Different industries. Different companies. Different priorities.

But the conversation kept circling back to one thing:

Shadow AI.

It was showing up inside organizations through employee behavior, data risks, and unclear governance.

There were questions about data leakage.
About employees pasting sensitive information into tools like ChatGPT.
About teams experimenting with Claude Cowork or Perplexity AI without visibility.

And underneath all of that, one consistent question:

How do we control this?

But listening to the discussion, it became clear:

That is the wrong question.

Shadow AI is NOT a new behavior.

Employees have always found workarounds:

• Spreadsheets outside the systems

• Informal communication channels

• Personal tools to get things done faster

We called it “shadow IT.”

But this is different.

Because this is not just about tools.

It is about capability.

When employees use AI, they are not just bypassing systems.

They are:

• Producing output faster

• Approaching problems differently

• Completing work that previously took hours, in minutes

And they are doing it quietly.

Not to break rules.

But to meet the expectations of their role.

So instead of asking:

“How do we control shadow AI?”

A better question is:

“What is shadow AI telling us?”

Because what it reveals is uncomfortable.

It shows that:

• The tools provided internally are often slower than what is available externally

• Processes are too rigid for the pace at which work is now expected

• Employees are no longer waiting for permission to improve how they work

Shadow AI is not a Governance Failure.

It is a demand signal.

It reflects a simple reality:

This is where most organizations are getting it wrong.

They are treating shadow AI as a security problem to shut down.

When in reality, it is a productivity signal to understand.

Because in many cases, the employee using AI “in the shadows” is not the risk.

They are often the one quietly outperforming expectations.

The real risk?

That your most adaptive employees are redesigning how work gets done…
and the organization is not learning from it.

So what should companies do?

Not more controls.

Not blanket bans.

Those may create a sense of safety.

But they do not build capability.

1. Bring it into the light

Most employees are not trying to hide their use of AI.

They are uncertain about what is allowed.

Create space to ask:

• Where are you using AI today?

• What is working?

Without consequences.

Because the moment behavior is penalized, visibility disappears.

2. Treat employees as signal, not risk

The instinct is to track tools.

But the better approach is to understand intent.

If someone is using ChatGPT to shortlist candidates, the question should not only be, “Are they allowed to use this tool?”

The better question is:

Why did they feel the need to go outside the recruiting systems already in place?

Maybe they do not support the way recruiters & leaders actually work.
Maybe employees trust the external AI tool more than the internal workflow.

That is not just tool usage.

That is a signal.

It is workflow redesign happening in real time.

And that is where the learning is.

3. Set guardrails, not roadblocks

The risks are real:

• Data exposure

• Compliance gaps

But the response cannot be avoidance.

It has to be clarity:

• What data is safe to use

• What is restricted

• Where approved environments exist

Clarity reduces misuse.

Ambiguity drives it.

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4. Provide a better alternative

If effective AI usage is only possible outside the organization, employees will go outside.

Organizations that are moving forward are:

• Providing secure internal AI environments

• Embedding AI into workflows

• Reducing friction in adoption

Control does not come from restriction.

It comes from offering something better.

5. Redesign roles - not just tasks

This is where most organizations are still behind.

If employees are already using AI to:

• Write faster

• Analyze faster

• Make decisions faster

Then the role has already changed.

But:

• Job structures have not

• Expectations have not

• Performance metrics have not

This is where leadership needs to step in.

Not to slow adoption.

But to interpret what is already happening.

Shadow AI is not an exception.

It is an early signal of how work is evolving.

Stay curious.

AI Lady 💫

About the Author

I’m Priya Tahiliani, and I’ve spent the last 15 years at the intersection of People and Technology. Most of my career has focused on SAP HCM and SAP SuccessFactors consulting, working with Big Four firms and clients worldwide.

I built and launched my company’s first AI tool by forging a great partnership with IT, and today I continue to work with HR leaders to help shape the future of work and drive AI enablement.

Beyond work, I serve as Vice President of Public Relations at Toastmasters. I’m also the Founder of the AI Collective – Oakville Chapter in Canada, part of the world’s largest community for AI professionals - a network dedicated to learning and leading responsibly with AI.

And of course, I write the AI Lady newsletter, where I share my experiences, insights, and thoughts about how AI is reshaping our workplaces.

If this article sparked something for you, share it with someone else navigating this shift. These conversations matter more when we have them together.

Pass it along to a leader, an HR partner, or a curious mind who’s thinking about where AI is taking us next.

A connection; someone who has watched my AI journey closely reached out with a question. She knew that I had been doing something at my company that did not have an official title then but was producing real results. She wanted to know: how did it work so well? What did you actually do? Because companies are now hiring for exactly this role, and nobody seems to agree on what it looks like.

It made me pause.

Because what I had done was not complicated. I had built a super strong partnership with IT. I built the bridge between the technology and the people. I ran training sessions, created guides, designed and built an AI tool from scratch, sat with skeptical employees until the tool clicked for them, and tracked what actually got used versus what got ignored. I measured adoption - not licenses, but genuine usage.

It worked. Here is what I measured:

• Active usage went from a small group of early adopters to the majority of the team within a few months.

• Employee sentiment shifted - the questions changed from “do I have to use this?” to “can I use this for X too?”

People moved from skeptical to genuinely excited. And my company saw the value.

So when my connection forwarded me a job posting she had come across one of many she had been collecting. I read it with fresh eyes. The title? “AI Enablement Lead.” Reputable company. Exciting language about driving AI transformation across the enterprise.

And then I read the requirements.

Technical degree required. Python preferred. Experience with ML model deployment a plus.

I stopped.

Read it again.

This was NOT an AI Enablement role.

This was an ML Engineer with a softer title slapped on top.

And that gap between what companies are calling the role and what the role actually needs is exactly what I want to talk about today.

A New Category Is Taking Shape

Open LinkedIn right now and search “AI Enablement.” Or “AI Adoption.” Or “AI Strategy Lead.”

You will find hundreds of real roles in North America. Real companies. Comcast. Lockheed Martin. Steel Partners. Organizations of every shape and size, all creating positions dedicated to getting AI off the ground - not just building it, but making it stick.

This is genuinely new.

Two years ago, these roles barely existed. Today, AI-related job postings have grown by over 140% And one of the fastest-growing segments is not the engineers. Not the data scientists.

It is the enablement roles.

The people roles.

And Here Is Where It Gets Interesting

Not all of these roles are the same.

This role is very fluid.

Because no company has figured out who they truly need.

There is a spectrum forming. And where a role sits on that spectrum changes everything: the skills required, the person who should fill it, and what success actually looks like.

Let me break it down.

On One End: The Technical Builders

These are the roles that genuinely require deep technical expertise.

AI Engineers. Machine Learning Engineers. ML Ops Leads. building the models, pipelines, and infrastructure that power AI systems.

If you are hiring for these roles, technical depth is non-negotiable. These professionals need to understand how models are built, how they fail, and how to deploy them responsibly at scale.

We know these roles. We know how to hire for them.

This is not the gap I am worried about.

In the Middle: The Translators

This is where it gets interesting.

Roles like AI Product Manager, Prompt Engineer, and AI Solutions Architect sit in the middle of this spectrum. They need enough technical understanding to talk to engineers but their primary job is to translate.

Translate between the business and the builders. Translate between what AI can do and what the organization actually needs. Translate a complex output into a decision a leader can confidently make.

The technical depth here is real but it is conversational, not constructional. You need to understand how the technology works. You do not need to build it yourself.

On the Other End: The Change Makers

And then there are the roles that I believe are the most undervalued, most misunderstood, and I will say it: the most important for 2026.

AI Enablement Manager. AI Adoption Specialist. AI Change Lead. AI Learning & Development Designer.

These are not technology jobs wearing a people-friendly costume.

They are change management jobs with an AI label.

Here is what I see in practice.

Most AI tools do not fail because the technology is bad.

They fail because people do not use them. Because teams were never properly trained. Because the why was never communicated clearly. Because the manager did not understand enough to champion the change. Because no one mapped how the workflow would actually shift.

That is not a technology problem.

That is a people problem.

And this is exactly what AI Enablement roles are designed to solve.

What These Roles Actually Do

When I look at job postings for AI Enablement and Adoption roles right now - the well-designed ones- a clear picture emerges. Here is what these people actually do:

• Design and deliver enterprise-wide AI training and onboarding - not a one-time workshop, but a sustained program.

• Build practical libraries of prompts, use cases, and how-to guides that employees actually reach for.

• Partner across HR, L&D, and Communications to make AI fluency part of everyday culture.

• Measure real adoption - not who has a license, but who is genuinely getting value and how that changes over time.

• Sit with resistant teams and figure out why the tool is not clicking.

• Act as the internal advisor who helps an organization move from “we have AI” to “we use AI well and measure its output in terms on value generated”

None of that requires a computer science degree.

They require deep curiosity about AI. Strong communication skills. The ability to work across functions and earn trust quickly. A change management mindset - because that is fundamentally what this work is. And yes, enough AI literacy to explain the concepts credibly.

You do not need to understand how a large language model is trained.

You do need to know what it can and cannot do - well enough to help others navigate it with confidence.

The Mistake Organizations Are Making

Here is what concerns me.

Many organizations are conflating these roles. They post “AI Enablement” positions and fill them with technical requirements that have nothing to do with the actual work.

Or worse - they are not creating them at all. They assume the tools will somehow drive their own adoption. That employees will figure it out. That the rollout will handle the change.

It will not.

The data is clear. According to Gartner, only 32% of organizations have achieved healthy AI adoption but 64% of CHROs say their managers cannot lead change effectively.

We are building tools faster than we are building the human infrastructure to support them.

And that gap? That is exactly what the enablement roles are meant to close.

A Quick Guide to the Spectrum

Here is a simple way to think about what each layer of these roles actually needs:

🔧 Deep technical expertise required: AI Engineer · ML Engineer · Data Scientist · Build the models, pipelines, and infrastructure. Non-negotiable technical depth.

🔁 Technical fluency needed - but not depth: AI Product Manager · Prompt Engineer · AI Solutions Architect Translate between builders and business. Conversational, not constructional.

🤝 AI literacy needed - curiosity over credentials: AI Enablement Manager · AI Adoption Specialist · AI Change Lead · AI L&D Designer Move people. Drive adoption. Change the mindset. No CS degree required.

If you are hiring for that third category and screening out candidates without technical backgrounds - you may be eliminating exactly the right people.

