Demystifying skills during digital transformation with help from AI

Managing change isn’t just a priority; it’s a business imperative. As previous installments of this series outlined, technology alone isn’t enough to generate value from a transformation. And while change management is an increasingly critical enterprise skill, many organizations lack strategic plans to implement it. Increasingly, putting change management and employee experience at the center of a business transformation strategy is the surest path toward delivering tangible results.

A digital transformation can increase efficiency, generate value and uncover new ways of working. But it also involves a massive shift in how business processes, models and activities unfold.

Inevitably, a transformation journey requires careful attention to skills management. This approach is particularly true in the era of AI. As the IBM Institute for Business Value has found, historically only 6% of the workforce has needed reskilling. By 2024, that number rose to a full 35% of the workforce—or over 1 billion workers across the globe.

Digital transformations are only as effective as the workers adopting new technologies. The importance of skills management during transformation truly cannot be overstated, as they’re the functional backbone of any successful technological shift. 

Digital transformation is not a one-time event but a continuous journey, and skills are the lynchpin of that journey. Strategic skills management through the planning, implementation and governance phases of a transformation determine its success. Currently, 87% of CEOs expect jobs to be augmented, rather than replaced, by generative AI. This means workers need to quickly acquire new ways of working, and embrace different workflows, to succeed.

Deploying key technologies to manage workforce skills supports this process. As AI disrupts the business world, it can also be used to reimagine how skills are measured and obtained.

By using AI for skills analysis and job displacement assessment, organizations can plan and execute their digital transformations more effectively. With these tools, enterprises create an iterative and ongoing process of reskilling and upskilling, remaining competitive and relevant in the digital age. This approach ensures a future-ready workforce capable of unleashing the potential of technology, for both the near- and long-term. 

During the planning phase of a digital transformation, organizations should audit existing skills and consider future skills requirements—this strategy allows them to leverage a transformation for the greatest possible effect. During this phase, job redesign is paramount.

Job redesign involves considering both skills that will become redundant and skills that will be necessary for future success. Throughout the planning process, enterprises should identify tasks ripe for automation. Concurrently, they should pinpoint areas where reassigned human workers can focus more deeply on complex problem-solving, creativity, empathy and critical thinking.

Such strategic realignment leads to a more efficient and human-centric work environment. And by framing every workflow automation as a potential for new, more creative work, the planning process unlocks potential value.

AI can play a pivotal role during the planning phase. Given job architecture data and access to historical trends, AI can predict which skills will be critical in the future. It can also analyze the workforce’s capabilities to spot potential skill gaps. This proactive approach enables businesses to create focused, data-informed training programs, ensuring the workforce is equipped for the future. 

During the implementation phase of a transformation, upskilling and reskilling become critical. Upskilling involves enhancing existing skills—for instance, empowering a digital marketer with data analytics training, or providing a manager with advanced leadership training. Reskilling, in contrast, teaches entirely new skills sets. During a transformation, employees might reskill by learning how to appropriately query AI tools and use new systems.

Both upskilling and reskilling are essential to realize the full potential of a transformation. These processes are iterative: As new technologies are introduced, and older ones evolve, so do a workforce’s required skills. Continuous learning and development must be embedded in an organization’s culture to keep pace with these changes.

Through an enterprise’s governance phase, organizational structures, processes and policies must adapt to reflect the evolving skills landscape. In designing a governance structure, leadership must ensure fair talent allocation, matching employee expertise with demonstrated job requirements. Such job and skills mapping not only reduces turnover rates, but boosts productivity and job satisfaction. Leading companies can leverage AI to facilitate this process, providing managers with comprehensive skill-matching assistance.

Moreover, addressing job displacement is a vital aspect of the governance process. AI can forecast which jobs are most likely to be displaced by automation, empowering organizations to think strategically.

Currently, only 19% of executives use succession planning software with predictive capabilities. But these tools assist leadership by forecasting which jobs are most likely to be displaced by automation, allowing organizations to mitigate negative impacts as early as possible. Data-driven insights can spur enterprises to retrain workers for emerging roles and provide personalized support during a transition. 

With such a vast number of jobs expected to change over the next few years, job design remains one of the most essential aspects of a transformation. As AI automates routine tasks and reimagines workflows across roles and departments, nearly every job in an organization will be modified. Many roles filled by human workers will require higher-value skills such as creativity, empathy and critical thinking. Automating and augmenting work processes allows for a more efficient allocation of these people-centric skills, ensuring that employees are engaged in meaningful and impactful activities.

Deep, data-informed skills analysis helps leadership manage the talent of the future. AI can help identify the skills required for new jobs, as well as assess a current workforce’s competencies to determine gaps or areas for improvement. By using nuanced skills data, businesses and talent developments teams can develop targeted training programs and upskilling initiatives, preparing employees for success.

AI’s capacity for forecasting and analysis can support leadership in planning for, and mitigating, the effects of new technologies on existing jobs. By ingesting enterprise-specific internal and third-party data, AI can identify which jobs are at a high risk for displacement. It can also predict which jobs, and skills, are likely to emerge. Such forecasting helps businesses develop strategies to mitigate the negative impacts of automation and supports workers who might be in danger of displacement. 

With AI, organizations can optimize talent allocation across different roles—essentially providing workforce planners with the tools to reimagine roles more accurately. AI-powered talent matching ensures that workers are deployed in areas of a business where their skills are best used.

According to IBM research, nearly three-quarters of executives have optimized their methods for evaluating employee potential; 87% of leading CEOs maintain the same practice, suggesting that talent optimization provides a significant competitive edge.

In addition to increasing productivity and helping businesses stay agile, talent matching can improve the employee experience, placing workers in appropriate roles and facilitating appropriate job expectations. This strategy, in turn, reduces turnover and improves general satisfaction. 

Without appropriate skills management, even the most well-designed transformation will fall short of expected outcomes. By strategically planning for job redesign, skills analysis, job displacement analysis and talent allocation, organizations ensure a smooth transition into the digital age. And, as with any aspect of the change management process, skills management is an active and iterative process. Continuous attention to upskilling and reskilling are necessary to keep up with the rapid pace of technological advancements, solidifying a business’ competitive edge.