By Rashika Fazali
We spent decades teaching employees how to navigate HR systems. AI is beginning to reverse that relationship.
Think about the last time you wanted to know something.
Maybe you wanted to know how to fix something at home, understand a medical term, compare two products, or plan a trip.
Not long ago, you probably would have Googled it. Typed a few keywords. Opened several websites. Scrolled. Read. Refined your search. Opened another tab.
So time-consuming when you think about it now.
Today, increasingly, we do something different.
We ask.
We ask ChatGPT, Claude, Copilot, Gemini. We ask the AI assistant built into whatever application we’re already using.
And instead of receiving 10 blue links and being told, essentially “good luck on your little information expedition,” we increasingly expect an answer.
That behavioural shift may have much bigger implications for HR than we realise. Because for decades, HR technology has been built around a simple assumption: Employees need to learn how the system works.
AI in HR may reverse that relationship. The system can begin learning how employees ask.
We spent decades learning how software thinks
Consider something as ordinary as checking your leave balance.
An employee might log into an HR system. Find Employee Self-Service. Navigate to Leave. Select the appropriate leave category. Find the balance.
The information was always there. The employee simply had to know where the system had decided to put it.
The same thing happens across HR.
- Where is my payslip?
- When is my performance review?
- What training have I been assigned?
- How do I update my bank details?
- How much annual leave do I have?
Employees have spent years learning menus, modules, dashboards, workflows, and terminology simply to communicate with software. When you think about it, that’s rather peculiar.
Imagine walking into a hotel and asking what time breakfast starts. Instead of giving the exact answer, the receptionist says: “You’ll find that under Guest Services, then Dining, then Restaurant Information, then Operating Hours.”
We’d think the hotel had lost the plot. I’m leaving and never going back to that hotel.
Yet digitally, we’ve accepted versions of this experience for decades because there was a perfectly sensible reason.
Software couldn’t understand what we wanted. So, we had to learn how software organised the world.
And then walked in AI.
AI in HR is changing the interface
A lot of discussion about AI in HR focuses on automation, and understandably so.
AI can support recruitment, workforce analytics, employee service, learning, talent management, and many other HR activities. Current AI agents can also move beyond retrieving information toward executing authorised workflows. But there’s another change happening that may be just as important.
AI is changing the interface between people and technology.
Instead of: Login → Menu → Module → Search → Filter → Result we’re moving toward: Ask → Understand → Respond
An employee doesn’t necessarily need to know where leave information lives. They ask: “How many leave days do I have left?”
A manager doesn’t necessarily need to know which report contains performance information. They ask: “Who in my team hasn’t completed their goals?”
An HR leader doesn’t necessarily need to navigate multiple dashboards. They ask: “Why has employee turnover increased this year?”
The technology underneath may be enormously complex, but the interaction becomes remarkably simple.
Employees largely don’t care about how something works or how complex it is. They want information at their fingertips. Instantly.
This isn’t just about Gen Z
It’s tempting to frame this entirely as a generational story.
Gen Z grew up with smartphones, recommendation algorithms, instant messaging, streaming platforms, and increasingly personalised digital experiences.
But that doesn’t mean every Gen Z employee behaves identically, nor that Millennials, Gen X, or Baby Boomers somehow enjoy digging through seven menus to find a payslip.
Nobody wakes up excited about navigation architecture. What’s changing is the reference point.
Consumer technology has spent years teaching us that technology should become increasingly intuitive, immediate, and personalised.
Now generative AI is teaching us something else: Technology should understand natural language. Once that expectation becomes normal outside work, it’s difficult to leave it at the office door. And this is where the next evolution of employee experience becomes interesting.
Why asking feels so much easier than searching
There’s psychology behind this. Traditional software often creates cognitive load – the mental effort required to process information and complete a task. Think about the effort it takes to pick something to wear each day – how much brain power do you expend to decide what to wear to the office?
This is a good enough reason why famous people like Obama, Steve Jobs, and Mark Zuckerberg wear the same type of clothes every day. Who has the time to deal with those kinds of decisions when you have government-level and global-level decisions to make?
But strangely, we don’t use this psychology in HR software.
Suppose an employee wants to understand their parental leave entitlement. Their actual intention is simple: “What parental leave am I entitled to?”
But a traditional system may require them to translate that intention into its architecture:
- Where would this information be?
- Benefits?
- Leave?
- Policies?
- Employee handbook?
- Knowledge base?
The employee is no longer thinking about parental leave. They’re thinking about how the software thinks about parental leave.
Conversational AI potentially removes some of that translation. Humans express the intention. Technology interprets it. That’s a subtle but profound change.
For decades, humans adapted themselves to computers. Natural-language AI increasingly allows computers to adapt themselves to humans.
From Employee Self-Service to employee conversation
Employee self-service was itself a major evolution in HR technology. Before self-service, many routine activities depended heavily on HR teams. Then technology allowed employees to perform more tasks themselves.
- Check leave.
- Download payslips.
- Update information.
- Submit requests.
- View benefits.
- Complete workflows.
That gave employees more autonomy while reducing administrative work for HR. But self-service still largely meant: Here’s the system. You operate it yourself.
Conversational AI introduces another possibility: Tell the system what you’re trying to do.
That’s different.
We may be watching HR technology evolve through another stage:
HR Administration → Employee Self-Service → Conversational HR → Agentic HR
The final step is particularly interesting.
Agentic AI in HR goes beyond answering questions. AI agents can potentially interpret a request, reason through the required steps and execute authorised actions across HR workflows. That’s already the direction in which current enterprise HR technology is moving.
