No matter the workforce tools, it’s people who are ultimately visioning, building and experiencing the seismic shifts in the status quo of work. Chances of sustainable business and capability growth hinge on whether organisations keep a people-first mindset while integrating new technologies.

The ability of generative artificial intelligence (AI) to convert user input into valuable deliverables — custom code, data analysis, drafts of reports — is big business. Forrester estimates spending on AI software will reach US$64 billion by 2025, during a turbulent time when digital transformation is converging with labour market, economic and geopolitical uncertainty. Each challenge requires organisations to think of multidimensional business strategies that maximise efficiency, minimise cost and risk, and cultivate a future-ready workforce.

No single tool or solution can address the myriad changes, challenges and potential opportunities facing the modern workforce. As organisations refine their approaches to flexibility in how and where work gets done, they also need to upskill, reskill, attract and retain the right people amid an evolving and long-running race for talent. Organisations need to deploy the most efficient tools and processes to create sustainable value while still investing in the skills, career and personal growth of the workforce to create a more exceptional employee experience.

To realise the full potential of generative AI — or any technology — organisations need to bring a holistic, people-centred perspective to an increasingly more digital world of work. Instead of just focusing on the capabilities of generative AI, it’s important to consider how its use might enhance both the operational and experiential realities of the “next normal” of work.

The ability of AI systems to enhance the experience of work is already rippling through the budgeting, hiring and operating strategies of global businesses. According to the World Economic Forum’s Future of Jobs Report,⁵ businesses are anticipating sustained labour-market churn with an increasing focus on AI investment and the potential for automation. Respondents expected a 23% churn in jobs over the next five years between the creation of new roles and the reduction of others. The highest growth-job categories in relation to their size today are technology-related, with AI and machine-learning specialists at the top of the list.

Respondents expect a

23%

churn in jobs in the next five years

This corresponds with findings from the EY 2023 Work Reimagined Survey, where 84% of employers say they expect to have implemented generative AI within the next 12 months. This expectation is paired with a generally positive sentiment around the technology, especially as it relates to new ways of working of both employee and employer respondents to the survey, a net positive 33% see potential benefits for productivity and new ways of working, with a net positive 44% seeing benefits to the reality of flexible working.

For employees, this positivity may be connected to the potential of generative AI rather than experience with it, as just 49% of employees are using or expect to use the technology in the next year.

A heat map showing the net positive perception of the impact of GenAI based on sector. Organisations in the technology, media and telecommunications industry are generally more positive about GenAI than those in the defence industry, for example.

While the potential of generative AI may have largely broken into the wider mainstream relatively recently, this disruption is a chapter in a broader reimagining of the realities of digital work. Just as with the growth in capability and availability of tools and processes for remote and hybrid working, generative AI may fundamentally change how work is done, while also influencing the experience of work.

Importantly, this deeper integration of AI into the world of work doesn’t mean a wholesale replacement of people because of AI. Instead, there is likely a percentage of tasks for every employee that might be supported by AI tools, building an organisation’s capacity while better equipping employees. AI might present a first draft of a piece of work, but it is well-trained and trusted people who make final decisions. People are then freed to focus on higher-value tasks, fuelled by innovation and creativity.

Sustainable value doesn’t come from technology itself, but from what people accomplish with it.

Still, it’s difficult to realise the full potential of these tools without clear purpose. Adopting technology just for technology’s sake carries inherent risks: new technology plus an outdated process will only equal an expensive old process, creating a sub-par experience for employees and customers. The more sustainable value of technology adoption doesn’t come from what the technology does, but what the user can do with it.

With this in mind, we can plot the potential impact of generative AI in two powerful categories of use cases: AI collaboration and talent and governance strategy.

Organisations will have to assess how AI could influence both back-office functions and customer-facing work. For example, AI might be thought of as an added digital assistant taking on bulk analytical or technical tasks in the first instance. Employees could utilise their AI tools for mundane or repetitive tasks. The Digital Worker Experience Survey from Gartner found that 47% of digital workers struggle to find the information or data needed to effectively perform their jobs, presenting an opportunity for AI-powered tools and collaborative technologies to increase efficiency.

More wide-reaching for internal use is generative AI’s influence across business functions, like HR and Payroll (via ey.com US). AI tools can perform rolling analysis on performance indicators of employees and recommend training and upskilling opportunities. Through individualised credentials and authentication, AI tools can give different levels of information appropriate to the seniority and job title of the requestor. These digital tasks can run independently, around the clock, allowing for reports or red flags to be delivered constantly. This is more robust than traditional automation because these new tools have deeper context and generative abilities.

Generative AI’s ability to identify training opportunities for employees can also contribute to an overall assessment of the skills organisations have now and will need in the future. The ability to customise these technologies can enable new ways to create a career development track attuned to employee experience and business need.

Importantly, the enterprise-level implementation of generative AI with large data sets and confidential materials can help ease compliance exercises, while also creating areas for potential risk. These AI tools built for purpose can automate the gathering, cleansing and interpretation of data, while summarising findings and offering recommendations from it. These tasks need to be done within the context of an organisation’s ethical AI governance framework, maintaining guardrails that are appropriate for an organisation’s particular needs. Generative AI outputs should be seen as first drafts, with people making adjustments and final assessments of the work.

