How AI Can Improve Onboarding Without Losing the Human Touch

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Hannah Toney: If you want to accelerate performance, reduce uncertainty. It's not about more tools. It's not about more systems. It's not about faster. It's about reducing uncertainty for people.

[Intro Music]

Monique: I want you to take a moment and think. On a scale of one to 10, how would you rate your current orientation process?

Now reflect on that number, and your answer can reveal a lot about how welcoming and effective your current process feels for new hires. And if you helped design your onboarding process, you might consider this: Are you evaluating it as the architect or as the employee experiencing the process for the first time?

Onboarding can be more than just paperwork and training. It's a new employee's first impression of your company and your culture. Welcome to a special live recording of Honest HR, where we turn real issues facing today's HR teams into actionable insights and honest conversations. I'm your host, Monique Akanbi.

Let's get honest. New employees don't need another portal to navigate or more compliance modules to complete. What they need is clarity, support, and just time to breathe. However, with mounting pressure to shorten ramp times and increase productivity faster than ever, how do you balance speed with a sense of belonging?

Joining us today is Dr. Hannah Toney, an educator, leadership practitioner, and learning architect who has spent more than two decades designing human-centered performance systems. Dr. Toney has developed the Kinder 30/60/90 framework, an approach that positions AI as a quiet co-pilot offering timely nudges while keeping people at the center of the onboarding experience.

Welcome to Honest HR, Dr. Toney.

Hannah Toney: Thank you so much. I'm glad to be here.

Monique: I am excited to dig into employee onboarding and the experience. My first question is: you've said that AI does not fix onboarding — it magnifies whatever systems you already have in place. Can you explain what you mean by this and why so many organizations are getting this wrong?

Hannah Toney: Sure, absolutely. AI is not going to fix something or make something better just by being added onto a system. I think we sometimes have that challenge in our heads.

If your system is clear, well-structured, well-sequenced, well thought out and well planned, and the learning within it is solid, then AI is going to amplify that. It's going to amplify that well-thought-out, clear system. If that system is chaotic, confused, or not human-centered, it's just going to scale the confusion.

And I think that's where a lot of organizations get things wrong. They think, "How can I use AI right now?" instead of taking a step back and asking, "Is this a system that was built for learning in the first place? Is this actually a system where people have the capacity to learn and to concentrate? Or is it just a system where we're inundating them with more and more information?"

You've got to think about what you're amplifying to begin with, because AI is just going to amplify what's already there.

Monique: Got it. With onboarding, we're often faced with bringing employees into the organization and balancing a lot at once — orienting them to the culture, letting them know where the restroom is if they're going into a brick-and-mortar on that first day, and immersing them into their role and responsibilities.

So in your experience, what is the biggest misconception HR leaders have about accelerating new-hire performance? And why do traditional 30, 60, and 90-day plans often miss the mark?

Hannah Toney: I think the biggest misconception I see in organizations is that speed equals compression. The idea that if we front load all of this information, people will learn, acclimate, and fit in. And I'm so guilty of this — I'm owning this too.

If there are learning professionals and training professionals here, we know that's not how a human brain works. You cannot just throw more information at the human brain and have it learn more. In fact, the exact opposite happens — it will start to freak out and shut down.

In layman's terms, you get that fight, flight, freeze thing happening. People will not learn as well because they are so inundated with information. What we really need to do is decrease uncertainty.

When people know what is expected of them, performance increases. They're not just completing checklists and compliance lists — they honestly understand how they belong, how they perform, and why that performance matters.

Where I see a lot of traditional 30, 60, and 90-day plans fail is that they are very compliance driven and very checklist driven. They focus on "do this now, do this next," but they don't focus on the why. They miss that human element — how does this actually relate to who I am in this role and who I am becoming in this role?

Some of that understanding has to come through human conversation and human connection. AI is fantastic at supporting all of this, and we'll talk more about that as we get into the questions. I'm a huge AI user — I love AI and use it every day. I am not anti-AI by any means. But it has to be used in a very informed way, with that human layer and human component, focusing on the overall experience.

Monique: So I want to dive into the Kinder 30/60/90 framework. At a high level, what makes this framework different from traditional 30, 60, 90-day onboarding plans?

Hannah Toney: The Kinder 30/60/90 is different because, as I mentioned, most traditional 30/60/90 plans are milestone-based and very checklist-based — do this, then move to this, then move to this.

I frame this as experience-based. It looks at cognitive load, which is one of the key components we'll discuss more. From a total experience perspective, it asks: What should people be learning, when should they be learning it, and how should they be learning it?

And that's probably going to be a little different from person to person. AI can help individualize that, which is fantastic — but you've got to figure out how it will differ first.

