Let's get acquainted
This info will help our workforce agility experts personalize your experience.
If your heading is spinning from the headlines in the world of AI and its impact on work and the workforce, you’re not alone. I’ve been studying and following the future of work since 2012 when I was a senior consulting partner at Deloitte leading our research on human capital and business trends. As I publish this research round up, we’re just over 1,000 days since the launch of ChatGPT (November 30, 2022) and there have been at least 10 new ChaptGPT versions released by OpenAI, in addition to more than 10 models from Anthropic as well as models from Google’s Gemini, Meta, DeepSeek and others. Hundreds of millions of people use these models daily, in both consumer and enterprise markets. My focus for this research round up is exploring where we are today and the future opportunities and impact on work and the workforce from Gen AI, and now AI Agents, and cybernetic teammates (more on that later on).
One of my favorite metaphors comes from the title of a Nate Silver book, The Signal and the Noise. Every quarter I collect articles, podcasts, and reports which are helping me make sense of what’s happening in the changing world of work, workforces, and workplaces. My goal is not to be exhaustive, but rather to curate practical and provocative ways of thinking about the future of work trends and opportunities for business, technology, and people leaders.
First, we’re in the early chapters of the AI story but we’re IN the story. As consumers and as workers and managers, we’re experimenting and using GenAI models– hundreds of millions of us on a daily basis.
Second, we’re using AI in a range of ways including as
(to help us do our work)
(to do some parts of our work, and some tasks for us)
(to partner with AI as a colleague, working on projects together)
Third, we’re moving from a focus on what AI can do to how we can work with AI. How can we leverage AI to redesign work, processes, and the organization? And perhaps, most importantly, how can we develop and deploy our talent to effectively leverage and partner with AI, in all its forms, to empower the human edge and build future-orientedworkforces?
In asking how to deploy people and AI agents on “hybrid human-machine teams,” our focus at Gloat reflects the priorities we see for leaders and employees across industries. We are committed to providing platform tools for the AI era which focus on moving beyond adoption to work and workforce transformation through:
Empowering leaders and managers across the workforce, with the tools and data to assess and target where to make AI investments and where to reskill and redeploy the workforce to take advantage of AI’s potential. (top down);
Empowering employees and team leaders to take advantage of work and task deconstruction and in real time to have connected access to all of the skills and expert advice from employees across the enterprise and to access and use the range of software and AI tools available and to make these people and technological resources available in the flow of work (bottom up);
Empowering every employee to build AI fluency and to navigate new career paths across the enterprise to pursue their personal ambitions and to grow in the direction of the future work priorities of the organization—the emerging future of work priorities for 2030.
It’s becoming increasingly clear that the business and workforce impacts of AI extend beyond access and adoption (it’s not enough to make AI tools available) and likely also extend beyond work and task and workforce intelligence and analytic tools.The move from adoption to transformation is about action informed by insights.
That’s where the exponential impact happens: when leaders use AI insights to target AI investments with clear plans for enterprise impact and focus on employee and management development and deployment to work with AI as tools and teammates; when employees and project managers can deconstruct work to tasks and in real time access the skills and expertise of the company’s workforce alongside AI tools and agents; and when employees can continually grow and move to the future priorities of the enterprise.
In short, after 1,000 days, we are recognizing that the future of work is a verb, not a noun. It’s more than the technology whether we’re talking about robots, predictive AI, generative AI, AI agents or AI (cybernetic) teammates. The future of work is about more than analytics (in part, yes, but it’s more than talent intelligence, task intelligence, and now work intelligence). The reason I have chosen these articles, posts, and videos is because they help us frame and formulate the actions we need to take as leaders, employees, and managers to take advantage of new technologies and new ways of working by redesigning work, reskilling, and redeploying workforces, reimagining and exploring the careers of the future. What actions are you taking today? How can we help you to build and empower your exponential workforce?
