Let me say this upfront: I’m not anti-AI. I’m actually very pro AI.

But I’m also pro-reality. And here’s the reality I’ve seen across HR Shared Services (HRSS) teams, both in conversations and in practice: we’re being flooded with AI tools and promises, but there’s not enough clarity, caution, or honest conversation happening around what it really takes to get value from them.

In just the past year, I’ve seen no shortage of vendors promising “AI-powered” transformations. Faster service. Fewer tickets. Intelligent insights. They lead with high production demos, polished use cases, and compelling promises.

But all of that excitement comes with a big question, one too many teams skip:

Are you actually ready for this?

So, if your team is even thinking about implementing AI - whether for chat, case management, document processing, or analytics - here are five things I wish more HRSS leaders understood before signing anything.

Every demo looks amazing when the sample data is perfect. But that’s rarely real life.

If your team has legacy job codes, inconsistent ticket categorization, duplicate employee profiles, or open cases that haven’t been updated in months, those issues won’t disappear when AI enters the picture. In fact, AI will learn from and repeat those errors.

Before investing in any AI solution, take the time to audit your data hygiene. Look at the way case types are labeled. Check for gaps in metadata. Normalize your categories and naming conventions. And make sure your key data fields are being populated consistently across the board.

It’s not the glamorous part of AI readiness, but it’s what makes everything else possible.

One of the most common traps HRSS teams fall into is assuming all AI is the same. It’s not.

Some vendors are using machine learning models that truly improve over time. Others are using pre-set decision trees that don’t actually “learn” at all. And many just slap the “AI” label on automation or analytics features because it helps close deals.

That’s why it’s so important to dig deeper during vendor discussions:

• What kind of AI is being used?
• Is it learning from our data or just running static rules?
• How does it handle exceptions or new scenarios?
• Can the tool explain its decisions in plain language?
• How does it adapt over time?

The goal isn’t to catch vendors off guard. It’s to ensure the technology aligns with your needs and your level of readiness. If they can’t explain how the AI works or how it delivers real outcomes, keep looking.

This is a tough one, because it’s tempting to believe a tool can “smooth things over.”

But here’s what I’ve seen: AI doesn’t fix broken processes. It magnifies them.

If case routing is already unclear, AI will just make the wrong decisions faster. If your knowledge base is outdated, AI will confidently share incorrect information. If there’s a lack of process documentation, AI won’t magically invent it.

That’s why process clarity and documentation are so important before AI enters the picture. Walk through your end-to-end service flows. Where do handoffs break down? Where are exceptions being handled manually? What happens when a case is misrouted?

Start there. Strengthen the process first. Then layer in AI to enhance it, not patch it.

Not every AI implementation needs to be a sweeping transformation. In fact, some of the most effective efforts I’ve seen began with a focused pilot that solved one small, specific problem with measurable impact.

For example, teams have seen real value by using AI to automate simple case tagging, assist with first-touch resolutions, or suggest relevant knowledge articles based on keywords. These small wins generate momentum and trust while giving you data to support expansion.

Here’s a rule of thumb I like to use: if you can’t measure the value of a pilot in the first 90 days, it’s too big.

Keep it contained. Measure what matters. Build internal champions along the way. When you scale, you’ll have a story to tell and support to go with it.

There’s often a sense of urgency when vendors are involved: special pricing, quarter-end deals, limited licenses. The pressure to move fast can feel intense, especially when leaders are eager for results.

But when it comes to AI, speed without clarity can lead to regret.

Make sure you do the work up front:

• Ask for customer references, especially ones in similar industries or org sizes
• Request a sandbox or pilot environment
• Clarify how implementation, training, and change management are supported
• Push for transparency in their roadmap and yours

Most importantly, don’t let the promise of AI overshadow your internal priorities. Your roadmap should serve your people and your strategy, not a sales target.

If you’re exploring AI for your HRSS function, here’s a practical place to begin:

• Get your data house in order. That’s step one, always.
• Document your biggest service or process pain points. Know what you’re solving for, not just what you want to buy.
• Define success clearly. What outcome are you hoping for, and how will you measure it?
• Choose one high-impact, low-risk use case. Prioritize simplicity over scale at the start.
• Equip your team. Build up internal understanding and capacity before leaning on a vendor.

It’s easy to focus on tools and workflows, but real transformation happens when your team feels included, equipped, and secure

If AI is introduced without transparency, it can feel like a threat, especially if employees worry about being replaced instead of empowered.

Bring your team along early. Communicate the “why.” Provide upskilling opportunities tied to these new tools, not just to use them but to help shape how they evolve.

Because when employees feel informed, valued, and invested in the process, they stop resisting change and start driving it.

AI is powerful, but only when you’re ready for it.

The best AI investments don’t start with tech. They start with clear goals, good data, honest assessment, and the right mindset. Don’t let the hype derail your judgment.

It’s not about being first. It’s about being prepared.

And if you’re leading HR Shared Services, your job isn’t just to adopt AI. It’s to make sure it actually works for your people, your processes, and your business.

About the Author

Amy Wang shares real-world insights of organizational transformation across HR, IT, finance, and shared services. With experience spanning higher education, healthcare, and automotive industries, she brings a grounded perspective to leading change in complex environments. Amy also serves as a strategic advisor on AI integration, helping organizations align technology with workforce strategy.

She started using #HRRealTalk to open up more honest conversations about leadership, change, and the human side of complex systems. She writes about what actually works, lessons learned, and how to lead with both clarity and empathy.

Connect with Amy on LinkedIn: linkedin.com/in/amywang168

Amy Wang | Voice of Real Talk in HR and Organizational Transformation