Four workforce development challenges keep showing up in budget meetings and C-Suite conversations. Most organizations are still searching for answers.
- Onboarding cycles are too slow for the pace of business (and definitely for growth).
- HR tech ecosystems have gotten expensive and complicated, and still can’t provide comprehensive data on workforce capabilities.
- Traditional development is not able to close the AI skill gap fast enough.
- Enterprise training is inconsistent and ineffective across the business.
Every one of these ladders up to the same fundamental problem in the age of AI: the gap between where the business needs to go and how fast the workforce can get there. A handful of organizations have already closed that gap. Here’s what they did.
Problem #1: Onboarding Isn’t Getting Employees to Productivity Fast Enough
Every day that a new hire isn’t contributing costs the business. In sales-driven organizations, that cost has a precise number attached to it: an empty revenue pipeline and low deal generation.
At TEKsystems, a global technology and business services company, new sellers traditionally took 36 to 40 months to land their first services meeting. What was missing: hands-on sales practice. Sales leaders were managing 15 to 40 direct reports, which made it challenging (and costly) to provide the frequency of coaching that new sellers needed to build confidence quickly.
Moving deals forward fast was a revenue differentiator for TEKsystems.
“What we know as a company is: If we can move that business deal velocity at a particular rate and get to qualifying, we are 60% more likely to win the business,” said Chris Harry, Chief Talent and Learning Officer at TEKsystems.
To close that gap, TEKsystems embedded Degreed Maestro AI roleplays into its sales onboarding program. New sellers practiced four specific skills: elevator pitches, objection handling, discovery questions, and full prospect calls. They could repeat each experience as many times as needed, on their own schedule.
“I can create these micro-gyms of skill building that you do that work like anybody who’s an athlete, a musician, or anything. It’s a practice environment that gets you ready for the human connection piece,” Harry said.
Each member of the cohort spent about an hour total across all four roleplay experiences. The results were unmistakable.
Within six months of joining, 25% of new sellers in the cohort had landed a services meeting, compared to that previous baseline of 36 to 40 months. Before the program, no new seller had hit that milestone in under six months.
“Overall, we found astonishing feedback. Their confidence grew,” said Stefanie Kuehn, Senior Program Manager, Organizational Development at TEKsystems. “They were able to roleplay in a safe environment, and that meant not having a leader or a mentor over their shoulder listening in.”
When employees can practice before the moment of truth—repeatedly and without judgment—they ramp faster and perform better when it counts. The coach’s time gets redirected toward higher-value conversations, because sellers arrive having already done the reps.
The result: faster ramp for new hires, higher-value coaching for managers, and less administrative overhead for everyone.
Problem #2: The Tech Ecosystem Has Gotten Complicated and Costly, and Still Can’t Find the Skill Data You Need
Over the years, most organizations have been accumulating technology platforms and tools. Each one made sense at the time. But collectively, they’ve created an HR tech stack that’s expensive to maintain, tough to navigate, and too disjointed to produce a clear, actionable data picture of what your workforce can do.
In a world where change is constant, that’s not a sustainable position.
Pernod Ricard, one of the world’s leading premium spirits companies, operates in a dynamic market where consumer expectations, regulations, and supply chain demands are in constant flux. Staying stagnant just wasn’t an option.
“Our business is changing and it’s changing all the time. We need to bring change to our employees fast. We need to understand where they stand and what maturity they have to bring them towards the future,” said Louis Bosche, Head of HR Tech at Pernod Ricard.
Pernod Ricard had learning experiences and development opportunities available, but those resources were scattered across fragmented, redundant platforms. Employees were eager for them, but they were difficult to find and the outcomes were difficult to measure.
To resolve this and simultaneously build a skills-based enterprise, Pernod Ricard partnered with Degreed to build a unified learning hub natively integrated with Workday. The bidirectional integration meant that when an employee updated skill proficiency, that data updated in real time across both systems and became immediately available for talent decisions. Once set up, more than 70% of employees across the entire organization declared 15 or more skills in Workday, including populations that had historically been difficult to engage.
The result was a simplified HR tech ecosystem and real cost savings.
“Degreed became our one-stop shop to deliver that experience for employees having everything linked to their skills. They’re performing and talking the same language as they have in Workday,” said Anna Gosteva, Product Owner, Learning and Employee Engagement at Pernod Ricard.
Decommissioning redundant platforms alone saved €600,000 just by. The bigger shift: unified skill data that now connects directly to business outcomes.
