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Skills Management: What is the Secret Sauce? GP Strategies’ Matt Donovan

by Dani Johnson and Stacia Garr | April 3rd, 2024

How do we define work and the skills needed to do the work? The way we view and assess skills is often through assessing and appraising someone’s output. But the problem is that most organizations aren’t capturing the right data and using it to gain insight.

According to Matt Donovan—the Chief Learning and Innovation Officer at GP Strategies—Job descriptions and skills in general describe the baseline. They are not what makes someone great at what they do. 

So how do we define the work and the skills needed to do the work? How can we capture a high-performer’s secret sauce? What are they doing that’s making it a successful experience versus what’s written in the job description?

We dive into a fascinating conversation about where we are now, how AI is going to both help and disrupt organizations, and what the future of skills assessment could look like.

Performance context matters

Matt’s built marketing academies for a variety of large organizations and he’s used skills taxonomies to drive the work. If Matt is creating a brand manager academy, he can identify the general elements of what a role does. 

But someone has to be able to come in and do the work to generate the necessary results. If they check the boxes of necessary skills yet can’t do the work, it’s a problem. How do they tie down what the work looks like to execute and generate a specific output? Performance context matters. 

A job description doesn’t tell us whether or not someone will be good at a job. A project manager in finance may look completely different than one in marketing—even in the same organization. The skills may be the same. So what’s the secret sauce? 

Capturing the “secret sauce” in an organization

Some organizations know what they do. Others don’t have an infrastructure in place that captures their high-performer’s secret sauce. In a space like manufacturing, it might be easy to define the necessary skills and track performance. But when you get into softer spaces like sales and marketing, it becomes harder to define the data. It’s a barrier that must become easier to capture and document. 

Skills taxonomies are often anchored to job descriptions. Many organizations assume that this means “mission accomplished.” But won’t the use of AI change the job description for a marketing analyst who now uses AI to do their job? It may not change the job description much but what the human actually does may change significantly. So you can’t just anchor to the job description.

So what does Matt do? He starts by looking at the high performers. Who is doing the job really well? It’s focusing on the anatomy of their performance. It’s not perfect and Matt believes we need a better process but doesn’t have an answer yet. 

Assessing and measuring the work being done

If you’re in a business and you’re looking at performance, what is the end goal? Can the organization describe what looks “good” with its current metrics? Do they have a connective map or visualization of the most lagging business indicators of measuring business success through leading indicators/behaviors? What interventions do they have in place to drive it through? 

That’s why Matt starts with performance analysis. How does an organization decide who to promote? What clues tell you that someone is doing things well enough to deserve that promotion? 

What if you have high performers defying the architecture in place? Are you in line with the systems and measurements you’re using? Or do the high-performers check a different box?

Data-driven performance management

The right data will allow us to better understand productivity. AI or large language models can and should help capture data so that humans can make better decisions. 

Matt believes that the growth of AI changes everything. AI will disrupt the way work gets done in any organization. The work will change as we integrate AI tools. Without a skills-based infrastructure, it will be hard to tackle that disruption at scale. The evolution is happening quickly. You have to be intentional about filling gaps. 

Organizations need to come up with a responsible and ethical way to use data to benefit the organization and the employee. We need a sophisticated system to collect and use the data and monitor who it’s used for under what conditions. The goal is to gain meaningful insights without compromising the individual’s rights.

 

Resources & People Mentioned

 

Connect with Matt Donovan

 

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