How to Succeed in the Field of Artificial Intelligence

By: Blake Coleman, FGP Technology
Artificial Intelligence Defined

It’s no secret that data science jobs are hot. In fact, the Bureau of Labor Statistics reports that computer and IT occupations are expected to grow 12 percent from 2018 to 2028, which will translate to approximately 546,200 new jobs. Specifically in the area of Artificial Intelligence (A.I.), Indeed’s 2018 study finds that employer demand for A.I.-related jobs has more than doubled in the last three years, even as the number of candidates has plateaued or dropped.

When 600 executives were surveyed by IFS, a global firm specializing in ERP/EAM enterprise software solutions, 90-percent of them voiced plans to implement Artificial Intelligence into their operations. Before delving further, it’s worth defining what, exactly, we mean by A.I. While the term is often used incorrectly to simply mean “automation,” true — or core — A.I. centers on machine learning, the ability for a machine, a “program,” to independently perform data-driven analysis and the related corresponding actions that a human’s logic, or “intelligence,” is typically relied upon to do.

A.I. Career Opportunities

There is not an industry that will not be impacted by A.I. and we are already seeing very real A.I. solutions in industries that are driven by, and have access to, large amounts of data. For example, automobile manufacturers are examining how Artificial Intelligence can improve the quality control process, healthcare providers are looking into what role A.I.-enabled wearables could play in predictive medicine, and banks are investigating the uses of A.I. to enhance fraud prevention and protection.

The broad range of industries that stand to benefit from A.I. equates to a broad range of opportunities for candidates seeking to enter this field, we recommend a focus on analytics roles in the following areas, and to focus on positions that emphasize a foundation within relational database skills combined with coding skills:

• Descriptive data analytics. While this is not A.I., this is where most companies currently are, at a BI and data visualization layer. Companies rely on this to tell them the story of where they are at today so that they can interpret what actions might need to be taken. It is important as it related to A.I. because it is a building block in how companies use data, it is very relevant with regards to technology stack, and it should be remembered that this is where the general business understanding of data typically is.
• Predictive analytics. Building upon descriptive data analytics, predictive analytics uses complex algorithms to predict future outcomes based off historical data. It’s a step further than interpretation. The automation of these logic algorithms combined with a software’s ability to use natural language processing is true A.I. This is what many companies currently have very real initiatives behind. The well-known “tech giants” are making A.I more accessible than ever with Microsoft’s Azure Machine Learning, Google’s Cloud Prediction API, IBM’s Watson, and Amazon’s AWS Machine Learning all offering enterprise solutions.
• Prescriptive analytics. This is the “brass ring” that companies aspire to. This is where a company can define an end goal and A.I. can be used to determine the actions needed to make that goal happen. It will look both at historic and real-time info to continuously evaluate if the predicted outcome will be the previously defined goal, advise on the needed adjustments to meet the original goal, but now can also predict new potential outcomes. It could help a company identify new potential revenue streams, help a Physician to look at how different treatments could, in conjunction with one another, impact patient care, etc.

Who will be in Demand?

We always say everyone can have a career in the data world, but companies are currently spending a lot of time looking at people in roles where IT is creating solutions centered around their business processes, a focus on automation, and making data both accessible, but also highly interactive. Not every company will have a role that screams A.I., but there are some roles tied to automation and data analytics that will be relied on heavily to lead the charge on that front. Some of the roles that companies are hiring both now and in the future are:

• Business Process Manager / Business Solution Architect: Rather than re-engineering the process or workflow of the business this focuses instead on IT working with the business to build solutions that fit within their processes, where automation can be used, etc. More and more we are seeing an ask for robotics process automation (RPA), and while RPA is not A.I., when you take an RPA solution and add machine learning to the mix you are now in the A.I. wheelhouse. BPM/BSA roles will be champions for both the tech team, but also the business. An example of this is how companies are looking at how to leverage Microsoft’s 365 and Power Platform offerings.
• Data Analyst: The role of a Data Analyst is morphing and has been for quite some time. More and more the ask has been for Data Analysts to have strong database skills (ex. T-SQL or PL/SQL) rather than simply advanced Excel and raw SQL skills, but the drive for companies to have real-time analytics in the palm of their hand has meant that companies also want candidates who have strong data visualization (ex. Power BI or Tableau) skills.
• BI Developer / Database Developer: BI and Database Developers who have backgrounds specifically tied to cloud environments (namely Azure and AWS), strong reporting/data visualization skills, and are adept at ETL via the use of APIs will be leading the way. Couple that with any Python and/or R skills and watch out!
• Cloud Architect / DevOps: Yes, we know their different, but we’ll take more the approach of looking at these roles from an infrastructure classification. Architects and DevOps specifically tied to cloud environments are and will continue to be in great demand. The stronger their integration and scripting skills, the more their background highlights the design and administration of a highly fluid and scalable environment, the greater the demand.
• Software Engineer: It’s a great time to be a Developer and there is no end in sight. Like their database brethren, full stack Software Engineers who have focused on integration and consumption of APIs will be in the greatest demand.
• Data Scientist: Data Scientists will also continue to be in great demand, especially those who truly understand information architecture and are able to think both practically and holistically. It’s one thing to devise a system that operates in a perfect environment; it’s an entirely different matter to develop solutions that work in the real world.

The list of potential roles could go on and on (ex. Application Support Specialist, Systems Engineer, Business Analyst, Project Manager, etc.), but if you’re looking for a trend, it is this: holistic, collaborative, solutions-oriented, data-driven professionals will do well. Whether you are a database or analytics expert, in an infrastructure role or work on the business side, your ability to understand goals, be part of a team, gather requirements, and understand individual functions as well as the entire process will be key.

The Role of Continuous Learning

I should add that one of the most important contributors to success for the new A.I. workforce will be a near fanatical obsession with continuous learning. The field is changing rapidly and shows no signs of slowing down. As a result, tech professionals will need to take control of their own learning — not only to remain professionally “viable,” but also to create and define their career paths. As the industry continues to become more specialized, key stakeholders may not have a full idea of what can be done with Artificial Intelligence. As a result, it will fall to the tech industry to outline the possibilities and what it will take to get there.

Blake Coleman has nearly 15 years of technical recruiting experience and has partnered with some of the most recognized global organizations, as well as high-tech startups in the building out of data science and A.I teams from leadership levels down. To learn more about careers in technology, visit http://www.fgp.com/practice-ar... or

Originally published at http://www.fgp.com.