Working in Learning and development (L&D) and HR for over a decade, I have frequently engaged in discussions about the distinctions between professional development and performance management. The same questions came up in these conversations over and over again, namely, if a significant difference exists between professional development and performance, how can an employee’s growth align with the opportunities presented by performance management, and if someone fails some aspect of professional development (i.e., assessments), can we performance manage them?
Most of the time, everyone eventually agreed that there is a distinct and meaningful segregation of these two processes, even when performance management can inform and strengthen professional development activities. For learning to really thrive, learning activities need to occur in a safe space away from any judgement, ridicule, and performance management impacts. This is something that most L&D leaders understand and advocate for. Yet, the rise of skills data and the ‘skills-based organisation’ means we need to re-establish the boundaries around learning once again.
Everyone is talking about data, and in HR circles, that means skills data. Among this discourse, skill ratings are becoming the loudest ‘data voice’ — which brings learning and performance management together again. This is because learning is a potent source of skills data, whether that is someone rating their skills, getting a peer or manager to do it, or completing courses, content, and pathways that build their skill levels. This data, in the wider vision of the skills-based organisation, feeds into every workforce decision from internal mobility and external hiring, to performance discussions, promotion, and pay. You can see how the lines are blurring between learning and performance.
But therein lies the challenge. How can we allow data to flow through the boundaries between learning, HR, talent, and performance, so that every stakeholder benefits, but also keeps the sanctuary of the learning environment?
Technology changes the boundaries
We have a plethora of platforms and vendors exploding onto the market, offering new ways to collect, track, report, and infer skill development. Not to mention technologies like generative AI that make skill inference even quicker. With this comes the temptation to connect skill measurement to performance rating scales. But this could be a mistake in the long-term, putting tremendous pressure on employees to ‘measure up’.
Yet, there is a positive and meaningful path forward that can be driven by L&D and accepted by employees. Coupling both performance and learning together, with individuals at the centre, can ensure it doesn’t become a punitive exercise. Our performance, across the spectrum from novice to expert, can be used to inform our learning opportunities, and then completed learning can inform our performance.
For example, a digital marketing manager could have a skills rating of 4 in ‘team management’. To take the next step in their career, they may wish to increase their rating to 6. To achieve this, they can acquire new knowledge and build their skills using a learning platform, then apply this knowledge to a task or project. Based on the outcome of that project, their skill rating can be updated based on their performance. This can continue until the manager reaches their desired skill level, or for other skills. In doing so, the combination of learning and performance becomes a virtuous cycle that makes employees stronger, more resilient, and better at what they do.
Lessons from on-the-job
On-the-job training (OJT) can provide another lesson for learning and HR professionals. Historically, OJT has always been a safe space to learn while doing. It’s most commonly afforded to new hires when they are first learning a specific trade or profession. However, this is becoming more popular among employers today because of the dwindling half-life of skills and the need to build talent, quickly. Theories show that learning and acquiring a new skill can best be supported by way of application, with guided, practical experiences and the support of someone who knows more than you do.
The key here, however, is providing this on-the-job learning experience without the fear of being performance-managed. The ‘more knowledgeable person’ guiding the experience needs to be a neutral individual — allowing someone to learn at their own pace, make mistakes, and fail. All in the pursuit of learning something new. For tenured employees, it’s worth making this distinction clear, since they will have previously been exposed to performance management processes and may be more wary or cynical as a result.
Trust is the foundation of safe spaces
Trust is fundamental to effective learning and performance. Providing a safe space for people to learn, explore new skills, and expand their career horizons, while also giving HR the ability to analyse their skills data, three components need to be in place.
1. Organisational culture
Psychological safety is vital to effective performance, As Amelia Haynes, Associate Researcher at Korn Ferry Institute writes, “Neuroscience research shows us that being talented, well-resourced, and highly skilled is not enough for top teams to perform well. Instead, studies have found that to excel, team members need to know that their mistakes won’t be their downfall. They need their workplaces to be psychologically safe—that is, where interpersonal risk-taking is accepted and welcomed. And this means organizations need to invest in their culture to take a team from surviving to thriving.”
You can build a culture where failure is part and parcel of innovating and learning by voicing how mistakes are human and part of how we improve. Sharing lessons learned at company town halls and team meetings can also help.
2. Responsible (and clear) use of skills data
Providing clarity on the collection, protection, and use of skills data is vital to building trust in its use. Only using it for the benefit of employees, for instance, in recommending learning resources, will also go a long way in establishing trust. We also need to be able to trust the data being used and the insights it delivers. Having multiple data sources, from HR, learning, recruitment, project management and more, can help with this. Just like how a GPS device’s accuracy increases with the increasing number of geospatially separated satellites locked onto our location, our skills data becomes more reliable the more data points we’re able to collect.
Self, peer and manager ratings are a solid start. Layering in actual work-based, observable feedback adds more strength to the signal. And in turn, we can begin to build a more holistic, unbiased picture of our development needs based on our performance.
My colleague Susie Lee, Client Innovation Officer and skills/learning governance lead at Degreed explains this well, “People must clearly understand what their data is going to be used for, how it benefits them, the protections in place, and who is ultimately responsible for its governance. Skills data needs to be secured. The collection and use of skills data should be transparent. Many will be tempted to use AI to sift through and analyse data, but these algorithms and their results should always be clearly explainable. The accuracy and timeliness of data also need to be considered for the most reliable results — and processes need to be implemented to reduce any potential bias in the AI and the data it is being fed.”
3. Explainable AI
This segues well into the final area, being transparent in the use of AI and how it influences someone’s career opportunities. AI is the bridge over which our personal skills data is travelling across the organisation. Algorithms are being used to infer skill levels, understand related skills (if you know Python, you may also have R or other data skills), recommend learning opportunities, or suggest mentors. It’s essential for such algorithms to never make the final decision in someone’s career or learning — there must be human oversight. At the pace that things are evolving, it is likely we’ll get to a stage of “trusting but verifying” when using AI.
It’s an exciting time to be working in people functions, whether that’s improving or assessing performance, or providing learning opportunities. Years from now, employees will be thanking us for shaping their careers and helping them navigate an ever-changing workplace. But for that to happen, we need to protect the boundaries that lie between learning and performance.