The HR professional with ten years of change management experience and genuine AI curiosity? The L&D leader who has spent a decade building capability programs at scale? The internal communications expert who knows how to land complex messages with a skeptical audience?

These are your AI Enablement candidates.

Are You in the Room Where This Gets Decided?

Here is the question I want to leave you with - whether you are in HR or not.

When your organization creates an AI Enablement role - and if it has not yet, it will - who is writing that job description?

Is it IT? Is it a hiring manager who defaults to technical requirements because that feels safer? Is it a recruiter working off a template that has nothing to do with what the role actually needs to accomplish?

If you are in HR: this is not a role you should react to after the fact. It is a role you should be designing in close partnership with IT from the very first conversation. These positions sit squarely at the intersection of people, process, and technology. Organizational change. Adult learning. Stakeholder trust. The ability to make something complex feel safe for someone who is scared of it. That is your domain. That has always been your domain. The only thing new is the AI label on the door.

If you are a business leader, a COO, a department head: this is your problem too. You are the one sponsoring these initiatives. If HR is not in that room, ask why. Because if the people function is not shaping how AI gets adopted across your teams - you are going to get a technology rollout, not a transformation.

The gap between those two things is exactly what this role is meant to close.

If your organization is creating these roles without HR at the table you are not just missing an opportunity.

You are letting someone else define the future of work at your company.

Do not sit this one out.

Stay curious.

AI Lady 💫

About the Author

I’m Priya Tahiliani, and I’ve spent the last 15 years at the intersection of People and Technology. Most of my career has focused on SAP HCM and SAP SuccessFactors consulting, working with Big Four firms and clients across the globe.

I built and launched my company’s first AI tool by forging a great partnership with IT, and today I continue to work with HR leaders to help shape the future of work and drive AI enablement.

Beyond work, I serve as Vice President of Public Relations at Toastmasters. I’m also the Founder of the AI Collective – Oakville Chapter in Canada, part of the world’s largest community for AI professionals - a network dedicated to learning and leading responsibly with AI.

And of course, I write the AI Lady newsletter, where I share my experiences, insights, and thoughts about how AI is reshaping our workplaces.

If this article sparked something for you, share it with someone else navigating this shift. These conversations matter more when we have them together.

Pass it along to a leader, an HR partner, or a curious mind who’s thinking about where AI is taking us next.

AI is making it easier to shrink work than to redesign it.

Everybody keeps saying AI is not a tech-only problem.

But many organizations are still approaching it as one.

They are investing in tools, pilots, copilots, and automation, but not asking the harder organizational questions early enough.

What happens when AI starts taking over parts of …

Read more

We landed on this topic because neither of us could stop thinking about it.

The manager has become the shock absorber!

Take a moment to read that again!

According to LinkedIn’s Workforce Confidence survey, only about 30% of individual contributors say they want to move into management roles.

Look around you.

Think about the last big change e.g. a reorg, a systems rollout, a layoff.

Who absorbed the shock? Who translated it for their teams while being expected to just keep things moving?

More often than not, it was the manager.

At the same time, the workplace itself is becoming more complex.

The role changed. Most organizations haven’t caught up.

AI is entering everyday workflows.

Teams are navigating constant technological change.

And leaders are increasingly expected to guide teams working alongside intelligent tools and automation.

AI. Org changes. Layoffs. A multigenerational workforce. Hybrid, virtual, in-person.

And sitting right in the middle of all of it is the manager.

Not the CHRO.

Not the HRBP.

The Manager.

The one supposed to hold their team together, adapt to every new initiative, communicate every change with clarity and calm and somehow still show up as a human being while doing it.

We’ve all either said it or had it said about us: “If someone is a manager, they will have to figure it out.

When anything new launches, default responsibility lands on managers to make sure their teams adapt.

Who helps them? Checklists? HRBP calls? Handbooks? Emails?

We’re asking more of managers than ever. And fewer people want the job. That’s not a pipeline problem. That’s a signal.

We are not overthinking this. The numbers tell the same story.

What Leadership requires now.

Here is what more organizations need to say out loud: if a leader does not understand how technology is shaping their team’s work, they are not fully leading it. They are reacting to it. And teams can tell.

Trust, empathy, and psychological safety are still the foundation. But they’re not enough on their own anymore. If a manager can’t engage with the tools their team uses every day, they become a bottleneck. Someone to route around, not lean on.

That’s not a technology problem. That’s a leadership gap.

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What does the new manager actually need?

Still deeply human. The ability to build trust, create safety, communicate clearly when everything feels uncertain. AI cannot do that.

Enough AI fluency to lead it is more important than the skills to build it. Just enough to understand how it shapes decisions, where it helps, where it misleads, and how to ask better questions of it.

Systems thinking. Work doesn’t move in a straight line anymore. A manager who sees how it all connects where the friction is, how a change in one place ripples somewhere else, that person is invaluable right now.

Human + AI fluency + systems thinking. That’s not a framework. That’s just what leading looks like now.

What L&D can actually do:

We keep asking if AI will replace managers. Wrong question.

The real one is are we making leadership a role the right people actually want?

Take away the admin. AI can handle the reporting, summaries, and documentation. That frees managers to coach, decide, connect.

L&D’s job is to help leaders use that space well.

1. Use AI to remove low-value administrative work. Reporting, summaries, documentation, and routine follow-up can be automated. That gives managers more time for coaching, judgment, and connection.

2. Build leadership pathways that value technical and operational depth. Too often, strong experts are expected to leave their expertise behind to become “people leaders.” Instead, organizations should create pathways where domain knowledge strengthens leadership credibility.

3. Make AI literacy part of leadership basics. Managers do not need to build the technology. But they do need enough fluency to understand how it affects decisions, workflows, fairness, and team expectations.

4. Measure leadership outcomes that actually matter. Not just program completion or training attendance. Look at trust, retention, change adoption, manager effectiveness, and how well teams navigate disruption.

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The conversation that started this and why it stayed with us :)

An intro chat!

Ekta and I connected over LinkedIn and our backgrounds were intriguing enough to have a chat. One of the topics we talked about was the importance of authenticity.

Ekta mentors emerging women leaders through the Ascentpoint Leadership Program. Five years in. This year felt different. The conversations weren’t about strategy or execution. They were about authenticity. About what it means to bring yourself to work when the ground keeps moving.

We got stuck in a conversation about what authenticity even means in a role that keeps telling you to just figure it out.

I thought I was overthinking it.

I wasn’t.

That’s why Ekta and I wrote this. Because the AI + EQ balance isn’t a nice-to-have. It’s the job. Trust and empathy don’t scale on their own; someone has to carry them into the room.

That someone is the manager. It’s time we treated developing them like it actually matters.

Stay curious. Continuous Learner. Powered by Technology. Grounded by Empathy.

Ekta and Priya

Suddenly, we had to make dozens of decisions all at once.

Cabinet finishes.
Appliance placements.
Floor tiles.
Where the sink should go.
Bathroom fixtures.
Tapware.
Ceramic.

It felt endless.

Because once construction starts, changing anything later becomes very expensive.

Move the kitchen sink?

Now you’re tearing out pipes.

Shift the oven?

You’re ripping up cabinets.

Change your mind about the island countertop?

That’s thousands of dollars gone.

So the logic is obvious:

Plan everything before you build.

For decades, organizations have operated exactly the same way.

The Old Economics of Building Software

In the past, building something new inside a company was expensive.

A new HR system.
A new workflow.
A new digital product.

You would need:

• months of requirements gathering

• long architecture documents

• planning committees

Why?

Because once the build started, change was COSTLY.

Just like moving that kitchen sink.

If you discovered halfway through that the idea wasn’t quite right, fixing it meant:

• rewriting code

• rebuilding systems

• blowing the budget

So organizations optimized for planning.

Think first. Build later.

But Something Fundamental Has Changed

AI is quietly rewriting the economics of building.

Today, teams can:

• prototype tools in days (sometimes hours)

• generate code with AI assistance

• automate workflows quickly

• experiment with internal tools without massive engineering effort

What used to take months can now take weeks.

Sometimes days.

Tools like Lovable and Claude Code have made it dramatically easier to turn ideas into working software.

I experienced this firsthand recently.

I decided to build a small app using Lovable.

My instinct was to do what we’ve all been trained to do - sit down and design everything first.

The screens. The buttons. The flows. The functionality.

Then my husband walked by, looked at what I was doing, and said:

“Why don’t you just ask Lovable to build it and see what happens?”

I hesitated.

Part of me resisted the idea.

Planning is something I genuinely enjoy. I like thinking through the screens, the buttons, the flow. It’s how I’ve always approached building things.

So letting AI do all of it felt… uncomfortable.

Almost like skipping the part I love.

But I tried it anyway.

And honestly, what it generated in minutes was better than what I had spent time planning.

The layout made sense. The buttons were exactly where they should be. The flow worked.

All I ended up changing was the color.

Two hours later, I had a working app.

If I had tried to design every detail upfront, I’m almost certain I wouldn’t have planned it that well.

The cost of trying something has collapsed.

And when the cost of trying something drops…

The cost of waiting goes up.

The Kitchen Sink Analogy Flips

Now imagine a different world.

You decide to renovate your kitchen.

But instead of tearing down walls to move the sink, you could simply slide it across the counter in minutes.

Cabinets could be rearranged like furniture.

Appliances could be swapped instantly.

In that world, would you spend weeks planning every detail beforehand?

Probably not.

You would do something much simpler.

You would try things.

Move the sink.
Step back.
See how it feels.

Adjust.

Build a little.
Learn a little.
Adjust again.

But Most Organizations Still Behave Like the Old Kitchen:

Even as technology has changed, many management habits haven’t.

Inside companies, we still see the same patterns:

A small idea triggers a large planning process.

A simple experiment requires:

• approval meetings

• project charters

• roadmaps

• cross-functional committees

Weeks are spent debating the plan.

Very little time is spent testing the idea itself.

Ironically, the tools have made building cheaper…

…but our processes still assume building is expensive.

This Is Where Leaders Need to Adjust

The shift AI is creating is more behavioral than technological.

We have spent a lot of time planning and, at times, have failed to get projects off the ground because of overplanning.

But when the cost of change falls dramatically, the optimal strategy shifts.

The smartest organizations will no longer optimize for perfect plans.

They optimize for fast learning.

That means asking different questions.

Instead of:

“Have we fully thought this through?”

The better question becomes:

“What’s the smallest version we can test this week?”

