A lot of HR teams I talk to are in the same place:
“We’re exploring AI.”
There are demos, pilots, steering committees… and not a lot of change in how work actually gets done.
Exploring isn’t bad. It just isn’t the finish line. In this research, we stopped obsessing over maturity and started looking at something much more honest: Momentum.
Are you actually moving? And if not, what’s in the way?
When we pulled the data apart, seven things kept showing up. Not abstract stages. Real, practical drivers of momentum that you can see inside almost any HR function if you know where to look.
The 7 Drivers of AI Momentum in HR
1. AI Literacy: The Permission to Try
You can’t build momentum if everyone’s faking their way through AI conversations.
Literacy isn’t about turning HR into data scientists—it’s about enough fluency to separate useful from nonsense, and to ask, “What problem could this actually solve for us?”
Leaders: HR can explain, in plain language, how their AI use cases work and where the boundaries are.
Laggards: Conversations stall at “I heard a vendor say…” and no one feels confident enough to commit.
As one participant put it:
“The question I keep asking my team is: are we building an ‘AI strategy’… or a business strategy where AI actually makes us better at it?”
That’s literacy in practice: understanding AI well enough to keep it in its proper place.
2. Strategy Posture: Is AI on the Page or in the Hallway?
Here’s a quick diagnostic: open your HR strategy deck. Does AI show up in the actual plans, or only in the hallway chatter?
When AI is truly in the strategy, it’s threaded through workforce planning, hiring, learning, performance. When it’s not, it becomes a side hustle for the one person who cares the most.
Leaders: Name 2–3 AI plays they believe will move the needle on outcomes like agility, retention, or quality of hire.
Laggards: Talk about “keeping an eye on AI” with no owners, no milestones, and no tradeoffs.
One leader described what “on the page” looks like:
“The most sophisticated teams knew what their strategy was. They were very specific—do we need this for scheduling, for workflow management, for motivation? They went looking for tools that matched the strategy, not the other way around.”
If AI only exists in your hallway conversations, you don’t have posture yet—you have vibes.
3. Risk Orientation: How You Respond to “This Makes Me Nervous”
The emotions around AI are real: risk to jobs, to fairness, to brand, to compliance. The question isn’t whether people are nervous. It’s what you do with that.
Some teams freeze. Others say, “Okay, what would make this safe enough to try?” and design from there.
Leaders: Use pilots and guardrails to learn into the risk, then bake those lessons into policy and practice.
Laggards: Treat risk as a stop sign, waiting for regulators, the board, or “everyone else” to go first.
One person shared a painfully familiar story:
“Our AI committee told us, ‘You can implement all of it… except any component that AI will touch.’ They think they’re mitigating risk, but really they’re just avoiding it entirely.”
That’s the difference: treating risk as something to manage versus something to run from.
4. Ownership & Coalitions: Lone Wolves Don’t Scale
If AI in HR touches data, ethics, productivity, and people risk (it does), then “HR owns it” or “IT owns it” is already the wrong answer.
Momentum shows up when there’s a named coalition that can actually clear the hurdles: tech, legal, security, operations, HR, and a business sponsor who cares enough to fight for it.
Leaders: Can point to a working team with clear roles, decision rights, and a shared agenda.
Laggards: Have lots of people “looped in,” but no one truly accountable—and it shows up as drift.
One leader described HR’s role in that coalition this way:
“On the implementation team I’ve seen huge global change in a short time. HR’s role is really to be the advisor on the organizational and cultural changes—upskilling, org design, change management—in addition to compliance.”
If your “coalition” is just a long CC list, you don’t have ownership—you have spectators.
5. Governance: Guardrails That Speed You Up
This is one of my favorite reversals from the research: the teams moving the fastest are not the ones with less governance—they’re the ones with clearer governance.
When people know what “responsible” looks like in your context—what’s allowed, what needs review, how you’ll monitor outcomes—they stop relitigating the basics in every meeting.
Leaders: Have a living set of principles, approval paths, and checks for bias and unintended impact.
Laggards: Either have nothing, or governance is buried in PDFs and policies practitioners never see.
One European leader described the model they’re building:
“We’ve set up an AI data, governance and ethics team that reviews each use case before we do anything. We look at it from a societal, individual, and company perspective, and we collaborate closely with legal, compliance, IT, and cybersecurity.”
That’s governance as an accelerator: clear rules that let people move faster with confidence.
6. Integration: Getting Out of Pilot Purgatory
A lot of AI optimism dies in integration. Not because the model is bad, but because its outputs never make it into the systems or workflows where decisions actually happen.
When AI is truly integrated, it disappears into the flow of work: surfacing insights at the right moment, updating records automatically, feeding into planning and reporting.
Leaders: Start with the workflow—“Who needs this insight, where, and what happens next?”—and design the tech around that.
Laggards: Collect disconnected tools and pilots that look great in isolation and go nowhere.
One person cut right to the heart of it:
“Where does data integrity fit into all of this? If your recruiters aren’t following the process in the ATS, the data isn’t an accurate reflection of reality. You can put any agent or analytics on top of it—bad data is just bad data.”
Integration isn’t just about APIs. It’s about habits, process, and whether the data you’re feeding these models is even worth the compute.
7. Investment: Your Real Priorities, in Numbers
We can talk about values and vision all day, but your budget and headcount will tell the truth.
Momentum requires fuel: people who own the work, partners who fill the gaps, time to rethink processes instead of just bolting “AI” onto old ones.
Leaders: Are increasing investment in AI for HR and tying it explicitly to decision quality, workforce outcomes, and manager capacity—not just “efficiency.”
Laggards: Try to run AI on leftover budget and goodwill, then wonder why nothing reaches escape velocity.
One leader gave a line I can’t stop thinking about:
“We’re probably technically considered a laggard—but a laggard for good reason. We’re investing in getting the foundational stuff in place first.”
That’s the difference between starving the work and funding the bedrock.
What You Do With This
You don’t need a 40-page assessment to get value here (though you can—and should!—download our 60-page white paper here lol).
Take these seven drivers into your next leadership meeting and ask three simple questions:
Where are we strong?
Where are we clearly stalling?
If we improved just one of these in the next 12 months, which would unlock the most progress?
That’s the work.
If you want a more structured read, we’ve built an AI Momentum diagnostic and a full report that map these drivers to the archetypes we’re seeing in the wild. But even without that, you can stop just “exploring AI” and start pulling the levers that actually move you forward.