As businesses race to scale generative AI (gen AI) capabilities, they are confronting a range of new challenges, especially around workforce readiness. The global workforce is now comprised of a mix of generations, and this inter-generational divide brings different experiences, ideas, and norms to the workplace. While some are more familiar with technology and its potential, others may be more skeptical or even cynical about its role in the workplace.
Compounding these challenges is a growing shortage of AI skills, despite recent layoffs across major tech firms. According to a study, only 1 in 10 workers in the UK currently possess the AI expertise businesses require, and many organisations lack the resources to provide comprehensive AI training. This skills gap is particularly concerning as AI becomes more deeply embedded in business processes.
Prioritising AI education to close knowledge gaps
A lack of AI knowledge and training within organisations can pose significant risks, including the misuse of technology and the exposure of valuable data. This risk is amplified by a report from Oliver Wyman, which found that while 79% of workers want training in generative AI, only 64% feel they are receiving adequate support, and 57% believe the training they do receive is insufficient. This gap in knowledge encourages more employees to experiment with AI unsupervised, increasing the likelihood of errors and potential security vulnerabilities in the workplace. Hence, to keep businesses competitive and minimise these dangers, it is crucial to prioritise AI education.
Fortunately, companies are increasingly recognising the importance of upskilling as a strategic necessity, moving beyond viewing it as merely a response to layoffs or a PR initiative. According to a BCG study, organisations are now investing up to 1.5% of their total budgets in upskilling programs.
Leading companies like Infosys, Vodafone, and Amazon are spearheading efforts to reskill their workforce, ensuring employees can meet evolving business needs. By focusing on skill development, businesses not only enhance internal capabilities but also maintain a competitive advantage in an increasingly AI-driven market.
Leaders’ role in driving organisational adoption of generative AI
Scaling generative AI within an organisation goes beyond merely adopting the technology—it requires a cultural transformation that leaders must drive. For businesses to fully capitalise on AI, leadership must cultivate an innovative atmosphere that empowers employees to embrace the changes AI brings.
Here are key considerations for organisational leaders aiming to integrate generative AI into various aspects of their operations:
Encourage employees to upskill
Reskilling can be demanding and often disrupts the status quo, making employees, , hesitant. To overcome this, organisations should design AI training programs with employees in mind, minimising the risks and effort involved while offering clear career benefits. Leaders must communicate the purpose of these initiatives and create a sense of ownership among the workforce.
It’s important to emphasise that employees who learn to leverage generative AI will be able to accomplish more in less time, creating greater value for the organisation. All departments, from sales and HR to customer support, can benefit from AI’s ability to streamline tasks, spark new ideas, and enhance productivity. For example, tools like ChatGPT can help research teams analyse content faster or automate responses in customer service, driving efficiency across the board. However, identifying how AI fits within workflows is crucial to fully leveraging its capabilities.
Empower employees to drive AI adoption and innovation
To successfully scale generative AI across an organisation, leaders must first focus on empowering employees by aligning AI adoption with clear business outcomes. Rather than rushing to build AI literacy across all roles, it’s important to start by identifying the business objectives AI investments can accelerate. From there, define the necessary skills and identify the teams that need to develop them. This approach ensures that AI training is targeted, practical, and aligned with real business needs.
Equipping teams with the right tools and creating a culture of experimentation empowers employees to innovate and apply AI to solve real-world challenges. It’s also crucial that the tools used are secure and that employees understand the risks, such as the potential exposure of intellectual property when working with large language models (LLMs).
Focus on leveraging the unique strengths of specialised teams
Historically, AI development was concentrated within data science teams. However, as AI scales, it becomes clear that no single team or individual can manage the full spectrum of tasks needed to bring AI to life. It requires a combination of skill sets that are often too diverse for one person to master and business leaders must assemble teams with complementary expertise.
For example, data scientists excel at building precise predictive models but often lack the expertise to optimise and implement them in real-world applications. That’s where machine learning (ML) engineers step in, handling the packaging, deployment, and ongoing monitoring of these models. While data scientists focus on model creation, ML engineers ensure they are operational and efficient. At the same time, compliance, governance, and risk teams provide oversight to ensure AI is deployed safely and ethically.
Empowering a workforce for AI-driven success
Achieving success with AI involves more than just implementing the technology – it depends on cultivating the right talent and mindset across the organisation. As generative AI reshapes roles and creates new ones, the focus should shift from specific roles to the development of durable skills that will remain relevant in a rapidly changing landscape. However, transformations often face resistance due to cultural challenges, especially when employees feel that new technologies threaten their established professional identities. A human-centered, empathetic approach to learning and development (L&D) is essential to overcoming these challenges.
Ultimately, scaling AI successfully requires more than just advanced tools; it demands a workforce equipped with the skills and confidence to lead in this new era. By creating an environment that encourages ongoing development, leaders can ensure their teams remain competitive and adaptable as AI continues to transform the business landscape.
- Data & AI
- People & Culture