Instead of:

“Is this the right solution?”

The better question becomes:

“What will we learn if we try it?”

Try this:

1. The “2-Hour Experiment Rule”

Before launching a full AI initiative, ask your team:

What’s the smallest version of this idea we can test in two hours?

Not two months.
Not two weeks.

Two hours.

AI tools now make it possible to prototype workflows, dashboards, copilots, and internal tools incredibly quickly.

The goal isn’t perfection.

The goal is learning faster than planning.

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2. Prototype before PowerPoint.

Before writing the strategy document or scheduling the steering committee, build something small.

Even a rough prototype changes the conversation.

Instead of debating ideas, teams can react to something real.

And learning accelerates.

3. The “Kill the 30-Page Plan”

Replace the 30-page plan with three questions:

1. What problem are we trying to solve?

2. What’s the smallest version we can test this week?

3. What would success look like after one experiment?

If those questions are clear, the best next step usually isn’t another meeting.

It’s trying something.

This Matters More Than It Sounds

The biggest barrier to AI adoption is management habits designed for a world where change was expensive.

Approval layers.
Long planning cycles.

These made sense when every change required massive effort.

But when building becomes easier, those same processes can slow organizations down.

The friction shifts.

Not in the tools.

But in the decision-making.

A Small Shift in Mindset

There’s a quiet realization emerging across many companies right now.

The hard part is no longer building.

The hard part is deciding what’s worth trying.

In a world where change is cheap, the advantage goes to organizations that are comfortable experimenting.

Trying small things.

Learning quickly.

Adjusting often.

And perhaps that leads to a simple question for leaders:

If changing things has become easier than ever…

Why are so many organizations still planning as if it isn’t?

Stay curious.

AI Lady 💫

About the Author

I’m Priya Tahiliani, and I’ve spent the last 15 years at the intersection of People and Technology. Most of my career has focused on SAP HCM and SAP SuccessFactors consulting, working with Big Four firms and clients across the globe.

I built and launched my company’s first AI tool by forging a great partnership with IT, and today I continue to work with HR leaders to help shape the future of work and drive AI enablement.

Beyond work, I serve as Vice President of Public Relations at Toastmasters. I’m also the Founder of the AI Collective – Oakville Chapter in Canada, part of the world’s largest community for AI professionals - a network dedicated to learning and leading responsibly with AI.

And of course, I write the AI Lady newsletter, where I share my experiences, insights, and thoughts about how AI is reshaping our workplaces.

If this article sparked something for you, share it with someone else navigating this shift. These conversations matter more when we have them together.

Pass it along to a leader, an HR partner, or a curious mind who’s thinking about where AI is taking us next.

Click here to redeem your special offer!

Its only valid until this weekend :)

Invite 26 people.

Put them in a conference room.

Ask, “So… what do we think about this AI use case?”

And then watch the project slowly turn into a group therapy session.

I met with a senior leader today, and she said something so simple and so true!

Most AI projects fail because too many people sit around chirping opinions.

Not because people are unhelpful.

Because the room fills up with opinions and runs out of owners.

Ants. Yes, ants.

A professor at the Weizmann Institute of Science ran a classic “piano movers” puzzle: maneuvering a large, oddly shaped object through a tight, constrained space.

He tested ants versus humans.

Here’s what happened:

• Ants, in groups, got dramatically better.

• Humans, in groups, did not.

• Ant teams scaled beautifully- groups of around ~80 ants got dramatically more effective while large human groups (about 16–26 people) didn’t improve in the same way, and sometimes performed worse than the best individual in the group.

• When communication was restricted, human performance deteriorated.

The ants didn’t debate about hypothetical failures.

They moved.

They relied on movement feedback, and what the researchers describe as a kind of shared memory - sliding the object along walls in a near-deterministic way.

Humans did what humans do.

They debated. They conflicted. They tried to align. They often optimized for consensus over efficiency. They discussed random & hypothetical risks.

Which is… painfully familiar.

Because corporate AI projects are basically “piano movers puzzles” in disguise:

• tight constraints (data, privacy, workflows)

• uncertainty hence high risk (AI isn’t deterministic)

• high visibility

• high fear of being blamed

And our default response is to add more people.

More stakeholders. More meetings. More “alignment.” More people to pass the puck.

But the experiment suggests something uncomfortable:

More brains in the room doesn’t automatically create collective intelligence.
Often times it just creates collective drag.

The real enemy of AI projects isn’t the technology

AI work is messy. It needs iteration. It needs feedback loops. It needs decisions that can be reversed quickly.

Big rooms are bad at that because they produce:

• unique strategies (everyone has a different mental model)

• conflict disguised as “We need to align on XYZ and ABC”

• risk inflation (“what if this goes wrong?” read “How do we avoid getting blamed?” and that becomes the project)

• consensus gravity (the group drifts to the most obvious, safest, suboptimal path - nobody hates it but nobody loves it.)

When ownership is fuzzy, people focus on being involved - not on being correct.

That’s how you end up with 26 people “contributing” and no one delivering.

A quick story from the field

We recently went live with an AI tool, and it worked because we stayed disciplined about the humans.

We kept the working team lean.

Not “small for the sake of small.”

Read more

Login.
Navigate.
Click.
Submit.

HRIS. ATS. Payroll. LMS. Benefits

That’s how SaaS was designed.

Humans enter through the INTERFACE.

And that interface is what companies pay for.

Per seat.
Per user.
Per year.

But I keep thinking about the moment that’s coming next.

A manager says:

“Open a new role for a senior analyst and adjust my team structure.”

And instead of logging into the HRIS…

They just ask.

And it’s done.

No navigation.

No Clicks.

Just conversation.

That’s the shift quietly unsettling SaaS.

Who Owns the Front Door?

At its core, this transformation isn’t about systems disappearing.

It’s about entrances changing.

Two doors.

Same building: Enterprise Systems.

Door A: HRIS Login (Traditional UI)
Door B: AI Assistant (Conversation Layer)

The infrastructure stays.

The database stays.

But the entry point shifts.

And whoever owns the entry point…

Owns the experience.

That’s why HR vendors are racing to build embedded agents.

And why horizontal AI platforms are building orchestration layers on top of enterprise systems.

This is a positioning battle.

Not a feature battle.

The Stack Is Reordering Itself

Let’s go deeper.

If AI becomes the interface, where does it actually sit?

Read more

In fitness, it’s “10,000 steps a day.”

We know it’s not a perfect scientific threshold, but it works as a behavioral anchor: simple & repeatable (Harvard Health has a great breakdown of the “10,000 steps” story.)

So here’s the question I can’t stop thinking about:

What’s the equivalent of 10,000 steps for AI at work?

Not “nice-to-hav…

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“I would like to thank my leader and my team for giving me the opportunities…”

People smile.

Some reply: “Wishing you the best. Let’s stay in touch on LinkedIn!”

And then… the organization starts to forget.

In a slow, expensive way.

Because while we treat offboarding and exits like an HR checkli…

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Rowena Slattery and her partner flipped their double kayak during their second outing.

And she described it as a lesson in balance, resilience, and respecting the swell.

Honestly, that’s how AI change feels in organizations right now.

I am co-authoring this week’s piece with Rowena. She works with leaders and teams navigating change, especially as AI and new systems reshape roles and ways of working.

She is deeply people-centered in her approach to this work, and she’s developed the PASA Frameworks to help organizations navigate change with clarity, confidence, and genuine care for their people.

Rowena loves engaging with people, sharing ideas honestly, and helping leaders make sense of complexity before rushing into action. This year, her focus is on building her dream work, which is helping teams achieve safe, strong outcomes as they adopt AI in practical, human-led ways.

And a quick credit where it’s due: this article was inspired by an episode of Beyond the Prompt featuring Prosci leaders Tim Creasy and Paul Gonzalez, which hits hard, because it put words to what I keep seeing too often: organizations keep underestimating the “human” side of AI change.

Many leadership teams are rolling out AI strategies right now.
Big vision. Big promises. Big urgency.

And I keep thinking about the moment that follows…

The moment an employee asks:

“Okay… so what changes for me next week?”

That is THE one question..

And if leaders can’t answer that clearly, the vision doesn’t inspire people.

It unsettles them.

We keep treating AI adoption like a rollout:

Pick a tool. Train people. Send comms. Done.

But AI change doesn’t behave like regular change. It moves fast, shifts constantly, and lands emotionally:

Fear. Pressure. “Am I behind?” “Is my job safe?”

So here’s the reframe:

When AI moves faster than people, leadership becomes the work.

Not the deck. Not the announcement. Not the hype.

Leadership is the work that happens when real humans try to change how they work.

The 5 steps people actually go through (whether we plan for it or not)

This part matters because leaders often assume: If we tell people, they’ll do it.

But change is still a sequence.

People usually move through five steps:

1. They understand what’s happening

2. They decide if it’s worth engaging

3. They learn what to do

4. They try it enough times to feel capable

5. They keep doing it because it’s reinforced

AI didn’t remove these steps.

It just shrunk the timeline.

Which means when leaders skip one… adoption stalls.

The 5 Leadership Moves That Make AI Change Stick

If you are leading HR, a function, or a team through AI change, these are five moves that consistently help.

Move 1: Make “next week” clear

Big vision is fine. But people need the next step.

Say it plainly:

• This month, we’re standardizing 3 use cases.

• This quarter, we’re stopping 2 workflows that don’t make sense anymore.

• This is what we’ll measure (and what we won’t).

When the present is clear, the future feels less scary.

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Move 2: Stop cheerleading. Learn alongside your team.

Teams don’t need leaders to sound excited.

They need leaders to sound real.

The trust-builder is a leader saying:

“I tried it. I got a weird result. Here’s what I learned.”

When leaders learn out loud, it tells everyone: it’s safe to be new at this.

And then something powerful happens:

• uncertainty gets normalized

• fear of getting it wrong drops

• experimentation becomes “what we do,” not “what the brave do”

• adoption becomes cultural, not compliance-based

Presence creates momentum.

Move 3: Protect time for practice

Experimentation doesn’t happen “in spare time.”

If people are already at capacity, “just try AI” becomes:

• rushed

• performative

• avoided

So protect time. Even small. Even imperfect. But consistent.

Adoption needs repetition.

Move 4: Lower the stakes

People won’t experiment if mistakes feel expensive.

So lower the risk:

• “Start with low-risk tasks.”

• “We’re learning, not judging.”

• “Share what worked and what didn’t.”

