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Building AI Fluency: How to Thrive in the Midst of the Skills Gap

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Orla Daly, Chief Information Officer, Skillsoft

Orla Daly has the proven ability to drive success through effective IT and business partnerships. She has a successful track record of managing multi-facet strategic programs. She is also an experienced facilitator in defining and making operational governance and support models across business and IT functions.

In a conversation with CIO Insider Magazine, Orla Daly, Chief Information Officer at Skillsoft shared her views and thoughts on the key AI-related roles and skills that are currently in high demand but are challenging to find in the job market. Also, she shared about the common barriers that prevent organizations from bridging the AI skills gap effectively.


In your opinion, what strategies are more effective in bridging the AI skills gap within an organization?
It starts with understanding the different types of AI capabilities, assessing your business, and identifying the areas where AI can be a differentiator or accelerator. Next, understand your organization's level of awareness and knowledge, and define a strategy to upskill and reskill based on your organization’s AI goals. As there is still so much new about AI, experimentation is a key component of any effort to advance skills in this area. Additionally, some types of AI, such as ChatGPT, make AI capabilities more accessible to a broader audience. Therefore, you might want to look across your organization (outside of traditional technology-related roles) to create opportunities for team members to become familiar with the capabilities and build the skills to leverage appropriately.

In your view, what are the key AI-related roles and skills that are currently in high demand but are challenging to find in the job market?
Some of the key components required to gain the benefits of AI include technical skills around Machine Learning, access to quality data, and knowledge to apply human intelligence to validate the results and ensure AI is responsibly leveraged. The first two are in short supply and are skills that take time to develop. The third is a developing area

where one can learn fast, and non-traditional technology roles can engage, making it easier to upskill or reskill.

Additionally, it’s important to have a culture of curiosity and experimentation to take advantage of AI. This requires a commitment at the company level, and the right support frameworks to encourage motivated individuals to learn new skills and deliver new business value quickly.

How can we approach upskilling and reskilling the current employees to fill AI-related roles within the organization?
Similar to closing the skills gap in other areas, start by understanding the skills you need by assessing the strengths and gaps in your existing talent pool. This can be done with various skill benchmarks or other micro-assessments to gauge the organization’s talent profile in skills areas needed to leverage AI. While assessments can sometimes be negatively perceived, they are an important baseline for career growth. They can be positioned positively when partnered with on-demand and self-service learning recommendations for employees with marketable badges and credentials for a skill area of growing demand. Using this approach, the workforce will be inherently motivated to improve their skill set, and the organization will create generative capability at scale.

I have also found that providing room for experimentation through Hackathons are a great way to allow people to learn. This can help identify the skills needed to deliver a working product but also highlights individuals within the organization that have found their niche in leveraging AI, whether on the technical side or non-technical. As we see sparks of success, we find ways to replicate and augment these, whether with training, tools or updated ways of working.

Leveraging AI successfully requires a shift in the fabric of how an organization operates, which is necessary to develop, attract and ultimately keep talent.

In your experience, what are the common barriers that prevent organizations from bridging the AI skills gap effectively?
Challenges to bridging the AI skills gap are no different from bridging other skill gaps. It takes time and focused effort to leverage the right tools and support framework (assessment, different learning modalities, coaching) to support team members on a development path. Often, the barrier to overcome is helping team members find the time to learn. However, there are some additional barriers to bridging the AI skills gap, which is two-fold–building skills in a rapidly evolving area, which requires higher levels of experimentation and establishing the necessary foundations to ensure AI is leveraged responsibly and ethically in a secure environment where your company data is protected. Leveraging AI successfully requires a shift in the fabric of how an organization operates, which is necessary to develop, attract and ultimately keep talent.

How do business professionals ensure that the IT team stays updated on the latest AI technologies and methodologies to remain competitive in the industry?
It is by making space for experimentation, while educating team members on the limitations and boundaries of leveraging AI. In addition, they need to stay up to date with industry whitepapers, learn from how their network is applying AI, and partner with key vendors to understand what they are bringing to market and how to leverage the capabilities being added to their products.

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