So the framework focuses on the experience, cognitive load, and clarity of expectations and instruction, ensuring that what people are learning and when they're learning it is very clear. The why is also very clear.

The sense of belonging is emphasized starting at day one — or really day zero. That's part of the hiring process. It should come from day negative 15, meaning it should be built into how you're hiring as well. The sense of belonging really begins even before hiring takes place.

And then ensuring that there are feedback loops so you're constantly gathering information and improving the system. It's not a set-it-and-forget-it — you should be continuously improving as well.

Monique: Right. If I think back to starting a new job — and our audience can probably relate — no matter where you are in your career, whether you're a senior-level executive or it's your very first job, that first day is always terrifying. It's almost like being a fish out of water. You don't know anyone.

And especially as adult learners, it can be challenging and overwhelming to learn something new. So I'm thinking about all of the emotions and feelings you experience at the beginning of starting a new role or joining a new organization.

There are four layers I want to talk about: cognitive load design, AI as scaffolding, recognition architecture, and early feedback intelligence. How do these show up in practice?

Hannah Toney: Sure, absolutely. Cognitive load design — and this is a great place to use AI as a support — shows up in sequencing and in exactly how things are summarized. It might look like policies being summarized specifically for the role and the individual, or determining exactly when someone needs to interact with specific components of a policy or a manual when they start a role.

The key is ensuring that things are delivered at the right time, in the right way, in the right place. Part of that is recognizing — and I'm glad you mentioned it — that there's a lot more than just work going on for people in those first days. You are experiencing a lot of emotions, and those emotions can block or influence your ability to learn.

We have to recognize that and build learning experiences that account for it, ensuring that content is more trickled and spaced. Spaced learning is a big deal — people need to receive information over time rather than in a data dump, because that doesn't work for anybody. That works for no one.

Monique: Don't ask me what happened yesterday. I can't even tell you.

Hannah Toney: Data dump. So — cognitive load. Then there's AI as scaffolding, and an important part of that is understanding that AI is scaffolding, not supervisor.

The AI serves as the support system to help determine what training is needed. It can even provide some training. It can help summarize what's coming out of onboarding to identify patterns — for example, if a whole cohort is stumbling at the same spot, that's a signal. Houston, we have a problem. We might need to look at that specific area or revisit the overall onboarding training.

That really ties back to the feedback architecture. AI can provide scaffolding, assistance, training, and support for your team. But any evaluation of training or assessments needs to be done by a human being.

Monique: I'm going to pause right there because my brain is in overdrive. I'm trying to imagine what integrating or using AI in the onboarding process actually looks like in terms of complementing or summarizing the process or the employee's experience.

Hannah Toney: It's going to depend on your specific onboarding process. It might look like pulse surveys — sending out quick surveys to ask, "How's it going? What have you learned today?"

It might also be skill-based tests, depending on what you're onboarding for. Or it might be something softer, like a pulse survey on a scale of one to five asking, "How are you feeling about your integration into work today? How are you just feeling today?"

Those are ways to get instant feedback from your onboarding cohort and get a sense of how things are going.

The manager should then receive that information, review it, and respond to it. That's the really important part — it should not be the AI reviewing and responding to it.

I actually attended a session earlier today where the speaker talked about this at length, and I thought it was fantastic: there always needs to be critical human oversight, particularly when it comes to evaluation.

Recognition architecture is another component, and this one is very near and dear to my heart. One thing I've found — particularly in those first 30, 60, and 90 days — is that people can feel like they're not really contributing. I hate to say that, but I've heard it in many workplaces: "I want a real job. I came here because I'm a professional, and I want to contribute."

Recognition architecture is about ensuring that during those first 90 days, onboarding is built to include ways for individuals to meaningfully contribute to real projects — and to receive real recognition somewhere within those 30, 60, and 90 days. Not just platitudes, but honest-to-goodness real projects.

Monique: So as AI continues to evolve, how do you see the role of human managers changing in the onboarding process? We have a saying here: AI plus Human Ingenuity (HI) equals ROI — artificial intelligence plus human intelligence, or human intelligence, equals return on investment. So what will always remain uniquely human?

Hannah Toney: I really think the role of the manager is going to increase in importance, at least in my view. AI is great at sorting, sequencing, and answering basic questions — if it has the correct data and the correct inputs for the algorithm.

But managers need to interpret that information based on the culture, the context they're working within, and the individuals they're working with. They need to calibrate. They need judgment. They need to build trust within the organization. They're going to be the culture builders.

In my mind, the role of the manager is going to be more important than ever for building the culture of the team and the company. That's the uniquely human connection people are going to get, and it's the uniquely human judgment that needs to be applied on top of AI outputs.