No safety = quiet avoidance.

Move 5: Reinforce it every week

AI change doesn’t stick because of one training.

It sticks because managers keep it alive:

• they ask about it

• they share examples

• they reward the attempt

• they normalize the learning curve

Without reinforcement, people revert — not because they’re resistant, but because old habits are comfortable under pressure.

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The questions I’m sitting with

• Where are we asking people to move fast without making it feel safe?

• Where are we selling a big AI future without explaining the next step?

• Where are leaders cheering from the sidelines instead of learning alongside their teams?

• And which of the five human steps are we accidentally skipping right now?

Because when AI moves faster than people…

Leadership isn’t the speech.

Leadership is the environment.

Stay curious :)

Rowena and Priya

Over the break, I caught myself thinking about what I want AI Lady (both me and my newsletter) to be this year.

Not just “Interesting AI takes.” Not just “Use Cases & AI tools.”

My 2026 goal is way more human:

I want to uplift more people to adopt and build with AI - and do it through real 1:1 connections.
The kind where you can say: “Here’s what I’m trying,” and we figure it out together.

Because I’ve learned something that keeps repeating across every technology wave:

Tools don’t change careers. Participation and Momentum does.

Why this is my Goal?

15 years ago, I was told I couldn’t be a SAP Consultant.

Why? You ask.

Because I didn’t have a “Computer Science degree”.

And here’s the plot twist:

I went on to become a successful SAP consultant anyway, traveled the world and worked on multi million $$$ high stakes projects.

The kind of work people name-drop when they want credibility. Enterprise Transformation. Technology Implementations. Big Programs. The Accenture-and-Deloitte flavor of SAP. The 3 Michelin-star level of SAP consulting.

And along the way I definitely became more “technical” than anyone expected.

It was not easy, but I did what it took.

And I learned.

You don’t earn credibility by having the perfect label.
You earn it by being curious, learning fast, shipping, and showing proof.

Which brings me to AI and surprisingly things have not changed much.

“AI is democratized”… and yet

So when people tell me “AI is democratized,” I nod.

Because yes… with vibecoding, copilots, low code/no code tools… it’s never been easier to build.

But I also pause.

Because “easy to use” does not automatically mean “everyone benefits equally.”

The thing Elena Verna said that stuck with me:

(Head of Growth at Lovable) wrote that she expected their users to be pretty balanced… but when the stats were out - women are only ~20% of users.

There’s no barrier to entry anymore. You can just show up, start chatting and start building - no code, no gatekeepers. But still, no such luck.

Hard truth: women often need that extra push, a reason, an excuse, a shove out of comfort. But her fear? For too many, that push will come too late.

That line matters because Lovable product is literally built to feel welcoming.

So if the gap shows up there… it’s not about aesthetics. It’s not about intelligence. It’s not about interest.

It’s about what gets people to actually start.

And yes - this shows up in broader data too. Deloitte has reported meaningful differences in how women and men are incorporating gen AI into daily life/workflows and highlights that parity in “adoption” doesn’t automatically translate into parity in integration and benefit.

And that means if women lag in adoption now, it doesn’t stay a “tool or a learning gap.”

It becomes an influence gap.

That’s DEIB in a brand new costume :) Yeah - I see that coming very soon with glass ceilings and the same old story.

Here’s the clearest “why” I’ve found

This is the simplest way to say it:

Adoption isn’t just technical. It’s social.

Adoption depends on things like:

• Who feels comfortable being a beginner out loud

• Who has time during the workday to experiment

• Who gets invited into pilots / “AI task forces” / prompt-sharing circles

• Who feels they can try something imperfect without getting judged

• Who trusts the tool enough to use it on real work

Research backs that up. A BIS working paper on the “GenAI gender gap” found women report lower gen AI usage than men, and the biggest driver isn’t ability, it’s self-assessed knowledge (basically: “Do I feel like I know enough to use this?”).

That’s not a talent gap.

That’s a confidence + context gap.

This is what needs to be addressed.

The most uplifting proof I can offer: my own inbox

Oh btw - I got an email from Lovable with my “2025 - year in review” stats and I just sat there smiling.

It said I’m in the top 10% of builders globally for code created.
I “vibed out” 23,204 lines of code, saved 773 hours of coding time, and apparently I’m an “Elite Vibe Coder.”

The same person who was once told “you can’t” without a Computer Science degree.

And the best part?

I built 4 apps. Not by waiting until I felt ready but by being vulnerable, failing, but working toward building the muscle of coding through practice.

That’s the energy I want to spread in 2026:

You don’t need permission to be a builder.

I’ve learned something practical which I am happy to share with you

Doors tend to open without you having to narrate your résumé. I represent HR in my company’s AI Taskforce.

My HR team and I built our company’s first AI chatbot and shipped it in September 2025.

That should tell you something.

That’s the energy I want more people to have access to.

What this means for HR leaders?

(and anyone who cares about the future of work)

AI is becoming a career multiplier.

Early adopters get visibility. Their learning compounds. They become “the person who knows it.” That turns into opportunity.

So in 2026, I’m not interested in “encouraging AI.”

I’m interested in designing adoption so it doesn’t default to whoever already feels most confident.

A few practical moves that work:

1) Give people protected build time.
Not an after-work hours hobby.

If it’s optional, it becomes unequal by default.

2) Create small peer pods, not big one-off trainings.
Confidence grows through practice and hands-on building, not PowerPoint slides.

3) Make “beginner mode” socially safe.
Leaders learning out loud changes everything.

4) Tackle trust head-on
Clear guardrails on what’s safe to input and what isn’t (ambiguity kills adoption).

5) Celebrate & reward builders in every role.
Building isn’t only coding. It’s better onboarding, smarter recruiting workflows, cleaner HR ops, faster internal comms, stronger manager toolkits.

My New Year ask

If you’ve been curious but hesitant: this is your sign.
Start small. Start today.

And if you know someone who needs a boost - share this article with them.

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Also: if you want a 1:1 nudge to build your first thing (or your next thing), DM me. This is literally my 2026 intention.

This year, I want to be more than a newsletter.

I want AI Lady to be a signal in someone’s week that says:

You don’t have to be a “Technologist” to build with AI.
You just have to start.

Let’s make more AI Ladies this year - meaning: more people who claim the builder identity.

Stay curious 🙂
AI Lady

About the Author

I’m Priya Tahiliani, and I’ve spent the last 15 years at the intersection of HR and Technology. My career has centered on SAP HCM and SAP SuccessFactors consulting, working with Big Four firms and clients worldwide.

I led various AI adoption initiatives, developed and launched my company’s first AI tool by building a strong cross-functional partnership with IT, and I continue to collaborate with HR leaders to shape the future of work through AI.

Beyond work, I serve as Vice President of Public Relations at Toastmasters. I’m also the Founder of the AI Collective – Oakville Chapter in Canada, which is the world’s largest community for AI professionals - a network dedicated to learning and leading responsibly with AI.

And of course, I write the AI Lady newsletter, where I share my experiences, insights, and thoughts about how AI is reshaping our workplaces.

My newest hobby - learning how to produce music and remixing songs!

Feel free to comment if you want to listen to my latest creation :)

“We can’t use that tool. What if it hallucinates?”

Heads nodded.

No one asked what hallucination actually meant.

And that’s when I realized - we are making decisions about AI based on words we don’t fully understand.

There are two sentences I hear all the time in conversations about AI:

“AI hallucinates.”
“AI keeps le…

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“Do I really need to understand all these AI terms such as tokens, GPT’s etc.?”

Here is what I believe:

AI terminology isn’t about turning you into a technologist.
It’s about helping you feel in control.

When you understand the language, you stop feeling like a spectator in AI conversations and step back into the driver’s seat of your HR strategy.

💛 Why this glossary matters for every leader:

• It lets you follow and lead AI conversations instead of silently nodding.

• It helps you challenge vendors and look beyond buzzwords.

• It supports HR’s shift from tech consumer to tech creator.

• It helps you rewrite the narrative that HR “doesn’t get” technology.

I’ve explained these terms in the exact order your brain naturally learns them.
We begin with the simple concepts - your words, your instructions, your prompts.

Then we move into how AI models work, what they’re capable of, and how they use your data.

Once that foundation is set, we explore accuracy, safety, integration, and finally the agentic tools that let HR automate and create.

Each bucket builds on the previous one, helping you understand AI without ever feeling lost or overwhelmed.

1. Foundation:

Before we talk about AI systems, we begin with the basics - your words, your instructions, and how AI responds.

Prompt

Your instructions or questions to the AI.

A prompt is what you tell the AI to do - the input that guides its output.
It can include:

• the task (summarize, rewrite, draft, analyze)

• the audience (employees, managers, leaders)

• the tone (formal, friendly, neutral)

• the format (bullets, email, table, policy summary)

• the constraints (keep it short, use plain language, use our policy only)

Better prompts → better outputs. Prompting is a skill HR leaders can and should build.

Example prompt framework:

• Role – Tell the AI who it should act as.

“You are a senior HR Policy & Program advisor.”

• Context – Provide background or sources.

“Use the attached HR policy document and our employee handbook.”

• Action – Say exactly what you want and in what format.

“Summarize the key changes in 3 bullet points for managers.”

HR Leader Tip:
Ask vendors:

• “How much prompting skill is required to use your tool effectively?”

• “Does your system support structured prompt frameworks?”

AI Model

A trained system that recognizes patterns and produces outputs.

An AI model is a mathematical system trained on large amounts of data.
It doesn’t memorize; it learns statistical patterns such as how language flows, how concepts relate, how questions map to answers.
When you use an AI tool such as ChatGPT, you’re interacting with an underlying model that has learned these patterns and uses them to generate responses.

HR Leader Tip:
Ask vendors:

• “What model powers your system, and why that one?”

• “Is it optimized for HR tasks or a general model?”

• “Where does the model process data (cloud vs on-prem)?”

• “Do you plan to add or support additional models in the future?”

Training an AI Model

How an AI model learns from example data.

Training is the process where a model is exposed to huge datasets (like text, code, or other content) and learns relationships within that data.
In HR language: training is like “onboarding + years of experience” for the model - it’s how it becomes capable.

HR Leader Tip:
Ask vendors:

• “Is your model trained on general data, industry-specific data, or HR datasets?”

• “How often is the model retrained or updated?”

• “Can we add our own organization’s data safely?”

Inference

The process of an AI model producing an output from your input.

