Which Capabilities Will People Analytics Teams Need Most in the Next 12–24 Months?

Why It Matters

Equipping people‑analytics teams with data‑engineering, low‑code AI, and a product mindset accelerates insight delivery, enabling HR to make faster, more strategic decisions in a rapidly evolving talent landscape.

Key Takeaways

• Invest in data engineering and low‑code AI platforms now.
• Business acumen remains essential for translating analytics into impact.
• Emphasize rapid prototyping and continuous product iteration cycles.
• Adopt a product mindset to envision ideal analytics solutions.
• Leverage sentiment analysis from surveys for ongoing insight improvement.

Summary

The discussion centers on the capabilities people‑analytics teams will need over the next 12‑24 months, emphasizing a shift toward AI stewardship, data‑engineering depth, and a product‑centric approach. The speaker notes that while some organizations already have in‑house AI expertise, most can achieve “80% viability” quickly by focusing on problem definition rather than pure technical prowess.

Key insights include investing heavily in data‑engineering foundations, leveraging low‑code AI platforms to bypass talent gaps, and cultivating business acumen to translate insights into actionable HR strategy. Rapid prototyping and continuous iteration—mirroring product development cycles—are highlighted as essential for gathering early feedback and refining tools such as sentiment‑analysis surveys.

Notable remarks underscore that “AI is not an insurmountable hurdle” and that “understanding the problem” drives success. The speaker cites Autodesk’s practice of updating survey‑analysis tools after each launch, using sentiment data to guide the next feature set, illustrating a real‑world application of the proposed mindset.

For HR leaders, adopting these practices means faster insight delivery, deeper integration of analytics into decision‑making, and a competitive edge in talent management as organizations increasingly rely on data‑driven people strategies.

Original Description

People analytics isn’t just about insight anymore — it’s about direction. That shift requires moving from a project mindset to a product operating model, and from reporting to decision enablement at scale. Where to invest, what to stop, and what risks are emerging.

In this conversation, David Green and Ashar Khan (Autodesk) highlight what that looks like in practice.

Key takeaways:

🔹 Business acumen remains the #1 skill — even in the age of AI. Understanding the problem and the decision context matters more than technical sophistication.

🔹 Product thinking is becoming core — designing solutions aligned to strategy, launching MVPs, iterating continuously, and measuring adoption and impact.

🔹 Rapid prototyping beats perfection — 80% viability is often achievable quickly with low-code tools; early feedback drives better outcomes than “showtime-ready” releases.

🔹 Data engineering, governance, and quality are the real AI differentiators — AI is only as good as the business-relevant data behind it.

🔹 Storytelling is evolving — AI can help generate narratives, but human judgment is critical to validate the story and guide decisions.

🔹 Metric fixation creates false confidence — success comes from using data to shape choices, not just report activity.

🔹 The future of people analytics isn’t about more dashboards — it’s about building decision products that scale impact.

Tune into the full podcast here: https://www.myhrfuture.com/digital-hr-leaders-podcast/why-people-analytics-needs-a-product-mindset-to-thrive-with-ai