- AI is constantly evolving the way we work, requiring organizations to reassess what skills will drive long-term success.
- AI does not make expertise less important, but it may place greater value on adaptability and continuous learning.
- Learning agility, the ability to build on your existing knowledge as technologies and ways of working change, can help professionals and organizations navigate fast-moving environments.
Table of Contents
AI is evolving, expanding the range of tools and capabilities available to businesses. From traditional machine learning to generative and Agentic AI, these technologies are already impacting how businesses operate. As the landscape continues to evolve, professionals will need to build the skills required to evaluate and use these tools effectively.
We are also seeing signs that the fundamentals of conventional work are shifting. While expertise generally supports long-term success, our new environment may require reskilling.
That does not mean deep expertise is becoming less important. Rather, the ability to adapt and to learn continuously may become increasingly more important for professionals and for organizations as tools, working environments, and needs change and evolve.
How AI Is Changing the Value of Expertise
As AI tools become more sophisticated and increasingly integrated into workflows, organizations may need to look beyond the technical skills employees possess today and consider how effectively they can continue developing as technology advances.
This shift may also influence what organizations look for in talent. Rather than prioritizing knowledge in a specific subject area alone, the AI era may increase the value of people who combine deep expertise with learning agility and sound judgment.
According to McKinsey, organizations increasingly value adaptability rather than specialization, placing less emphasis on an employee’s academic background and more on their ability to respond to new demands. It also notes that companies are expanding their focus on cognitive capacities, such as problem-solving, logical thinking, creativity and resilience.
The question, then, is not whether deep expertise is still valuable, but what makes that expertise durable. We believe its long-term value will increasingly depend on professionals’ ability to build on existing knowledge as technology advances, with human judgment remaining integral to how that knowledge is applied.
Why Learning Agility Matters in an AI-Enabled Workplace
As AI continues to accelerate the need to acquire new skills, learning agility is critical to sustaining talent. It goes beyond tool fluency and reflects a person’s ability to recognize what needs to be learned and continue developing as new demands emerge.
This approach highlights that professional learning does not require us to predict which skills will be relevant in the future but instead, cultivate the ability to keep pace with technological shifts.
For organizations, this means fostering healthy work environments that support talent development by allowing employees to remain curious, upskill or expand their knowledge, and importantly, receive feedback.
Continuous Learning Requires Application
Learning agility can be demonstrated by how effectively employees apply newly acquired skills to real-world workplace problems. As McKinsey states, actively incorporating learning into the flow of work allows employees to expand what they know while tracking their development over time.
As AI integration continues to change the state of work, using AI as a collaborative partner can help close the learning gap. This framework starts with employees training directly in real-world situations and producing tangible deliverables. We believe that hands-on learning not only helps employees develop new skills in real time but also strengthens active problem-solving.
AI can also become part of the learning process, giving employees another way to assess their work and identify areas where additional knowledge or guidance may be useful.
This approach can support two objectives:
- It gives employees autonomy over their learning journey by allowing them to course-correct.
- For managers, this can reduce time spent on routine review and create more opportunities to focus on coaching and guiding their teams.
While frameworks and training processes may vary throughout organizations, one thing remains true: leveraging AI is not about replacing talent; it is about using it as a partner to strengthen existing teams while helping them navigate fast-moving technical environments.
Why Workforce Adaptability Matters to Long-Term Success
The evolution of AI has made it clear that adapting is critical for the future of work. Although AI adoption is still in its early stages, it is essential for businesses to incorporate the necessary resources into workflows that empower teams to keep pace with technological change, especially as AI is already making an impact in the workplace.
For businesses, learning agility is not only a talent-development priority. A workforce that can rethink existing processes and apply new technology effectively may also help an organization respond more quickly to shifts in its operating environment.
Expertise remains essential, but its long-term value increasingly depends on a professional’s ability to keep learning as technology and working environments change. Organizations that foster that adaptability can better position their teams to navigate the technologies reshaping how work gets done.
The durable advantage may not rely on predicting what is next but on having the resilience to keep learning through uncertainty.
For more career insights, follow Robert F. Smith on LinkedIn and subscribe to his YouTube channel.