What Happened:
Insight222’s sixth annual People Analytics Trends Study found the function continuing to expand despite economic pressure, growing about 60% since 2020 across participating organizations. The research covered 372 companies worldwide and showed reporting lines moving closer to the CHRO, signaling increased strategic influence.
Organizations deriving measurable value from people analytics were significantly more likely to unlock value from AI in HR. Common AI use cases included survey text analysis, process automation and attrition prediction, while generative tools were widely applied to chatbots, sentiment analysis and job description writing.
AI adoption is uneven and has changed work more by adding responsibilities than eliminating them. Most companies reported minimal task displacement, with new work emerging around governance, prompt management, context validation and measuring adoption across the enterprise.
Our Take:
This research reinforces a broader lifecycle shift unfolding across HR functions. AI adoption is not primarily a technology story. It is a maturity story. Teams that already operate close to business priorities, measure outcomes and translate data into decisions are positioned to extract value from AI. Teams still focused on reporting or dashboard production struggle to move beyond experimentation.
The implication for HR leaders is practical. Analytics capability is becoming a foundational layer for AI readiness, not an adjacent capability. The study’s “A teams” stood out not because of tooling but because of prioritization discipline. They align work to business performance outcomes such as productivity, sales or workforce deployment rather than limiting impact to engagement metrics or reporting requests. That focus determines whether analytics and AI initiatives are seen as strategic or administrative.
Another notable signal is how AI is reshaping work content. Task elimination remains limited, but scope expansion is accelerating. Governance, context validation and responsible deployment are emerging responsibilities alongside traditional analytics work. This mirrors broader enterprise patterns where automation compresses execution time while increasing expectations around interpretation, oversight and integration into workflows. For workforce planning, this suggests capability development should emphasize technical literacy and judgment rather than role reduction narratives.
Investment patterns also indicate a structural shift in the technology stack. Spending is moving away from standalone analytics tools toward AI-enabled platforms embedded in core systems. At the same time, adoption measurement remains immature. Many organizations cannot quantify usage or impact, underscoring a gap between experimentation and operationalization. Lifecycle management of AI initiatives may therefore become a differentiator, especially for HR teams tasked with scaling adoption and tracking behavioral change.
Perhaps the most grounding takeaway is the uncertainty revealed in the data. A substantial share of respondents reported not knowing how AI affects work or how widely it is used. For practitioners, this reframes competitive anxiety. The market is early, fragmented and exploratory rather than settled. Progress is less about finding definitive answers and more about building environments that encourage experimentation, skill development and transparent evaluation of outcomes.
For HR professionals, the trajectory is clear. Competitive advantage is likely to come from strengthening analytics maturity, aligning work to business impact and treating AI adoption as an organizational change process rather than a tool rollout. Teams that develop those operating muscles now may shape how workforce decisions are made in the next phase of AI integration.
Listen to the full interview here.