It’s no secret that AI changed the conversation around workforce capability almost overnight.
As AI adoption accelerated, leaders moved beyond access to tools and experimentation to a more pressing question: how do you build workforce capability at scale?
Leaders started asking harder questions:
- What skills do people actually need to work effectively with AI?
- As AI takes on more agentic and technical work, which human skills and behaviors become even more important?
- How should those skills be defined across teams?
- And how do organizations turn scattered experimentation into something scalable?
That shift exposed a deeper challenge.
Many organizations already had skills initiatives in place. The challenge was turning those disconnected efforts into a scalable system.
Gartner recently warned that, while organizations continue to invest heavily in AI, access to tools alone doesn’t create sustainable business value. The real challenge is operationalizing AI capability across the workforce.
At HiBob, we began our AI workforce transformation with skills-based learning.
Rami Tzafrir, VP of people growth, was already strategizing in 2023 about how skills could serve as the connective layer across the business, connecting hiring, performance, and learning into a single system.
When Yael Rotem Sher, head of global learning at HiBob, joined the company, she shared the same belief: skills are foundational to how people grow and how organizations build capability.
“Skills are very close to my heart,” Yael says. “They’re not just something I work with. They’re how I think about growth at the individual and organizational level. I’ve personally experienced this shift more than once in my career by being offered opportunities based on my skills and not my job title or position.”
That perspective eventually became part of a much broader conversation across HiBob about how skills could support growth, workforce planning, and AI readiness.
The shift behind the change
The biggest shift was moving conversations away from training programs and toward the capabilities behind the work itself:
- What capabilities do teams rely on most?
- Which skills are becoming more important?
- Where are the biggest gaps starting to appear?
Skills gave teams a consistent way to define capability and make workforce decisions as AI transformation accelerated change across the business.
As AI takes on more tasks, human skills like judgment, collaboration, adaptability, communication, and critical thinking become even more important for identifying talent and closing skill gaps.
Step 1: Start with clarity, not completeness
How the shift started
At HiBob, teams needed a better way to discuss capability as AI accelerated change across the business. Conversations across the business kept circling back to the same questions:
- How do people grow?
- How do managers assess potential?
- How do teams identify capability gaps?
- How does learning connect to real business needs?
At the same time, product teams at HiBob were laying the foundations for Bob Skills alongside L&D, Talent, and HR teams, shaping the strategy that would eventually become a shared source of truth for roles, skills, and compensation bands.
Rather than waiting for a perfect system, Yael and Sofia Moll, Head of Talent Management, focused on creating enough alignment for teams to move in the same direction.
By early 2025, conversations shifted from “What training does your team need?” to “What skills does your team need to succeed?”, opening the door to more practical discussions about business priorities and future readiness.
What changed as skills became visible
Rather than starting with rigid frameworks or waiting for fully mature systems, the team prioritized creating a shared language for capability across the business.
The focus was simple: create enough consistency for teams to discuss capability in the same way.
Operationalizing skills at HiBob
One example was AI Day, a company-wide program built around a five-tier AI literacy framework. Through Bob Learning and LinkedIn Learning, Bobbers received personalized learning paths tailored to their AI proficiency and capability needs.
These personalized learning paths covered topics such as workflow design, AI-assisted research, and prompt engineering. Creative workshops, including AI-generated videos and headshots, helped drive engagement and adoption.
That thinking shaped the broader HiBob skills philosophy: skills work best when they connect workflows, people decisions, and organizational strategy through one shared framework.
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Step 2: Consolidate into a single source of truth
How the shift started
As Bob Skills matured, the focus shifted from alignment to visibility. Like many growing organizations, HiBob needed a way to connect emerging capability frameworks before they became fragmented.
Skills became embedded in hiring, performance, growth, and workforce planning workflows.
Performance conversations expanded to include AI skills development and learning goals tied to departmental needs.
Hiring managers also began assessing candidates based on AI-related capabilities, practical experience, and proficiency, rather than just role history. Teams also began operating from aligned definitions and shared visibility, rather than disconnected frameworks and inconsistent definitions.
What changed as skills became visible
The strategy behind Bob Skills was never to centralize capability for the sake of control.
It was to create a connected source of truth that makes capability visible across the business while still allowing teams to maintain context around how work actually happens.
