a prospective candidate is a right fit for an organisation via just a couple of discussions or online tests?,\u201d said Ashish Sam, senior partner, people and operations at TheMathCompany. Some of the measures that companies can take to shortlist the right candidate is to give them situations\/case studies and understand their reactions\/solutions and set up an interaction session with the leadership team.\n\nFurthermore, one cannot just randomly pick a person who has significant experience in the field of AI or ML. Why? Because that might have enough experience but might not be a perfect fit for the job you want to get done. How to overcome this challenge? There is a challenge in that a lot of data science and AI recruits are generalists, meaning that they come with a broad set of skill sets unlike who can work in specific areas such as NLP or image processing. \n\n\u201cExperts have specific skills in one technology or domain and are an excellent fit for solving their domain-specific problems. There are, however, challenges associated with having only experts in a team\/organisation as expertise comes with a limited shelf life. Having a generalist on board helps in the cross-pollination of ideas not just across technologies, but across industries. However, a generalist may not aid in understanding the depth and expanse of the problem one is trying to solve,\u201d said Sam.\n\nPrior to going the right way with the hiring process of AI experts, pen down the business challenges you want to deal with. Also, list the pain-points you want to solve and what expertise you want your candidate to have to solve those pain-points. This approach of knowing the business challenges and pain-points will help you define key components of building an AI team. Meaning, when you know exactly what you are looking for, you reduce the chances of hiring the wrong candidate.\n\nThat is not all, another reason is the cost behind setting up a dedicated AI and ML team. Therefore is always advised to take time and figure out the important pieces of the puzzle.\n\nInvesting In Training & Reskilling \n\nThe job is not done by simply hiring the talent and assigning them work. If you want a team of experts to work on your AI and ML challenges, you have to provide them with an ecosystem that fits their needs. And an ecosystem also includes training and briefing. \n\nPeople might wonder, if we are hiring people who have significant experiences, then why is the need to train them. To answer that, we have to understand the fact just because they have enough experience that doesn\u2019t mean they would be familiar with the business the company has. So, in order to make it easier to understand what are the things that the business is trying to solve and how things work, training is imperative. Also, investing in training is something that delivers value and it is considered to be one of the best practices.\n\nComing to another point about the ecosystem, tools play a major role. When professionals are provided with the right tools to work on, efficiency and productivity increase significantly. For instance, if your AL and ML consist of professionals such as number-crunchers, a modern day term of data analyst, then s\/he should be provided with the top-notch tools that help them carry out analysis.\n\nOutlook\n\nIt is not always necessary to build a dedicated team for a specific vertical, you have to hire new talent; sometimes, the void can be filled by the existing employees. There are many companies out there that follow the \u201cTraining and Upskilling\u201d formula.\n\nSo, if you think your company has employees who are capable of taking on new responsibilities and work on a complete AI and ML field, then definitely educate them and provide them with the necessary training. Many companies also provide online courses and hands-on experience to their employees in order to assign them new responsibilities.