So eventually: “How many leave days do I have?” can become: “I’d like to take next Friday off.”
The first requires an answer. The second requires action. And that distinction could fundamentally reshape Employee Self-Service.
The same change is happening to HR analytics
There’s another side to this transformation.
HR systems don’t only serve employees. They contain enormous amounts of workforce information used by managers, HR teams, and business leaders.
Traditionally, extracting insight from that information required knowing which report to run, which dashboard to open, which filters to select, which metrics to compare, and sometimes which analyst to call.
Generative AI changes that interaction.
Imagine a CHRO asking: “Why has employee turnover increased this year?”
Then: “Which departments are contributing most?”
Then: “Are there common patterns among employees leaving?”
Then: “Has this been developing gradually or did something change recently?”
Each question builds on the previous one. The leader isn’t merely retrieving data. They’re exploring it conversationally.
This is where AI in HR becomes much more interesting than another dashboard. The interface between a decision-maker and workforce data begins to resemble a conversation.
But asking is easy. Trusting the answer is harder.
There is, naturally, a rather large elephant sitting beside our shiny AI assistant.
What happens when it gets the answer wrong?
The more invisible technology becomes, the easier it can become to forget how much complexity sits underneath it.
An employee asks: “How much parental leave am I entitled to?”
AI answers, but where did that answer come from? Was it based on the correct policy? Did it consider the employee’s country, employment category or tenure? Does the employee have permission to access the information being surfaced? Was the AI retrieving organisational information or generating an interpretation?
And what happens when the question concerns salary, performance, health information or another sensitive area?
These aren’t minor implementation details. They are central to Responsible AI in HR.
Current HR research similarly stresses that AI’s value needs to be combined with human judgment, governance, and appropriate oversight rather than treated as an autonomous substitute for HR decision-making.
The easier HR becomes to ask, the more important accuracy, permissions, privacy, transparency, and accountability become.
A chatbot confidently inventing a pizza recipe is irritating. An HR assistant confidently inventing company policy is a rather different Tuesday.
From finding information to getting things done
This shift is already influencing how we think about AI at MiHCM.
With MiA ONE, the opportunity isn’t simply to add AI to an HR system. The more interesting question is: What if employees didn’t need to understand the HR system to use the HR system?
Instead of navigating through technology to reach an HR process, employees, and managers can increasingly begin with what they’re actually trying to accomplish.
Wouldn’t it be great to have a lovely conversation with your HR AI Assistant? It would go like this:
Me: I would like to take next Friday to next Wednesday off.
MiA ONE HR Assistant: Instantly pulls out the Leave Application, identifies an Annual Leave request, and fills in the data for me.
All I have to do is check and submit the application, so goodbye to those application-filling days!
And on the workforce intelligence side, Syntra explores the same shift from another direction. Instead of requiring leaders to navigate reports and dashboards to understand workforce information, the interaction can begin with a business question.
Me: Based on the information you have, give me a performance indicator for Branch A and Branch B.
Syntra: Analyses and pulls out information as a comparison between Branch A and Branch B, taking in the different performance indicators and providing them as a graph where needed.
The distinction is useful.
MiA ONE: I want to do something.
Syntra: I want to understand something.
Different needs. Same behavioural shift.
Start with human intent rather than software architecture.
Could the best HR technology eventually become invisible?
Think about the technologies we use every day.
When you send a WhatsApp message, you don’t think about networking protocols.
When Netflix streams a movie, you don’t think about content delivery infrastructure.
When Google Maps gives you directions, you don’t think about satellite calculations.
The complexity still exists. It simply disappears from the user’s experience.
AI-powered HR technology is heading in a similar direction.
Employees shouldn’t necessarily need to understand databases, modules, reporting structures or workflow architecture. They should be able to express what they need:
- “What skills should I develop for this role?”
- “Show me my team’s learning progress.”
- “What does our parental leave policy mean for me?”
And leaders should be able to ask:
- “Where are we losing talent?”
- “What skills are we missing?”
- “Why has absenteeism increased?”
- “What’s changed in our workforce this quarter?”
The technology underneath becomes more sophisticated. The experience on top becomes simpler.
The next generation won’t learn the system
For decades, one of the challenges of HR technology was adoption.
- How do we teach employees to use the system?
- How do we make navigation easier?
- How do we help people find the right report?
- How do we train managers?
AI is beginning to make those questions less relevant.
One of the easiest demos I’ve ever done has been Syntra because there is nothing much to explain to the user about how to use Syntra. The experience is straightforward. One user interface lets you ask your pressing questions using your data points, and answers appear in the same chat.
Today, we’re moving toward a world where employees need to know less and less about how HR technology is organised. Instead, the system increasingly needs to understand what the employee means.
That changes the fundamental relationship between people and enterprise software.
For decades, we designed HR technology around the question: How do we make HR systems easier for employees to use?
AI may be forcing us to ask a better one: What if employees didn’t have to learn how to use the system at all?
Because the next generation of employees may not search HR. They’ll simply ask it.
Rashika Fazali
Business Operations Lead – Data & AI
(Rashika Fazali is the Data & AI Business Lead at MiHCM and Deputy General Manager at Futura Tech Labs. She holds a Master’s degree in Business Psychology from London Metropolitan University and has over 16 years of experience spanning the media, entertainment, and technology industries. Her work examines the human factors that shape organisations, brands, and communities, bringing together psychology, artificial intelligence, leadership, and broadcasting, combining psychological insights with real-world experience to help organisations build more engaged, productive, and human-centred workplaces.)
