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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 organizations 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 labor market, economic and geopolitical uncertainty. Each challenge requires organizations to think of multidimensional business strategies that maximize efficiency, minimize 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 organizations 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. Organizations 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 realize the full potential of generative AI — or any technology — organizations need to bring a holistic, people-centered 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.
Reimagining digital 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, businesses are anticipating sustained labor 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.
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.
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 organization’s capacity while better equipping employees. AI might present a first draft of a piece of work, but it’s well-trained and trusted people who make final decisions. People are then freed to focus on higher-value tasks, fueled by innovation and creativity.
Still, it’s difficult to realize 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 subpar 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.
AI collaboration
Organizations 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 a first instance. Employees could utilize 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 individualized 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 to be delivered constantly.
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.
Broad productivity gains across sectors and occupations
The magnitude of the productivity boost from GenAI will depend on the speed of its diffusion across organizations and industries. While GenAI has already spawned many innovations, it has yet to show a visible and meaningful boost in the aggregate productivity data. The productivity boost from GenAI will likely occur with a lag as there has generally been a long delay between the inception of paradigm-shifting technologies and their diffusion across the economy and society.
While the majority of previous technological advancements have focused on automating manual labor, GenAI stands poised to revolutionize the automation or assistance in complex cognitive functions such as sophisticated predictive analytics, interactive 3D data modeling, advanced natural language processing for document summarization and the development of intricate algorithms for machine learning. Its usage is expected to span a wide range of sectors, occupations and tasks.
As such, information and knowledge workers across a diverse array of sectors are poised to experience significant impacts due to the diffusion and integration of AI tools. These sectors include, but are not limited to, technology, banking, life sciences and retail. In these fields, GenAI specifically offers substantial enhancements in productivity across four key domains by performing a wide range of skills:
1. Office and administrative support: This includes roles such as customer service representatives who can leverage GenAI for handling complex queries, information clerks utilizing GenAI for efficient data retrieval, desktop publishers employing GenAI for advanced layout designs, inventory managers using GenAI for predictive stock management and research assistants harnessing GenAI for data collection and analysis.
2. Business and financial operations: This covers roles such as financial analysts who can utilize GenAI for deeper market insights, human resource specialists applying GenAI in talent acquisition and management, logisticians leveraging GenAI for optimized supply chain solutions and credit analysts employing GenAI for more accurate credit risk assessments.
3. Life, physical and social sciences: This encompasses roles like political scientists using GenAI for predictive modelling of political trends, medical scientists employing GenAI in drug discovery and personalized medicine, economists leveraging GenAI for complex economic forecasting, and biological technicians utilizing GenAI in experimental design and data interpretation.
4. Mathematics and computer programming: This includes software developers integrating GenAI to create more sophisticated applications and data scientists using GenAI for advanced predictive analytics and machine learning model development.
In these major domains, GenAI stands not just as a tool but as a transformative force, reshaping the way tasks are approached and executed, which can lead to unprecedented levels of efficiency and innovation.