Those AI Transformation jobs you're generating with ChatGPT are actually 3 jobs in a trench coat

Quick questions: What is AI enablement? What is AI transformation?

If your team can’t answer it, then you shouldn’t be asked ChatGPT to generate a new AI Transformation role for your company.

Companies need people who can wrangle AI inside their organizations. So they’re creating new AI jobs to help with AI implementation. Job boards are full of positions like AI transformation VPs, AI enablement directors, AI adoption specialists, and everything in between.

LinkedIn is full of new AI jobs that look like this:

But take a quick stroll through the actual requirements of these jobs and it’s clear everyone has a different idea of what AI transformation requires.

For example, each of the three jobs below are AI transformation jobs, yet they’re all different in their requirements.

The first is focused solely on change management for AI.

The second is heavy on process automation with AI with a bit of project management and AI training thrown in.

The third is sits at the intersection of AI case identification, AI workflow development, and AI implementation, with a focus on building light-weight solutions with AI tools, partnering with engineering on more in-depth projects, which includes both prompt engineering and AI testing.

Side note: As a former AI UX designer and prompt engineer, prompt engineering and AI testing in itself is a full time job when you have multiple AI solution in production.

This is an AI transformation role focused on the change management aspect of AI Transformation

More often than not, these new AI roles (often listed under AI transformation, AI strategy, AI adoption, and AI enablement) are a combination of multiple roles jammed into a single role. Many of these roles combine the roles of program management, AI implementation, AI product management, and change management into a single role.

In the job below, the person is expected to rearchitect roles for AI, while ensuring the workforce is trained and evaluated on AI skills, and on top of that, build and deploy AI agents! That’s at least three roles right there, but it severely underestimates the work it takes to 1. identify where AI agents belong 2. Build and test them (because all AI agents need extensive testing before launching) 3. Then deploy them (with or without engineering support - very unclear). There also seems to be no plan for process development and documentation around those newly built AI agents either.

Even worse for the poor employee who takes on this role (no shame in this, after all we’re all trying to make ourselves relevant amidst the deluge of AI layoffs), this role takes place in a “resource-constrained environment.” But hey, maybe that team of two they’re also responsible for leading will help?

Red flags all around.

This is but one of many in a sea of AI transformation jobs.

There are jobs where the person hired has to 1. find the use cases (Identify & Shape AI Opportunities), 2. design and build them (Design & Deliver AI-Enabled Solutions) and 3. develop the infrastructure around AI usage (Drive AI Transformation Execution) and 4. train others on how to use AI (Enable Adoption & Change)

These are all full-time jobs wrapped up into a single job.

Worse, many of these AI jobs don’t come with any authority, which risks dooming the new hire to political purgatory.

AI Transformation is not a one person job

AI transformation is a catch all term to describe the process of the organization changing with (or because of) AI. It is not a single role. Within the AI transformation process, there are four sets of role clusters, that rely on a specialized set of functional skills.

Role clusters are based on the kind of work being done. They are not always defined by department, title, or reporting structure.

People preparing the organization for AI - These is the AI enablement piece, made up of people who focus on people infrastructure. This includes the people teaching AI skills (L&D) and redesigning job families (HR), as well as the people evaluating risk and creating governance policies (legal + other departments).

People implementing AI solutions - This is a huge group with two sub-role clusters. This includes people who find high impact AI use cases, map AI workflows, prototype and experiment with AI, document new AI processes, and partner with engineering to implement the AI solutions. The other group are the actual engineers who build, test, and monitor AI in production (this includes internal solutions as well as customer facing AI solutions).

People building the tech infrastructure AI solutions - This includes the people preparing the data for AI (data engineers), people selecting and evaluating the approved AI tools (vendor procurement or IT), and monitoring security risks (devsecops).

People driving the strategy behind AI transformation - AI adoption has to map to business outcomes. The people who work across departments to create the AI roadmap (internally and externally), ensuring experiments turn into ROI, relentlessly prioritizing when politics pop up (as they will) or resources are constrained (as they will be). They manage the over all AI program goals and they’re also responsible for communicating the progress and expectations around AI in the context of the company’s goals.

AI Transformation jobs require a multi-disciplinary skillset

AI transformation work blends responsibilities that traditionally belonged to separate roles, requiring a multi-disciplinary skillset. People who lead and execute on AI transformation need skills across technical, business, and people. The work requires people to think about data, system access, process, operations, and how the work fits together. And also requires an understanding of the impact of AI on work, including the cost and ROI of using AI. And most importantly, the work requires an understanding of people and politics, with the skills to collaborate and persuade in highly ambiguous environments.

None of that fits together easily in a traditional job families with clear reporting structures. That’s what makes generating these new AI jobs so risky. There’s a nuance in how AI transformation roles come together. They need to be written with an understanding of the workload, AI goals, and skill sets required to do these jobs.

Perhaps not shockingly, that nuance is not captured in the job postings that are AI generated. After all, ChatGPT/Claude/Gemini are only as good as the prompt and subject matter expertise of the person generating the job. And most people posting for these jobs aren’t experts in emerging AI roles and AI transformation. And no fault to them, this is an incredibly chaotic space.

As a result we’re seeing AI transformation jobs that are really just 3 jobs in a trench coat.

How to prevent your AI transformation jobs from being 3+ jobs in a trench coat

If your company has AI transformation goals, you need the right AI roles in place to support that. Here’s how create them:

Well-scoped AI transformation roles look like this:

AI enablement and training, with clear call outs for collaboration with other teams.