Inference is what happens after you send a prompt to an AI system.
It’s the real-time process where the model:

1. reads your input

2. breaks it into tokens

3. applies its learned patterns

4. generates a response

No new learning happens during inference! Learning happens during training.
Inference is simply the execution phase where the AI uses what it already learned to respond, summarize, classify, or take an action.

Every time you chat with an AI, ask a copilot a question or use a voice agent you are triggering inference.

Systems with faster inference feel more responsive and are more suitable for:

• employee-facing tools

• high-volume HR queries

• real-time support

Example:
An employee asks:
“What’s the process for updating my bank details?”

The AI model instantly interprets the question, retrieves the relevant policy, and generates a clear answer.
That entire behind-the-scenes process from reading → reasoning → replying — is inference.

HR Leader Tip:
Ask vendors:

• “How fast is your model’s inference time during peak HR periods?”

• “Does inference happen on-device, in the cloud, or in a private environment?”

• “What affects response speed — model size, context window, or compute limits?”

• “Can inference be optimized for employee self-service scenarios?”

Tokens

A small unit of text that the model reads.

AI models don’t process an entire paragraph at once.
They break it into tokens that are small chunks of text (often pieces of words).
The number of tokens in your prompt and documents affects:

• how much the model can “see” at once

• how long it takes

• how much it costs (many tools bill per token)

Every AI model has its own way of calculating tokens, below is an example for OpenAI.

A helpful rule of thumb is that one token generally corresponds to ~4 characters of text for common English text. This translates to roughly ¾ of a word (so 100 tokens ~= 75 words)

HR Leader Tip:
Ask vendors:

• “How are costs calculated? By tokens, messages, or usage tiers?”

• “Will this tool become expensive with long documents?”

• “Does your tool optimize token usage automatically?”

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2. Model Types: Understanding the “Engines” Behind AI

Once you know how you interact with AI, the next step is understanding the models themselves - the engines that power every AI tool. These terms explain the different kinds of AI and what makes each one useful.

LLM (Large Language Model)

A model trained on very large amounts of text to understand and generate language.

LLMs, like GPT-4, are powerful AI models that can:

• summarize documents

• write emails or policies

• answer questions

• analyze text for patterns or sentiment

They learn from broad, large-scale datasets so they can handle many topics and tasks.

HR Leader Tip:
Ask vendors:

• “Which LLMs does your product support or integrate with?”

• “Do you use a single model or switch depending on the task?”

• “How do you ensure accuracy for HR-specific content?”

Small Language Model (SLM)

A smaller, more efficient language model.

SLMs are compact models designed to be:

• faster

• cheaper to run

• easier to deploy inside organizations

They’re ideal for focused internal tasks, like answering FAQs from your own documents.

HR Leader Tip:
Ask vendors:

• “Can SLMs be deployed privately for sensitive HR content?”

• “How do you balance speed and accuracy?”

Generative AI

AI that creates new content.

Generative AI uses models (like LLMs) to generate original text, images, or other content - not just retrieve information.
In HR, it can draft job descriptions, write communication drafts, summarize survey comments, or propose development plans.

HR Leader Tip:
Ask vendors:

• “Where does your tool generate content versus retrieve information?”

• “What safeguards exist to prevent incorrect generation of data?”

• “Can we restrict generative functions in sensitive areas?”

Classic AI

AI built for one specific task, following fixed rules or narrow logic.

Classic AI systems do one job at a time like classifying data, detecting patterns, routing tickets, or playing chess.
Each use case traditionally needed its own separate model, designed and tuned for that task.

They are predictable and stable but not flexible or adaptable.

By contrast, Generative AI is general-purpose AI:
the same model can perform many different tasks simply by changing the prompt or instructions.

HR Leader Tip:
Ask vendors:

• “Which parts of this workflow use classic rule-based automation?”

• “Can we configure the logic ourselves?”

• “How do you combine classic automation with AI-driven reasoning?”

Reasoning Model

An AI model optimized to think step-by-step and solve complex problems.

A reasoning model is designed not just to generate text but to analyze, plan, and make structured decisions. It breaks tasks into steps, evaluates possible actions, and follows a chain of logic.

These models improve accuracy for analytical HR tasks such as interpreting surveys, summarizing audits, or evaluating employee data patterns.

Example:
An AI agent that explains why it categorized certain employee comments as “workload concerns” by walking through its reasoning.

HR Leader Tip:
Ask vendors:

• “Is your system built on a reasoning model?”

• “Can we see the reasoning steps or chain-of-thought?”

• “How does your model handle complex HR scenarios requiring judgment?”

Deterministic Output

The same input always produces the same output.

Deterministic AI behaves predictably. When given identical data or instructions, it generates identical responses every time unlike generative AI, which may produce variations.

Deterministic outputs are useful for compliance-heavy HR workflows requiring consistency.

Example:
A deterministic classification model that always assigns a specific type of HR ticket to the same category.

HR Leader Tip:
Ask vendors:

• “Where in your system do you guarantee deterministic behavior?”

• “Can we choose between deterministic and generative modes?”

• “How do you manage consistency in high-risk HR scenarios?”

3. Capabilities: What AI Can Actually Do

Now that we understand the types of AI models, we explore their practical capabilities - how fast they respond, how well they reason, and the different types of inputs they can handle. These terms help you understand what the real experience will feel like for employees and managers using AI tools every day.

Chain of Thought

The step-by-step reasoning an AI uses to arrive at an answer.

Chain of thought refers to the internal reasoning steps an AI may generate to solve a problem. It’s the “thinking trail” - the sequence of logic the model follows before producing a final answer.

In many systems, this chain of thought is hidden, because exposing it can reveal sensitive model behavior, create security risks, or confuse users.
Instead, models usually provide only the final answer, unless specifically built to show a simplified reasoning summary.

Understanding chain of thought helps you know how the AI arrived at a conclusion, especially for tasks requiring logic, calculations, or multi-step decisions.

Example:
If AI is asked:
“Is an employee eligible for parental leave?”

The chain of thought might be:

1. Check employment type

2. Check tenure

3. Check local legal requirements

4. Check company policy exceptions

5. Provide eligibility decision

You see only the answer, but the model followed these steps internally.

HR Leader Tip:
Ask vendors:

• “Does your system use chain-of-thought reasoning for HR decisions?”

• “Can we get a simplified rationale so decisions are explainable?”

• “How do you ensure the reasoning is transparent without exposing sensitive model logic?”

Compute Power

The processing capacity the AI uses to “think.”

Compute power is the technical resource behind the scenes.
More compute allows:

• faster responses

• deeper reasoning

• handling bigger inputs (more tokens, longer documents)

Understanding compute at a high level helps HR leaders make sense of why some AI tasks are more resource-intensive and more expensive than others.

HR Leader Tip:
Ask vendors:

• “How resource-intensive is your system?”

• “Will response times slow down with bigger documents?”

• “How do you scale compute for peak HR cycles?”

Multimodal

An AI model that can process multiple input types (text, images, audio, etc.).

Multimodal models can:

• read text

• interpret images (e.g., a screenshot of a policy)

• listen to audio

• in some cases, work with video

This broadens how AI can support HR beyond just chat into document images, slides, forms, videos and more.

HR Leader Tip:
Ask vendors:

• “Can this system read image-based documents like employee files or screenshots?”

• “Does it handle audio or video in employee experience scenarios?”

• “Are multimodal features included or an add-on?”

Latency

The time the AI takes to respond to a request.

Latency is the delay between your prompt and the model’s reply.
Some tasks finish almost instantly; others take longer because they involve:

• large documents

• complex reasoning

• multiple tool calls

For HR, reasonable latency is important for user experience especially in employee-facing tools.

HR Leader Tip:
Ask vendors:

• “What is your average response time?”

• “Does speed change with longer documents or more users?”

• “Can latency be guaranteed during peak HR seasons?”

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4. Data + Context: How AI Uses Your Information

Now we shift into how AI handles your company’s documents, policies, and knowledge. These terms explain how AI retrieves, grounds, and interprets your organizational data.

Document Grounding

AI answering based only on your approved documents.

Grounding means that when the AI answers, it relies on your content (like HR handbooks, policies, FAQs) rather than generic internet knowledge.
This is critical in HR because:

• policies differ by organization

• legal and compliance risks are high

• accuracy builds employee trust

Grounding is the foundation for making AI useful and safe in HR contexts.

HR Leader Tip:
Ask vendors:

• “Which documents does your tool use for grounding?”

• “Can we control which sources it can or cannot reference?”

• “How do we ensure outdated documents aren’t used in answers?”

• “Is document grounding included in the price?”

RAG (Retrieval-Augmented Generation)

AI that retrieves relevant documents before generating an answer.

In RAG systems, the AI:

1. Searches your document store for relevant content

2. Retrieves key passages

3. Uses generative AI to craft an answer based on those passages

This combination of retrieval + generation delivers responses that are both fluent and grounded in your actual data - which significantly reduces incorrect or generic answers.

HR Leader Tip:
Ask vendors:

• “Do you use RAG to minimize hallucination?”

• “Can we see which documents were retrieved to form a given answer?”

• “Can we restrict responses to retrieved content only?”

Vector Database

A database that stores and searches by meaning, not just keywords.

A vector database converts text into numerical vectors that represent meaning.
This allows for “semantic search” - finding information that is conceptually similar, even if the exact words don’t match.

Example:
Search for “employee happiness” and get documents tagged as “engagement” or “well-being,” because the system recognizes they’re related in meaning.

HR Leader Tip:
Ask vendors:

• “Does your system search by meaning or just keywords?”

• “How do you ensure the vector database stays updated with our latest HR documents?”

• “Where is the vector database hosted?”

Context Window

How much information the model can consider at once.

The context window is like the model’s short-term memory.
If the window is small, it can only “see” a limited number of tokens at one time.
If it’s large, it can read long policy documents, full email threads, or multiple files in one go - and respond with a better, more holistic answer.

HR Leader Tip:
Ask vendors:

• “How long of a document can the AI consider at once?”

• “Can it handle entire policies or only snippets?”

• “How do you support context windows for audits or investigations?”

Anchoring

Forcing the AI to stick to a specific document or source.

Anchoring restricts the AI so it only answers using the documents you specify. This prevents hallucinations and ensures consistency with approved HR policies, handbooks, and guidelines.

It’s one of the most important methods for making AI reliable in HR.

Example:
“Answer only using content from the official employee handbook dated 2024.”

