Artificial Intelligence (AI): 4 novel ways to build talent in-house

IT and analytics leaders share four not-so-common ways to build artificial intelligence skills in-house when external AI talent is scarce or expensive
245 readers like this.
digital transformation customer focus

The analytics leader of a US-based Fortune 200 company was under severe pressure. Her team supported 45,000 employees of the global energy company, and the business users weren’t happy. The analytics deliverables were often late and suffered from poor quality.

The analytics team was a part of the IT organization and was struggling to fill their open positions. The skills needed couldn’t be found within the IT team. Their office was a 60-mile drive up north from a large metropolitan area in the US, and it wasn’t easy to attract talent.

Training the few people they managed to hire wasn’t easy, and they often fell short in their business understanding. As a result, the analytics team was notorious for being understaffed, overworked, and facing the wrath of business users.

Does this scenario sound familiar?

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

Recruiting data science talent is one of the biggest challenges facing companies today. O’Reilly’s 2021 survey on Artificial Intelligence (AI) Adoption in the Enterprise found that the “lack of skilled people or difficulty hiring required roles” was the topmost challenge reported. With increasing investments in AI across organizations, the war for AI talent has heated up.

5 skills you need to make AI work

It’s a misconception that building AI solutions call for just data scientists. Your AI is as good as the data you have. You need to collect, curate, and store good-quality data. Once you have the data in place, you need these 5 data science skills to design, build, and adopt AI successfully:

  • Domain expertise: To pick the right business problems and frame a sound approach
  • Machine learning (ML): For identifying data insights and building the AI models
  • Software engineering: To package the models into a software application
  • Information design: For designing the workflow and help users consume model insights
  • Managerial expertise: Manage uncertainties in data projects and ensure user adoption

Here are four not-so-common ways to build these multi-functional skills in-house when external talent is scarce or comes at a heavy premium.

1. Look for talent beyond your IT team

“Every organization is underutilizing their current staff due to a lack of awareness,” says Lisa Palmer, chief technical advisor at Splunk. Teams often restrict their internal search to technology teams. “You’d be surprised by the versatility and depth of talent available outside IT, in your lines of business,” she adds.

To discover the gems hidden across your organization, you must start maintaining a self-identified list of skills for every employee. The list must be updated every six months and be openly searchable by associates to make it useful and usable. Palmer recommends self-classifying each individual’s skills into four categories: expert, functioning, novice, and desired stretch assignment. This allows teams with hiring needs to scout for individuals with ready skills and those with growth aspirations in the five competencies needed for AI.

2. Tailor your data science curriculum using public content

Finding the right content to upskill your in-house teams is a challenge. Despite the rapid mushrooming of training portals and MOOCs (massive open online courses), the curriculums may not meet your organization’s specific needs. However, with access to such great content online, often for free, it may not make sense to recreate your content.

“You must design your own curriculum by curating content from multiple online sources,” says Wendy Zhang, director of data governance and data strategy at Sallie Mae. Base the training plan on your team’s background, roles, and what they need to succeed. This can help you get the best of both worlds – reusing valuable online content while avoiding the limitations of a cookie-cutter approach.

[ Struggling with how to start your AI strategy? Read Artificial Intelligence (AI): How to plan a pilot project. ]

To motivate teams to upskill, you can gamify the experience. Zhang ran a fun contest to help her teams acquire new skills during her stint at a US financial services major. The simple reward of lunch with an executive led to fast-paced learning while creating healthy competition among team members.

3. Bridge your team's technical skills with domain expertise

Good AI solutions need the right combination of domain and technical expertise. People who go through the upskilling are often siloed in their perspectives. Technical training often fails to provide exposure to business applications, while business orientations aren’t grounded in technology.

The online Analytics Academy at Fidelity Investments helps associates from business and technical backgrounds develop their skills in artificial intelligence, big data, and analytics. “When we started our AI journey, it became clear that we needed to close the AI awareness gap between our data science and business teams,” says Todd James, SVP of Intelligent Automation at Fidelity Investments.

“To address the challenge, we created an Agile routine called Learning Days. This routine provided a platform for the data scientists to educate our business teams on AI use-case identification using practical examples and share how best to work with data science teams. The data science teams, in turn, received similar briefs from business partners on strategy, products, and business processes,” he adds. Learning Days helped bridge the AI awareness gap and led to higher quality ideas and better implementation of projects.

4. Enable experimentation and learning on the job

To paraphrase Julius Caesar, experience is the best teacher. You internalize any new skill only when you apply it in practice. The best courses and training methodologies will amount to nothing if you don’t let your teams experiment, make mistakes, and learn on the job. 

The best courses and training methodologies will amount to nothing if you don’t let your teams experiment, make mistakes, and learn on the job.

“We’re big believers in on-the-job training,” says Michael Cavaretta, senior manager of manufacturing analytics at Ford Motor Company. “Our team has a mix of backgrounds from Industrial Engineering to Computer Science. So, it’s rare for someone to come onto our team with the right combination of technical and domain skills,” he adds.

When internal candidates have a growth mindset and an aptitude for learning, you can design on-the-job training. You must pair up novices with more experienced employees and set clear expectations for the shadowing period. “Define beginner tasks that the shadow employee can take on immediately to help them apply their learning. To provide clarity, create laddered tasks for the novice to perform as they gain proficiency,” adds Palmer.

Balance your team's skills

Methodical training and application can help your teams upskill and hone their competencies through these four approaches. You must balance these competencies with soft skills such as curiosity, creativity, and communication by nurturing a conducive environment. That’s when your team will be ready to build data science solutions that are not just interesting but impactful for your business.

[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]

Ganes Kesari is an entrepreneur, AI thought leader, author, and TEDx speaker. He co-founded Gramener, where he heads Data Science Advisory and Innovation. He advises executives of large organizations on data-driven decision making.