The Capacity Equation Is Changing: Are You Ready?

I’ve spent the last several weeks neck-deep in research for Kantata’s upcoming State of the Professional Services Industry 2025 research, and one finding in particular is sticking out to me as I develop the report.

89% of professional services leaders say their future growth will depend more on scaling AI than on scaling headcount.

For resource managers, the implication of that shift is enormous. The equation you’ve spent your career balancing — matching people to projects, keeping the right tension between slack and overload, maximizing utilization — is being rewritten. The denominator isn’t just human headcount anymore. The numerator isn’t just billable hours.

The capacity equation is being rewritten. And resource managers have the chance (and I’d argue, the responsibility) to take the pen.

The Changing Math of Professional Services

For decades, professional services growth has been a fairly linear equation: more work = more people. Utilization was the guiding star, and scaling meant a constant treadmill of recruiting, onboarding, and retention.

But here’s the problem with that old math: it caps your growth. You can only hire so fast. You can only stretch your teams so far before burnout sets in. And you can only justify rate increases to clients for so long when your entire value equation is “hours of human effort.”

Now, the equation is changing. Driven by AI as both an enabler and disruptor, the industry is shifting from selling time to scaling impact.

• Instead of projects priced solely on hours, firms are increasingly offering packaged services and PSaaS models where outcomes are the deliverable.
• Instead of delivery being handled only by people, AI agents are becoming part of the team, taking on routine work, accelerating knowledge retrieval, and allowing human consultants to focus on higher-value judgment and client engagement.
• Instead of growth being tethered to headcount, AI opens the door to non-linear growth — scaling revenue faster than the rate of hiring, without the crushing overhead that used to define expansion.

This adds up to a wholesale rewriting of the laws of services physics. And resource managers, who have always sat at the center of the utilization equation, are suddenly dealing with a new growth equation entirely.

Visibility Into Variables: Hard Enough Already

That all sounds very utopian, but the reality for many resource managers is that, even without AI in the mix, visibility into resourcing was already challenging enough. According to Kantata’s upcoming research:

• 40% of PS leaders say they can’t accurately forecast resource needs beyond two months.
• 63% admit they aren’t sure what skills they’ll need over the next six months.
• 65% had to turn down work in the past year because they simply didn’t have the right people available.

And behind those numbers are human realities every RM knows all too well: project managers calling in panic because the right specialist isn’t available; benches in one practice sitting idle while other teams burn out; new hires onboarded reactively because the pipeline wasn’t trusted enough to plan ahead.

Those gaps aren’t going to magically disappear in the AI era. If anything, they’ll compound — because now you’re not just forecasting people and skills, you’re also forecasting where AI agents fit, what they’ll cost, and how they’ll be integrated into delivery workflows.

The margin for error shrinks. Which means the strategic resource manager of tomorrow can’t just be a scheduler. They must be an orchestrator of a hybrid workforce.

It won’t be enough to simply ask, “Who’s available next month?” You’ll need to know:

• Which agents are trained on the right data sets for this client?
• Where does human judgment create differentiation clients will pay for, versus where an AI can handle the heavy lifting?
• How do you model the costs of agent time in a way that’s consistent, transparent, and trusted by finance and clients alike?

If you don’t trust the information you’re seeing now, imagine trying to answer those questions. The margin for error is shrinking even further. Which means the strategic resource manager of tomorrow can’t just be a scheduler. They must be an orchestrator of a new hybrid workforce.

The New Hybrid Workforce Is Here

The shift to a new definition for the hybrid workforce (from remote and onsite to human and AI) isn’t theoretical anymore. According to Kantata’s upcoming research, the top resource management or project management challenge services organizations are facing in 2025 is managing and integrating AI agents into delivery workflows. Additionally:

• 87% of PS leaders say their org is preparing to manage AI agents as part of their delivery workforce.
• 90% say their systems will soon need to attribute work, cost, and value to both humans and AI.
• 92% believe AI agents will play a measurable role in delivery within two years.

That’s a good deal of consensus about what the next year or two will look like. But not every firm is equally ready to take advantage.

Some are dabbling in AI without a strategy. Some are dabbling in AI without a strategy. Some are bolting on assistants that save a little time here or there but don’t add up to transformation. Others are frozen at the starting line, waiting for clarity or clinging to the belief that clients won’t care how delivery happens.

