AI Won’t Save You from the "Boring" Stuff

I’ve been hanging out with Mercedes Sullivan and Martha Curioni on the HR Tech Couch Club for almost a year now. This whole thing started because back in November 2024, the three of us got together and bonded over our mutual “What on Earth is happening with this whole AI thing?!” (Granted, we still don’t quite have the answer to that question, but more on that in the next newsletter).

When we host the shows, one of the questions we’re almost guaranteed to get in each live show is “How do you stay up to date with everything that is happening in AI?”.

Honestly, we don’t.

I spend most of my days studying the HR tech industry and tracking what is happening in the world of AI. Even then, when I go offline for 24+ hours, I come back to what feels like a knowledge whiplash. It’s as if the world of AI decided to go on hyperspeed while I was gone.

If I feel like this, I can only imagine what this pace of change and growth can feel like for HR professionals who are spending 10+ hours a day on their day jobs, and then somehow are miraculously expected to know everything about how to select, implement, and make their organizations better with AI [insert massive eye roll here as you read this].

Now, I also realize that while telling people about how I struggle to keep up with everything that is happening in AI may be an empathetic approach to answering the question, it also doesn’t help anyone (welcome to the world of a content creator, where “is this actually helpful?” is a standard gut-check question).

So, as I was debating whether I should talk about how all of your ChatGPT conversations and documents are legally discoverable, or how OpenAI has put in place human moderators who will alert the authorities of any extreme conversation content, I decided to acknowledge my own AI fatigue this week and go back to the basics.

You don’t need to stay up-to-date on everything happening in the AI world and its impacts on HR to effectively use AI and create value for your organization.

Ironic right? Hear me out.

At the end of the day, AI is another form of technology that is transforming the way we live and work. We have seen this movie before, with the internet, ERP digitization, the HR cloud transformations, and the move to mobile, among others. The approach we took with all previous rounds of technology disruption was straightforward: figure out what happens today, identify opportunities, and design for an optimized future.

Easier said than done; I know.

Having spent 10+ years running HR digital transformations hands-on, I can say that most HR teams are set up for transformation failure right out of the gate. Not because of motivation, resources, or other factors. But because everyone is attracted to the shiny future state design, and not the boring current state discovery.

When I used to run discovery workshops (both as a consultant and as in-house HR), a common feedback I’d get was “Do we really have to waste our time on the current state? Does it even matter if we are going to transform anyway?” And I am watching this all take place again now as we embark on this round of AI-driven transformations.

Here’s the deal:

• What you know about the process is at best 80% of the full picture. In every discovery session I have been a part of, it’s guaranteed that even the process SME learns something new from breaking down the current state workflow with a larger team of stakeholders

• Just because someone knows HOW the work is done, it doesn’t mean they have the full view on WHY the work is done. What appears as an optional and mundane document-check step could make all the difference in a potential court case

• Technology buyers and decision-makers don’t always appreciate the intricacies and pain points of those executing the process. What appears to be an unreasonably long turnaround time that can be easily solved by AI may unveil itself as a highly coordinated and manual workflow that touches four other functions/systems.

So, while in the name of time and resources, you may not want to spend too much time on current state discoveries, it’s important to remember that this work is the very foundation of how the rest of the transformation will unfold (and quite honestly, nobody likes a UAT surprise of “oh, you didn’t build that in?”).

You don’t need to know everything about AI to utilize it in your organization, but you do need to revisit the basics and gain a detailed understanding of how and why the work is done today.

Here are some thoughts on how you can get started with the basics today:

Stage 1: Frame the Value

Collaborate with your counterparts from other functions to identify 3-5 core business workflows that will yield improvements in both the P&L and workforce experience within the next quarter. Steps can include:

• Defining a North-Star KPI: Select a metric for the business to focus on in the next 90 days

• Map the value stream: Determine the high-level process flow, value chain, and system involvement

• Size the prize: Determine the ROI and align on project go/no-go

Stage 2: Map the Process

Engage process SMEs, IT, Legal, and other experts as applicable to capture how the work is done today through event logs, shadowing, or interviews. Steps can include:

• Pull the Data: Identify the systems that are facilitating the workflow today, and review the system process and workflow for opportunities

• Mine Tasks: Determine the work tasks in the current process, the estimated time for each task, and the associated pain points

• Gather employee input: Collect first-hand feedback from SMEs on what may be slowing them down

Stage 3: Deconstruct the Work

Work with IT and process SMEs to break down the workflow into bite-sized decisions, steps, or content chunks. Steps can include:

• Build a Task Inventory: Identify all activities in the process that require a decision (e.g., yes/no, classification, generation, routing, escalation, etc.)

• Identify AI opportunities: Any decision that can be made using rules (e.g., yes/no or classification) or requires generation (e.g., draft email) can be an AI opportunity

• Overlay risks: Rate potential AI-enabled activities for bias exposure, explainability, and possible regulatory scrutiny

Mercedes also wrote a piece on this recently. Check it out here.