Monique: There's also this risk of subtle erosion when AI is poorly integrated — more generic content, minor inaccuracies, an automated tone. With that in mind, and focusing on keeping things human-centered, how do organizations prevent this erosion while still gaining efficiency benefits?

Hannah Toney: That erosion tends to happen when efficiency replaces intention. We see that all the time. AI is fabulous for summarizing, sequencing, and pattern detection — but people can tell when something hasn't actually been thoughtfully designed.

One of my big go-to rules is: design it, then automate it. Don't do it the other way around. When people start by automating and then try to design around it, that's exactly how those subtle erosions creep in.

Design with solid learning in mind. Design with certainty. Design with belonging in mind. And automate after.

And I think it's really important to emphasize that human oversight has to be good human oversight. It has to be accountable and knowledgeable. It can't just be, "Yeah, it looks fine." It has to be real, rigorous human oversight, because AI makes mistakes all the time.

I'm someone who uses AI constantly, and I literally argued with it last week about basic math. This was a paid AI — a well-known one that people use for coding and engineering. And I was just asking it to lay out my calendar. The math was not right. I'm not a math person, but even I could tell something was off.

I had to push back, and finally it said, "Oh, you're right. My math was wrong." But if I hadn't stopped to check it, my entire 2026 calendar would have been a mess. I would have just trusted it and loaded it into my calendar app, and it would have been a nightmare. And that was just one of the mistakes it made last week — there were multiple.

So check the outputs. You've got to have good human oversight, because AI makes mistakes all the time.

Monique: We know that AI will create automations, generate efficiencies, and help streamline processes to drive toward those business outcomes we're looking for. I love the idea of incorporating that into the onboarding process while keeping the human at the center. AI complements your onboarding process, but at the center should be the person.

How can HR professionals measure the success of their onboarding program?

Hannah Toney: Attrition is definitely one way to measure it. Pulse surveys are another great option. I've also really enjoyed, in the past, sending prior hires through new onboarding programs — people who went through the old onboarding, sent through the new one to test it.

I've done this with people who'd been with the company for six months, two years, and 20 years. They all went through the new onboarding program, and it was an amazing way to get feedback.

Monique: I bet they probably said, "I wish I had this when I started."

Hannah Toney: Oh, it was fantastic. But you got some really honest feedback, and some great insights from the long-tenured folks. We actually uncovered some real holes in the program from people who had been around for a long time. So running a pilot with people who know the organization is a great way to get meaningful feedback early.

Monique: In terms of evaluating the onboarding process, what would you say is a good timeframe? Let's say you've just done an overhaul. You're continuously evaluating, but is there a certain point where you'd say it's time for a redo or an update?

Hannah Toney: That's going to depend on your industry. I don't think there's a hard-and-fast answer, and I'm probably not the best person to ask because my honest answer is: look at it every year.

Look at it annually. I know that might not be what people want to hear, but keep it fresh. Keep those questions fresh. If you're trying to hire people who are fresh in the workforce, coming out of college, reading blogs about what's happening in hiring — your onboarding process should probably be refreshed regularly too. So I'd say annual.

Monique: I don't think annually is unrealistic. Things change so rapidly. If you could design the perfect first day for a new hire, what would it look like?

Hannah Toney: It depends significantly on the job, but here are the things I'd include. First, everything they need would be there. No, "Your computer will be here next week" or "Your keyboard hasn't arrived." All their equipment and materials are ready on day one.

Then, I'd have them shadow someone doing the job they're going to do — not for the whole day, because that would be overwhelming, but for at least two hours. They get to truly see and talk to someone who is doing the actual work they're about to step into.

I'm also a very big fan of human-to-human connection. You meet with your supervisor, your supervisor's supervisor, and so on. At one of my prior companies, everyone — including summer interns — met with the CEO and the president. I'm a huge believer in that. It sets a tone: this is a human place. We're going to have conversations. Meeting these people matters more than sitting down and reading a bunch of policy handbooks.

Because you're not going to remember the policy handbooks anyway, and you'll be able to find them when you need them.

Monique: If you could leave our audience with one key insight about creating onboarding that truly accelerates performance while respecting people, what would that be?

Hannah Toney: If you want to accelerate performance, reduce uncertainty. That's my biggest takeaway. It's not about more tools. It's not about more systems. It's not about faster. It's about reducing uncertainty for people.

If people understand the why, know where to go with questions, and feel that sense of belonging — the rest will fall into place. It really will. But if you truly want to accelerate performance, what you need to do is reduce uncertainty.

Monique: Well, thank you so much, Hannah, for joining us for this special episode of Honest HR. And thank you to our live audience, both online and in person. That's going to do it for this episode. We'll catch you next time.