But resource managers don’t have the luxury of waiting. But resource managers don’t have the luxury of waiting. You are on the front lines of orchestrating this new operating model — deciding how people and agents work together, how capacity flexes, and how outcomes are delivered. You’ll be the one asked to explain why a project is staffed the way it is, why costs look different than they used to, why the team mix includes fewer junior consultants and more AI-driven accelerators.

And the stakes aren’t just internal. Clients are asking sharper questions: What part of this work is being done by people versus agents? If you’re using AI to deliver faster, where does that show up in my price? If you can’t answer, you risk eroding trust — and trust is the currency that wins renewals and expansions.

There’s a lot at stake as the new hybrid workforce emerges, and the winners will be the firms that manage it intentionally, not reactively.

Four Shifts Resource Managers Must Lead

So how do you seize this moment instead of being steamrolled by it? Four shifts stand out as essential for resource managers ready to lead in the AI era:

1. Optimize Utilization Across People and AI Agents

There’s a temptation to see AI as “free” capacity — a bottomless well you can dip into without constraint. But that’s a dangerous illusion. Yes, AI never sleeps. Yes, it looks cheap on paper. But AI can be overutilized (becoming a bottleneck if too many workflows require too many babysitters to validate outputs). And it can be underutilized (if agents sit idle because no one knows how to integrate them, blocking return on investment against AI’s true costs).

So treat AI agents like any other resource: with cost rates, availability, and fit-for-purpose roles. Build staffing models that show not just who is working, but what is working — and ensure both people and agents are optimized for their highest-value contributions.

2. Become Outcome-Aware While Packaging Success

Too often, success stories live and die in the heads of individual project managers or consultants. In the AI era, that’s wasted gold! If a particular team mix, skill set, and AI configuration knocks a project out of the park, that’s more than a success worth celebrating. It has the potential to be a reusable asset.

Outcome-aware resource managers go beyond staffing projects or managing capacity. They capture and package what worked. They turn wins into playbooks. They feed those playbooks into proposals, so sales can promise with confidence. They transform delivery knowledge into intellectual property that scales — and even into productized services that drive recurring revenue

3. Understand the Economics of AI

AI-driven time savings create higher margins today, but clients are already asking tough questions. They want transparency on what was human work vs. what was agent work. They want to understand the cost implications. Resource managers must know the economics: how AI affects project speed, cost-to-serve, and profitability per client.

The best RMs will model AI as a costed resource, understand its impact on margins, and communicate it confidently to clients. That doesn’t mean giving away the value created — that’s a race to the bottom. Instead, it means being able to justify pricing with clarity, so AI becomes a differentiator, not a discount trigger.

4. Think in Terms of Elasticity, Not Just Utilization

In a previous post, I compared the professional services workforce to a rubber band: the goal is optimal tension, and it can’t be too loose but it definitely can’t be so tight it snaps.

According to the linear capacity equation we’ve lived with up until now, maintaining the right level of tension on your rubber band required agile reflexes, as what team size and skill mix was optimal changed granularly with fluctuations in demand. Being slow to adjust tension could be costly, if not catastrophic.

But what if the answer isn’t developing ever-finer reflexes to keep the old band from snapping — but trading it out entirely? Instead of the old math’s aging, overly sensitive rubber band that constantly threatens to fray under pressure, imagine transitioning to a new synthetic blend: a more elastic band that adapts with you. It stretches further when demand spikes, and it holds taut when things slow down. Not brittle, not slack — but right-sized and resilient, no matter how team size and skill mix flex. That’s the promise of AI as a force multiplier: to deliver more with fewer people when needed, but also to weather low ebbs with less overhead.

And here’s the actionable shift for resource managers: if you’re still measuring success by utilization alone, you’re tracking the wrong signal. Elastic capacity demands elastic metrics. Revenue per head becomes the more telling measure of scalable, AI-augmented value creation: are you creating more value per resource than you did before? Are you stretching further during peaks without breaking, and staying profitable when demand dips? Start reporting, modeling, and steering against revenue per head now, because that’s the KPI that will set apart the firms that thrive in the AI era.

This article originally appeared on ResourceManagementInstitute.com