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Creating A Productive AI Workforce

2018-12-11T17:13:32+00:00October 4th, 2018|Artificial Intelligence, Trill Takes|

Google, Facebook and Amazon have invested billions of dollars into artificial intelligence research and development. But they aren’t the only companies looking to integrate AI. Companies across nearly every industry are evaluating AI, but most lack access to Amazon-sized investment dollars.

The primary objective for most companies is to make small, incremental AI investments that produce high-impact results. It all comes down to return on investment; steadily demonstrating the value of AI via proofs of concept that could save or make the company money. With steady progress on proving the ROI of AI, teams can have the support needed to make the case for additional AI investments.

This strategy that hinges on small and steady wins sounds simple. Unfortunately, it’s more complex than most realize.  

Companies who see AI as a tactical initiative, as opposed to a strategic one, end up under prioritizing it and yielding low returns on investment. Often, significant losses in ROI come from a lack of productivity from the AI team. But the key to driving productivity in AI is to have a balanced workforce in place. Without a balanced workforce, companies will always struggle with effective AI integration and proofs of concept.

Overcoming the AI workforce imbalance

When looking to build out AI applications, most companies race to hire data scientists and machine learning engineers. These hires are coveted for their ability to implement machine learning techniques and solve necessary problems. But they are also often the only new hires for an AI initiative.

While machine learning engineers and data scientists excel at algorithmic experimentation tasks, they may only have tangential skills in data cleaning and processing, and survival skills at best in cloud operations, backend API development, and frontend API visualization. In order to optimize an AI initiative, it is therefore beneficial to have dedicated team members for each of these roles so that every member can stay focused on specific types of tasks.

A successful AI initiative will require multifarious tasks from varying disciplines in order to:

  • Clean, process, and deliver data to support a live application.
  • Identify and develop algorithms to utilize the data.
  • Plan and develop cloud operations to host and access the data and its output.
  • Develop APIs and user interface visualizations to connect users with the solution.

How to make the workforce shift

Hiring strictly machine learning engineers and data scientists to solve problems along the length of an AI initiative, from planning to implementation, is likely to result in low productivity and ROI. Instead of forcing these expensive hires to perform outside their scope of work or rely on other departments that have competing priorities, consider a different approach. Create a dedicated team of varying disciplines and skill sets to effectively support your machine learning engineers and data scientists to deliver better AI applications and a higher ROI.

By adjusting how your workforce is structured, you can build a more productive operation with faster results. Integrate the skill sets of data scientists and machine learning engineers alongside a database engineer, backend developer and data visualization developer.

With a collaborative team structure in place, your hires will begin to form a unit and become more productive as they plow through projects with ease and build real-world applications, which can be tested and deployed, instead of just experimented on.

Detailed skill sets required: Is your team getting it right?              

Your AI team needs dedicated team members with a variety of skill sets and personalities in each of these roles for optimal performance.

  • Methodical, detail-oriented database engineers focused on cleaning, processing and delivering high-quality data in a timely manner to support a live application.
  • Scientifically-minded machine learning engineers and data scientists highly-skilled at working with machine learning algorithms or sets of algorithms, to utilize and produce valuable data output.
  • Responsive backend developers skilled at managing cloud servers at scale, with the ability to build robust APIs to deliver data and outputs.
  • Creative data visualization developers focused on creating applications that leverage API outputs, with expertise in visualizing information and making connections between user and solution.

The best value of AI comes from teams that are structured in the right way.

Let’s get started now so that we can succeed together.  

Image: Unsplash

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