How artificial intelligence can augment a people-centered workforce

Talent and governance

Generative AI’s ability to identify training opportunities for employees can also contribute to an overall assessment of the skills organizations have now and will need in the future. The ability to customize 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 summarizing findings and offering recommendations from it. These tasks need to be done within the context of an organization’s ethical AI governance framework, maintaining guardrails that are appropriate for an organization’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 organizational culture and capabilities. Industrializing 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 organization is operating and organizing 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 in 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 prioritizing 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.

Organizations 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:

Assess how generative AI can empower your people

It’s important to understand how generative AI might best fit within your organization, 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 organization-wide talent considerations, influencing talent acquisition, onboarding, performance management, learning and skills development, and more.

Explore potential risks and security concerns

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, cybersecurity 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 organization’s diversity, equity and inclusion (DE&I) strategy. Similarly, organizations should gauge potential resistance to generative AI from employees, and assess what further actions may help support positive organizational culture.

Consider size, scope and cost

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. Organizations 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.

Chart the path forward with people at the center

Technology’s effectiveness ultimately depends on how people use it. Organizations should develop a comprehensive roadmap 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 organizations adjust their strategy as necessary through the digital transformation.