Why Does NHRMA Partner with AIHR? Does Saving $ on Training Interest You?

Quoted from NHRMA's Weekly Pulse...

The hype around AI in HR feels eerily familiar. In the early days of people analytics, teams struggled to prove ROI, shift HR’s mindset, and move from isolated pilots to scalable impact. Today, we’re seeing AI follow the same pattern.

Consider this: Gartner predicts over 40% of Agentic AI projects will be canceled by 2027. S&P Global reports that 42% of companies have already abandoned their generative AI projects, and nearly half of organizations say that no single business objective has seen a strong positive impact from recent AI investments.

In short, the early promise of AI is crashing into organizational reality. That’s why the best way forward is to apply lessons we've already learned. People analytics has taught us what it takes to translate emerging tech into real business value.

Let’s break down the five most important lessons from people analytics that every HR and business leader should apply when implementing AI.

1. Start with Strategy

The mantra in people analytics has always been to start with a business case. Whether it is a burning platform, a strategic opportunity, or a regulatory requirement, the goal has always been to create clear organizational value, not just to implement technology. The same applies to AI. When aligned with strategy, AI has the power to lower costs, increase consistency, and scale with precision. Done right, it becomes a competitive advantage.

Yet most AI strategies today start in the wrong place: with tools, not problems. Vendors push the latest features, and organizations jump in without a clear use case. That’s why so many initiatives fail to take off. It also explains why many CHROs are still unsure what their next move should be. The starting point for any AI implementation should be a clearly defined business need: a pain point, a missed opportunity, or a process that’s ready for intelligent automation. Without this anchor, AI is just another shiny object.

2. Tools ≠ Solution

In the early days of people analytics, we often got the question: "Which tool should I buy/use to get started with people analytics?" Our message has always been that people analytics is a process, not a tool. Analytics is a way of thinking. It enables you to approach problems in a data-driven way, approach strategy with clear KPIs and metrics, and approach decision-making with a better understanding of statistical thinking that helps reduce bias and gut feel.

AI is no different. It’s not one tool; it’s an ecosystem. From personal productivity enhancers and generative chatbots to AI-powered team platforms and enterprise-level applications, the value of AI doesn’t lie in the tool itself. It lies in how we apply it. Impact comes from a deliberate combination of four elements:

• Mindset. Taking ownership of AI’s role and using it where it creates real value

• Toolset. Knowing and mastering the tools that enable human-AI collaboration

• Skillset. Building AI fluency to apply and adapt technologies effectively

• Heartset. Embracing the responsibility to guide, protect, and uplift the workforce through thoughtful AI adoption

Organizations that invest in all four, rather than chasing silver-bullet tools, will be the ones that lead.

3. Identify relevant skills

Over the past decade, many organizations have invested in upskilling their HR teams in people analytics. And for good reason: without data literacy, adoption stalled. We’ve spoken with multiple PA leaders who built beautiful dashboards, only to find them underused. The root cause? A lack of skills and confidence to interpret and act on the data. Combine that with a lingering discomfort around data and rising expectations for HR to be more evidence-based, and it’s no surprise that data literacy quickly became a core competency for forward-looking professionals.

We’re now seeing the same skill gap emerge with AI. AI fluency is becoming the new baseline. Those who learn how to integrate AI into their own workflows (and their team’s) will quickly outpace those who don’t. This isn’t just about technical skills. It’s about mindset, adaptability, and experimentation.

Key competencies for the AI-enabled HR professional include:

• AI awareness – Spotting opportunities to apply AI to improve workflows, decision-making, and outcomes
• Prompt design – Structuring and refining prompts to guide AI systems toward useful, high-quality results
• Responsible AI use – Understanding AI’s limitations, risks, and ethical considerations
• AI experimentation – Continuously testing and learning what works, iterating based on performance and insights

4. Build for scale, not pilots

A common pitfall in people analytics has been the overemphasis on reporting. Dashboards and custom reports played a critical role in building HR’s data foundation: improving data hygiene, increasing democratization of data, and building credibility. But too often, teams remained stuck there. Reporting became the end goal, rather than a stepping stone. These efforts, while necessary, were often manual, reactive, and rarely led to deeper insights or scalable business value. The real impact of people analytics came when teams moved beyond reporting into predictive, proactive, and strategic analysis. Yet even then, many efforts remained in experimentation mode: never fully embedded into the day-to-day rhythm of the business.

AI is following a similar path. Many implementations start as isolated pilots, often detached from core operations. They generate excitement, but not outcomes. Without a clear path to scale, they stall, or worse, fade out entirely. These small experiments rarely survive leadership transitions, budget cycles, or shifting priorities.

To succeed, AI needs to be designed for scale from the outset. That means prioritizing use cases that align with real business workflows, not one-off demos. It means putting the right process, governance, and capability structures in place to support sustained adoption. Just like people analytics had to move beyond data reporting, AI must move beyond proofs of concept to drive strategic impact.

5. Regard AI as a change management process

One of the key breakthroughs in people analytics was realizing that analytics can’t sit on the sidelines. Insights only create value when they’re embedded in core business processes like workforce planning, talent reviews, succession, performance cycles. The more deeply analytics was integrated into the way HR and the business made decisions, the more impact it had.

The same is true for AI. When AI is treated as an add-on, owned by innovation teams or explored in standalone sandboxes, it stays disconnected from real workflows. That leads to disjointed user experiences, low adoption, and unclear value. AI needs to live where work happens: in the tools teams already use, the processes they follow, and the decisions they make.

Integration means embedding AI into how people hire, learn, develop, and lead. It means moving beyond isolated experiments to AI-powered capabilities that enhance day-to-day execution. The most successful use cases don’t even feel like “AI”: they just feel like better work.

To move from hype to value, AI must be embedded in the fabric of work, not bolted on. Just as people analytics gained traction by integrating into the business rhythm, AI must do the same.

Taking the lessons home

The parallels between people analytics and AI in HR are instructive. We’ve seen what it takes to move from experimentation to impact: start with strategy, focus on mindset and skills, design for scale, and integrate deeply into the business. These are not just best practices; they're prerequisites for turning potential into performance.

AI is not a trend to watch; it's a capability to build. And just like with people analytics, the organizations that move with purpose, clarity, and discipline will be the ones that lead. The rest will be left wondering why their AI investments never lived up to the promise.