Companies Are Hiring Engineers to Do a People Job!

It finally feels like organizations are getting serious about AI. Now let's make sure they're hiring right.

I want to tell you about a message I received on LinkedIn recently.

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.

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