RightFoot supports a Gulf company in preparing for AI upskilling
A Gulf-based company wanted a clear view of its workforce's AI readiness. It brought in RightFoot to map current skills, find gaps, and launch learning that actually moves the needle. The aim was simple: build capability quickly without guessing who needs what.
Why the company acted
AI is moving into everyday work, but most HR tools weren't built to assess readiness at this pace. Leaders needed visibility: who has baseline digital skills, which roles need AI-specific upskilling, and where automation or innovation could land first. Without that map, training stays generic and adoption stalls.
How RightFoot approached it
RightFoot used its OpusAnalytics platform to map technical and soft skills for each role across the organization. The analysis flagged employees with digital readiness, pinpointed roles needing focused AI skills, and highlighted departments primed for automation or new workflows.
"Using structured skill libraries and real-time employee data, OpusAnalytics surfaces job-relevant gaps that align with both company goals and individual growth," said Somaya El Sherbini, founder of RightFoot. Within weeks, the client launched targeted learning programs based on those gaps-no broad, one-size-fits-all training.
What changed for the business
The company moved from generic training to data-driven upskilling. Capability gaps were identified at scale and addressed faster than before. Teams are now better prepared to adopt AI responsibly, reducing reliance on new hires for AI-heavy roles and improving strategic workforce planning.
The collaboration also tightened coordination around AI use. Many employees were already experimenting with tools on their own; formal skills development and governance made adoption safer and more consistent. For guidance on risk and governance, see the NIST AI Risk Management Framework here.
What HR leaders can do next
- Map skills by role, not by department. Start with digital readiness, data literacy, prompt writing, and AI-assisted workflows.
- Identify role-specific AI capabilities (e.g., forecasting with AI for finance, content generation quality control for marketing, AI-assisted ticket triage for support).
- Build short learning sprints tied to real projects. Measure impact with adoption and performance metrics, not course completions alone.
- Set governance early: tool access, data-use rules, human-in-the-loop checks, and clear accountability.
- Create internal mobility paths so newly skilled employees can move into AI-enabled roles without leaving the company.
Practical next step
If you're shaping learning paths by job family, explore curated AI learning by role here: Complete AI Training - Courses by Job. Build from foundational literacy to role-specific skills, then embed those skills in live workflows.
Bottom line: the value isn't in more courses-it's in precise skill mapping, focused upskilling, and clear governance. That's how you turn interest in AI into real performance.
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