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Artificial Intelligence (AI) and machine learning jobs: 4 hot skills

Artificial Intelligence (AI) ranks as one of the fastest-growing professions, with practitioners highly sought after in 2021, according to LinkedIn. In addition to having proficiency in C++, Python, or Java and an aptitude for math, the strongest AI/ML professionals and teams are well-rounded in their general business knowledge and ability to communicate.

Organization-wide adoption of AI/ML technologies is the next phase of digital transformation, so a powerful team of programmers, developers, and data scientists is critical to improving AI literacy from the top down. It is important for technology leaders to communicate that AI/ML is meant to enhance the organization’s teams, not replace jobs.

4 non-technical skills AI jobs require

Savvy AI/ML professionals and new hires alike can strengthen these four non-technical skills to drive career success and business growth:

1. Adaptability and continuous learning

One of the strongest non-technical skills your AI/ML teams can employ is something they likely have already: a natural curiosity about the problems at hand and a creative approach to solving them. Together, these skills will be invaluable in leading the adoption of AI/ML technology within your organization.

Beyond being a leader in AI/ML implementation, it is important for your team to have a grasp on the ever-evolving technologies themselves. As you grow your team, look to hire individuals who are quick on their feet and adapt easily to new ideas. The pace of innovation is not slowing in 2021, and it will strengthen your business to grow a team of natural learners.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

Further, we are seeing an increased emphasis on low-code/no-code industry solutions that enable citizen developers to optimize their workflow with the click of a button. Even your most advanced developers could soon be challenged to adapt no-code platforms, so it’s important to build a team that can think on its feet.

2. Ability to communicate data value

Though a solid grasp of the technology is critical to the success of your AI/ML teams, what separates your all-stars from your average players is an ability to communicate the value of your data in a non-technical way.

Are your teams interpreting the data, drawing conclusions, and making valuable recommendations based on their understanding of the technology and of larger business concepts? The best teams can generalize the tech lingo in a way that other, non-data teams understand without losing the integrity of the concepts.

Bonus tip: When vetting new talent, you can gauge their ability to ingest and explain complicated concepts by asking a few thoughtful questions. Invite your candidates to come up with a use case for the technology as it relates to your organization’s industry. Even if the proposed solution isn’t groundbreaking, it gives you insight into the candidate’s line of thought. 

To gain insight into a candidate’s line of thought, invite them to come up with a use case for the technology as it relates to your organization’s industry.

3. Excitement and enthusiasm

Although simple and perhaps obvious, excitement and enthusiasm are often overlooked when talking about highly specific technical roles. However, both are valuable to your organization’s growth.

For example, consider two employees on your AI/ML team: One energizes your team with new ideas and a positive attitude. The other keeps their head down and gets the work done but is relatively uninterested in the day-to-day operations of your company. The former strengthens your teams by uplifting others and reinforces the culture of your organization.

In times of stress and uncertainty, enthusiasm and excitement turn into resiliency and help drive innovation forward. Look to bring these kinds of employees into the fold across all seniority levels.

More on artificial intelligence

4. An understanding of AI’s social implications

It is easy to get lost in the deeply technical language of AI/ML development and implementation. However, a standout data practitioner will look beyond the complex concepts to understand the larger global significance of changing technology.

Amid ethical concerns around deep fakes and bias in AI algorithms, it is important that your teams remain an active part of the conversation. Cultivating business leaders that are ethically minded and strive to see the global impact of your work could save you from bigger problems down the road – and perhaps even set your organization ahead of the competition in the public’s eye.

[ How does AI connect to hybrid cloud strategy? Get the free eBooks, Hybrid Cloud Strategy for Dummies and Multi-Cloud Portability for Dummies. ]

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