Feature
For decades, business leaders relied on static organization charts and well-defined job roles when plotting their companies’ overall strategies. However, the rise of artificial intelligence (AI) and automation is threatening to upend traditional business architecture and planning. With many job roles in flux, and companies reordering every aspect of their operations, leaders must lean harder than ever on people analytics to figure out their org structures and capabilities.
In many ways, AI has helped workers do more. Software engineers generate code, marketing professionals spin up the first drafts of press releases and social-media postings in minutes and analysts ask a specialized tool to digest and interpret huge amounts of data. At the same time, AI threatens to make life complicated for managers and executives who must figure out the most efficient ways to use talent. For example, which tasks and jobs should be left to humans, and which should be offloaded to software?
The time to make these crucial decisions is now. According to a January 2025 study by the World Economic Forum, some 86% percent of employers expect AI and a new generation of information-processing platforms to transform their business by 2030. Meanwhile, 39% of workers believe new technology will render their current skill sets outdated within that same period.
With businesses under pressure to evolve, and workers rushing to upskill to nascent technologies, conventional job architecture — the old-fashioned framework of job levels and titles that executives have long depended upon — could be on the verge of a meltdown.
If business leaders want to build an effective org structure and get the most out of their talent and AI tools, they’ll need to master the fundamentals of a new, AI-powered generation of people analytics.
How People Analytics Drive Organizational Change
People analytics tools, including Crunchr, ChartHop, Oracle Fusion Cloud HCM, Agentnoon and Visier, crunch corporate datasets to give organizations several capabilities, including:
- Insights into workforce performance, such as employee engagement.
- Turnover and retention trends, including which employees might choose to leave in the near term.
- Modeling future scenarios and org structures
- Planning and budgeting, including compensation adjustments
No matter what tool they select, when integrating people analytics into an organization, it’s important that business leaders go beyond basic metrics such as headcount. To accurately capture the full scope of how work is performed, companies must merge people data — including job performance, turnover and compensation — with work data such as ticket resolution and sales conversions, according to Dr. Andrea Derler, principal of research and customer value at Visier, provider of an AI-based workforce insights solution.
“This enables organizations to break jobs into component tasks, identify patterns and then assess which are optimal for automation,” Derler said. “A task like summarizing large texts or tracking bugs can seamlessly be automated, but presenting to executives or managing complex team dynamics requires human nuance. At scale, this shows what AI can do and how people can focus on the work that matters most.”
It’s also important for companies to remember that not all jobs are created equal when it comes to the potential for automation. “Human roles are already changing, though not uniformly across all industries or functions,” Derler added. “Transformation doesn’t have to be urgent for all industries. In fact, it’s often subtle. Individuals are quietly redefining their roles, delegating repetitive tasks to AI, allowing for more time spent on ideating, coaching others and quality-checking the work of AI.”
People analytics likewise has a role to play when it comes to bringing the right employees into an organization, and how they rise through the ranks. “People analytics and intelligent systems are transforming hiring by shifting the focus from pedigree to potential,” said Chris Daden, CTO at Criteria, which produces software for job candidate assessment and interviewing. “Traditional hiring often relies too heavily on static credentials like degrees, years of experience or job titles — none of which predict future success particularly well. With modern analytics, we can now prioritize dynamic indicators: learning agility, problem-solving ability, emotional intelligence and actual on-the-job performance potential.”
In theory, AI-augmented employees with these skills will encourage companies to move away “from rigid hierarchies and static job titles toward a more fluid, skills-based architecture,” Daden continued. “This opens up new paths for internal mobility and more equitable access to opportunity because talent can be evaluated based on what someone can do — not just what’s on their resume. And as the nature of work becomes increasingly cross-functional, AI-powered tools can help dynamically assemble teams around projects and skill gaps, supporting a more agile, responsive organizational model.”
When reworking an automation strategy, Daden recommends the following:
- Establish an organization-wide governance strategy: Define the purpose and scope of AI tools to be used within the organization, including their risks and guardrails for ethical use. “The worst mistake organizations can make is treating AI governance as an afterthought,” Daden said. “Without it, even well-intentioned tools can introduce blind spots or erode trust.”
