Artificial intelligence (AI) is now on a mission to permeate every industry. From e-commerce and healthcare to travel and finance, AI has made its way to just about every type of industry. In fact, the adoption rate of AI has increased by more than 270%, according to Gartner, Inc. (*) Moreover, 37% of all types of businesses are now using AI-driven technologies such as natural language processing, predictive analysis, machine learning and robotic process automation.
Therefore, if you’re still not using AI in your business, then it’s highly likely your competitors are already doing so, and very soon, you’ll be left behind. That’s why we have come up with this article that will allow you to build your own AI team to eliminate your existing bottlenecks and achieve your business goals.
Planning
Building an AI team isn’t about blindly following the trend by jumping on the bandwagon. First, you carefully need to analyze your current standing, allocate cost, determine the most important problems and launch a pilot project. All of these steps require proper planning and evaluation. Your business needs a step-by-step AI implementation plan, from laying solid foundations to crafting a vision for the future. Consider starting small to gain expertise gradually by learning from your mistakes at each step of planning.
Focus On Your Business Values
Your whole AI adoption plan can be doomed and crushed in the bud if you fail to focus on your business values. You don’t want to waste both energy and time to implement your AI projects that don’t deliver any tangible ROI. A better approach is to thoroughly communicate the business values with your employees as well as with clients, which can help you achieve success.
Lack Of Related Talent
Working in a well-developed economy is like playing with a double-edged sword. It undoubtedly allows you to envision the digital transformation’s long-term effects. However, you’ll also need to deal with the talent scarcity challenge. Assembling your own AI team isn’t an easy task. Great AI developers are rare, and you also need to pay them large salaries. At this point, you’ll need to choose from a couple of available options: Building an in-house AI team or Outsourcing an AI team.
Building In-House Vs. Outsourcing
Choosing between these two options is no less than a challenge for many C-level executives. With the increasing security concerns, building an in-house AI team seems better. However, when it comes to salaries and availability, the suited talent often resides overseas. Another option that isn’t yet fully explored is staff augmentation, which is a collaboration model. In this approach, you’ll need to augment your team with experienced and expert AI developers from abroad, but the fact of the matter is that it totally depends upon your unique needs and current situation. If you think building an in-house AI team suits you better, here are the typical roles you’ll need to look for.
AI Team Roles
Bear in mind that a successful AI team will require versatile skill sets and multiple roles. Additionally, your whole AI team will need to work in tight cooperation with your organization’s other departments. Only by doing this will you be able to craft a personalized solution that meets all your business needs. The most critical roles on an AI team are usually related to working with your organization’s data, and you’ll need:
Data modelers, Deep learning specialists, Data engineers, Software engineers, Domain experts, Product designers, AI sociologists and ethicists, Operation professionals, IT leaders, Strategists and executives, Business analysts, Mathematicians or statisticians, Applied machine learning engineers, UX or graphic designer, QA specialists and testers, Human resources leaders, Lawyers…
Python and R are the most commonly used languages when it comes to AI programming. Bear in mind that the roles mentioned above are general, and you’ll need to hire people depending upon your project’s needs.
Infrastructure
Mostly, AI solutions are resource-intensive and data-intensive. Before jumping into the development of any project, you need to make sure that you have a sufficient amount of data available. Moreover, you’ll also need to store it in a database that is easily readable and workable instead of keeping it in multiple disparate databases.
Infrastructure is also very important, which means you’ll need to make sure that your hardware capacity is enough to run the AI project. That’s because AI projects mostly involve a sheer volume of high-velocity and unstructured data that require expensive and considerably powerful infrastructure.
Hiring And Maintaining An AI Team
The hiring process can be very time-consuming, and it becomes a challenging task if your current team doesn’t already possess relevant skills. In such a scenario, you’ll need to train your current employees in data science and machine learning to make sure they hire the right talent. You can also partner up with some educational institutes in your area to get connected with some fresh graduates. Moreover, don’t forget to take part in hackathons, boot camps and conferences to build awareness about your AI project to attract potential candidates.
Verdict
Undoubtedly, AI can solve many problems that your business is facing. However, at the same time, it also creates new roadblocks and challenges as well. We hope that this guide will help you to counteract these challenges and build a great AI team to help your business to achieve new heights.
This article discusses the findings of the Gartner, Inc. 2019 CIO Survey, which shows that the number of enterprises implementing artificial intelligence (AI) has grown 270 percent in the past four years and tripled in the past year. The survey also highlights that organizations across all industries use AI in a variety of applications, but struggle with acute talent shortages. The deployment of AI has tripled in the past year, rising from 25 percent in 2018 to 37 percent in 2019. The article suggests that CIOs need to be creative in order to stay ahead, by investing in training programs for employees with backgrounds in statistics and data management or creating job shares with ecosystem and business partners.
