What Data Foundations Do You Need To Start Automating Career Pathing & Mobility?
Improving how you approach internal mobility and career pathing is one of the most impactful ways organizations can improve retention, close skills gaps, and increase workforce agility. Automation of (most of) this process is possible, and desirable – but automation is only as effective as the data it’s built on.
Before you can trust AI to recommend career moves or upskilling paths to employees, you need to build the right data foundations.
Let’s break down exactly what data you need, and how to structure it, to make career mobility automation a reality.
1. A unified view of your workforce 🗂️
Most companies have fragmented workforce data scattered across systems like the HCM, the ATS, Learning Management Systems, and performance platforms. To power automated mobility recommendations, you need to bring that data together into a single, enriched view. Without a unified profile, neither AI nor humans can accurately match employees to future opportunities or learning paths.
At a minimum, this should include:
- Current role and team
- All skills and experience (past and present)
- Proficiency and level
- Learning history and certifications
- Performance and potential indicators
Career aspirations or interests would be useful too, if you can get it.
AI can build these profiles by inferring the skills people have and use based on Job Descriptions, CVs, and other unstructured data. The crucial thing is that you can pull in the skills people have but may not be using – and the “adjacent skills” they could very easily pick up.
Note: Some of the richest data about your employees may already exist in your Talent CRM. If you use a CRM like Beamery to create skills-based profiles for your wider talent pool, this could be another valuable source to pull into your unified data set.
2. A dynamic, skills-based Job Architecture 🚀
Traditional job architectures based on titles or levels are too rigid for automation. What you need is a dynamic, skills-based job architecture that maps:
- The tasks performed in each role
- The skills required for every task and therefore role
- The proficiency level expected
- Possible adjacent or feeder roles
Again, utilizing AI tools to bring this information from disparate (often unstructured) sources means you get more precision – and you get a holistic view of the jobs to be done and how they interact at your company, really quickly.
This dynamic job architecture becomes the blueprint AI uses to assess role-fit, recommend upskilling, or suggest lateral and vertical moves.
3. Real-time signals from your Learning & Talent systems 🏁
Automation needs fresh, evolving data. That means pulling in signals like:
- Courses completed or certifications earned
- Internal applications submitted
- New skills acquired through project work
- Increased skill proficiency overtime
- Engagement with learning content
These signals help the system adjust recommendations in real time and understand when an employee is “mobility ready.”
Note: Where your HR tech stack is well connected, these insights can be surfaced in the day-to-day tools (like Slack or Teams) that HR leaders and managers use – so they are nudged when a team member is ready for a new challenge.
4. Employee intent and preferences 💬
Even with perfect data, automation fails if it doesn’t reflect the human side of mobility. Wherever possible, layer in data about:
- Career interests or growth goals
- Geographic preferences
- Willingness to relocate or change roles
- Preferred pace of progression
This allows the system to offer relevant, personalized guidance – without pushing people toward unwanted paths.
5. Visibility into internal opportunities 👀
Career pathing automation is only useful if there are visible, meaningful opportunities to move into. That means:
- A consistent, searchable inventory of open roles
- Transparency into team and project needs
- A culture of talent sharing, internal hiring and gig-style mobility
Without this, automation becomes a recommendation engine with nowhere to send people.
Speaking to industry experts, we get the clear sense that a major secondary obstacle to talent mobility – beyond data and technology – is talent hoarding: managers who are unwilling to support mobility where it means losing people from their team.
Of course, where you have a more connected HR data ecosystem – a layer of workforce intelligence that sits across tools – you can support managers to easily fill any roles that are about to become vacant, with passive talent from your CRM or even another internal candidate.
6. Governance and data quality 🔐
However you choose to approach the data foundations for better internal mobility – whether automated or otherwise – you do need to ensure that:
- Data is standardized across systems (with skills as your common currency)
- Insights are continuously updated with real-time signals
- Any AI models you use are auditable and explainable, with clarity around why recommendations were made
- You and your tech vendors are compliant with regulations and ethical standards
These governance foundations allow you to build trust in the system and its outputs.
Career pathing automation isn’t about replacing managers or HR, it’s about scaling personalized growth. But to do it well, you need a connected, skills-based data foundation that reflects both the realities of work and the aspirations of your people.
Once that foundation is in place, you’re ready to let AI do what it does best: connect the dots at speed and scale.