HR Leader Tip:
Ask vendors:

• “Can your system anchor responses to only our approved HR documents?”

• “How do you prevent the AI from going outside those sources?”

• “Can we control which documents the AI must anchor to?”

AI Memory

AI that remembers previous interactions or preferences.

Memory allows AI systems to:

• recall what you asked previously

• remember your role (e.g., HRBP vs manager)

• adapt responses to your tone and style

This creates continuity and makes AI feel more like a partner than a tool.

HR Leader Tip:
Ask vendors:

• “What does the AI remember between interactions - and what does it NOT remember?”

• “Can memory be controlled or turned off for compliance reasons?”

• “How does the system store or protect personal preferences?”

5. Behavior + Safety: How AI Stays Accurate and Compliant

Once the AI is connected to your data, we focus on accuracy, risk, and trust. These terms explain how AI avoids errors, stays aligned with policy, and operates safely in HR environments.

Hallucination

It is when Gen AI makes up an answer with confidence.

Imagine a friend who doesn’t know the answer…
but refuses to admit it so they improvise beautifully and incorrectly.
That’s an AI hallucination.

Now that you’ve seen model, grounding, RAG, and context, we can define this clearly:

Hallucinations happen when:

• the model is relying only on learned patterns (not your data)

• there is no grounding or retrieval step

• the model fills gaps by “guessing” what seems plausible

This is why:

• Document Grounding and RAG are crucial because they anchor responses in your real content.

• Well configured copilots (for example, a custom Microsoft Copilot Agent with restricted knowledge sources and instructions) tend to hallucinate much less, because they are designed to retrieve from defined repositories rather than invent answers

HR Leader Tip:
Ask vendors:
“How exactly does your system reduce or handle hallucinations in HR-sensitive use cases?”

Guardrails

Rules and safety boundaries that control how an AI behaves.

Guardrails are the constraints that prevent an AI system from giving inappropriate, unsafe, or irrelevant outputs. They define what the AI can and cannot do, including tone, confidentiality, allowed actions, and restricted topics.

Guardrails ensure consistency, reduce risk, and protect employees by keeping the AI aligned with organizational policies and compliance expectations.

Example:
A Copilot agent may be allowed to summarize policies but not generate legal interpretations.

HR Leader Tip:
Ask vendors:

• “What guardrails are built into your system?”

• “Can we add our own HR-specific guardrails?”

• “How do you prevent inappropriate or non-compliant responses?”

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6. Infrastructure & Integration: How AI Connects to HR Systems

These terms describe the technical pathways that allow AI to read, write, update, or trigger actions inside your HR systems — securely and with the right permissions.

MCP (Model Context Protocol)

A standard way for AI to safely connect to tools and data sources.

MCP defines how AI models can talk to external systems (HRIS, LMS, knowledge bases, etc.) in a structured, permissioned way.
It gives your regular Chatbot like ChatGPT other capabilities such as:

• look up data

• trigger actions

• update records

• orchestrate workflows

All while respecting security and access controls.

HR Leader Tip:
Ask vendors:

• “Which systems can your tool connect to using MCP?”

• “Does MCP access require IT or HR configuration?”

• “What guardrails prevent unauthorized access?”

API Endpoint

The specific digital location where an AI system connects to another system.

An API endpoint is like a doorway that lets two systems communicate.
AI uses endpoints to:

• read data

• update records

• trigger actions

• retrieve employee information

• execute HR workflows

Endpoints define where the request goes and what it can do.

Example:
An AI agent uses an API endpoint to update an employee’s contact information in your HRIS.

HR Leader Tip:
Ask vendors:

• “Which HR systems does your AI integrate with, and through which endpoints?”

• “Do you require IT to set up or approve these endpoints?”

• “What data does the AI read or modify through each endpoint?”

API Key

A secure digital key that controls access to an application or system.

An API key is like a badge for software.
It tells a system:

• which application is calling

• what it’s allowed to access

• under what permissions

When AI tools integrate with HR systems, API keys control and limit that access.

7. Agentic Capabilities: When AI Moves From Answering to Doing

At this stage, AI becomes more than a chat interface. These terms introduce how AI agents plan tasks, execute workflows, take actions, and act like a digital teammate.

Chatbot

An AI system that follows rules, predefined logic, or simple pattern matching.

A chatbot is designed to handle conversations within a defined boundary.
Modern chatbots may use natural language understanding, but they:

• operate inside predefined logic flows

• answer repetitive, predictable questions

• cannot genuinely reason or act beyond what they were configured to do

They are useful for FAQs, but they do not have the autonomy or decision-making abilities of agents.

HR Leader Tip:
Ask vendors:

• “Is this a rule-based chatbot or a true agent?”

• “Can we update flows ourselves?”

• “Can the chatbot escalate to a human when needed?”

AI Agent

An AI system that can take actions, not just respond.

Agents understand goals and can:

• plan steps

• call other tools and systems

• read and write to data sources

• execute workflows end-to-end

Example: an HR agent that can take a policy question, look up the policy, summarize it, log the query, and send a follow-up email - without you manually doing each step.

HR Leader Tip:
Ask vendors:

• “What actions can your agent actually take today?”

• “What systems can it interact with?”

• “Is the agent fully autonomous or approval-based?”

Agentic AI

The logic that lets an agent plan, decide, and correct itself.

The agentic algorithm is the “decision engine.”
It lets the agent:

• choose which tool to use

• decide the next best action

• recover from errors

• change strategy based on results

This is what differentiates a true agent from a simple chatbot or script.

HR Leader Tip:
Ask vendors:

• “How does the system decide what to do next?”

• “Can we see the reasoning steps for transparency?”

• “How does the agent correct mistakes?”

AI Workflow

A sequence of tasks automated by AI.

AI workflows join multiple steps together for example:

1. Collect employee questions

2. Classify them

3. Answer simple ones automatically

4. Escalate complex ones

5. Summarise trends for HR

They move AI from “one-off answers” to “ongoing process automation.”

HR Leader Tip:
Ask vendors:

• “Which HR workflows can this automate end-to-end?”

• “Can we design workflows ourselves without IT?”

• “What level of customization is allowed?”

Orchestration

Coordinating multiple AI actions, tools, or steps into one workflow.

Orchestration is what allows AI to move from a one-off answer to a multi-step, end-to-end automated process. It stitches together tasks, decision points, and tools so the AI can execute complex HR workflows.

Example:
For onboarding:
AI collects information → updates HRIS → sends welcome email → creates IT tickets → schedules orientation.

HR Leader Tip:
Ask vendors:

• “Which HR workflows can your AI orchestrate end-to-end?”

• “Can we modify the orchestration ourselves without IT?”

• “How does your system handle exceptions or approvals?”

Voice Agent

An AI system that can understand, process, and respond to spoken language in real time.

A voice agent is an AI assistant you interact with through speech instead of typing.
It uses automatic speech recognition (ASR) to convert your voice into text, a language model to process the request, and text-to-speech (TTS) to reply back verbally.

Voice agents can:

• answer questions through conversation

• follow verbal instructions

• handle employee calls

• provide hands-free assistance

• guide users through steps (e.g., onboarding tasks)

• integrate with HR systems to retrieve or update information

Unlike chatbots, which rely on typed input, voice agents are built for natural, human-like conversation making them ideal for environments where speaking is easier or faster than typing.

Example:
An HR voice agent that an employee can call and say:
“Tell me how many vacation days I have left,”
or
“I want to submit a sick leave request,”
and the system responds conversationally and performs the action.

HR Leader Tip:
Ask vendors:

• “Does your voice agent understand diverse accents and speaking styles?”

• “Can employees complete actions through voice, or is it Q&A only?”

• “How does the system handle sensitive HR information over voice?”

• “Is the voice agent grounded in our data or just generative?”

• “Can it integrate with our HRIS to take real actions, not just answer questions?”

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8. HR-Building Tools: How HR Can Create With AI

After understanding AI’s capabilities, we shift to the tools HR can actually use. These terms empower you to build automations, copilots, and workflows — no technical background required.

Low-Code Tools

Tools that let you build with minimal coding/ configuration.

Low-code platforms provide visual interfaces plus optional bits of code.
They’re ideal when HR wants power and flexibility but can partner lightly with IT or technically confident team members.

Examples:

ServiceNow (workflow automation)
Build HR case routing, approvals, and workflows with some logic blocks.

Microsoft Power Apps
Build HR apps, forms, workflows, and dashboards.

No-Code Tools

Tools that let you build without any coding.

No-code tools use drag-and-drop interfaces and configuration instead of code.
They enable HR teams to design workflows, simple chatbots, and automations independently.

Examples:

• Airtable
  Create HR tracking boards, workflows, and repositories visually.

• Zapier
  Connect systems and automate HR tasks with simple triggers.

• Notion AI
  Build internal HR knowledge hubs + AI assistants inside pages.

Vibecoding

A development method where natural-language instructions are used to direct an AI model to generate code, reducing or bypassing manual coding

Vibecoding is an emerging approach in software development. In this method, the user provides a high-level goal or natural-language prompt (for example, “Build a login form with third-party authentication”) and a large language model (LLM) generates the underlying code.

This shifts the developer’s role from “coding line by line” to “guiding + reviewing” the AI outputs. Proponents say it accelerates prototyping and lowers the barrier to building applications. Critics raise concerns about maintainability, security, code quality, and oversight when using the approach in production settings.

Fine-Tuning

Training an existing model further using your organization’s own examples.

Fine-tuning customizes a pre-trained model (like GPT) using your internal data so the AI learns your company’s tone, terminology, processes, and preferred outputs.

It doesn’t train the model from scratch but it refines it using your examples, so results feel more accurate and on-brand.

Example:
Fine-tuning a model using hundreds of your company’s job descriptions so future drafts match your exact structure and language.

HR Leader Tip:
Ask vendors:

• “Is fine-tuning included, or is it extra?”

• “What internal data should we provide for the best HR outcomes?”

• “How do you maintain privacy when fine-tuning with HR documents?”

9. Future State: Where All of This Is Heading

Finally, we zoom out. These terms look ahead at what’s coming next, so HR leaders can understand the long-term direction of AI and prepare strategically.

AGI (Artificial General Intelligence)

A future, hypothetical AI with human-like general intelligence.

AGI describes a system that could understand, learn, and apply knowledge across any domain at a human level or beyond.

We are not there today.

Current systems are powerful but still specialized, strong at many tasks, but not equivalent to human-level general intelligence.

