The transformative powers of AI are undeniable. Every day, AI is becoming more imperative in the operations of workforces, including aiding decision-making with real-time data, increasing productivity, and creating new opportunities.
While there are common fears that AI will replace workers, we argue that it has enormous potential to augment and even replace manual work, helping people move on to higher-value tasks that require more ingenuity and creativity.
Learn more about how AI can have a positive impact on workforces, including:
AI is becoming more integral to how we work, but are we putting it to work in the right ways? In this e-book, we present how to best deploy AI in your organization as a partner to build up talent and beat your bottom line — all with an eye on ethical and responsible use.
In this e-book, we examine how GenAI can automate decision-making to boost productivity and create new labor market opportunities. While many people might fear that AI will take their jobs, we argue the opposite will happen — but only if you embrace learning how to work with AI.
As they say, “AI won’t replace you, but someone who knows how to work with AI will.” We also provide a framework for assessing how GenAI will impact workforces through upskilling, strategic technology adoption, and fostering people-machine collaboration.
By advocating for a responsible approach, we are setting workers and organizations up for success with the knowledge and skills they need to thrive alongside AI’s remarkable capabilities.
Rather than rendering people obsolete, GenAI’s true potential lies in augmenting our unique strengths to elevate economic growth and human potential. This series illuminates pathways for you to successfully navigate this AI-driven revolution and unlock a future of unprecedented innovation and prosperity.
Generative AI will have multifaceted effects on work and the economy, including the potential to reshape traditional roles, create new job opportunities, and transform productivity.
The economic and ethical implications of GenAI include its effect on the labor market — it’s not just changing how we work, but also reshaping the nature of work itself. Traditional roles are evolving, some jobs face disruption, and new roles are emerging in their place. This impact is already in progress, but a lot is still unknown. What applications of AI will have the most commercial success? What kinds of jobs will be created out of new opportunities? What kinds of work can GenAI fully replace versus augment? Despite these uncertainties, here is a framework to assess AI’s impact.
First, let’s look at what GenAI does for businesses. There’s a lot of conversation about the automation of tasks that’s possible with AI, as well as the text-generation abilities of tools like ChatGPT. But even more fundamentally, GenAI can analyze enormous sets of data and learn how to recognize patterns much like the human brain. That data digestion and pattern recognition then informs the AI decision-making model. Unlike us, AI models can look at huge amounts of data in a short period, massively accelerating the process of deciding what actions to take in a given situation.
If AI is thought of as “prediction machines” that enable you to make cheaper, more abundant, and better-automated decisions, then the best generative models can reshape the economy due to their widespread relevance. If AI automates decisions in the economy, this increases productivity, which has significant effects on labor and investment. Since information and knowledge work dominate the U.S. economy, the potential for AI systems to boost overall productivity is vast.
Productivity can be divided into two components: total factor productivity (a measure of the impact of technology) and the contribution of the labor composition and capital intensity. Recently, the U.S. has experienced slow TFP, or tech-related growth, which has made it harder to fight inflation, eroded wages, and worsened budget deficits.
However, the 1990s saw a surge in productivity growth driven primarily by an investment in computers which drove business transformations. Despite the stock market bubble and a reallocation of labor, workers were better off. The federal budget was also balanced from 1998 to 2001. Digital technology can drive economic growth.
As the capabilities of GenAI systems grow, allowing us to perform many tasks that used to be reserved for cognitive workers, the broad applications of the system will impact a large portion of the U.S. workforce in some form.
According to recent research, LLMs could affect 80 percent of the U.S. workforce. A recent report by Goldman Sachs suggests that GenAI could raise global GDP by 7%, a truly significant effect for any single technology.
The good news is that these gains are not hypothetical — they are significant in the real world. The ability of AI systems to create value by capturing knowledge and conveying information on a wide range of tasks was previously only learned on the job. This is a key indicator of AI’s effect on the labor market.
AI enhances output efficiency — and that means greater output. AI also accelerates innovation as we not only produce output but also invent new things and engage in discoveries, which boosts future productivity. As we become more efficient, we contribute more significantly to technological progress, compounding productivity growth over time.
However, for these productivity gains to materialize, AI advances must disseminate across the economy, a process that takes time and adaptation. Upskilling and training workers to effectively use these new technologies is critical for realizing productivity gains.
The productivity effects of GenAI go hand in hand with disruption in the job market. However, much of the current discussion is around displacement, because it’s much easier to conceive of straightforward machine-people replacement in completing tasks, reducing the demand for labor.
AI’s potential for augmentation, capital deepening, and creating new tasks and industries indicates an overall positive impact on labor demand. For instance, an improved AI recommendation system like Netflix’s could increase company returns and, consequently, the demand for workers. Investment in AI systems enhances worker productivity, increasing labor demand. Additionally, as AI creates new tasks and industries, this further boosts labor demand.