Skill intelligence that used to live in L&D dashboards is now moving into the hands of functional leaders and the C-suite to make real talent and mobility decisions.
Problem #3: Your Workforce Can’t Build Skills and AI Capabilities Fast Enough
By the end of 2026, 90% of enterprises will be facing critical skill shortages, according to an IDC analyst brief. That means organizations that figure out how to build AI fluency and AI-adjacent capabilities at scale will have a real competitive advantage.
Two global companies have shown what it looks like to get ahead of this critical upskilling.
Capgemini needed to build AI fluency across a workforce of 360,000 people, fast. To do this, it launched a Gen AI Campus on Degreed in just 10 weeks, deploying thousands of learning plans, pathways, and curated learning resources. Since then, more than 150,000 employees have engaged with the academy, and 50,000 certifications have been awarded.
Capgemini drove what could have been a mulit-year initiative at the speed of the market. The result: €32M in new client contracts directly tied to the team’s AI upskilling.
Meanwhile, for ZS, a management consulting and technology firm, employee skills are its product, so expertise (especially in emerging tech like AI) continues to be a competitive differentiator. It took a complementary approach to skill building by embedding AI into the learning workflow itself, not just building AI skill content.
The ZS team deployed “My Virtual Coach,” their internal deployment of Degreed Maestro, to help employees prepare for high-stakes conversations, work through competing feedback, and act on development insights from their managers.
ZS is also using Degreed’s skill data to tie learning activity to focused business expectations, mapping development to one of seven defined competency areas. That means whatever an employee is working on, it connects back to what the firm actually needs. Simple and focused.
The lesson from both organizations: Building AI capabilities at scale requires deliberate infrastructure to make AI fluency and upskilling measurable and specifically aligned to business goals.
Problem #4: Training Isn’t Consistent and Effective on a Global Scale
Rolling out a learning program is one thing. Rolling out a personalized learning program to thousands of employees across dozens of roles, geographic locations, and skill proficiencies is something far more complicated. Yet also far more important for business success.
Crédit Agricole, the ninth-largest bank in the world, operates with 150,000 employees across global markets. Keeping that workforce current across simultaneous industry and market changes requires a fundamentally different approach than instructor-led cohorts and static course libraries can provide. That’s the job of IFCAM, Crédit Agricole’s corporate university.
“We have to face multiple challenges and multiple changes at the same time,” said Guillaume Lefebvre, CEO of IFCAM. “Technology gap. Behavioral gap. Demographic gap. Competition gap. Now, even geopolitics and transitions. It’s not a challenge, it’s an opportunity.”
To tackle this, the organization needed one learning system (in this case, Degreed) that interconnected with its HR systems. That unification would strengthen learning as well as workforce mobility.
Twenty thousand employees have started using Degreed so far, and the IFCAM team is working toward a goal of 70,000. “The ones who have adopted, all of them are happy,” Lefebvre said. “They see the potential in terms of the mass of information and learning that they can get, thanks to the platform. And they also see the benefits in terms of optimization—cost, cognitive capacity, and time.”
Now, local branches in various regions are asking to create their own academies with support from Lefebvre’s team. The connection is driving better learning and training all the way to the regional level and boosting workforce transformation. It helps people keep pace with a business that never stops changing.
“I don’t believe the barrier for entry is too high between the workforce and AI,” Lefebvre said. “There’s no limit. The older we get, the smarter we can be, thanks to experience, plus training.”
Building an Infrastructure for AI-Powered Learning
None of these organizations solved their problem by adding more content or more training hours. They solved it by building smarter infrastructure: a learning ecosystem that generates better data, enables more personalized experiences, and connects directly to business outcomes.
In each case, AI-driven workforce learning didn’t replace the human elements that make learning effective. It amplified them. TEKsystems’ sellers still work with human coaches, but now the sellers have practice reps in Maestro so those face-to-face coaching conversations can go deeper. Pernod Ricard’s L&D team still makes strategic decisions, but now the skill data they need is finally in front of them. ZS’s employees still apply human judgment, but Maestro makes sure they get the most out of their feedback in AI coaching conversations.
These four common workplace challenges likely won’t be common for much longer. The organizations that solve them first (like TEKsystems, Pernod Ricard, Capgemini, ZS, and Crédit Agricole) will shape the work world of the future, which will have none of the pesky problems of today.
It’s time to build workforces that are ready before the business needs them.
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