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68%

68% of TA teams are hiring with limited visibility — receiving headcount on a rolling basis or just 1–3 months’ notice. (HIGHER)

88%

Using conversational AI to handle initial screenings led to an 88% reduction in financial costs compared to traditional methods.(WEForum)

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Over half of enterprise teams say that their internal headcount stayed the same 2024 to 2025 — or even shrank in size. 

Meanwhile, the pressure is creeping up. Hiring is back in full swing — and enterprise teams are the most likely to end up scrambling. According to HIGHER’s State of TA 2025 report, 36% of TA teams are receiving headcount on a rolling basis, while 32% are working with just one to three months’ notice. With shifting business priorities and limited visibility, long-term planning is becoming increasingly difficult to maintain.

When you’re moving to fill seats at warp speed with the leanest of teams, you really need to be juicing every little process and workflow for maximum efficiency.

“When you’re scaling rapidly with lean teams, every process and workflow must be optimized for efficiency,” says Daniel Morales, Global Talent Operations at Chainlink Labs. The critical question becomes: “Are our systems robust enough to handle this?”

The thing that’s holding your team back isn’t just a resourcing issue — it’s misaligned processes, disconnected tools, and delivery models that were never built to scale. 

Zooming out to understand how your systems, structure, and tech infrastructure interact as a whole is how enterprise TA teams can build for consistency, efficiency and adaptability.

 

 

The TA layer cake: Why you need systems before software

Behind every talent acquisition challenge lies a whole hierarchy of potential root causes. Understanding this hierarchy is critical to designing systems that scale — and avoiding the trap of fixing what’s visible, not what’s actually broken.

Matt Bradburn, founder of AI consultancy People x AI and former people leader, looks at it from a culinary perspective.

Building efficient talent operations for enterprise is like making a layer cake,” he says. “You need all of the components for a great cake. But equally, you can’t just go in with the [frosting] and top layer before you’ve baked in the foundations.

In other words, you don’t start with tech — you start with systems.

Because what looks like a tech problem (or frosting, in this case), is often actually a systems problem — a breakdown in how your people, processes, and tech infrastructure work together (or don’t). And that’s when the whole cake collapses.

Recruiters creating workarounds outside of your ATS, for example, or hiring managers struggling to get to decisions quickly are symptoms of broader systemic problems.

So, how do you figure out where the system is failing?

Start with what the experience looks like for all users,” Bradburn says. “Internal users, hiring managers, interviewers, recruiters, the finance team, the ops team, and your candidates — all those end users of your system. You’re solving for not just one set — you’re looking across these problems, balancing out which is going to be the most impactful and effective solution.

Your users’ experience is your systems audit. It tells you how people experience your processes, working models, and tech. To get a handle on this, ask:

  • Who are our core users?
  • Where are people working around the system instead of within it?
  • What parts of the hiring experience feel most inconsistent, confusing, or manual for all users?
  • Where do handoffs break down?
  • Which tools are being used inconsistently — or not at all?

Systems thinking helps you spot where the cracks are. Fixing them means zooming in — on process, team design, and tech.

The 3 levers of scalable TA systems

To build a talent system that actually holds under pressure, you need to get three key components working in sync: processes, team design, and your tech infrastructure.

Each lever matters on its own — but none of them operate in isolation. Your delivery model shapes what your processes can handle. Your tech stack sets the pace for how efficiently people can work. 

And when all of these support one another, you build a talent acquisition system that helps you hire better, faster, and more efficiently.

1

Design efficient, consistent processes with flex

Building an enterprise hiring engine means knowing when your processes are helping you scale — and when they’re holding you back. 

“You need the right people designing the right processes — that’s the bedrock for an efficient TA system,” Bradburn says. “Trying to swap out what you think is a bad piece of tech — to another one isn’t going to solve a process problem.”

To Bradburn’s point, this is exactly the type of challenge Morales faced scaling Better.com ‘s team from 600 to 8,000 in just two years. Identifying that their tech was actually causing a  hiring process breakdown meant the team could propose the right fix — going analog.

“At Better.com, our hiring challenges required unconventional solutions because traditional recruiting systems simply weren’t built for that scale and speed,” Morales explains. “The ATS couldn’t handle our pace; clicking through 50+ candidate statuses manually wasn’t sustainable. 

“We needed speed and efficiency, but without sacrificing candidate experience. Our solution was to go analog: we built a master spreadsheet tracker, implemented a blitz hiring model, standardized hiring manager scorecards, and used appointment-scheduling software to allow candidates to self-book interviews. Ultimately, we created a high-throughput hiring machine tailored exactly to our needs, enabling a lean team to process hundreds of candidates weekly.”

 

What this looks like in practice

Standardized, repeatable processes enable global scale while maintaining local consistency. But if there’s one thing Morales’ example proves, it’s that this doesn’t necessarily mean rigidity.

Instead, it means reducing the processes that slow you down, and building processes that can bend under pressure — without snapping.

“You might see breakdowns in a recruiting funnel — like a more significant number of people dropping out after one stage because we’ve asked them to do a 4-hour test,” Bradburn says. “That might not necessarily be a problem — maybe we want people who will complete a 4-hour test. It’s about working out where the problems are in the process.”