- Determine how data will be used: What data is the organization collecting as part of its AI efforts, and how will that data be used? Which teams (legal, technical, HR, etc.) will “own” the use of those tools?
- Build out a feedback loop and stakeholder review: Employees who are using AI to automate tasks should be given a way to report how their work is changing. In addition, any automation logic should be subject to “diverse stakeholder review,” in Daden’s words, especially in arenas such as hiring and performance management.
Once AI tools are in place, and decisions made about which skills and roles to automate, organizations can consider whether to deprioritize a rigid org structure in favor of dynamic teams built to respond to specific projects and challenges. Employees brought together based on their existing skills, potential for growth and fit with other teammates could quickly solve problems using new technologies.
“This shift signals a change in how we think about the traditional org structure to one that puts a stronger emphasis on the skills and unique attributes an employee brings to the table and less on the skills laid out in a job listing,” said Darin Patterson, vice president of market strategy at Make, which builds AI workflows and agents. “Building teams in this way allows for more fluidity. Teams can form and re-form quickly around priorities.”
Patterson has some additional tips for automating business processes:
- Start small: Start with one discrete task and ramp up. You don’t need to overhaul your business operations and automate across the board all at once.
- Automate the good processes: Does a process exist simply because it was the best compromise at the time? A company-wide automation push is a good time to eliminate some processes that are no longer needed.
- AI is probabilistic: “The more you need to guarantee consistent decision-making, and the more complex the inputs to those decisions, the more you’ll want humans in the loop,” Patterson added.
Whatever the path forward, leaders in charge of people analytics should work with HR to track the entire employee lifecycle, including recruitment and promotion.
“Investments in fair AI may also create measurable benefits in innovation and effectiveness,” said Sarah Beth Todd, vice president, sales enablement at Equifax Workforce Solutions, which develops HR management products. “Companies can track how quickly their workforce adapts to new demands by measuring the rate at which new skills are acquired. They can also monitor an AI-driven innovation rate by tracking how many new ideas are enabled by these tools.”
Using People Analytics to Define a ‘Human’ Job
People analytics give managers insight into repetitive and rule-based activities that are candidates for automation, while leaving humans to tasks that require nuanced problem-solving, emotional intelligence and ethical judgment. After any kind of automation analysis, managers must take the time to define how their reports’ jobs will change, both in terms of workflow and expectations.
“The real benefit of AI isn’t just speed, but scope: It handles the heavy, repetitive or expertise-focused tasks, which creates capacity,” Derler said. “For an entry-level software developer, this doesn’t mean waiting around for inspiration to strike. It means using that extra time to learn new frameworks, support colleagues, test features more rigorously.”
Derler relies on AI to save hours every week in data-gathering and brainstorming, and uses that time to support teams and other creative tasks. “But here’s the catch,” she added. “None of this happens unless organizations actively redefine jobs — aligning them to evolving goals and encouraging employees to experiment and grow.”
Making people analytics into a core element in an overall strategy is just the start for managers trying to define “human” tasks. Only with data, and a strong AI governance framework, can they redesign jobs and decide what to automate.
There’s also a cultural element to consider.
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An AI-driven shift to a more skills-based architecture might allow for faster workflows, but it also “assumes people feel safe enough to name what they are good at and how they work best,” said Tia Katz, CEO of Hu-X, which offers leadership development and other services. “Many employees, especially in leadership, still cover aspects of their identity that could reveal those skills. If a culture rewards sameness, the structure will default to it. For skills-based models to work, the environment must allow for honest visibility.”
Editor's Note: Read more about redefining jobs for our current moment below:
- Workforce Planners Say AI Isn't Ready for Prime Time — The volatility of the current business environment is making workforce planning tough. AI can help, but not in the way you might think.
- The Disappearing Entry-Level Job — Entry-level jobs are disappearing , but we can't leave it to recent grads to figure out next steps.
- Waking Up to Our Power: Digital and Human Capabilities for a Future-Ready Workforce — Beyond technical know-how, the future-ready worker needs a new blend of human and digital skills — anchored in awareness.
