What We Learned at Transform 2026: AI, Skills, and the Future of Hiring

HiringBranch headed down to sunny Las Vegas for the Transform 2026 event, and it did not disappoint. The venue was gorgeous, the content was top-notch, and the attendees were extremely savvy. While the global theme was The Human + AI Equation: Forging the Next Era of Work, there were four talk tracks happening across three floors of The Wynn, including:

• AI and Humanity
• Health and Wellbeing
• Culture and Belonging
• Performance Reimagined

With only so many staff on-site, HiringBranch covered many of the sessions, but certainly not all of them. Still, a few overarching messages emerged across different presentations signalling the shifts being felt by the onset of agentic AI in the workplace. Let’s start there.

Change Management is the Real Implementation Challenge

One of the clearest themes across sessions was that AI transformation fails when organizations treat it as a purely technical exercise. The cultural and human dimensions of adoption are just as critical.

The biggest barriers to adoption were around trust and time-to-learn. Misinformation about how AI is being used inside organizations is eroding employee confidence, and in some cases, that mistrust is entirely justified. Listening tools and surveillance technologies have the potential to create real tension between workers and employers. For example, one HR leader talked about their one-on-one meetings using an AI note taker, and the employee felt somewhat unsafe with this tool listening to them, not knowing who would have access to these recordings after the fact, which leads to the employee not being able to be authentic in that experience.

That said, the picture isn't all fear. Businesses that are embracing AI are actually hiring more people, not fewer, as automation enables growth. The challenge for HR is helping employees shift their mindset from how will AI affect my job to how will AI shape my future. Advice centered around building a positive AI culture, cultivating recognition around successful use cases, and communication practices that emphasize the benefit to the employee or team. These shifts will help the organization make the AI shift with more ease and less fear.

A Practical Framework for Building AI Readiness

To facilitate AI upskilling across an organization, one panel emphasized a practical three-tier model. 

The foundation is individual AI literacy, helping employees understand how to interact with AI tools effectively, what the risks are in a corporate environment, and how to apply critical thinking and judgment when working with outputs. Critically, this isn't a one-time training event. The technology is evolving too fast for that. Organizations need to build ongoing cultures of learning and share AI success stories internally.

From there, organizations can build team-level technical and functional skills, pairing AI tools with the workflows and domain knowledge that make them actually useful. Early career employees, who often arrive with greater digital fluency, can serve as reverse mentors here, though some senior leaders mentioned it's important to set clear boundaries around usage, especially for younger users who need to develop non-AI skills in the workplace.

The final tier is enterprise-level value creation, understanding how AI capabilities translate into competitive advantage, product improvement, and customer value. This is where ROI conversations happen, and where leadership buy-in becomes essential. Speakers across many sessions consistently recommended starting with one to three high-impact use cases to prototype and prove value before scaling, rather than trying to transform everything at once.

The AI Skills Gap Is Already Here

As AI in the workplace is taking over, HR has to be mindful of who is getting left behind. One presentation shone a spotlight on this discussion, stating that women are 20–25% less likely to use generative AI than men. Similarly, senior leaders are adopting AI tools at nearly double the rate of non-leaders. And finally, workers aged 35–60 are also most at risk of falling behind, especially as early career talent arrives in the workforce with stronger digital literacy than ever. If the workforce doesn't address these gaps proactively, AI adoption won't lift everyone; it will deepen inequalities that already exist.

Meanwhile, another gap is evident. Human skills like communication, empathy, and critical thinking are growing in demand as fast as technical ones. Organizations are asking how they can assess candidates for these soft skills in new and evolving roles. With technical skills changing rapidly, these foundational skills yield more long-term success in comparison.

Rethinking How We Measure TA Success

The panel on measuring TA success in an AI-driven world surfaced a debate that's long overdue. Roughly 90% of hiring departments still measure success primarily through time-to-fill, a metric that, as one panelist bluntly put it, incentivizes bad recruiter behaviour by rewarding speed over quality.

Time-to-fill has its place as a health metric (a signal that something is wrong when it spikes), but it tells you nothing about whether the right person was hired. Quality of hire is universally cited by CHROs as their most important metric, yet definitions vary wildly, and the standard proxy of 90-day retention has real limitations. For frontline, high-turnover roles, 90 days might be meaningful. For senior or technical roles where onboarding isn't even complete at that point, it's nearly useless.

The more useful shift is toward predictive metrics and funnel-stage data, including pass rates, interview-to-offer ratios, and performance indicators gathered earlier in the process. This is where skills-based screening becomes strategically important: the earlier you can surface real performance signals, the better your downstream metrics will be. The data to support better hiring decisions already exists in many organizations. The question is whether organizations will actively adopt the right tools and processes to act on it.

The Vendor Opportunity and Final Thoughts

Perhaps the most forward-looking observation across the conference was about the role technology vendors have to play in closing the AI adoption gap and in fixing the fragmented HR tech stack. For example, a single practitioner can only drive change in one organization. In contrast, a vendor can drive innovation across hundreds of organizations potentially. That's a significant responsibility, and several speakers called out the current state of HR technology as part of the problem: too many disconnected systems, too many logins, too little interoperability. The opportunity, and the expectation, is for vendors to use agentic AI to anticipate what HR leaders need and bring data and systems together, rather than adding more complexity.

As AI unfolds in the workplace and under various HR tech, the most important bottom line will be keeping humans in the loop and building the trust that makes real adoption possible.

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Image Credits

Feature image: Property of HiringBranch

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