Contrary to fears of a job apocalypse, this model suggests that AI systems enhancing labor productivity could also bolster labor demand. However, it’s essential to recognize that the net effect of new technology on labor demand could be negative if the AI systems are “mediocre” in that they are only productive enough to displace workers, but not productive enough to increase labor demand through the other channels.
While AI’s impact on the labor market is complex and multifaceted, it holds the potential for significant economic growth and job creation, provided the challenges of displacement and the need for skill adaptation are adequately addressed.
Investing in advanced AI capabilities and complementary infrastructure can steer labor market effects in a positive direction.
Not all AI is created equal — the kind and caliber of systems that your organization adopts will strongly influence your labor force outcomes.
Recent technological change has been biased toward automation, with insufficient focus on creating new tasks where labor can be productively employed. In other words, AI systems are primarily replacing some tasks that were previously performed by workers, which pushes down the demand for labor, and people lose their jobs.
Displacement is without question a negative impact of AI on the workforce — that aforementioned Goldman Sachs report predicted that up to 300 million jobs could be disrupted. Your organization may see short-term gains from reducing head count and speeding up the completion of some tasks, but if your workers lose value and income, the whole economy suffers.
On the other hand, if you invest in and adopt AI systems that augment tasks and create completely new ones by generating valuable predictions and insights, the result is increased labor value and demand.
Here’s an example. You could use AI to automate the résumé-screening process and generate a narrowed-down list of promising candidates. If that were your primary task, AI could take your job.
However, the time you save by no longer scanning résumés and filtering candidates can be used for more personalized communication with candidates and improving their experiences, which helps you nab the best talent for greater success.
At the same time, AI could help you upskill by identifying trends and patterns from the recruitment data would be beneficial for the firm. You could then focus on analyzing these insights to strategize future hiring needs, workforce diversity, and talent management.
AI can replace one tedious task, but it also complements and even creates others that it couldn’t do alone. This is something that current AI-driven systems can do, and businesses are seeing the benefits. But take this a few years into the future, and AI systems can be creating entirely new industries and roles.
Right now there’s a real risk that firms will primarily adopt middling AI systems that are productive enough to displace workers, but not productive enough to increase labor demand through other channels.
It’s easy to imagine how machines can speed up existing tasks — it’s much harder to conceive of ideas and functions that don’t exist yet.
This goes for AI firms and the organizations that buy their products. Both ambitious technologists and forward-thinking business leaders are needed to build advanced AI systems and invest in possibilities beyond their current frame of reference.
The predictive and creative capabilities of AI that can have a positive impact on the labor market are difficult to explain and understand. To truly increase productivity and the value of labor, AI needs to be accompanied by complementary investments in IT infrastructure, skills development, and business processes.
These investments may involve the accumulation of intangibles such as data, information, and knowledge that are critical inputs for the AI model. Intangibles are hard to protect, imitate and value, and their creation often involves lengthy and uncertain processes of experimentation and learning-by-doing.
But there are tangible reasons to make these complementary investments. For example, if there’s no investment in upskilling, reskilling, and evolving transferable skills, there will be a big risk of labor polarization, in which there are both labor shortages and unemployment.
As technology evolves, it will require new and different skills to implement and use effectively, creating new roles and organizational structures. If there aren’t enough people with those skills, jobs will go unfilled, and AI’s impact on productivity and growth will be limited. At the same time, growth will slow in jobs that don’t require higher skills or can’t be replaced with AI.
If you are eager or curious about adopting AI, the process can feel like stepping into a pitch- black room. But the solution to this isn’t to start slow with simpler automation systems — it’s to embrace the experimentation and look beyond short-term business milestones to transformative opportunities.
The true power of AI lies not in replacing workers, but in augmenting and enhancing the unique strengths that only people possess.
The enormous potential of AI is in how it can accelerate and complement the things that only people can do. The real threat of GenAI is that we will not use it responsibly and dismantle its progress and promise.
We are slowly understanding where AI excels and where it doesn’t perform as well. AI that automates mundane and repetitive tasks is effective and in full swing. This opens the way for us to instead focus on the creative, strategic, and interpersonal work that highlights our strengths.
The challenge lies not in the technology, but in how to integrate it into workstreams, steer its development, and manage its impact.
If ethical considerations are prioritized, more investments into education and retraining are made, and a collaborative environment between AI and human intelligence is cultivated, society can avoid the pitfalls of disruption, and instead unlock a future where technology amplifies our potential.
Navigating this new age will take strategic and talent-centric leadership coupled with a clear vision. Once your organization can prioritize, identify, and execute AI software, your leadership needs to bring employees along for the transformation.