✨ Final thought: This isn’t about terminology - it’s about agency

Once you understand this language:

• vendor demos will feel less like theatre and more like dialogue.

• internal conversations will feel less intimidating and more collaborative.

• you will stop nodding silently and start steering the direction.

HR isn’t just in the room for AI transformation.
HR is fully capable of leading it.

And now, you have the vocabulary to do exactly that.

About the Author

I’m Priya Tahiliani, and I’ve spent the last 15 years at the intersection of HR and Technology. Most of my career has focused on SAP HCM and SAP SuccessFactors consulting, working with Big Four firms and clients across the globe.

I built and launched my company’s first AI tool by forging a great partnership with IT, and today I continue to work with HR leaders to help shape the future of work with AI.

Beyond work, I serve as Vice President of Public Relations at Toastmasters. I’m also the Founder of the AI Collective – Oakville Chapter in Canada, part of the world’s largest community for AI professionals - a network dedicated to learning and leading responsibly with AI.

And of course, I write the AI Lady newsletter, where I share my experiences, insights, and thoughts about how AI is reshaping our workplaces.

I depended on public transport. It worked - I got from Point A to Point B most days.
But I had to follow the schedule. There was no flexibility. Routes were fixed.

I dealt with delays and sudden cancellations. There were limitations.

I tried driving multiple times back in India. Each time, I stalled, panicked, and quietly decided: maybe driving just isn’t for me.

So I stayed dependent.
And that came at a cost - you give up some freedom.

Years later, I tried again. Different country. Same fear.
But this time, I didn’t quit.

It also felt easier and safer because the roads were smoother and the car was smarter, filled with automations that supported me.

It gave me confidence instead of anxiety.

And when I finally drove alone - really drove - something changed.

Agency replaced dependency.

Now, I love driving so much that I sometimes dream of driving on the Abu Dhabi F1 Ferrari track.

And that’s when it hit me - HR has been living the same story.

🚌 HR’s Public Transport Problem

Old HR systems were designed to record data, not draw insights.
They made sure policies were followed and learning courses were completed.

We spent years running transactions - payrolls, appraisals, benefits, attendance -
important work, but invisible work.
Necessary, but there is nothing transformational about it.

The truth is, the systems we inherited shaped the stories we told about ourselves.
When your daily work revolves around checklists, compliance, and forms,
you start to believe that’s all HR is meant to be.

That’s how we built the muscle for accuracy -
but lost the confidence for experimentation.

And somewhere along the way, administration became our comfort zone.

For years, HR has relied on IT, external vendors, and enterprise HRIS systems - SAP, Oracle, Workday that serve millions but move slowly.

They’re powerful but rigid.
You can request a feature and wait for years to get it, or forget about it.

Every automation, every dashboard, every new workflow had to go through someone else’s queue.
So we learned to adjust - and do things manually.

And let’s be honest - most of us in HR aren’t technology nerds.
We didn’t grow up writing code or programming.

And over time, we told ourselves the same story I once did behind the wheel:
Maybe technology just isn’t for us.

That’s how HR became the department of administration, not acceleration.

We’ve been great passengers - compliant, careful, consistent, but we could never take charge of where we take our technology.

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🚗 The Shift: When HR Learns to Drive

That story is changing.

Today’s AI tools are low-code and no-code platforms that have redrawn the map.
You don’t need to know coding to automate an HR process.
All you need is curiosity and a continuous improvement mindset - one that does not accept the status quo.

AI has the potential to turn HR from a consumer of technology to a creator of solutions.

When HR learns to drive, three shifts happen:

1. From waiting to building.
   No more IT tickets for every change. HR teams can now automate, design, and iterate on their own.

2. From systems to experience.
   HR stops serving platforms and starts designing journeys that are personal, intuitive, and human.

3. From administration to strategy.
   AI doesn’t replace HR; it releases HR to focus on culture, foresight, and business alignment.

⚙️ Case Studies: HR behind the Wheel

What I am quoting above isn’t just theory, there are real world examples of HR teams in companies actually doing it!

🔹 KFC Canada (Yum! Brands)
KFC’s HR team is proving that AI can amplify people.
Using Microsoft Copilot, they draft job descriptions, internal communications, and engagement calendars in minutes - freeing time for strategic work.

They even launched an internal AI Academy to train employees on how to use generative AI responsibly.

The result: HR now spends less time filling forms and more time shaping culture.

🔹 Zapier’s HR Playbook
At Zapier, HR led the company’s internal AI adoption.

They built 30 AI workflows and achieved 97 % adoption across the organization - not through mandates, but by modeling curiosity.

Their HR team became the engine of transformation, guiding others on how to integrate AI into daily work.

⚠️It all sounds good but…

I know what you are thinking and I get it.

We can talk about AI, low-code, no-code and HR’s new identity all day…
but there’s something deeper holding us back.

It’s not the tools - it’s our “reputation” that has deterred our confidence!

Our trainings haven’t landed well in the past.
Our people don’t always listen to us!!

For years, HR has been seen as the function that “doesn’t get technology.”

We also have an image - one we partly built ourselves -
HR is a department that polices policies, sends reminders, and enforces rules that don’t always make sense to the people.

Somewhere along the way, we convinced ourselves that leading AI transformation isn’t our cup of tea - that it belongs to IT or Data teams.

And if all else fails, we’ll just hire an external vendor to do it for us.

This is our moment to rebuild trust -
not by trying to be more like IT,
but by being more like us: curious, creative, and deeply human.

It’s time to leverage AI to learn about AI and stay informed.

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Strategic recommendations

Here’s how companies like Gartner recommend getting started:

• Build an HR-first AI strategy. One that aligns with business goals and has clear ownership. AI in HR can’t live in someone else’s roadmap - it needs to be ours.

• Partner with IT, Legal, and Compliance early. That’s how we keep AI reliable, accurate, and transparent, not just impressive in a demo.

• Prioritize what matters most. Focus on HR use cases that actually move the needle - the ones that save time and shift mindsets.

• Invest in skills, not fear. Help employees understand how AI will shape their work - starting with our own roles and what new skills will keep us in the driver’s seat.

• Redesign HR roles for what’s next. If AI can handle the transactions, let’s use that time to rethink culture, strategy, and the employee experience itself.

I have led AI transformation for my HR team, and it was more than just obtaining Microsoft Copilot licenses for my team and conducting prompting sessions.

My HR team led the company’s first AI chatbot, proving HR can do it!

Suddenly, employees were asking questions we had never heard before. They were exploring, engaging, and pushing boundaries.

And now they want more chatbots because they don’t want to spend hours searching for information.

That’s when we realized: once you build and release something real, you don’t just uncover efficiencies - you uncover more possibilities!

We shifted the conversation.

If your HR team is ready to take the wheel - let’s talk.

About the Author

I’m Priya Tahiliani, and I’ve spent the last 15 years at the intersection of HR and Technology. Most of my career has focused on SAP HCM and SAP SuccessFactors consulting, working with Big Four firms and clients across the globe.

I built and launched my company’s first AI tool by forging a great partnership with IT, and today I continue to work with HR leaders to help shape the future of work with AI.

Beyond work, I serve as Vice President of Public Relations at Toastmasters. I’m also the Founder of the AI Collective – Oakville Chapter in Canada, part of the world’s largest community for AI professionals - a network dedicated to learning and leading responsibly with AI.

And of course, I write the AI Lady newsletter, where I share my experiences, insights, and thoughts about how AI is reshaping our workplaces.

Empty seats on a flight.
Empty rooms in a hotel.
Unused M365 Copilot/ ChatGPT licenses across your company.

Because every empty seat is a sunk cost in days of lost productivity that can never be recovered.

The Illusion of ROI

When companies talk about “AI ROI,” they talk in broad strok…

Read more

“Let me get back to you.”

It was the professional pause button. A graceful, tactful way to buy time while you scrambled behind the scenes to phone a colleagu…

Read more

Undoubtedly, the most reassuring FOUR words of the year.

It sounds like someone has our backs.

It is like a corporate version of mac and cheese - warm, familiar, reassuring.

To me, “human in the loop” sounds more like comfort food than anything else.

What does HITL really mean, and where- or when- is the human even being looped in?

…

Read more

Departments as Knowledge Monopolies

• Finance didn’t just balance books- they controlled the budgets, because only trained accountants could navigate complex ledgers.

• Engineering wasn’t just about code- it was the sole gatekeeper of systems that powered the business.

• HR and Legal didn’t just manage policies- they interpreted frameworks most employees couldn’t access or understand.

Each function became a kingdom, where knowledge wasn’t just expertise- it was power. Scarcity created control, and control defined status.

If you weren’t in the guild, you stayed out.

Scarcity Defined Status

Degrees, MBAs, Certifications, Titles- they were tickets into the kingdom.

The harder the knowledge was to access, the higher the social and organizational status of the people who held it.

This model made sense- until now.

Because AI just shattered the logic of scarcity.

From Scarcity to Abundance

With a few prompts, anyone can build, automate, or analyze.

• A store manager generates local ad copy and flyers for their branch’s marketing campaign.

• A customer service rep creates a training video for their new hires.

• An HRBP designs a survey and instantly summarizes results.

What once took years of training can now be attempted with curiosity and a keyboard. The walls of the kingdoms are cracking.

Lessons from Etsy and YouTube

We’ve seen this play out before:

• Etsy gave millions of creators access to markets once reserved for brands. Fashion houses didn’t disappear - but creativity multiplied and new forms of value emerged.

• YouTube let anyone broadcast to the world. Hollywood didn’t die - but culture was redefined by creators who didn’t need permission.

Both platforms turned spectators into participants. They didn’t erase professionals- they expanded the stage.

AI is now bringing that same DIY culture into companies. It’s Etsy-for-processes. YouTube-for-problem-solving. And it’s happening faster than most leaders realize.

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What Happens to Departments?

My question: If knowledge is abundant, do rigid departments still make sense?

Departments were built to protect expertise. But what happens when expertise is no longer scarce?

Do we need Marketing and IT as separate kingdoms, or do we need fluid teams that form around problems, not titles?

Picture this:

On Monday, a customer onboarding problem pops up.
By Tuesday, a product designer, an HRBP, a salesperson, and an ops analyst team up.
Each uses AI to build - not just brainstorm.
By Friday, the prototype is live.

No tickets. No waiting in line. No silos. Just speed, ownership, and creativity.

That’s not chaos. That’s transformation.

And the companies that lean into this fluidity will move faster than those clinging to old silos.

