What if the real power of AI in HR isn’t automation—but trust? Angela Briggs-Paige, Chief People Officer at Acelero Learning and a Head Start alum, shares how her team uses AI to elevate human-centered practices across a mission-driven, early childhood education workforce.

She explains how AI transforms thousands of open-ended survey comments into timely insight—capturing tone, emotion, and patterns—so leaders can respond in hours, not months.

Angela details how the time saved gets reinvested into designing better experiences: welcoming, belonging-rich onboarding; ongoing coaching; and real career pathways. In hiring, Acelero automates credential and compliance screening while keeping judgment and empathy with people—anchored by the principle “AI recommends, humans decide.”

She also unveils a pilot for AI-personalized learning paths tied to role, goals, and feedback, plus the change practices that make adoption safe: transparent modeling, peer champions, and psychological safety.

Expect practical guidance on measuring “time to value,” choosing best-of-breed tools, and leading AI efforts that start—and end—with people.

Watch the full interview below.

Key Takeaways

• Use AI to synthesize open-ended survey feedback in hours, track tone and momentum, and act while trust is fresh.
• Reinvest saved time into human touchpoints—design onboarding, coaching, and career pathways that deepen belonging.
• Apply “AI recommends, humans decide” to hiring: automate credential checks, keep empathy and mission fit with people.
• Write inclusive, values-driven job posts and targeted interview questions with AI’s help to attract mission-aligned talent.
• Pilot AI-personalized learning paths tied to role, goals, and feedback; measure engagement and real-world application.
• Normalize AI use through executive transparency and peer champions; judge pilots by 30-day time to value and added capacity for high-touch work.

Key Timestamps

[00:45] – Angela intro and Acelero Learning’s mission in early childhood education

[01:17] – Where AI moves the needle: listening at scale and turning comments into action

[04:40] – Using freed capacity to design human experiences: onboarding, coaching, career paths

[06:11] – Hiring in a tough market: “AI recommends, humans decide” and values-aligned job posts

[09:51] – Piloting personalized learning: AI-curated paths, micro-learnings, and early success signals

[12:22] – Change that sticks: transparency, peer champions, psychological safety; ROI as time to value

[16:19] – Lightning round: top misconception, the metric that matters, and best-of-breed vs consolidation

[21:44] – Closing advice: lead with your people and start with one real problem

Full Transcript

Barb (00:00)
Welcome to the Human Element presented by Wisq, where we explore how AI and human insight are reshaping leadership and the future of HR. Our guest today is Angela Briggs-Paige, the Chief People Officer at Acelero Learning, a national organization dedicated to expanding access to high quality early childhood education. Angela leads people strategy, organizational development, culture, DEI, and L&D to improve outcomes for children and families. I'm excited, Angela, to talk to someone who's building people strategy and culture to support such expanded access to early childhood education. I think this is going to be a great show today and a different and unique perspective from some of the guests that we've talked to in the past. So thanks for coming on the show.

Angela Briggs-Paige (00:53)
Thank you so much for having me. I'm looking forward to this conversation.

Barb (00:57)
I am too. So let me dive right in and see where this takes us. Where has AI-driven automation made the biggest dent in administrative HR work within your team today?

Angela Briggs-Paige (01:13)
That's such a good question. I think the biggest impact AI has had for us at Acelero isn't probably what most people expect. Yes, we've talked about how we're automating onboarding and how we're automating case management and HRIS workflows. But when we think about AI and how great the impact has been, it's really about how we listen to our people.

So we run surveys every quarter—quick, honest check-ins to understand how our employees are really doing. And we know that's so important, especially now. We want to know what's working, what's breaking, where they need support. And they tell us everything, which is great. The problem was that it used to bury us. We had thousands of comments, and there was no way that we could get to all those comments and provide a readout in a timely fashion.

Before, by the time we were able to read all the comments—and it would take weeks—it was time to launch the next survey again. AI really helps us understand what people are saying within hours instead of waiting for weeks. And it doesn't just count the words; it picks up on tone, emotion, even spots patterns we might have missed.

It helps us keep track of shifts over time, the momentum of the organization over time. So we're not just reading what people are saying; we're seeing how that's changing. But the reality is we still do the listening. We still read all the comments. AI just helps us clear the noise so we can focus on what really matters—and that's the story behind the data.

We're not waiting for months to respond anymore. There's nothing worse than someone taking a survey and then not seeing any action. Leaders have time to have conversations in real time while the feedback is still fresh. That's how we continue to build trust in our employee listening strategy. We're really excited about that.

