How can businesses prepare their workforce to have the digital skills of tomorrow’s AI-powered workplace?

While the past few years have left us with a business landscape scarred by the impact of economic and geopolitical uncertainties, the current AI movement has become a rocket ship for significant transformative changes set to accelerate new opportunities.

Alongside this AI buzz is the exponential data growth within every enterprise; studies show that global data creation is projected to grow to more than 180 zettabytes by 2025. Yet in this intelligence era, it’s no longer about how much data one company can generate but about how they use it to fuel decision-making while upskilling their workforce to ask the right questions for the right answers.

A recent Alteryx survey of 300 enterprise board members across four countries including Australia echoes this anxiety and confirms that the sudden rise of generative AI has moved beyond hype and become a focus of the enterprise. Of the board members surveyed, 43% of Australians stated that generative AI is currently their “main priority above anything else” and 39% are experimenting with generative AI in certain projects or departments.

Whether it is a CFO pursuing a reduction in the time to close the quarter to a Head of Supply Chain wanting to optimize complex logistics, today’s enterprises pull data from multiple input sources—from legacy databases and applications to modern cloud data warehouses and platforms. This suggests that AI-driven automation will remain a key characteristic of future enterprises, creating a perfect storm for data and AI-related skills while reshaping the roles and skill bases required for the future workforce as more organizations strive to unlock the potential of these innovations.

The World Economic Forum further underscores this in its “The Future of Jobs Report 2023”, which highlighted ‘AI and Machine Learning Specialists’ and ‘Data Analysts and Scientists’ roles to be the top 10 fastest-growing jobs between 2023 and 2027.

Whether greeted with excitement or anxiety, it’s clear that AI is expected to transform the business landscape over the next three years. Our recent Alteryx research into the future enterprise reveals that organizations across Australia, India, Japan, and Singapore have a strong appetite for AI and automation. In fact, nearly nine in 10 (86%) say AI is already impacting what their organizations can achieve.

No matter what the future brings, for generative AI to be successfully integrated into every facet of the organization, it requires a business-wide approach to data-driven decision-making that empowers the entire workforce to take full advantage of the technology. That’s why business and tech leaders must build for the future now.  Working with people managers to develop the skills stack to support the tech stack ensures organizations can take advantage of current and future AI capabilities—all powered by data.

Laying the foundations for an AI-infused future

Data is dirty, and it’s everywhere, and it’s growing in volume. Investments in the tech stack ecosystem alone will not convert these increased data volumes and variety into business opportunities. Rather than facilitating value extraction from data at the speed and scale needed for real-time intelligence, they silo the process to a limited few. This inability to extract meaningful insights from data at scale impedes the capacity to gain the decision intelligence necessary to meet evolving business objectives.

However, the key lies in the realization that every company possesses a substantial reservoir of untapped data talent poised to unlock its full potential.

While AI is set to shape how future enterprises operate and perform, the current skills gap poses significant barriers that will stall this journey if not bridged. Preparing for this increasingly complex, data-driven future requires a focus on developing non-technical soft skills that enable a broader spectrum of individuals to contribute to insightful decision-making rather than being the exclusive domain of traditional data analysts.

Building a data-literate workforce from within

As the use of AI and Large Language Model (LLM) technologies accelerates among businesses, it is crucial for individuals across the board to grasp the art of extracting valuable insights using these advanced tools. According to Gartner’s projections for 2025, analytical and soft skills will emerge as the most sought-after skills in the data and analytics talent market. Fostering data curiosity and analytical thinking are foundational tools in cultivating the next generation of data science talent. However, transferable soft skills such as collaboration, curiosity, creative problem-solving, and communication are equally pivotal.

For instance, an Alteryx research found that 76% of Australia business leaders state that it is more important for their employees to be multi-skilled than specialised in one area. While hard skills in areas such as AI and ML remain important, having the right AI tools will help these employees manage the increasing volume and variety of data and find the competitive edge their organizations need. 

Employees with a blend of technical expertise and soft skills are immensely valuable to businesses, even if this value is not immediately apparent. This group includes mid-career professionals regardless of educational background and age, individuals considering upskilling for career advancement, or those seeking a return to the workforce. Their unique understanding of the broader business context is their most valuable asset. This equips them to translate data into pivotal business decision-informing insights, showcasing their ability to pose the right questions, implement effective data techniques, and yield actionable outcomes.

While this expertise may not fit the traditional idea of a data scientist skillset, it serves as the linchpin for unlocking valuable insights.

Shaping the workforce for tomorrow’s AI-powered workplace

So how can companies upskill their workforce with the essential data literacy and expertise needed to deliver data-driven insights? Business leaders should follow these steps to engage with, encourage, and upskill their workforce:

1. Assess current technical and soft skills: Creative problem-solving is crucial. Ensure that training aligns with an employee’s skill set, allowing for flexibility and time for learning.
2. Leverage the cloud for democratizing data and analytic access: Simplify and enable data and tool access to encourage more time for learning.
3. Provide easy-to-use self-service tools and data access: Advances in no-code/low-code self-service analytics make it accessible for anyone to solve business challenges and deliver decision intelligence, regardless of qualification.
4. Treat upskilling as an investment: Upskilling creates a more inclusive workplace and a culture that empowers everyone to use data for strategic decisions.
5. Make it fun: Gamifying the learning experience and incorporating hands-on activities like datathons will make upskilling engaging, in turn incentivizing team members to continue learning.

Following these steps will enable the workforce to acquire the necessary data and analytic skills to drive transformative change within their companies; neglecting them puts one at risk of falling behind the competition.

The importance of humanity increases in an AI world

Combining high-quality data, diverse human intellect, and business context is paramount for AI to enable businesses to understand the ‘what’ and ‘why’ behind crucial business decisions. Data, in isolation, cannot provide insights needed to resolve business challenges, and AI without domain expertise to formulate informed questions will not deliver reliable, secure, and trustworthy outputs.

The AI wave will create new data interaction paradigms and faster ways to discover patterns and insights hidden within data—insights that deliver business value. Therefore, the organizations that will flourish will be the ones that have nurtured and equipped their domain experts with essential critical thinking, domain knowledge, data literacy, and analytical skills to navigate this era of AI-driven intelligence.

Undoubtedly, data-driven decision-making will remain the lifeblood of tomorrow’s business. Only by supporting the upskilling and reskilling of their current employees—from knowledge workers in lines of business to those in more technical roles—can businesses successfully transform and be ready to harness generative AI.