How to Create a Strong AI Talent Development Strategy

Check out all the Smart Security Summit on-demand sessions here.

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

However, there are growing concerns that progress in AI is being slowed by a shortage of skilled talent needed to scale AI solutions across organizations. This talent shortage is expected to lead to a massive imbalance in AI adoption and scalability across the enterprise.

But what is causing this talent shortage? Is there really a shortage or is the problem our inability to use talent effectively?

There is a lot of discussion on the forums regarding the right talent activation and management strategy for AI. But the underlying problem is not a lack of skills, but a lack of the right people connecting to the right opportunities. There are many amazing people out there who would be a great fit for a career in AI, but the industry just isn't doing enough to provide the right platform to launch their careers.

Event

On-Demand Smart Security Summit

Learn about the essential role of AI and ML in cybersecurity and industry-specific case studies. Watch the on-demand sessions today.

look here

This is because there are no best practices or developed standards for the next generation of deep learning and AI skills, and adoption in most organizations does not exist. is only in its infancy. Even several well-established players do not have a solid talent development strategy in place to grow their existing AI/ML talent.

An AI talent development strategy

The solution lies in creating a strong talent development strategy and the right platforms and frameworks for talent to be nurtured, by:

Identify those best suited for enabling programs: From backgrounds such as math, statistics, computer science, and economics, we can get a pool of talent already acclimated to solving structural problems. Similarly, there are people with experience as data engineers, data scientists, and machine learning (ML) experts who can be coached and mentored into AI roles with very little transition time. . A proper screening mechanism that selects candidates with the right skills and learning potential is key to solving the skills gap problem. Enable career transitions: In addition to identifying the most suitable talent, there must be well-designed enablement programs to equip talent with the right skills. These empowerment programs can take the form of short bridging programs or a comprehensive six to eight month training. Other than that, creating personalized growth plans that step the aspirants closer to the desired career profile will be another vital ingredient in the transition process. Build robust, best-in-class internal learning platforms: Building learning platforms is essential for upskilling and retraining in niche areas. 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. Personalized guidance can be offered to users for better engagement and better learning outcomes. Nurture partnerships with startups, MOOC platforms: Companies must invest in partnerships and employee training with