Barb (03:37)
Totally. We all can remember the days of tagging survey comments thematically. I'm sure we were doing a lovely job, but AI is making quick work of that and probably doing a much more consistent job than we ever were able to. The nuances you must be learning from that must be fantastic. And the turnaround time—there’s no denying. People want to see quick action when they've provided feedback. It encourages them to continue giving feedback when they see action.

Have you been doing anything, Angela, to redeploy the capacity you've gained back from not having to pour over survey results in the ways we used to? Are you directing that capacity toward anything fun?

Angela Briggs-Paige (04:31)
Yeah. We're trying to figure out how we design experiences that matter instead of focusing on manual coding of spreadsheets and analysis. For us, it's about designing onboarding that doesn't feel like paperwork hell but feels like a genuine welcome and a personal sense of belonging. We're also focusing on coaching and development and career pathway conversations that happen more than once a year.

We're trying to figure out what matters to you—what makes the people team still feel human—and really focus our energies on doing that. AI gave us more time and clarity to be more human. It freed us up to do what actually helps our people: listening, responding, being present. It's really helped us improve the employee experience at Acelero.

Barb (05:31)
That's awesome. I know that you must do a lot of hiring. I'm super curious about how you're thinking about AI to strengthen hiring and staffing. When I think of hard-to-find talent, or screening for folks who align with the mission of an organization, or forecasting staffing across a large footprint—how are you using AI in the hiring space?

Angela Briggs-Paige (06:00)
Listen, early childhood hiring is definitely not for the faint of heart. You're not just filling positions; you're inviting people into purpose-driven work that's emotionally demanding and, let's be honest, chronically under-resourced. This is important for me because I am a Head Start alumna. I'm a Head Start kiddo. We want to make sure we're bringing in people aligned with our mission.

And then adding all that up, we're competing with Target, Amazon, Starbucks—companies paying $18 an hour with benefits where nobody bites you during your shift. The candidate pool is small, burnout is real, and posting-and-praying on Indeed doesn't work anymore.

Barb (06:43)
You—

Angela Briggs-Paige (06:55)
So for us, the question was and remains: How can AI help us find the right people faster without losing the heart in the process?

We've designed AI to help screen for the baseline stuff—credentials, compliance requirements, experience levels. Does the person have their CDA? Are they eligible to work? Do they meet state licensing requirements? That's what AI handles. Those boxes have to be checked before we can even have a conversation.

But this is where we jump in. We say AI recommends, humans decide. That's our golden rule. AI can flag someone who meets minimum qualifications, but it doesn't make hiring decisions. It can't tell us if someone has the heart for this work. It can't pick up the warmth in someone's voice when they talk about why they want to work with children. It can't sense how someone will show up for families in the moments that matter most.

We also use AI to help write better job descriptions, targeted interview questions—really making sure our postings sound like us: values-driven, inclusive, mission-aligned. But we make the hiring decisions. It just alleviates the manual effort.

Barb (08:07)
Yeah. It sounds like all the things that help you get to those great hires faster—removing the manual work. I loved the “AI recommends and humans decide.” AI gets you there faster so you can decide and select candidates with mission alignment. So important with educators for sure.

Angela Briggs-Paige (08:54)
Absolutely. It really helps our recruiters tell the story, build relationships, and connect our mission to candidates’ motivation. It’s about getting to know candidates as people.

Barb (09:09)
Something I haven't talked with enough guests about—and I'm excited you're here—is AI's role in tailoring learning pathways. We haven’t touched on learning in another episode. What role do you see AI playing in learning pathways, coaching, leadership development—for educators, center leaders, or other team members at Acelero?

Angela Briggs-Paige (09:37)
I love this because this is where I'm so energized. We're in the early stages of piloting AI to build personalized learning plans for employees and leaders. We're not claiming to have it all figured out. We're literally just starting. We built a bot, we're testing it, we're experimenting, we're listening—but it's promising.

We know the old model of professional development was one-size-fits-all. Everyone goes to the same training at the same time, regardless of experience or learning style.

AI is letting us change that. Imagine a system that looks at your role, your job description, your goals, even your feedback, and curates a learning path tailored to your growth needs. If you're a leader working on coaching, it might serve micro-learnings on feedback conversations or connect you with a peer mentor.

We're not measuring success yet in percentages. We're looking for early signs: Are people engaging more with content? Are they saying the learning fits them? Are leaders referencing their learning in team meetings or performance conversations? Are they getting value?

We're building a culture of curiosity—how this can work and how it can improve the experience of our people. I'm excited to see how it pans out. We literally just started, but I'm excited.

Barb (11:27)
That is super exciting. What sort of change management practices are you using? It's a huge change—from old learning models to personalized paths. How do you plan for that from a change management perspective so frontline teams adopt AI-recommended learning paths? And how are you thinking about ROI?