Employees need to embrace new technology, but they also need to be motivated to upskill along with it so that the jobs of the future are filled, and workers feel confident about using AI to grow their careers. As roles become automated and overall efficiency rises, there will be a constant pipeline of upskilling and reskilling needed to make the best use of their skills.
Take, for example, the work of radiologists who specialize in diagnosing diseases and injuries using medical-imaging techniques. AI and machine-learning algorithms have demonstrated remarkable accuracy in analyzing these images, often spotting nuances that may not be immediately apparent to the human eye.
That doesn’t mean people have no place in this field. While AI could handle a substantial portion of the preliminary image analysis, radiologists can instead focus on more complex cases, cross-verify AI’s findings, and provide a more personalized approach to patient care.
People are now available to focus on interventional procedures and collaborate with other health care professionals to determine the best course of treatment. These aspects of the job use empathy, critical thinking, and a nuanced understanding of patient care, which AI cannot replicate.
There is no template for building organizations, teams, and processes around AI and people together yet. However, for the productive future to succeed, you need to be willing to put in the work and ask the right questions: How will AI affect different tasks? How do organizations need to be redesigned? How do people need to be reskilled?
This developing new world of GenAI will become even more unfamiliar as AI tech improves. It will be critical to understand the tradeoffs like cost, speed, build versus buy, and risk mitigation.
There must be visionary organizations at the forefront to put in the work and model how you can flourish alongside AI. This depends on having leaders willing to experiment and devote enough time and patience to make it work, which includes supporting people in learning the technology’s capabilities and upskilling your workforces.
AI won’t be a threat if people are given the opportunities and resources to get good at using it to complement what they do. This people-first connection — not the potential of AI models alone — should shape where the technology goes from here.
It’s not a question of whether to implement AI. It’s a question of the best strategy to do it as soon as possible, while preparing both your organization and employees.
As more organizations adopt AI into everyday operations, the gap between those who choose to innovate with this technology and those who do not is only going to widen. The movement to adopt AI will not be limited to the industries that need these skills today — like renewables or chip manufacturers — it will include nearly every industry in existence.
The writing is on the wall: three out of four C-suite executives believe that if they don’t scale AI in the next five years, they risk going out of business entirely. Your organization needs to incorporate AI and natural-language processing into your business now to be viable and competitive.
The good news is that many already are doing so, but these initiatives tend to be one- offs or experimentation only. And while that’s valuable, how can you go from this point to successfully scaling AI across your entire organization?
There was a major disruption that occurred with “business as usual” as the internet came into play in the early 2000s. There was also a mindset shift that took place — tasks could be done in a digital world faster and more efficiently than in a manual world.
With AI, understanding where your business wants to be and what skills will be needed to get there will require you to take a step back and think differently. Focusing on understanding your employees’ skills rather than their job descriptions is a departure from “business as usual.” Identifying declining and rising skills enables you to create paths for your workforce to build those rising skills they’ll need for the future of work.
Just as with any other technological innovation, whether AI improves jobs or eliminates them will depend on how it’s deployed. If the mindset is to use AI to create value for the customer and improve the jobs of employees, then AI can be a great asset.
Many organizations are already doing this now. For example, major retailer, DICK’S Sporting Goods, adjusted its talent strategy by embracing new skills to serve customers better and create internal opportunities for employees. With AI, the company is well on its way to building the skills of its nearly 50,000 employees to deliver the kind of experience its customers demand.
This is where most organizations get stuck. They conduct AI pilots or experiments, but fail to achieve ROI, which hinders the ability to scale. The main issue here is putting the cart before the horse.
Instead, you should focus on what business challenges need to be solved, and how AI can help solve them.
Tie your pilot or experiment to a business challenge, need, or strategy and brainstorm ideal metrics. This way it will be evident whether the pilot delivers the expected ROI. Also, siloed initiatives often stay inside one department or team. It’s good to think holistically by engaging other teams, especially your C-suite. When the C-suite can see overarching analytics that prove AI can help your organization meet business objectives, increase revenue, and keep customers happy, that’s when there’s buy-in.
The other issues with scaling AI beyond pilots are unrealistic time frames and under- investment, which yield low returns. According to an Accenture study, a “lack of budget” was at the bottom of the list of pilot challenges. The key to getting the most value out of AI is scale.
Scale strategically focusing on the big picture
But it’s not just about rolling AI out across the entire organization as quickly as possible. Scaling requires strategy. Organizations that strategically scale AI report nearly three times the return from AI investments compared to organizations pursuing siloed proof of concepts.
Focus on “big picture” problems using advanced analytics to guide the way. It’s easy to get bogged down into the details of the data so keep a high-level view.
Establish realistic expectations in terms of time to scale. It’s better to go slow and steady making sure business objectives are being met than it is to speed up with no proof of ROI.