The headline whipped through the business press and social media: “Despite $30–40 billion in enterprise investment into GenAI, this report reveals that 95% of organizations are getting zero return.” Yet, my guess is that while many are quoting the headline, few have read the 23-page report (or even the 1000-word AI summary Adobe generates when you open the PDF version of it. While there have been some constructive criticisms of the report’s methodology, I think its core message is spot on: what can we learn from the five percent of companies that are seeing the impact of AI initiatives and how do we move from pilots to transformation at scale? What types of targets do we need to set? What teams of leaders and functions need to be involved? What combinations of technology, process, and people do we need to work together? Let’s not be surprised. While AI adoption and access have grown dramatically over the past 1,000 days, the work on AI-driven transformation is now front and center. As I wrote in a recent article, ”Now the real AI transformation work begins.” Finally, in reading this report, as a student of the future of work, I kept thinking about Amara’s law: the observation that we tend to overestimate the effort of a technology in the short run and underestimate its effect in the long run.
This is another widely cited report, so it’s valuable to take the time to read (or at least skim) it. Brynjolffson and his colleagues find “that since the widespread adoption of generative AI, early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment even after controlling for firm-level shocks. In contrast, employment for workers in less exposed fields and more experienced workers in the same occupations has remained stable or continued to grow. We also find that adjustments occur primarily through employment rather than compensation. Furthermore, employment declines are concentrated in occupations where AI is more likely to automate, rather than augment, human labor.” It’s early days but this research is an example of the impacts, in this case on early career workers, that we’re beginning to uncover. This is an important piece of research both for business and talent leaders thinking about their workforce strategies and career models and for educational and policy makers to understand the changing nature of education to work patterns.
Last spring and this September has raised concerns about the impact of AI on labor markets and the challenges for government oversight and regulation. In this 10 minute interview with Anderson Cooper, Anthropic’s CEO discussed these concerns including a common thread of the past few years: the rapid speed that AI appears to be developing. While the long-term trend appears to demonstrate that the labor force and the economy adjust to technological advancements, Amodei highlights the challenges we face in adapting to these changes when they happen so rapidly.I’m highlighting this here, to share concerns about how we prepare as individuals, companies, and societies to the changes we are seeing as AI progresses.
Timely article in The Wall Street Journal from the CEO of Walmart. “It’s very clear that AI is going to change literally every job,” he said recently in one of the most pointed assessments to date from a big-company CEO on AI’s likely impact on employment… Some jobs and tasks at the retail juggernaut will be eliminated, while others will be created, McMillon predicts. “Maybe there’s a job in the world that AI won’t change, but I haven’t thought of it.”
Inside Walmart, top executives have started to examine AI’s implications for its workforce in nearly every high-level planning meeting. Company leaders say they are tracking which job types decrease, increase, and stay steady to gauge where additional training and preparation can help workers.”“Our goal is to create the opportunity for everybody to make it to the other side,” McMillon said.
This is a powerful framework for business, AI, and HR leaders preparing to reskill and redeploy the workforce for the work of the future. First, understanding which jobs and work are sunsetting (decreasing), which are in the daylight (steady), and which are sunrising (increasing). Second, having workforce planning, development, and internal mobility to move workers from sunset to sunrise jobs. That’s the focus of the AI era platform we’re rolling out at Gloat.
Ethan is the AI researcher I make sure to follow every day (and I would encourage you to as well). He published his book, Co-intelligence: Living and Working with AI in April 2024. In this recent blog, he shares his thoughts on how Co-intelligence has changed since it was published 18 months ago. Ethan fed his book and 140 of his blog posts into Notebook LM to produce a video on the question: AI since Co-intelligence – the evolution of AI to Gen AI, AI agents, AI Reasoners, and now Cybernetic Teammates.
I found this great article from Joe Davis, Global Chief Economist and Global Head of the Investment Strategy Group at Vanguard in Azeem Ashar’s Exponential View substack. Davis reviews Vanguard’s Megatrends model with a focus on demographics—the Silver Tsunami and the aging of the population—and the impacts of AI technologies. As he points out, “For most jobs—likely four out of five—AI’s impact will result in a mixture of innovation and automation, and could save about 43% of the time people currently spend on their work tasks.”
