AI Talent in IT: Building Skills for the Future

The IT world is grappling with a significant skills gap when it comes to both traditional and generative AI. A survey conducted earlier this year by Pluralsight of 1200 executives and IT professionals indicated that 81% of IT professionals think that they can use AI, but only 12% actually have the skills to do so. And 70% of workers likely need to upgrade their AI skills. In a December 2023 article Reuters reported there will be a 50% hiring gap for AI-related positions in 2024 and the existing personnel up-skilling demand could approach 70% of workers needing such a skills upgrade. A 2023 survey of nearly 13,000 employees by Boston Consulting Group found that while 86% needed AI training, only 14% were receiving it. Bottom line, we have a large AI IT skills gap that is likely going to get worse as more and more companies deploy AI enabled solutions this year.

Here's a breakdown of the challenges:

Traditional AI:

• Deep Learning and Machine Learning: These are the foundational technologies behind most AI applications. There's a major demand for professionals who can build, train, and maintain these complex models 

• Data Science: Extracting meaningful insights from data is crucial for training effective AI systems. Data science skills like data wrangling, analysis, and visualization are in high demand

• Understanding AI Limitations: Traditional AI often lacks explainability, making it difficult to understand why it makes certain decisions. IT professionals need to be able to identify and manage these limitations.

Generative AI:

• Newer Technology: Generative AI is a rapidly evolving field. Finding professionals with experience in this specific area can be challenging.

• Focus Beyond Coding: While coding remains important, generative AI requires a broader skillset. This includes understanding creative processes, human-computer interaction, and ethical considerations

• Data Bias and Explainability: Generative AI models can inherit biases from the data they're trained on. IT professionals need to be able to identify and mitigate these biases, as well as explain the model's outputs.

Commonalities in the Skills Gap:

• Rapidly Evolving Field: Both traditional and generative AI are constantly changing. IT professionals need to be adaptable and lifelong learners to stay relevant.

• Educational Gap: Traditional educational programs may not be keeping pace with the latest AI advancements. There's a need for more specialized training and certification programs. This extends to software development education, which needs to shift from a development / coding focus to a conceptual modeling focus to keep pace with the evolution of generative AI coding tools. 

Overall, the AI skills gap presents a significant challenge for businesses looking to leverage these technologies. By investing in training and development, as well as fostering a culture of continuous learning, IT organizations can bridge the gap and unlock the full potential of AI.

Closing the Skills Gap

Closing the AI skills gap and building a team with the "right" skills for the future are crucial tasks for Chief Information Officers (CIOs) and IT leaders. Here are some strategies IT organizations can adopt to address these challenges effectively:

• Assess Current Capabilities and Needs:

• Start by conducting a thorough assessment of the current skills available within the organization and identify gaps specifically related to AI technologies and methodologies. This will help in understanding what skills are immediately needed versus those that can be developed over time.

• Create a Strategic Hiring Plan:

• Develop a targeted recruitment plan that focuses on the specific AI skills lacking in the organization. This might include data scientists, AI specialists, machine learning engineers, and experts in AI ethics and governance.

• Invest in Training and Development:

• Offer continuous learning opportunities for existing staff to upskill or reskill in AI-related areas. This can include sponsored courses, certifications, and workshops from reputable institutions.

• Encourage participation in online learning platforms that offer specialized courses in AI and machine learning.

• Leverage Partnerships:

• Partner with universities, research institutions, and other companies to access talent and resources that might not be available in-house. This can include internships, joint research initiatives, and innovation labs.

• Foster a Culture of Learning and Innovation:

• Encourage a company culture that embraces ongoing learning and experimentation. This could involve internal hackathons, project showcases, and regular tech talks by internal or external experts.

• Implement Mentorship and Collaboration Programs:

• Set up mentorship programs where experienced professionals can guide less experienced staff in AI projects. Promote teamwork and cross-disciplinary collaboration to spread AI knowledge across the organization.

• Stay Updated with Industry Trends:

• CIOs should stay informed about the latest developments in AI technologies and industry applications. This helps in adjusting training programs to be forward-looking and aligned with emerging technologies.

• Develop Ethical AI Practices:

• Ensure that the team not only focuses on how AI can be used but also on how it should be used responsibly. Building expertise in AI ethics and compliance is as important as technical skills.

• Evaluate and Adapt:

• Regularly review the effectiveness of the implemented strategies and make necessary adjustments. The field of AI is rapidly evolving, and strategies need to be dynamic to keep pace with technological advancements.

By adopting these strategies, CIOs can effectively close the AI skills gap and ensure their teams are prepared to leverage AI technologies responsibly and effectively.