That framework became foundational to how Bob Skills connects:
- Hiring
- Learning
- Performance
- Workforce planning
- Career growth
Conversations moved away from assumptions and toward shared evidence. Leaders could identify skills gaps more clearly, prioritize investment faster, and make more confident decisions across learning, performance, and workforce planning.
Step 3: Turn skills into an operating model
How the shift started
Building the Skills catalog required balancing employee input with leadership priorities.
Bobbers identified the skills they used every day, while leaders defined future capability needs. Talent, Learning, and Product teams then aligned those inputs into a shared taxonomy.
Teams used Bob AI to strengthen an operating model built around shared definitions and connected workflows. This became the foundation for how the Skills catalog in Bob was built: not from a rigid theoretical framework, but from the real work already happening across the organization.
As Rami explains, “Using the Bob Skills framework to manage skills mapping into roles and people skills is a new way to utilize our own product that we are part of building.”
What changed as skills became visible
HiBob’s approach treated skills as operational infrastructure rather than a standalone learning initiative.
The framework behind Bob Skills focused on:
- Defining skills clearly
- Validating definitions across teams
- Embedding skills into existing workflows
- Continuously refining capability data over time
This operational model helped ensure that skills reflected both business strategy and real employee experience.
The collaboration between Product, Talent, Learning, and business leaders also reinforced an important principle behind Bob Skills: capability systems only scale when teams trust the definitions and recognize them in their everyday work.
How to apply a skills-based operating model in your organization
Organizations don’t need a perfect skills taxonomy or fully mature systems before getting started. The most important first step is creating a shared understanding of the capabilities that matter most to the business.
Begin by aligning leaders around the capabilities that matter most to the business and identifying where skills data already exists across hiring, learning, performance, and workforce planning.
As your approach matures, create a shared source of truth that connects existing frameworks and reduces fragmentation across teams. Most importantly, treat skills as business infrastructure rather than a standalone learning initiative. When skills are embedded into everyday workflows and people decisions, organizations gain greater visibility into workforce capability and can adapt more effectively as business needs evolve.
What becomes possible when skills become infrastructure
At HiBob, skills evolved from a learning initiative into a business operating model. Connecting skills to hiring, learning, performance, and workforce planning created greater visibility into the capabilities we have today and those we’ll need tomorrow.
The biggest lesson: Don’t wait for a perfect skills framework. Start with the capabilities your business depends on most and build from there.
As AI continues to transform work, skills can no longer live solely within learning programs. They need to inform how organizations hire, develop, evaluate, and plan for the future.
That’s why we believe organizations should treat AI skills as infrastructure. The organizations that do will be better positioned to scale capability, adapt to change, and create long-term business value.
Key takeaways
- Skills-based learning works best when it connects directly to real work. At HiBob, skills evolved beyond learning programs to support hiring, workforce planning, performance management, and career growth across the business.
- Shared skills frameworks create stronger workforce alignment. Consistent skills definitions help leaders identify capability gaps, prioritize learning investment, and make faster workforce decisions during AI transformation.
- AI workforce transformation requires operational clarity, not just AI tools. Organizations create greater long-term business value when they build structured systems to define, validate, and scale workforce capability.
- Skills become more scalable when embedded into everyday workflows. Connecting skills data to hiring, learning, performance, and workforce planning creates visibility across the employee lifecycle.
- Disconnected skills taxonomies create friction as organizations grow. Fragmented skill models and inconsistent definitions of capability make it harder to scale workforce planning and AI adoption effectively.
- AI accelerates execution when strong workforce foundations are already in place. Shared language, connected systems, and clear governance enable organizations to scale AI capabilities more consistently.
- Skills-based operating models should evolve alongside the business. The organizations moving beyond AI hype are continuously refining how they assess, develop, and operationalize workforce capability over time.
From Dana Liberty
Dana Liberty is Senior Content Manager at HiBob, turning HR insights into clear, people-first content for modern organizations. She writes about HR operations, employee relations, documentation, engagement, workplace communication, and practical resources. Her work helps people teams create clearer employee experiences and translate HR topics into useful guidance. When she's not writing, you'll find her reading, planning her next adventure, or challenging her kids to a board game she fully intends to win.