Angela Briggs-Paige (12:02)
Especially now, when we see posts and news articles about people losing jobs because of AI—trust was the currency for us. We didn't want people to be afraid to use it or to admit to using it. It was about psychological safety.

We wanted to make AI normal. People should feel comfortable saying, “Yep, I used AI to draft that communication,” or “I used it to summarize a meeting.” No shame, no secrecy.

The executive team modeled transparency—sharing when and how we used AI, being open about what worked and what didn’t. We normalized experimentation.

We also developed internal change champions—people who were naturally curious—who piloted tools and shared their experiences. When peers saw them using it—not just the people team—that’s when adoption took root.

We measured two things: capacity and culture. Is it saving a director time in coaching? If it helps people spend more time in high-value human moments, that matters. We're building capacity without replacing connection. And we want everyone to feel safe using AI.

Barb (13:56)
Another thing we haven't talked about on the show—the mentality of using AI in secret. People almost feel like if they admit AI helped them, their work is somehow lesser. But making it safe encourages the opposite: using AI is the right thing. People often see it like a cheat code they shouldn’t admit to. But it can be a cheat code we all should be using—to free up time for what matters.

It sounds like you're doing great work to shift people out of that mindset.

Angela Briggs-Paige (15:03)
Yes. If people believe even for a second that AI is coming for their jobs, adoption will fail. It doesn't matter how good the tool is or how much training you provide. People will resist or quietly sabotage or refuse to engage. We want people to feel it's okay—that we want them to use it—and that they're safe doing so.

Barb (15:13)
100%. Does not, I agree.

That is fantastic. I am going to move us on to our lightning round—quick questions I’d love your thoughts on. What is one misconception HR leaders have about AI adoption?

Angela Briggs-Paige (15:52)
I think it goes back to our previous conversation. They think it's about the technology. The biggest misconception is thinking if you buy the right platform, AI adoption will magically happen. It won't. For us, AI adoption is about trust. Period.

Barb (16:11)
Love it. What is one metric you trust most when judging an AI pilot’s success?

Angela Briggs-Paige (16:22)
I say time to value. We're always talking about time to accept for offers or time to start. When I think about AI, I'm thinking time to value. How long does it take from implementation to when people actually say, “This is helping me”?

If my staff can't see the benefit within 30 days—not six months, not after the third training session, but 30 days—something is wrong. The tool is too complicated, it's solving the wrong problem, or you didn’t involve the right people in choosing it. But they’ve got to see some value.

Barb (17:00)
Sometimes the window is really short. Certain tools blow people’s minds—something that would've taken a week now takes 10 minutes with the right inputs. That’s serious time to value.

Here’s our question: keep one and drop the rest. If you had to choose between platform consolidation or best-in-breed AI, which would you select and why?

Angela Briggs-Paige (17:47)
I would choose best of breed every single time. Integration is messy, but innovation lives in the mess. I'd rather have one great tool that changes the game than 10 average ones that just check boxes. One-size-fits-all never fits anyone well. Let’s keep best of breed.

Barb (18:10)
We are on the same team in that regard. It's a new day for integrations. It's not as hard as it once was, and selecting the very best solutions is critical. I love that answer.

Lots of great takeaways here, Angela. I did my best to boil it down. I loved—and you didn’t say it this way, but it came through in every answer—that you're thinking about creating amazing experiences. Where AI creates efficiency and removes mundane work, you're thinking: How do we use that saved time to create an amazing experience?

We started with listening—really listening. Surveys generate a lot of comments. Before AI, it took too long to get results and actions back out. In the realm of surveys, being able to listen and act fast—even within hours—is huge.

I loved “AI recommends and humans decide.” Could not agree more. Humans still have a place in this mix. We decide. AI helps us get there faster and better. And the trust point—trust as currency for change—was such a powerful idea. Peer champions, making AI normal, encouraging people to share when AI helps them rather than hide it.

Thank you for all of that. We always leave our guests with the last word. What is one piece of advice you'd offer HR leaders starting their AI journey?

Angela Briggs-Paige (21:09)
That's such a good question. I would say lead with your people. We didn't get into HR to optimize systems. We got into it because we care about people. Before you do anything with AI—before you buy a platform, attend a conference, or draft a policy—ask yourself one question: Would this help me care for people better?

If you start there, you make sure you're offering solutions that keep connection at the center. Then ask another question: Where are you spending time on things that don't matter? Take one pain point—just one. Then find a tool that helps. Pilot it. Learn. Adjust. Share what you've learned. Then do it again. But start by solving real problems for real people.

Barb (22:07)
I agree. What a fantastic place to end. This was a fun conversation today. I am so glad you came on. Thank you for joining me on the show, Angela.

Angela Briggs-Paige (22:20)
Thank you so much for having me.