Generative AI in the Workforce: Key Trends and Implementation Strategies

10 big trends driving generative AI and the workforce: Report

1. Data-driven organizations are best placed to take advantage of GenAI Those quickest to adopt GenAI among their workforce can be described as data-driven, the report says. These organizations have a long history of establishing robust data quality, infrastructure, governance and security. They may not necessarily be faster at identifying GenAI use cases, but when they do they already have everything in place to deploy quickly. Many early adopters have moved beyond the initial experimentation phase. Among the key lessons they report is the importance of not rushing the implementation process and the benefits of testing solutions in small groups before a wider roll-out. This helps identify issues early and prevents employees losing interest if things don't work as planned. Most organizations featured in the report are highly conscious of the risks around deploying GenAI in the workforce. These include data breaches, privacy violations and bias in outcomes or other ethical aspects. To prevent reputational damage and avoid conflict with regulators and authorities, many are taking a cautious approach by conducting experiments and implementing pilots within the comparatively safe environment of their organization. While the report says it is currently difficult to assess GenAI productivity gains at a macroeconomic level, at an organizational level, such gains are being reported. One company claimed requests that would have once taken weeks to complete now take just minutes with automation - an example of how gains are particularly evident in routine and repetitive work. Empowering people in this way is frequently mentioned in the report - more than a quarter of respondents said that GenAI enables employees to do more enjoyable, creative and value-adding work. However, a number of organizations quizzed do not have a clear plan for what workers should do with any freed-up time. Productivity improvement is not the only driver for GenAI deployment, with improving the quality of work mentioned by respondents as equally important. If correctly implemented, the technology has the potential to be more accurate and consistent and make fewer mistakes than humans and can therefore lead to higher quality and customer satisfaction. From concerns around accuracy to the potential existence of bias and the ethics of replacing human work with that of GenAI, employees have many questions about the use of the technology. Frequently, IT professionals within an organization were among the quickest to embrace GenAI. Meanwhile, the most significant impact is being felt in departments that conduct a lot of administrative work, leading to uncertainty among those teams. Trust can be built through training that demystifies the technology and reskilling and upskilling that gives workers the potential to grow into new roles. This will be vital in the near future - as 44% of workers' skills will be disrupted in the next five years, according to the Forum's Jobs Initiative, which is working towards good jobs for all in the context of such labour market disruptions. With new initiatives, it is important to also understand the effect on the culture of the organization and the mindset shift it requires of employees, the report says. Effective leadership, from the very top of the organization, is vital. And there is a crucial role for middle managers, who understand workflows and processes and therefore where GenAI can have the biggest impact. In fact, the interviewed companies reported varying figures from 20% to 80%. Some stated that almost everyone was using the technology, or at least that they could because the whole organization had been given access to GenAI tools. How accessible these tools are to workforces depends on a company's appetite for risk - some respondents grant all employees access, while others limit to certain departments or require licences to be requested. Compared to smaller, task-specific AI models, large language models such as ChatGPT are energy intensive, with each prompt requiring calculations that consume a significant amount of power. While this is a problem most organizations in the report acknowledge, few have yet developed a strategy for acting on it and environmental considerations do not seem to be central to GenAI workforce deployment decisions. Most organizations interviewed for the report monitor the risks, quality and responsible use of GenAI through internal committees or councils, which establish rules and frameworks and assess use cases. Nearly all also say they have developed training programmes for the responsible use of tools. With knowledge of scandals around discriminatory algorithms and incoming legislation such as the European Union AI Act, companies are acutely aware of the importance of validation, verification and human intervention. "The biggest mistake you can make is to remove humans from your processes," one report interviewee said.

AI at work: A practical guide to implementing and scaling new tools

For a new report, the World Economic Forum and PwC asked more than 20 'early adopters' about the lessons they have learned while promoting job augmentation and workforce productivity growth with GenAI. From the boardroom to the breakroom, generative AI applications such as ChatGPT are the talk of companies everywhere. But how can organizations harness that energy and ensure that new tools lead to better outcomes for businesses and people? A new report from the World Economic Forum in collaboration with PwC - Leveraging generative AI for job augmentation and workforce productivity: Scenarios, case studies, and a framework for action - examines the experience of more than 20 'early adopters' of generative AI, who have been implementing GenAI into their operations since the end of 2022. The report shares lessons from those companies - and provides an actionable framework that organizations at all stages of implementing the technology to augment jobs and enhance productivity can adapt for their own use. The framework is based on two iterative stages: starting, where organizations minimize initial investment while testing and learning from GenAI applications; and scaling, where they build on those early results and implement informed decisions on broader measures. During both of these stages, organizations should address various elements that fall under two core themes: enabling GenAI and engaging workforces. Enable elements focus on establishing foundations and guiding principles and are essential for the early adoption of GenAI and developing use cases. Engage elements focus on ensuring GenAI is effectively adopted and built into workflows to generate the desired benefits. As the report outlines, lessons from early adopters show that engaging the workforce is a particularly important differentiator in driving successful job augmentation and job productivity growth. Of course, all businesses are different, so the framework outlined in the report is intended to be customized to specific organizational needs. But the broad framework it suggests is as follows. 1. Develop a GenAI vision and strategy that aligns with broader digital strategy and is agile enough to adapt to evolving business goals. To speed up adoption and create alignment across the business, this should consider multiple perspectives on the impact on jobs from stakeholders including leadership and employee representatives. And it should integrate with workforce planning strategies to facilitate the necessary upskilling and reskilling as roles change. 2. Build a robust and scalable data and technology infrastructure. This will help ensure accurate performance, reduce bias and see off potential legal issues that all lead to low trust, usage and adoption. These elements become increasingly more essential during the scale-up phase. 3. Ensure compliance with regulations and develop responsible AI programmes - vital to maintaining trust and mitigating legal or reputational risks and create a foundation of trust and sustainability for GenAI deployment, both internally and externally. The Forum report's insights from early adopters suggest that successfully deploying and scaling GenAI in the workforce is not about the technology but ensuring people are open to change and experimentation. Therefore, it is important to create a people-centred approach that empowers employees to adapt and promotes the right mindsets and behaviours across the organization. Continuous development of new skills, knowledge and capabilities are essential for the practical application of GenAI, both in helping employees successfully execute new tasks and transitioning to different roles in the business. Organizations should foster these while at the same time managing workforce redeployment and redesigning job roles. This is crucial to demonstrating benefits, getting stakeholder buy-in and improving business outcomes. As the report concludes, by aligning the organizational interests with those of the workforce, companies can create an environment in which GenAI enhances job quality, supports innovation and drives productivity. Ultimately, this can create job augmentation, not displacement at scale - a key focus for the World Economic Forum's Jobs Initiative. The initiative works to engage businesses, governments and civil society across industries and geographies to prepare leaders and workers for dynamic job transitions and support the creation of good jobs for all.