The narrative surrounding artificial intelligence and the workforce often feels like a foregone conclusion. If you follow the mainstream headlines, the story is almost always about subtraction. Through May of this year, employers announced nearly 90,000 job cuts tied directly to AI. Consulting firms like BCG have projected that up to 15% of jobs in the United States could be eliminated by AI over the next five years. For many professionals, especially those in the association world, this creates a sense of quiet dread. The assumption is that as the technology gets smarter, the need for human staff inevitably shrinks.
That dread isn't unfounded. AI-tied layoffs are real, and they've been climbing, not slowing down. But a closer look at the data shows that job cuts are only half the picture. While some organizations use AI as a tool for downsizing, others use it as a catalyst for significant expansion, and which camp an organization lands in isn't random. New research suggests the split has less to do with the technology itself and more to do with the strategy behind its adoption, particularly how much an organization is willing to invest and how long it's willing to wait before that investment pays off. For associations, that means growth isn't a guaranteed outcome of adopting AI. It's a choice that has to be backed with real commitment, and the payoff doesn't show up overnight.
A recent working paper from Ramp and Revelio Labs provides some of the most compelling evidence against the "AI equals layoffs" narrative. The study tracked AI spending and workforce data across nearly 22,000 companies. The researchers focused on what they called high-intensity adopters—firms that were spending roughly $30 per employee per month on AI tools early in the adoption curve.
The results were the opposite of what the conventional wisdom predicts. These high-intensity adopters grew their total headcount by about 10%. Even more surprising was the impact on early-career professionals. While many assume that entry-level roles are the most vulnerable to automation, entry-level hiring in these AI-forward companies actually rose. Meanwhile, organizations that merely dabbled in AI—those that bought a few subscriptions or ran small pilots without deep integration—saw no headcount growth at all.
This data indicates that AI adoption creates a widening gap between two types of organizations. On one side, you have firms that view AI as a way to trim costs and maintain the status quo with fewer people. On the other side, you have organizations that view AI as a way to unlock new capabilities. These organizations are finding that as they become more efficient, they discover more work that needs to be done. They are not using AI to replace their people; they are using it to empower their people to take on higher-value projects that were previously out of reach.
To understand why an organization would hire more people after automating its core tasks, you have to look at the sheer scale of the productivity gains. In some fields, such as software engineering, AI is not just making people 10% or 20% faster. It is acting as a force multiplier that can increase output by orders of magnitude.
Consider the process of building a new software application for an association, such as a custom form builder that integrates directly with a member database. In the past, a project like this might have required a team of three to five engineers working for a year to produce a stable, feature-rich version. Today, an engineer using advanced AI tools can build a functional version of that same application in as little as 24 hours. This is not a theoretical estimate; it is a shift that is happening in real-time.
When a team can produce 100 times as much output as they could two years ago, the organizational response is rarely to fire half the team. Instead, the response is to realize how much more the organization can now achieve. If you can build a year's worth of software in a few days, you don't stop after the first project. You start looking at the hundreds of other tools, features, and services your members have been asking for but you never had the bandwidth to create. This explosion in capacity creates a massive demand for more smart, motivated people to guide that output, ensure its quality, and align it with the organization's mission. The work doesn't disappear; it moves to a higher level of complexity and impact.
The rise in entry-level hiring among high-intensity AI adopters is perhaps the most important finding for association leaders. The common fear is that AI will handle all the "junior" tasks, leaving no room for new graduates to learn the ropes. But the reality in high-growth firms is that AI allows junior staff to skip the repetitive, low-level drudgery and move immediately into substantive work.
In a traditional association environment, a new hire in member services might spend their first year manually updating records, answering basic FAQ emails, and cleaning up spreadsheets. These are the tasks AI handles best. When these tasks are automated, that same entry-level employee can spend their time on proactive member engagement. They can analyze member data to identify who is at risk of not renewing and reach out with personalized solutions. They can help manage the AI agents that handle the basic inquiries, acting as a human-in-the-loop to ensure the association's voice remains helpful and accurate.
Organizations that invest heavily in AI are hiring more junior staff because they need people who can operate these new tools. They need a workforce that is comfortable working alongside AI to produce more value. The risk for young professionals is not that AI will take their jobs, but that they will end up at organizations that don't use AI, where they will be stuck doing the manual labor that their peers at AI-forward organizations have already automated away.
If the technology is available to everyone, why are some organizations growing while others are cutting? The answer often comes down to leadership and organizational change management. The study from Ramp and Revelio Labs noted that the winners in the AI era tend to be firms that already have the management bandwidth and the technical staff to experiment and scale.
For an association to be in the growth camp, its leaders must move past the mindset of AI as a cost-saving measure. If your primary goal for AI is to reduce your payroll, you are likely to see your organization shrink in both size and influence. If your goal is to use AI to serve your members in ways that were previously impossible, you will find yourself needing more talent, not less.
This requires a willingness to experiment that is often missing in traditional nonprofit governance. Leadership must be willing to fund small AI experiments, but more importantly, they must be willing to step on the gas when an experiment succeeds. Too often, associations find a successful use case for AI but then wait for the next budget cycle—sometimes 12 or 18 months away—to do anything about it. In the world of AI, that kind of delay is a recipe for falling behind. Successful AI workforce strategy involves creating a culture where staff feel safe to automate their own tasks because they know their value to the organization is based on their human judgment and creativity, not their ability to perform manual labor.
The most difficult part of this transition is the human element. Not every employee will be immediately ready to move from a reactive, manual role to a proactive, AI-augmented one. This is where the risk of job loss is most real—not because the AI replaced the person, but because the leadership failed to provide a path for retraining.
Consider a member services department. If an association deploys a real-time audio AI that can answer the phone, look up member records, and solve 80% of basic problems, the staff who used to do that work must be retrained. They have deep institutional knowledge and an understanding of member needs that the AI lacks. The leadership's job is to figure out how to apply that knowledge to new, higher-value activities.
This might mean moving staff into roles focused on community building, advocacy, or high-touch consulting for members. It might mean training them to become "AI orchestrators" who manage the various tools the association uses. The organizations that are growing are the ones that have a clear vision for what their people will do once the drudgery is gone. They are communicating this vision openly to their teams to put fears to rest and encourage adoption.
Association leaders today face a fundamental choice in their AI workforce strategy. They can look at AI as a way to manage decline—cutting staff to keep up with shrinking budgets or stagnant membership. Or they can look at AI as a way to drive a new era of growth.
The data shows that the organizations that go all-in on AI are the ones finding new ways to serve their markets and, consequently, needing more people to help them do it. They are producing more software, more content, and more member value than ever before. They are hiring entry-level talent to fuel this expansion.
This shift is not just about technology; it is about the willingness to imagine a version of your association that is 10 or 100 times more impactful than it is today. When you start from that vision, the question isn't how many jobs you can cut, but how many great people you can find to help you reach that new horizon. The risk is not the AI itself, but the lack of a leadership vision that is big enough to make use of it.