A Skills Conversation with Forrester: Removing Roadblocks to Launch Your Strategy

Organizations have been keen on the idea of a skills-driven strategy for decades, but the path to getting there has been either unclear or impossible to maintain given the manual processes required. In recent years, however, the emergence of AI has opened a window of opportunity to make the skills workplace a reality. Now that we have the technology to eliminate the heavy work, figuring out how to push through the roadblock of getting leadership on board has become a pricklier issue.

In a conversation with Betsy Summers, Principal Analyst at Forrester, and Claire Fang, Chief Product Officer at SeekOut, we discuss moving past common challenges to advance your skills efforts. We cover why organizations are investing in AI to support their skills strategy, how AI can build and maintain a skills system, and how teams can show the value of skills and AI to leadership to move these efforts forward.

Sitting on the sidelines: Getting past the apprehension of AI

Getting a skills-based strategy off the ground seemed like an impossible feat only a few years ago. Traditionally, organizations would build a skills roadmap through the painstakingly manual method of spreadsheets only to find them outdated months down the road. AI is giving us an exciting opportunity to adopt a pragmatic approach to managing skills that better reflects how people continually grow and change—but leaders aren’t so enthusiastic about hopping on board.

"What makes AI exciting can also make it incredibly scary,” says Forrester’s Betsy Summers. “AI is sometimes seen as being outside of our control and that gives people a lot of anxiety, especially HR. Their reticence to participate is a little alarming.”

According to Forrester research, leaders understand the importance of a skills strategy to reach business objectives. But Betsy notes that because of their apprehension to the technology, leaders are now at a crossroads. The conversation shouldn't center around whether they should adopt AI, but instead when they should adopt it. AI is dynamic and imperfect, and none of us have a complete understanding of the technology, but organizations cannot afford to let the unknown hold them back.

“The moment is about how to embrace this technology effectively, responsibly, and ethically so we can help our businesses move forward,” says Betsy. “Because without it, we’ll just be on the sidelines, and that is a risky position as well.”

Finding clarity: How AI supports skills-based organizations 

SeekOut's Claire Fang says there is often confusion from talent management teams and HR leaders about AI’s validity as a solution to skills and whether they can trust the data. She finds it helpful to simply illustrate how the technology works to ease the tedious work needed to build a skills-based organization and how human input remains at the heart of it all.  

Inferring skills from existing data across sources 

We all leave a digital footprint across the websites and systems we use every day. For example, you could have a publicly available project on GitHub, your resume is available in your organization’s internal HR system, and any project you’ve worked on is accessible in your organization's productivity system (e.g., Jira, Salesforce). AI can synthesize all your data across these resources to infer your skills, experiences, career interests, achievements, learning history, and more to unlock new use cases for HR teams.  

Claire says that HR frequently asks about the effort required on their end to ensure these inferences are correct. At SeekOut, the inferred skills of a specific use case (such as career recommendations) don’t require high precision or proficiency validation. The breadth of around 10 to 100 inferred skills per role or employee should be enough to work with confidently. However, there is a subset of critical skills and roles that do require human input to define. For example, you may need to identify the skills for a VP role and how you expect someone in a director-level role to progress on these skills.  

When a shortage of time isn’t a problem anymore 

Betsy points out that the inferences AI can make for time-consuming use cases like career recommendations can open new opportunities for employees faster and with less stress on managers.  

“Managers tell me all the time that they know career advancement for their employees is the right thing to do, but they struggle to find the time because so far, these processes have been entirely manual,” says Betsy.  

A manager’s job is not typically constructed to help their employees advance their careers.  AI is now making it possible for managers to skip these time-consuming barriers and focus on ensuring their employees get what they need from their organizations to stay long-term. For more insight on how AI can can enhance the recruitment process, check out our comprehensive AI Recruiting Guide.

Placing AI in the right role  

Another common concern from teams is how AI is used to make decisions. Claire notes that any technology that takes over decision-making is not a technology you should be using. Humans should stay in control and seek solutions that allow them to do that.  

“We have to put AI in the right role,” says Claire. “AI is here to assist humans to make decisions, not make decisions for us. There’s great risk in giving AI that power, and any platform you use should be as white box as possible in showing you why recommendations are being made.” 

How to get started: Start small and expand gradually to align with business goals

Both Betsy and Claire recommend starting small with skills. First, identify your talent problem and focus on translating that into language that aligns with what leaders care about to get their buy-in (aka, articulating the business impact).

Identify your talent problems

Typically, organizations start their skills journey backward:

• They spend most of their time building a skills taxonomy and skills inventory

• When they’re done, they try to figure out how to map the taxonomy and inventory to a job architecture.

• Finally, they end up struggling with how to tie the systems they’ve built into a solution for various use cases.

Organizations are left unable to articulate the talent problems they’re trying to solve, with outdated systems only months down the road.

Instead, Claire recommends starting with identifying talent problems first. "If you start with the outcome you want to achieve then it becomes easier to think through the resource you need to put around it to make that happen,” she says.

Spend time talking to business leaders to identify those problems (e.g., talent shortage, upskilling, retention) and how a skills approach can solve them. Gather this information by finding internal allies around the talent problem and identify the KPIs to measure. Once there’s a clear picture of that, the next steps are less strenuous to get through. Teams can pick and choose a solution to solve that problem and save time from focusing on efforts that don’t matter to your business objectives (e.g., you won’t need to bother building a skills taxonomy if all you need is a talent marketplace).

Getting leadership on board

HR teams deal with a lot of intangibles that don’t easily translate into dollars and cents. But what’s great about skills is that there are very clear business metrics that leadership can understand, especially when it comes to the common use cases that most teams first prioritize (e.g., talent mobility, upskilling). With talent mobility, for example, you can calculate time to productivity or time to market.

Once you have all the metrics you need, you can put together an experimentation model that identifies your talent problems, what’s been done in the past, and how past methods haven't solved those problems. Show examples of successes from similar companies, present a hypothesis and the model you’d like to pilot, and track your results. Start with a few use cases and expand to others over time.

Betsy notes that there will be lots of questions you need answered first, and they’ll inevitably spark more questions down the line:

• How should we define and measure our skills?

• How can we use skills data?

• How do we ensure adoption?

• What changes will this prompt?

• Who do we need to partner with?

• What is our governance and process?

The number of questions you’ll need answered may appear overwhelming at first, but you won’t be doing this alone. With help from the network of people you’ve gathered in your organization, you'll collect the information you need.   

"I hear most often from practitioners who have done this is that you just need to start,” says Betsy. “It's hard but that doesn’t mean it’s not worth doing.”

What do to next: Skills-based project worksheet

In the spirit of not trying to boil the ocean when it comes to skills, we created a worksheet to help you start small. Download it here for practical guidance to kick off your skills-based journey.