To ensure that AI is scaled responsibly, it’s a smart move to establish an employee resource group (ERG). This group can not only act as a champion of AI across the organization, but also create accountability and align efforts. Your ERG group can also set how the organization will measure success from AI.
The end goal of scaling AI is to create a culture of continuous real-time insights that drive business decisions so your organization can innovate to consistently improve customer satisfaction.
The best way to reach this end goal is with a clear vision that includes communication, accountability, and metrics so everyone knows and trusts in the business practices. The vision should also include ongoing training for your employees so they understand how AI applies to their roles.
Above all, incorporating AI demands a shift in thinking. If the prevailing attitude is that people are a cost to be minimized, then AI will simply be a substitute for labor.
However, by prioritizing skill development and fostering a culture that views AI as a tool to enhance rather than replace potential, you can change the way people work for a more positive impact that benefits both your organization and your employees.
With great technological advancements, like AI, comes great responsibility. While ensuring diversity and mitigating bias with data are important, taking a wider view to include educating workers and organizational accountability are critical.
We are at a point with information technology where every advancement over the past 50 years has led to this moment — the industrial revolution for information is here.
AI can now absorb all the data that has been collected over past decades and reason across that data to answer questions. That is fundamentally changing not only how organizations work, but also has the potential to reconfigure entire economies.
It’s crucial to use this opportunity to come together to ensure that AI benefits everyone to reinvent a future where we elevate the entire labor force.
Responsible AI isn’t limited to the typical responsible AI themes like transparency, fairness, accountability, and privacy. It’s not only about including diverse perspectives and voices when AI systems are being developed, or even including larger data sets to ensure that results aren’t biased.
While considering these aspects during implementation and shaping AI policy are both important, there’s also another critical aspect to consider — the impact on people.
In the past, technological innovation has often thought of the impact on people after the fact. This “move fast, break things” approach has arguably worked out for us in the long run, give or take some outliers.
What if we take a different approach? Instead of analyzing the effects on the labor force after widespread adoption, imagine instead if we did the research and analyses on how workforces can be set up for success. This would require a shift in thinking, creating a world where everyone can benefit.
Responsible AI would include all of the above in implementing these incredible systems, but it could also include making appropriate plans to upskill the labor force to be ready for the opportunities this technology is creating.
Focusing on the research now to pinpoint how specific tasks and the related specific skills used to complete those tasks are affected will allow us to create plans for moving people from declining tasks to upskilling for future tasks that will be AI future compatible. Identifying how your workforce can move from the skills which will be on the downward trend and creating a path for upskilling and reskilling to skills rising in importance will be essential.
The goal should be to use AI to deliver innovative services to your labor force while making it efficient for your employees working with it. If approached thoughtfully, and engaged responsibly, your organization could use AI to create real value for people.
How can we enable organizations to take a proactive approach with AI toward the workforce? It will take a collaborative effort among organizations, investors, and governments.
While workers need to have the right resources to upskill, investors can also play a key role in helping organizations understand the impact of automation and help assess development strategies to harness potential. Being proactive in addressing workforce implications not only acknowledges a social imperative — it also makes financial sense.
Boards should be informed about AI-implementation plans and ensure that management is addressing implications that come along with adopting new technologies and practices. On the other hand, governments can create policies to help their labor force upskill with incentive structures. Singapore used innovative thinking and implemented a monetary offer.
Governments, investors, and organizational management all have a shared goal — to identify risks emerging from these technological changes and provide the right resources to prepare people for the future. These can include monitoring worker and skills retention, to capturing the full potential of the workforce already in place, and using metrics to devote energy and resources to areas of need.
When search engines were first introduced, people had to learn how to create queries that yielded the best results. The same holds true for AI.
Think about how your workforce prefers to learn and match that method to begin upskilling within your organization. Does your workforce prefer the traditional classroom approach, or are they more interested in short bursts of snackable content.
Additionally, natural-skilling can also be a path to help people learn based on the context of their jobs and they can be rewarded for it. Learning how these AI tools work will be critical to stay competitive.
AI will help you identify those hidden gems in the workforce and their skill sets. You can also identify adjacent skills to areas of the organization where you need people to do the job because automation won’t be able to do everything. With an understanding of existing skills, you can then determine the best way to create paths for reskilling and/or upskilling.
Beware of the danger to speed the deep knowledge work that might become the norm when using AI. Skimming AI summaries of documents might be faster, but it might not have the same effect on memory retention that reading the document itself would have. It’s important to balance the benefits of consuming condensed information compared to the potential detriment to your mental health or memory.
This is a unique opportunity to set the stage for future success, and to pioneer responsible AI deployment that prioritizes the well-being and growth of your workforce. There is no predetermined path for this technology. How it affects us will depend on the choices we make right now. It’s imperative to approach this phase with empathy and a steadfast commitment to see and use AI as a tool to elevate humanity.