Davis helps us connect the impact of the growing retirement of Boomers and the productivity impact of AI. In many parts of the world, perhaps these two megatrends will complement each other. This is a thoughtful analysis of the macro issues. I liked the article so much, I bought Joe’s book, Coming Into View.
MBA Tech Program and Microsoft, explores what if your startup’s first hire wasn’t a person, but an AI agent? The article is based on an MBA class working on the question: “What is the future of work with AI agents?”
The results highlight several insights about human-machine interaction and collaboration. “The most successful teams sought to unlock AI’s ideation potential—building org charts, reshaping pitch decks, and providing hypothetical business models for human leaders to react to. Along the way, they uncovered a new paradigm for work—one where AI is a proactive, evolving team member.” Interestingly, the experience highlighted both the idea of “work as a conversation” (between humans and AI) and the evolving role of human knowledge (emphasizing the value of human knowledge as inspirers and curators.)
Perhaps the most radical insight from the project was how AI reshaped team dynamics. With AI handling execution, teams stayed smaller and moved faster. Students described a “multi-agent mesh” where different AI agents handled different domains—CRM, scheduling, finance—while the human acted as a conductor, coordinating the agents and making final calls. The idea was that each agent could have its own role, knowledge, and perspective, so that the curation of each agent’s skills became paramount. This model flips the traditional team structure. Instead of people using many tools, people manage many AI workers who use the tools. It’s a shift from humans using machine labor to humans using digital labor using machine labor. A new workflow is established.”
A recent article from Korn Ferry. We do love a good turn of phrase in the land of the future of work and “job hugging” may be my pick for the cute idea of the summer (in the northern hemisphere). We’ve moved swiftly in the past few years through the Great Resignation, the Great Reshulffling, the AI apocalypse (that one is still out there), and now job hugging. Of course, the economy and the job market go through cycles, booms, and busts. Yes, employees are holding onto their jobs and leaders are trying to figure out how to hold onto their employees. Perhaps more interestingly, leaders are now focusing on how to reskill and redeploy their employees to the work and jobs of the future. Job hugging is not about running in place. It’s about leading by helping our employees to get ahead and find and realize new opportunities.
His latest blog, Strategic Reset, is an instant classic prompting us to think about what customers will want in the future, what skills we will need in a world infused with AI and robots, and how we think about our jobs and careers. The section on implications for talent (that’s all of us) is a great case in point demonstrating how Rishad can get to the point faster than almost anyone: “Prepare for a completely different landscape by 2027: Knowledge will matter less as it can be accessed on demand for $20 a month. Experience gets less important as the ability to unlearn and learn becomes key. Almost everything that one does that can be done better by AI will be, and if one cannot build new skills to work alongside AI one’s career regardless of level will be at risk.”
A timely piece on AI and the evolution from adoption to transformation which focuses on tasks, talent, and teams, and what actions leaders need to take to fully reimagine workflows. This is the critical shift now: moving from adoption and experimentation to future horizons of human-machine teams where people drive strategy and oversight. Interesting observation, what do you think: “what feels advanced today will be table stakes by 2030.”
In August I had the opportunity to host my friend and colleague Josh Bersin on a Gloat webinar to explore his latest research on the rise of Superworkers and what it takes to build a 10X workforce. In this session, he discussed the organizational shifts needed to unlock exponential performance through AI-powered work design. Josh shares why mobility, talent density, and reimagined HR and IT relationships are key, and how platforms like Gloat are helping companies bring this vision to life.
This is a timely and front line view from two amazing leaders, Mark Jackson, Head of Future Workforce at Nationwide Building Society, and Chris Smart, Head of Global HR Services, Controls & Digital Solutions at MetLife, who share their real life stories on how they are leveraging technology (including Gloat) to build skills data, work and task data, and to connect the workforce to work with AI and develop the skills of the future. This conversation explores how two leading enterprises are moving past static job architectures and siloed talent strategies toward adaptive organizations that orchestrate work between people and AI.
This info will help our workforce agility experts personalize your experience.