Organizations must rethink skills to realize AI ROI

AI adoption in the workplace continues to grow, with 88% of businesses incorporating AI into their operations. Companies are striving to quantify the return on investment (ROI) from their AI technologies, aiming to increase productivity by automating routine tasks and improving efficiency. However, a significant challenge remains: a skills gap.

Half of London businesses report their workforce lacks the necessary skills to meet AI requirements, leading to a costly problem of needing to revamp skills and job roles to achieve ROI.

To bridge this gap, businesses need to adopt a holistic approach that balances technical knowledge with creativity and business understanding. Skills required extend beyond technical IT proficiency, encompassing digital literacy, critical evaluation of AI outputs, and seamless workflow integration. These skills are crucial at all levels, from entry-level to C-suite positions.

Job descriptions must be updated to reflect these evolving requirements, moving away from generic technical proficiencies to specific expectations tied to AI tools.

As the digital skills gap widens, some workers fear AI may replace them, but the reality is a bifurcated labor market. One tier focuses on roles requiring human judgment and human-AI collaboration, while the other allows AI to handle repetitive tasks entirely. Maximizing AI efficiency requires critical thinking and judgment that technology cannot provide. Job descriptions need to be revised to accurately reflect these expectations, ensuring employees are not overestimated in their capabilities.

The reshaping of job roles favors creative technologists—individuals who understand technology, creativity, and strategy. These professionals are more likely to contribute to successful digital transformations. As generative AI becomes commonplace, rising content demands, and cross-discipline collaboration increase, organizations must ensure their job descriptions align with the emerging reality of AI-driven roles.

Effective learning and experimentation are key to overcoming the skills gap. Encouraging a supportive environment where employees can try, test, and learn from both successful and unsuccessful AI implementations is vital. Dark data, which comprises unstructured information like documents, emails, and PDFs, poses a significant challenge. Organizations must convert this dark data into structured, contextual, and governable data to enable AI systems to work effectively and produce reliable outputs.

Despite AI's ability to streamline planning and documentation tasks, human skills remain irreplaceable. Judgement, storytelling, creative thinking, and the ability to lead change are essential and cannot be replicated by AI. The demand for AI-fluent specialists has given rise to empowered generalists who can navigate AI trends alongside managing stakeholder relationships and understanding customer needs. While AI can automate workflows, it cannot lead teams or comprehend human nuance and mission-driven objectives.

AI is reshaping roles, creating new positions focused on managing the human-AI relationship, such as AI evaluators and human-in-the-loop reviewers. This shift is not about job replacement but job transformation, potentially reducing workforce inequality by democratizing access to new skills. Addressing the skills gap requires both employees and employers to experiment and adapt, ensuring organizations can fully harness the benefits of AI while mitigating its challenges.