For nearly 20 years, I have attended the Human Resources Technology Conference and Expo to take the temperature of the market. This year, the energy felt different—less showy, more grounded. I was able to engage with more than 40 software providers for deeper-dive discussions, and I left with three big takeaways and one nagging concern that I can’t shake.
The first theme is about shrinking the distance between HR and the rest of the business. Historically, most HR systems were designed to route approvals and capture HR data. If other functions showed up, it was usually as a stop on a workflow. What I saw this year suggests a real shift toward purposeful collaboration in-app, with analytics that intentionally co-mingle HR metrics with finance, IT and operational data to tell a more complete talent story. Think compensation planning that lives next to budget variance, skills data that sits alongside project demand and workforce plans that finally answer the CFO’s favorite question: What’s the business impact? It’s not just dashboards for HR anymore; it’s decision support for leaders who run the P&L, the portfolio and the roadmap. This is overdue, and it’s the right direction.
The second theme is empathy in the employee experience, not as a tagline but as a design choice. The best product teams are making the system the supporting cast and putting the human—manager, employee, admin—in the starring role. That shows up in the small
things: fewer clicks to get to a decision, guidance that appears at the right moment, workflows that flex around real work instead of forcing people to contort themselves around the system. It’s still uneven, but the intent is clearer. The more providers anchor on reducing cognitive load rather than adding “more” for the sake of parity, the better the outcomes we’ll see. By 2027, two-thirds of enterprises will expect their HCM software to be utilizing AI for personalization in the interactions between employees and HR. That expectation raises the stakes for providers to deliver personalization that feels intuitive and genuinely helpful, not just algorithmic.
The third theme is AI with a point. For years, the show floor felt like it was wallpapered with “AI” just to check a box. This year, the conversations had a different texture. I heard more about specific, solvable problems—drafting a job description that aligns with internal skills taxonomy, surfacing pay outliers before comp cycles, creating a first-draft learning path grounded in role and performance signals—rather than vague promises that the machine will fix everything. That’s progress. But it’s only half the story.
Here’s the concern: The readiness gap is still vast, and too many providers are sprinting ahead of buyers. The last 12 to 24 months have produced a flood of AI-powered features, but adoption remains slow and, in some places, nonexistent. The reasons aren’t mysterious. HR teams are already stretched. Data foundations are messy. Governance and risk standards aren’t defined. People leaders aren’t trained to interpret AI-generated guidance, and legal is understandably cautious. What’s missing isn’t another feature; it’s sustained help getting organizations ready to use the features they already bought. Somewhere along the way, HR lost the muscle memory for organizational change management as a core competency. We need it back—urgently.
I want to be clear about this: I’m optimistic about AI’s impact on HR. I’ve seen enough well-implemented, well-governed use cases to know the ceiling is high. But I don’t buy the hype that suggests “more AI” equals “more value.” It doesn’t matter how many features sit on the roadmap if your buyer isn’t positioned to adopt them with confidence and success. When adoption lags, it’s not just a utilization problem; it’s a trust problem. It turns feature launches into internal political fights, and it pushes buyers toward safe, incremental changes that don’t move the needle.
The messaging-capability gap didn’t help. Across my conversations, I observed the same, persistent pattern: Marketing plays to buzzwords because the buyer isn’t an expert, and buyers invite the wrong providers into RFPs because they’re selecting for labels rather than capabilities. Everyone loses time and momentum. Providers burn cycles educating late in the process. Buyers waste attention on the wrong shortlist. And nobody builds the kind of shared understanding that makes implementation smoother and adoption faster.
So, what should change? For providers, shift some of your innovation capacity from net-new features into readiness, adoption and measurable value realization. Treat education and change management not as add-ons but as core services. Teach your customers what good
looks like—data readiness checklists, governance templates, decision-rights maps, manager enablement kits, comms sequences, risk scenarios. Put guardrails around where your AI is useful and where it’s not. Be specific about prerequisites. Show the delta between day-zero value and value at steady state, and outline how you’ll bridge it together. This isn’t a services land grab; it’s acknowledging that your product’s value depends on organizational behavior change. By 2028, two-thirds of enterprises will enable workforce readiness through HCM and skills development to optimize employee potential and performance. That future state won’t happen by accident; it requires deliberate investment in adoption strategies today.
For HR leaders, the work is equally clear and equally hard. Stop treating readiness as a project phase that happens right before go-live. Treat it as a strategy. Bring legal, IT, finance and communications in early and make them co-owners, not approvers. Clarify decision rights for how AI will be used and what will remain human-led. Clean up your data and document the assumptions behind it so that when AI produces an output, people know how to interpret it. Pilot intentionally with a few use cases that matter to the business—not to prove the tech works, but to prove the operating model works. Invest in manager enablement well beyond a one-time webinar. Track adoption like a product team would, with leading indicators and real feedback loops, and be willing to adjust when reality doesn’t match the plan.
If there’s a hopeful throughline in all of this, it’s that the market is maturing. The move toward cross-functional analytics, the centering of the employee experience and the more grounded AI conversations are all signs of progress. But maturity isn’t just a feature set; it’s a way of operating. The winners—on both the seller and buyer sides—will be the ones who can turn promising demos into durable habits at scale. That starts with shared language, honest scoping and joint accountability for adoption.
I left HR Tech encouraged, but I’m not letting us off the hook. We don’t need more slogans. We need better handoffs between vision and execution. We need providers to build readiness into the product experience and buyers to invest in change management as if the success of the project depends on it—because it does. If we can meet each other halfway, the next 12 months don’t have to be about chasing the next big thing. They can be about getting real value from the things we already have.
Regards,
Matthew Brown
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