This Could Feel Unsettling

For experts, this shift feels existential. Careers were built on scarcity. Identity was built on being the one who knew how.

For those who spent years mastering their craft, DIY at work feels threatening. It raises uncomfortable questions:

• If anyone can build, what does expertise mean now?

• If employees create outside their lane, what happens to the role of functions and departments?

It’s natural to feel defensive. For decades, careers were built on the scarcity of knowledge. But when knowledge becomes abundant, the currency changes.

In today’s world…

If tools can handle mechanics, then the real edge lies in vision, creativity, and judgment.

The New Currency of Work

But here’s the truth: when tools make mechanics easy, the edge shifts.

Let me say that again… in today’s world:

• Taste + imagination > Talent + qualifications.

• Vision, creativity, and judgment matter more than credentials.

• The ability to see what’s possible beats the ability to hoard what’s known.

Knowledge is no longer the scarce resource. Curiosity is.

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So, what about the Experts?

Democratization doesn’t erase mastery; it redefines it. Experts no longer win by being the only ones who can build. They win by being the ones who:

• Design platforms, build guardrails and Governance to reduce the blast radius.

• Take DIY prototypes and harden them for scale & security.

• Teach judgment, ethics, and business acumen. These are things AI can’t replicate.

Experts Sustain. Experts Integrate. Experts Safeguard. Experts Professionalize

In other words, experts shift from being Authors of output to Architects of ecosystems.

How Leaders Can Harness the DIY Wave

This is where leadership matters most. The organizations that thrive will not be those who fight DIY, but those who harness it.

1. Spot the Sparks
When employees hack together a tool, don’t dismiss it as “shadow IT.” Treat it as free R&D.

2. Build On-Ramps, Not Walls
Create sandboxes where employees can experiment safely, and pathways for good ideas to graduate into production.

3. Measure Creativity, Not Just Efficiency
Ask: How many employees built something new this quarter? How fast did an idea become impact?

4. Redefine Recognition
Celebrate curiosity, initiative, and collaboration - not just output. Show that building belongs to everyone.

A Different Kind of Courage

It’s tempting to tighten controls. To double down on approvals, rules, and red tape. That feels safe.

But safety is an illusion when the ground itself is shifting. The braver move is to lean into democratization- to trust that abundance won’t dilute value, but multiply it.

Because the past was about silos of expertise.
The present is about the abundance of tools.
And the future will be owned by those with the courage to reorganize around curiosity.

Work is no longer a place where a few hold the tools.
It’s becoming a place where anyone with curiosity can shape the outcome.

Creation belongs to anyone who is curious and willing to experiment.

So, Stay Curious,

AI Lady

Everywhere you look, people are asking the same question: Will AI replace our jobs?

But here’s the better question: What if HR could redefine how AI transforms work?

Yes, the truth is, Technology can’t determine the Future Of Work on its own. HR should and HR will.

We are the ones who decide whether AI is used to shrink people… or to unlock their full potential. That’s a power position, and it belongs to us.

HR at the Crossroads

HR has always been at the heart of change - from navigating globalization to guiding organizations through remote work. AI is simply the next great transformation, but it comes with a sharper choice:

• The Old Road: Efficiency at All Costs.
  AI gets deployed only to reduce headcount, cut budgets, and eliminate roles. HR becomes seen as the messenger of bad news. I can clearly see How HR Could Accidentally Become the Grim Reaper of AI.

• The New Road: Empowerment Through Augmentation.
  AI becomes the engine that clears away admin work, gives employees more capacity, and creates space for innovation and culture-building. HR becomes the function that makes people believe in the future again.

Which road we choose will shape how history remembers HR’s role in the AI era.

The HR Playbook for Leading with AI

So, how do we actually seize this opportunity?

1. Reimagine Our Own Roles

👉Start at home!

AI is changing every function, but HR has the unique mandate to shape how humans and machines coexist at work. Yet only 36% of Technology Executives rank HR Leaders as peers capable of co-leading enterprise culture change (Gartner Survey).

That gap is our invitation to lead. HR leaders need to redefine themselves not just as people leaders, but as digital change agents.

This means:

• Owning the narrative of how AI gets adopted in the workplace.

• Building confidence in digital decision-making, not outsourcing it.

• Acting as co-architects of the AI-enabled employee experience.

When HR stops being seen as “adopters” and starts being recognized as co-creators of digital culture, the role of HR becomes future-defining.

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2. Reimagine and Audit Org-wide roles

👉 Think Tasks, Not Titles

Don’t start with headcount charts. Start with tasks.

Ask:

• What’s repetitive and transactional that AI can handle?

• What’s strategic, relational, and judgment-heavy that humans must elevate?

For example:

• Production Planning

  • AI can: Forecast demand, optimize raw material usage, and generate shift schedules.

  • Humans must: Balance these forecasts with on-the-ground realities (machine downtime, worker safety, supplier relationships).

• Maintenance

  • AI can: Predict when a molding machine or equipment/tool is likely to fail, scheduling preventive maintenance.

  • Humans must: Use judgment to prioritize fixes, manage downtime trade-offs, and ensure worker safety.

• Supply Chain & Procurement

  • AI can: Analyze supplier performance, compare pricing, and flag potential disruptions.

  • Humans must: Negotiate, build trust with suppliers, and make judgment calls during crises.

• Quality Control

  • AI can: Run visual inspections on manufactured parts using computer vision, flagging defects instantly.

  • Humans must: Decide when to override or adjust, troubleshoot root causes, and coach teams on continuous improvement.

When HR reframes roles this way, AI stops being a threat. It becomes a teammate.

3. Reskill With Confidence

👉 Flip Fear into Fuel.

Employees are already anxious about being “left behind” by AI. HR’s job is to flip that fear into excitement.

That means building programs around:

• Human-only skills: empathy, leadership, critical thinking, collaboration.

• Human + AI skills: prompt engineering, data literacy, change navigation.

One powerful approach: make reskilling visible and celebrated. Instead of hiding AI training in a corner, put it front and center in your culture narrative. Show employees that you’re not just protecting jobs - you’re expanding futures.

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4. Redesign the Employee Experience

👉 Shape the Story, Shape the Future.

HR decides whether the workflows redesigned by AI feel empowering or alienating.

Imagine two companies rolling out AI scheduling assistants:

• Company A positions it as a cost-saver. Employees fear it means fewer admin jobs.

• Company B frames it as “We’re giving you back 5 hours a week to focus on meaningful work.”

Same technology. Different story. Different outcome.

That’s where HR shines: Shape the narrative for the tools implemented.

5. Redefine Success Metrics

👉 Measure Potential, Not Just Productivity.

Measure Potential, Not Just Productivity.

If the only thing we measure is efficiency and numbers, AI will always look like a downsizing tool.

But HR can set the bar higher. We have never done this before but why don’t and can’t we try to measure:

• Creativity unleashed.

• Time freed for strategic decision-making.

• Employee trust, engagement, and retention.

Why should HR dashboards and Scorecards only show Cost per Hire and Time to Fill. They could show how AI helped employees build careers worth staying for.

A Case for Courage

None of this is easy. It’s far simpler to let AI become a blunt instrument for cost savings.

But courage has always been the hallmark of great HR leadership. It took courage to champion diversity before it was mainstream. It took courage to push for hybrid work and work from home when it was untested.

Now it takes courage to say: HR won’t be the department of administrators. We’ll be the department of possibilities.

That’s how history will remember us.

They’ll say: “HR did.”

-AI Lady

I will let that sink in for a moment :)

But here’s the uncomfortable truth:

No one has a perfect playbook for WHO should actually lead it.

And that gap could define who thrives and who falls behind.

I’ve been talking to leaders in HR, IT, Operations, and beyond - and while every organization has its own story, four clear patterns are emerging.

This isn’t about control.

It’s about having both tech fluency and people fluency to lead change - and helping the entire organization navigate the biggest shift in how work gets done in our lifetime.

1. HR Wants to Lead AI

You know this group. They believe if AI changes jobs, skills, and culture, HR should be in the driver’s seat.

For them, AI is a people-first challenge!

They are taking an interest in setting up governance councils, building adoption playbooks, and talking about ethics and bias.

Questions they might not be asking out loud yet, but are definitely top of mind:

• How do we keep this human-centered?

• Who will lose their job?

• Who will gain one?

More than picking an AI tool, they are interested in building and protecting trust.

These are HR leaders who want to step into the driver’s seat, steering AI adoption with a people-first lens.

Mindset:

“If AI is transforming work, HR should lead the transformation.”

2. IT Wants to Lead AI

Here, AI is treated as a technology stack challenge. Infrastructure. Data security. Integration.

HR often gets called after the fact - when it’s time to train people or manage adoption.

In the best cases, IT listens deeply to HR’s people insights. In the worst case, HR is a passenger in the backseat, saying, “Wait… where are we going?”

Mindset:

“We’ll handle the Tech. HR can support adoption.”

3. HR + IT Merge

The boldest vision: HR and IT literally become one unified function, accountable for how work happens across humans and machines.

This is the future CPO as a Technologist’s vision.

Not “HR and IT report to the same leader.” No.
Literally one function - responsible for how work happens across humans and machines.

Shared budgets. Shared leadership. Shared accountability for productivity.

The future leader here? They need:

• Platform strategy chops.

• Systems thinking at scale.

• The ability to lead people and tech governance as ONE.

Some call it a Chief People & Technology Officer. Others? Chief Delivery Officer.

Mindset:

“People and Tech Governance are inseparable. The future leader will own both.”

4. HR Pros Jump to IT

This one’s bolder.
HR pros moving into IT to learn from the inside.

They’re learning product leadership. Automation. Platform thinking.
They’re making themselves the future leaders of integrated people + tech functions - not just traditional HR teams.

Mindset:

“The HR job I want doesn’t sit in HR anymore - I need technical fluency to be ready.”

Here’s the real takeaway:

This isn’t about who controls or who leads AI.

It’s about who has the fluency to lead the change - to guide the organization through a transformation that will touch every single role.

And here’s the big question I’ll leave you with:
Do you want to be the one driving the car… or the GPS navigator making sure we don’t end up in a ditch?

Both matter.
Both require courage.
Both require speaking tech and people in the same sentence - without losing either.

💬 Your Turn
Which of these patterns are you seeing in your industry or organization?
Are they working?
What’s missing?

Drop me a note - I’m collecting real-world examples for a follow-up edition.

Stay curious,
– AI Lady