In practice, this means:

  • Mapping end-to-end TA processes from sourcing to onboarding, and flagging unintentional points of friction
  • Systematically identifying the gaps between what your process is supposed to achieve versus its actual outcomes
  • Challenging inherited or legacy assumptions of how processes can be fixed
  • Being explicit about what can flex and what can’t, so that teams know where they can adapt

2

Design your talent delivery model for speed, scale, and change

Processes are good in theory — but it only works when the people running them day-to-day are set up to succeed. 

“Your system is only as good as the people using it — and people typically don’t scale as quickly as rapidly scaling businesses do,” Bradburn notes. 

Even the best-designed workflows will stall if your talent acquisition delivery model can’t adapt to handle the load. 

Morales uses a hybrid approach that allows for maximum flex to match current market volatility.

“When scaling talent teams, it’s crucial to create structures that can flex dynamically to business demands,” Morales says. “Hiring needs change quickly, and teams must handle both rapid growth and sudden contractions smoothly. 

“I’ve found success with hybrid models where team members have clear core responsibilities, but also spend roughly 30% of their time supporting other functions like employer branding, analytics, or strategic programs. This built-in flexibility allows generalists to pivot quickly, ensuring agility and resilience even when headcount projections inevitably shift.”

 

What this looks like in practice

While there’s no one best-fit delivery model for enterprise TA, talent acquisition teams on a global scale are changing how they hire and build a team that better matches the needs of today’s volatile environment. 

74% of talent teams are hiring for broader skill sets over specialism, because when the going gets tough, the generalists can stop every bullet like Neo in The Matrix. 

“Generalists who can flex across multiple areas tend to be most effective when there’s potential for volatility,” Morales notes. “That’s why understanding the potential for fluctuation — even if headcount plans almost always change — is so important.”

In general terms, teams need a small strong bench in-house, with the ability to expand to fit what’s coming next. 

Here’s what that can look like:

  • Map market and macroeconomic trends over hiring data to identify any patterns or trends.
  • Anchor roles with a 70/30 split that enable cross-functional skills development and strengthen secondary and tertiary skill sets.
  • Shift your TA hiring strategy toward adaptable generalists with cross-functional capabilities — including analytics, coordination, and employer branding.
  • Lean on external teams like embedded RPOs or agencies for capacity spikes — such as short-term seasonal hiring.

3

Build a future-focused tech stack that solves for your biggest bottlenecks

Your processes and talent organization structure should define your tech stack — and never the other way around. Otherwise, you’re not solving root cause issues — you’re just adding tools that become a band-aid.

Morales calls this approach the “Frankenstack” — a bloated TA stack that’s literally separate parts nailed together without too much thought into data flow or process fit. And where there’s a Frankenstack, clarity and consistency go to die.

“Teams end up with a Frankenstack when they try to solve every problem by adding new tools onto a core platform that falls short,” Morales says. “Soon, recruiters are bouncing between multiple systems, data becomes fragmented or duplicated inaccurately, and overall friction increases rather than decreases.”

Frankenstack tells, according to Morales, are when your tooling is actually increasing friction within day-to-day processes, not removing it. Users bounce between different tools to complete tasks. Siloed data leads to gaps in reporting. Tools have overlapping functionality. And worse — your tech spend is through the roof. 

“Before adding another tool, teams should evaluate carefully: Does this tool solve a critical gap not addressed by our existing systems? And is the benefit truly worth the cost and complexity of managing another integration, another siloed data source, and another step in the user workflow?”

What this looks like in practice

To dial in on which problems are a tech problem, you need to view your stack (and anything on your shopping list) through a critical lens. This will help you make more intentional investments that align with your size, priorities, and process setup. Ask: 

  • Will this tool still serve us when we’ve doubled in size, or increased in complexity?
  • Does it fully integrate with our current core systems — and will it work if those systems change?
  • Can it scale across multiple geographies, functions, or business units without custom rebuilds?
  • Is the value of this tool dependent on another tool we might outgrow?
  • Are we solving for today’s pain point, or building infrastructure that will still hold up in 2 years’ time?
  • How interoperable is our full tech stack? Does data flow between systems without anomalies or errors?

This approach is especially important when teams consider how and where to implement AI-enabled tooling.

“At Chainlink Labs, we’ve established a cross-functional AI working group including recruiters, sourcers, employer branding, and analytics,” Morales explains. “Our first priority isn’t the AI itself — it’s clearly defining the problems we’re trying to solve. 

“Each potential use case is carefully prioritized based on its projected impact, complexity, scalability, and risk. Only after clearly defining a problem statement and outlining desired user interactions do we determine whether a particular solution is best built in-house or bought off-the-shelf. Our structured approach, supported by strong AI governance, ensures every AI initiative drives tangible business outcomes.”

Fix the system, scale your talent machine.

Scaling enterprise-ready talent operations starts with zooming out, not zooming in. Not everything is a process, or a tech problem. More often, it’s a systems problem — one that shows up in brittle workflows, lengthening time-to-hire, and bloated tech stacks that add friction instead of removing it.

And that’s where you need to start — by fixing what connects everything else. 

Getting to the core of how your system works — who its users are, where it fails, and what it’s actually designed to do — will help you identify the changes you need to make across your processes, team design, and tech infrastructure to build talent operations that can flex with your company as it grows.


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