Upskilling the workforce for an augmented 2026
The global workforce has moved into a rapidly evolving phase of digital capability where human skills and AI systems operate side-by-side to deliver faster learning, stronger decision-making and inclusive growth. However, without a clear AI-adoption strategy companies will find themselves losing both money and talent because of mismatched expectations.
A study by EY has found that there are up to 40% of AI productivity gains lost because companies lack substantial talent strategies. Another survey by the firm has also highlighted the growing need to reduce the friction between AI adoption and employee engagement. Only 28% of companies have given employees the tools they need to benefit from the transformative value of AI and therefore both time and benefits are being lost because of limited tools and mindsets. These results illustrate that organisations who are viewing AI as a replacement resource instead of a tool will struggle to reap the benefits in a sustainable and profitable manner.
This article discusses the changing skills development environment and the importance of prioritizing AI readiness within the culture and fabric of the organization. The main points of this article are:
- Why 2026 marks a shift toward AI-augmented roles.
- What global research shows about digital skills demand.
- How continuous learning improves decision-making and innovation.
- How Mint develops AI-ready talent across internal teams and youth pipelines.
- What leaders can prioritize to support inclusive workforce growth.
What’s the value of an AI-ready workforce
Digital skills have evolved rapidly over the past two years, thanks to higher data volumes, AI-enabled tools, and new expectations for workplace agility. The State of Data and AI Literacy Report found that 69% of leaders value AI literacy – there’s a growing demand for employees with these skills, across all industries and sectors. However, capability isn’t created by technology. Confident decision-making depends on how well people understand, question, and interpret the information they acquire, and this means building a culture of intentional learning and knowledge sharing. How one department or role will use AI will differ from another, and if this application becomes territorial it will exacerbate natural organizational tendencies towards silos.
Developing an AI-ready workforce reflects the need for employees who can combine human judgement with AI-enabled insight in everyday work.
Human-AI collaboration in the workplace
Human-AI collaboration is emerging as a practical working style and it includes drafting, analysis, forecasting, scenario testing, and rapid content generation, all supported by AI tools that reduce manual effort and expand problem-solving capacity. Companies need to invest in structured enablement and role-based learning alongside practical AI use cases as these initiatives support employee skills development and engagement. They also need to provide guardrails and governance so that employees use AI responsibly and ethically.
When employees have data insights and AI support embedded into their daily roles, they are more inclined to explore the potential of the technology. With clear guidelines and a visible AI strategy, employees will naturally lean into the advantages of incorporating AI into their roles and effectively using the tools to enhance their own efficiency and capabilities.
Teams need to be equipped to collaborate with AI tools so that companies gain a workforce that learns faster and contributes more consistently to innovation.
Building AI literacy through continuous enablement
Companies often treat AI training as a single intervention, but real capability comes from the same continuous enablement that supports data literacy and workflow change. Mint has seen this repeatedly across projects where teams only change behaviour once training is tied to their real tasks and decisions. In data literacy programs we have found that our people became more confident when reports were redesigned to match the work that they do, rather than abstract dashboards that did not support their responsibilities. The same principle applies to AI skills. The training provided must be close to the work they’re doing because people learn best with tools that solve the problems they encounter every day.
This is why organizations need structured pathways that evolve alongside their technology. Skills improvement gains momentum when people have the space to experiment, repeat, and refine their use of AI. Continuous enablement also reduces the friction between curiosity and competence, which is often what separates teams that adopt AI confidently from those that disengage early.
The most effective AI skills programs treat learning as an ongoing operating requirement rather than an event, supported by clear expectations, repeated practice and visible leadership involvement.
