Ling-yi Tsai has spent over two decades at the intersection of human capital and technological evolution, guiding global enterprises through the labyrinth of digital transformation. As an expert in HR analytics and talent management integration, she specializes in shifting the narrative from technology as a standalone tool to technology as a fundamental architect of organizational culture. In our current landscape, where the initial hype of generative tools has settled into the reality of daily operations, her insights into the ELMO 2026 HR Industry Benchmark Report provide a crucial roadmap for leaders struggling to turn experimentation into tangible, high-quality output.
While nearly every Australian organization has begun experimenting with AI, there is a stark contrast between occasional usage and deep integration. Why are we seeing such a significant gap between the 93% of HR professionals using these tools and the mere 19% who have actually woven them into their daily workflows?
The gap we are seeing in 2026 is fundamentally a “redesign” problem rather than a “technical” one. While 93% of HR professionals are reaching for AI to help with various tasks, many are treating it like a faster typewriter rather than a new engine for work. According to the latest ELMO data, only 15% describe the impact of AI over the past year as truly transformative, which tells us that most people are simply using it to pave the cow paths—doing old tasks slightly quicker without changing the underlying process. We see that 57% of workers prioritize speed above all else, but that rush often leads to a superficial engagement with the technology. To bridge this gap, organizations have to stop asking how a tool can help an individual and start asking how a workflow can be rebuilt from the ground up to leverage automated intelligence.
The data suggests a divide in who actually “owns” the AI journey, with 39% of organizations leaving it to IT while only 19% share responsibility between HR and IT. What are the risks of leaving workforce transformation primarily in the hands of the technology department?
When transformation is viewed strictly through a technical lens, we lose sight of the “human” in human resources, leading to the visceral discomfort reported by 49% of the workforce. IT can implement the software and manage the “tokenomics” or consumption costs, but they cannot answer the existential questions that keep employees up at night. If HR doesn’t have a seat at the redesign table, no one is answering the critical question: “What will we do with the time saved?” Silence from leadership on this front breeds a deep-seated fear of job cuts, which stifles the very creativity AI is supposed to unlock. We need a model where the CEO endorses the change, but the people leaders and tech leaders co-own the outcome to ensure that as roles evolve, the capabilities of the staff evolve alongside them.
We are starting to hear the term “AI slop” used to describe low-quality, automated content that adds noise rather than value. With 32% of workers reporting excessive rework after using AI, how can leaders ensure that increased speed doesn’t come at the cost of quality?
The obsession with volume is the greatest trap of the current era, because AI makes producing massive amounts of content effortless, effectively destroying “volume” as a signal of merit. It is disheartening to see that 31% of workers struggle to even validate the outputs they receive, while 29% are dealing with flat-out inaccurate results. We have to adopt the mindset that “writing is thinking” and reject the notion that a longer document is a better one; otherwise, we end up with managers sending pages of indecipherable, AI-generated feedback that takes more time to decode than the original task took to complete. I strongly advocate for leaders to adopt quality proxies, such as publishing the “time to write” or “effort involved” alongside major outputs, to signal that human oversight and critical thinking remain the gold standards.
Many employees are still operating in a gray area, with 43% stating they are unclear on when AI usage is even appropriate. How can a team move from this state of confusion toward building a reliable, automated workflow?
The move from confusion to clarity begins with the discipline to unpack a single, repeatable workflow rather than chasing every new tool that hits the market. Most people lack the skill to break a job down into its constituent parts, which is a prerequisite for designing effective AI agents. A practical first step is to pick one process—like onboarding or performance reviews—define what a “good” outcome looks like, and then map out every decision point and task involved. You cannot build a reliable agent in ten minutes; that’s a red flag for poor quality. By encouraging teams to share “how I used AI this week,” you create a transparent environment where the 43% who are unsure can learn through the lived experiences and successes of their peers.
As organizations face increasing pressure to show a return on their AI investments, the focus is shifting toward “tokenomics” and financial discipline. How should HR leaders prepare for this more rigorous scrutiny of AI spending?
As we move deeper into 2026, finance leaders will no longer be satisfied with vanity metrics like the number of daily logins or license adoption rates. They are going to look at how AI consumption costs—the “tokenomics”—translate into actual business value or improved employee outcomes. HR leaders must move away from measuring activity and start measuring the efficacy of the time redirected; if AI saves a team 20 hours a week, where is 그 20 hours going? If that time isn’t being reinvested into high-value strategic work or innovation, then the investment is essentially being wasted on “AI slop” or redundant tasks. The maturity of an HR function will be judged by its ability to prove that AI isn’t just making people faster, but making the work itself fundamentally better.
What is your forecast for HR technology over the next two years?
I predict that by 2028, we will see a “Great Decoupling” where organizational success is no longer tied to the number of hours worked, but to the precision of the agents we manage. We will move away from being “users” of AI to being “architects” of automated ecosystems, where the primary skill of an HR professional is the ability to deconstruct complex human interactions into logical, machine-ready workflows. However, this will also lead to a premium on “human-only” spaces; as AI-generated content saturates our internal communications, the value of face-to-face strategic sessions and high-empathy leadership will skyrocket. The organizations that thrive will be those that use the 2026-2028 period to rigorously test their processes, ensuring that every saved minute is intentionally reinvested into the creative and emotional labor that machines simply cannot replicate.