“Generative artificial intelligence (GenAI) has the potential to drive significant improvements in workforce productivity at the level of tasks, organizations and economies,” said a recent report “Leveraging Generative AI for Job Augmentation and Workforce Productivity: Scenarios, Case Studies and a Framework for Action,” by the World Economic Forum in collaboration with PwC. “Delivering those gains depends, among other things, on the deployment of GenAI to augment jobs, i.e. to partially perform tasks in such a way that technology effectively supports or enhances human capabilities through human-machine collaboration.”
“With such a fast-moving technology, it is hard to predict how even the relatively near-term future will play out,” the report added. “To help think through the possibilities, it is useful to think in terms of scenarios.” When the present resembles the past, we can calculate the probability of future outcomes by analyzing past historical data. But, the situation is very different when trying to formulate strategy in the face of high uncertainty because when situations lack analogies to the past, we have trouble envisioning how the future will play out.
The object of scenario planning is to assemble all known facts, trends, and other information and use them to create a number of alternative stories rich in detail, typically three or four, about plausible future outcomes. Military strategists first developed scenario planning in the 1950s to help them envision the impact of a nuclear war between the US and the Soviet Union, - something that was clearly way beyond anyone’s experience.
Scenario planning has continued to be used by military and government policy strategists. For example, every four years since 1997, the US National Intelligence Council has been publishing a Global Trends report outlining the key trends that will shape the world over the following twenty years. The reports illustrate how the future might play out by developing different scenarios, typically three or four, each embodying the key issues, trends, decisions and uncertainties that will shape the global landscape twenty years into the future.
In the business world, scenario planning has become a valuable tool for strategic thinking over the mid- to long-term planning horizon. Alternative technology, market and political environments can be described in a few discrete scenarios while organizing the richness and range of possibilities into plausible future outcomes.
After interviews with over 20 early adopters from a range of industries across the world, the WEF considered four alternative scenarios that will shape the near future of GenAI jobs, productivity, and innovation based on two key uncertainties:
- The level of trust, high or low, that employees and organizations have in GenAI technologies, and
- Whether the applicability & quality of GenAI continues to improve or remains the same.
Then four near future scenarios were derived from interviews with over 20 early adopters from a range of industries and regions across the world.
- High Hopes: High trust — Current applicability and quality;
- Broken Promises: Low trust — Current applicability and quality;
- Lost Opportunities: High trust — Continued improvements;
- Shifting Gears: Low trust— Continued improvements.
Let me summarize each of these four scenarios as well as their key implications.
Scenario 1: High Hopes. Enthusiasm for GenAI is high in this high trust/current quality scenario. There is hope that GenAI will help address the expected labor shortages and improve the quality of work. Organizations invest heavily in GenAI technology, applications and training because they’re afraid of jumping too late on the fast moving GenAI train. The workforce is generally enthusiastic. There is quite a bit of experimentation with ChatGPT both at work and at home to help handle tedious tasks.
However, GenAI does not live up to its sky-high expectations. “Because confidence is so high, employees also use tools that are not validated or without proper knowledge. Without a thorough understanding of how GenAI works, employees are not able to effectively interpret or validate the results it produces. This leads to inaccurate decision-making or reliance on flawed insights.” Instead of increasing productivity, work has to be redone. Instead of integrating GenAI into all their products and processes as originally planned, organizations have now become reluctant and are tightening their risk management.
While GenAI assists people with administrative tasks, it does not generate short-term breakthroughs. Instead, “hope that GenAI is a panacea is fading, and organizations are scaling back on their investments in GenAI because the return on investment is disappointing. They are also reserving larger budgets for training on the responsible use of GenAI and risk management.”
Key scenario 1 implications:
- Organizations continue to develop GenAI, but the majority do so only within their internal organization for now.
- The risks are considered too high to integrate GenAI into externally facing products and services.
- Broader productivity gains will be minimal and mainly seen on an individual or team level.
Scenario 2: Broken Promises. There is little enthusiasm and willingness among employees to deploy GenAI in this low trust/current quality scenario. Reinforced by stories about bias and unreliability, workers are less inclined to emphasize the positive attributes of GenAI. Some employees do see the longer term potential of GenAI and experiment with the technology, but not enough to make a difference in the workplace.
“Workers in this scenario may have access to the tools but tend to trust the quality of human work and judgement over technology so they do not feel motivated to try them out.” Without humans-in-the-loop as well as policy there will be lower trust. This means that they do not experience the benefits GenAI can offer.
