Our latest McKinsey Global Institute (MGI) research, Agents, robots, and us: Skill partnerships in the age of AI, shows just how profound this shift could be. Today’s technologies could theoretically automate activities accounting for more than half of US work hours. That headline understandably fuels anxiety. But AI is not just a labor-saving tool. It’s a catalyst to rethink how work is done, how skills are used, how roles are defined, and how value is created. Companies that have approached generative AI, and now agentic AI, by transforming end-to-end processes as opposed to pursuing more siloed use cases, are the ones that have had the greatest impact.
Embracing partnerships with AI
The dominant narrative around AI still frames the debate in terms of jobs gained versus jobs lost. That framing is too narrow. What is changing fastest is the content of work—the tasks people perform and the skills they apply.
More than 70 percent of the skills employers seek today are used in both automatable and non-automatable work. In other words, most skills will endure, but they will be applied differently in partnership with AI-powered agents and robots. Writing, research, analysis, and even coding are not going away; instead, people are shifting from producing first drafts to framing questions, validating outputs, and applying judgment.
This is already visible in hiring data. Demand for AI fluency, the ability to use, manage, and work alongside AI tools, has grown nearly sevenfold in just two years, faster than any other skill category. Crucially, this demand is not limited to technical roles. It is spreading across management, finance, healthcare, education, and frontline services.
For leaders planning the year ahead, the implication is clear: reskilling is not about turning everyone into an AI engineer. It is about helping people develop AI fluency and build the capabilities that complement intelligent machines—critical thinking, adaptability, quality assurance, coaching, and decision-making.
Why workflow redesign matters more than pilots
Almost all companies (90%) report investing in AI, but fewer than 40 percent report meaningful bottom-line impact. The gap exists because many organizations are applying AI to individual tasks rather than redesigning entire processes or workflows.
Workflows—the connected sequences of activities that deliver outcomes—were built for a pre-AI world. Layering a chatbot or automation tool onto those legacy processes yields incremental gains at best. The real productivity unlock comes from reimagining workflows so people, agents, and robots each do what they do best to get work done.
MGI estimates that by 2030, AI-powered agents and robots could unlock about $2.9 trillion in annual economic value in the United States alone—but only if organizations redesign work around these partnerships rather than automating tasks in isolation.
Early movers offer a preview. In sales, AI agents now prioritize leads, manage outreach, and schedule follow-ups, freeing human salespeople to focus on negotiation and relationship building. In customer service, agents resolve routine inquiries while people handle complex, emotionally sensitive cases—improving satisfaction and cutting costs. In professional services, AI accelerates drafting and analysis while experts apply judgment and ensure quality. In pharmaceutical companies, AI is completely transforming the drug discovery and development process.
The pattern is consistent: productivity rises not because people do less, but because organizations achieve more as people do different work.
A New Year’s checklist for leaders
As leaders start to act on their goals for the year ahead, three questions can help separate limited AI adoption from real at-scale transformation.
First, are you treating AI as a core business transformation? The question is no longer which tasks can be automated, but which end-to-end workflows should be rethought to reflect what people, AI agents, and robots each can do best. Automating isolated tasks can deliver short-term efficiency, but without broader redesign, it risks reinforcing outdated ways of working. The greatest value will come from looking five to ten years ahead, identifying where future value will be created, and redesigning workflows accordingly—working backward to make changes today.
Second, are you investing in skills as a source of competitiveness? As work changes, skills—not jobs—become the anchor. Organizations will need to develop AI fluency alongside distinctly human capabilities such as judgment, communication, and leadership. This includes enabling managers to lead hybrid teams and decide when to rely on automation and when human judgment should prevail. Done well, this strengthens productivity while supporting internal mobility as roles evolve.
Third, are you owning the strategic decisions? AI adoption forces trade-offs that cannot be delegated. Leaders must decide how fast to move and where to slow down to build trust; how much to bank capacity gains or reinvest them in growth and workforce transitions; and whether to prioritize certainty or foster experimentation and continuous learning. Over time, these choices shape both performance and the organization’s capacity to adapt.
Every economic transformation brings uncertainty. The AI era is no different. But history offers a lesson worth carrying into the new year: organizations that invest in people alongside technology tend to capture more value—and sustain it over time.
The partnership between people, agents, and robots is already forming. The question for leaders is whether they will shape it deliberately or react to it. A year from now, the companies pulling ahead will not be the ones that automated the most tasks. They will be the ones that redesigned work to amplify human strengths.
That may be one of the most important New Year’s resolution leaders can make.
This article originally appeared in Forbes.