How to create a strong AI talent development strategy

Artificial intelligence (AI) is revolutionizing the way we live by automating decisions, predicting outcomes and optimizing processes. From our phones to shopping, medication, banking and manufacturing, AI is everywhere.

However, there is growing concern that AI progress is being held back by a shortage of trained talent needed to scale AI solutions across organizations. This talent shortage is expected to create a huge imbalance in AI adoption and its scalability across the enterprise.

But what causes this shortage of talent? Is there really a shortage, or is the problem our inability to leverage talent effectively?

There is a lot of discussion on forums about the right activation and talent strategy for AI. But the underlying problem is not the lack of skills, but the lack of the right individuals connecting with the right opportunities. There are many extraordinary people in the market who would be perfectly suited for a career in AI, but the industry just isn’t doing enough to provide the right platform to launch their careers.

That’s because best practices and standards have not been developed for the next generation of deep learning and AI skills, and adoption in most organizations is still in its infancy. Even several entrenched players don’t have a strong talent development strategy to nurture their existing AI/ML talent.

An AI strategy for talent development

The solution lies in creating a strong talent development strategy, along with the right platforms and frameworks for nurturing talent, by:

1. Identification of the programs best suited for activation programs: With backgrounds like mathematics, statistics, computer science and economics, we can get a talent pool that is already used to structural problem solving. Similarly, there are those with experience as data engineers, data scientists, and machine learning (ML) experts who can be coached and guided into AI roles with very little transition time. A good filtering mechanism that selects candidates with the right aptitude and learning potential is key to solving the skill gap problem.
2. Enabling career transitions: In addition to identifying the most suitable talent, there must be well-designed activation programs to equip talent with the right skills. These activation programs can take the form of short-term bridging programs or full six- to eight-month training. In addition, creating tailor-made growth plans that step by step bring aspirants closer to their desired career profile is another essential ingredient for the transition process.
3. Building robust best-in-class internal learning platforms: Developing learning platforms for upskilling and reskilling in niche areas is vital. These should be learner-friendly and provide engaging content and a wide variety of resources and content to enrich the talent pool. These portals can be monitored through analytics. Users can get personalized guidance for more engagement and better learning outcomes.
4. Nurturing partnerships with startups, MOOC platforms: Companies should invest in partnerships and training for employees with open-source experience and startups specializing in different AI domains. By means of partnerships, knowledge transfer is initiated in two directions, with mutual enrichment of talent being a natural consequence.
5. Maintain partnerships with universities and think tanks: Collaboration with academia, universities and research organizations, AI consortia and think tanks provides access to state-of-the-art training materials and research. Academia can also leverage industry feedback to tailor their courses to specific business needs.
6. Initiating mentoring programs from experienced AI professionals: Engaging experienced professionals who can provide much-needed support and knowledge to train the rest of the team is essential to disseminating much-needed added skills and technical know-how. Equipping and assigning trainers from the team ensures faster learning and promotes a learning culture within the team.
7. Create incentives: Focus on creating a good incentive structure to encourage employees to continue training.
8. Sponsorship of Temporary Gig Projects and Job Rotation: Creating a support system for employees to work on side projects and hobby projects within their organization’s framework, as well as rotating roles on a regular basis, is another strategy that can help strengthen skills and provide a better platform for talent development.
9. To set up Hackathons and Ideathons: Hackathons are one of the best ways to get the talent pool hooked on advanced technologies and teach them valuable knowledge. Employees participating in AI knowledge-building hackathons may see what AI is all about and may become intrigued and want to get more involved.
10. Creating a steady pipeline of emerging talent: Very few entry-level positions are available in AI, making it difficult to develop new talent. Often the hiring process is not adapted to identify potential candidates who are easily trained because hiring managers are not experienced in finding these easily trained candidates. This creates shortcomings in building a steady talent pipeline.
11. Create learning opportunities: Encouraging employees to contribute to technical white papers on AI topics, participate in knowledge sharing in various AI magazines, participate in round tables and collaborate with industry analysts are some of the other ways to create learning opportunities.

Top skills best suited for transitioning into AI roles

Reskilling/upskilling will ensure adequate scaling of business AI and leveraging of AI-related transferable skills. Today the top transferable skills for an AI career include linear algebra, probability, statistics, ML algorithms, data science, programming, AIOps, text analysis, image analysis, and data mining.

In general, math plays an important role in AI, and specifically in ML. Applied mathematics skills in linear algebra, probability and statistics, multivariate calculus, algorithms and optimization are particularly relevant. Since ML works with massive amounts of data, data science competencies help with predictive analytics, data modeling, analytics, and other aspects of AI. There are also multiple programming languages ​​to cater to the algorithms, libraries, and frameworks in AI related to text analysis, image analysis, compute deep learning, and neural networks.

Solving the skills gap by focusing within the organization and driving internal transformation will take some patience and conscious effort. But this is a worthwhile investment as creating a robust talent pool and pipeline will be one of the key requirements to seize the opportunities the next generation of the AI ​​revolution will bring.

Balakrishna DR, popularly known as Bali, is the executive vice president and head of the AI ​​and automation unit at Infosys.