But appropriate guidelines can make that first draft even better.

For business functions requiring bulk retention of certain types of data to maintain regulatory compliance, or more stringent privacy controls, generative-AI tools need to follow clearly defined and transparent access and retention policies and have processes in place to monitor for potential legal risks around the use or leveraging of intellectual property.

This need extends to AI-enabled HR tools to assess opportunities for improvements to internal communication, process change or technology investments, or other unseen challenges. By mixing quantitative and qualitative assessments of the workforce, both with AI tools and apart from them, leaders can better understand the state of organisational culture and capabilities. Industrialising the analysis of multiple data sources may help identify bright spots or hotspots related to employee retention, and quickly, deeply and consistently respond to and anticipate opportunities.

Similarly, this assessment can flag potential compliance or regulatory risks in real time. But the effectiveness and reliability of this activity relies on an appropriate ethical generative-AI framework and governance model, including countering potential bias transmitted through public-training data, and avoiding the recycling of toxicity received in certain inputs.

Even with the need for regular management and maintenance of generative AI tools, these systems can provide better visibility on how an organisation is operating and organising itself for the “next normal” of work.

As exciting as generative AI may be in reimagining work, its potential can only be realized by the people who use it. Achieving that potential also rests on expanding the capability that we each have inherently with new tools, with better-connected insights and with rapid delivery of them, while not losing our humanity in the process.

This embrace of new technologies should be considered part of a constant state of digital transformation, whose chances for success are tied closely to how well the emotional and rational concerns of employees are addressed and respected. A more thoughtful approach to generative-AI integration can lead to more positive influence on the overall employee experience.

All employees are influenced by their use of technologies in their personal lives — ecommerce, consumer interaction, access to information — that creates an expectation in the workplace, and that expectation can differ drastically between generations. Younger employees are more comfortable blending their physical and digital lives, seamlessly shifting between interactions and activities online and in-person without as much of a separation of the two. This combined with Gen Z placing a high value on authenticity, transparency and integrity (via ey.com US) in their interactions presents an important consideration for generative AI’s implementation and influence on talent retention and attraction.

All of this points to the fact there isn’t a one-size-fits-all approach to technology adoption. Younger employees may be afforded more AI-enabled self-services, for example, while more training and effort at acclimation might be offered for employees less comfortable with new tools.

Technology ultimately has a major role to play in how we think about ourselves and operate in the workplace and outside of it, and balancing our tendencies and needs in a thoughtful way will promote an employee experience that attracts and retains valuable employees. The EY 2023 Work Reimagined Survey shows just 17% of employees and 22% of employers are prioritising training in generative AI-related skills in 2023, ranking far below a desire for better training to improve remote working skills and tools, the top priority for both employees (41%) and employers (52%). Even if the skills focus now is more on improving the overall experience of work, increased deployment of generative-AI tools will increase the need for people to be able to use it.

Organisations should consider approaching generative AI with a balance of appreciation for its role in a comprehensive workforce strategy, and awareness of the potential challenges which may be ahead. Here are four areas of focus:

It’s important to understand how generative AI might best fit within your organisation, and how that implementation might create opportunities and risks for your operations and people strategy. Generative-AI tools can create first drafts of interpretive reports or analysis, saving time and energy for employees to focus on final assessments and steps forward. Generative AI can play a similar role for organisation-wide talent considerations, influencing talent acquisition, onboarding, performance management, learning and skills development, and more.

Any implementation of a powerful technology like generative-AI creates the potential for risks that should be considered and mitigated. These risks can be directly related to the technology itself, for example having appropriate regulatory, compliance, cyber-security and privacy guardrails in place for generative-AI tools to access disparate data sources. Processes should also be created to account for accuracy and reliability concerns with generative-AI outputs. Risks may also stem from how well integrated generative AI systems are in countering bias and reflecting your organisation’s diversity, equity and inclusion (DE&I) strategy. Similarly, organisations should gauge potential resistance to generative AI from employees, and assess what further actions may help support positive organisational culture.

Generative-AI systems can be bespoke, requiring a decision of which model of generative AI is fit for purpose. This includes evaluating performance and cost trade-offs with considerations related to potential talent-related benefits. Organisations also need to evaluate their current and future knowledge-based infrastructure considerations for using generative AI tools, and design and build the large language model to enable the system. Part of this assessment phase can include the exploration of potential industry partnerships and alliances.

Technology’s effectiveness ultimately depends on how people use it. Organisations should develop a comprehensive road map for how your people will be trained to use new tools in a way that helps them feel empowered to focus on higher-value tasks. Implementing metrics that measure workforce sentiment tied to confidence in and adoption of the new technology, will help organisations adjust their strategy as necessary through the digital transformation.

Generative AI is already disrupting how organisations think about how they create and offer sustainable value in an increasingly turbulent world. It’s not a question of when AI will affect how business is done, in any industry, but rather how prepared organisations are to maximise the technology for their clients and their employees, while keeping people at the centre. The leaders who leverage this approach will be best positioned to realise the benefits of reimagining digital work in a responsible and sustainable way.

Author:

Jonathan Sears

Contributor:

Maya Smallwood