“GenAI is thus primarily used for labour-intensive and low-risk tasks like drafting an email. Employees are unlikely to trust the outcomes and instead conduct additional reviews of the outputs, often resulting in redoing the work. To increase adoption, organizations invest heavily in change management and training, but even these efforts do not fully overcome the resistance to embracing GenAI.”
Key Scenario 2 implications:Adoption of GenAI is very slow, thereby limiting the impact of GenAI on job augmentation and workforce productivity.
- Individuals will use GenAI, but not on a large scale.
- Light individual productivity gains could be achieved by performing simple tasks with GenAI, but the effects will be negated by the time required to verify the accuracy of outputs.
- External pressures may slightly increase adoption, resulting in a marginally higher but still limited impact on augmentation and productivity.
Scenario 3: Lost Opportunities. A large part of the workforce is resisting or hesitant due to concerns about job displacement as opposed to fear of incorrect GenAI outcomes. In this low trust/expanding quality scenario. On the other hand organizations want to deploy the technology because the expanding applicability and quality of GenAI can potentially take over many tasks and even fully replace a range of jobs.
“In this scenario, pressure from shareholders or supervisory boards on leadership is very likely to increase the pace of deploying GenAI on a larger scale. Early adopters may achieve substantial competitive advantage by benefiting from the high applicability and quality of GenAI. These early adopters are likely to see benefits, which could lead to a gap between those who scaled early and those who did not.”In addition, there will be pressure on management relating to the workforce. Jobs of people who “cannot keep up” with developments are seriously at stake, which may lead to tensions within the organization and also require involvement of unions and worker representatives.
Key Scenario 3 implications:
- Slower implementation of GenAI in most organizations, due to lack of trust.
- There is a big difference between early marketplace adopters and those who follow, which only increases the pressure on the latter.
- Within organizations there will be pressure on workers who are reluctant or unable to adopt GenAI, potentially leading to conflict and unequal outcomes.
- Job augmentation and initial productivity improvements are evident in certain parts of the organization; however this has not led to widespread job augmentation and productivity enhancements, and benefits are concentrated among a few.
Scenario 4: Shifting Gears. Organizations and workforces are enthusiastic about experimenting with GenA in this high trust/expanding quality scenario. High trust and rapidly expanding applicability enables organizations to embrace and scale up different use cases. “GenAI becomes an integral part of daily processes, tasks, tools and systems. It seamlessly integrates into various roles, automating routine tasks and providing advanced decision-support capabilities, resulting in considerable productivity gains and the creation of new and augmented roles.”
In addition, organizations not only deploy GenAI in their own operations, but also begin to incorporate the technology into their products and services, potentially driving substantial innovation and business model transformation. They may also maximize GenAI-associated productivity increases, job augmentation and potentially even job creation.
“However, with a rapid pace of change, demands on organizations’ and workers’ agility and adaptability are equally high. For some workers, this means transitioning to other functions faster. Workers who are unable to adapt to the accelerated pace of change may face job losses, potentially leading to conflict between workers and the organization”
“Accelerated upskilling programmes may be required to enable employees to acquire new skills fast, while demand for GenAI and data experts will rise, intensifying the war for talent. Gaps between those whose jobs are exposed to transformations by GenAI and those whose are not could emerge or deepen, leading to significant societal impacts.”
Key Scenario 4 implications:
- Faster adoption of GenAI leads to large organizational efficiency gains. GenAI is deployed not only in the internal organization but also in products and services. This potentially leads to business model reinvention.
- Trust in GenAI is no longer something that only needs to be gained from employees, but also from customers and stakeholders.
- Maximum potential for productivity increases and job augmentation, but also higher rates of job displacement and, potentially, opportunities or democratizing access to job opportunities through GenAI tools.
- Some workers may benefit from these developments more than others, at both the workforce and organizational levels. This may have wider social ramifications.
“The emergence of GenAI in the workplace has created uncertainties, challenges and opportunities for workers, organizations and economies at large,” said the WEF report in conclusion. “The experience of early adopters interviewed for this report highlights that the path to harnessing GenAI’s full potential for job augmentation and workforce productivity growth is iterative and multi-faceted, requiring continuous learning, adaptation, and alignment with broader business and workforce strategies and goals. This journey is not without its challenges and its success depends not only on the technological but even more so on the human element.”
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