You’re Sitting On a Goldmine of Skills Signals
iMocha turns it into decisions inside Workday HCM

Yes. The Worker Profile within Workday is designed to hold exactly this — employee-level skills that are validated, proficiency-rated, and continuously updated. The challenge most enterprises face is that generating that data at scale requires more than asking employees to self-declare. iMocha reads employee context from Workday Employee Data — role, function, reporting structure — and uses that as the foundation for AI-driven skills inference and validation. Validated skills, backed by assessments and AI interviews, are passed directly into the Worker Profile with proficiency ratings on a 1 to 5 scale. Employee Data remains your system of record, unchanged. The Worker Profile gets populated with skills data that is evidence-backed and decision-ready.

Yes. The distinction between "this employee has this skill" and "this employee operates at this proficiency level" is what makes skills data genuinely useful for deployment, mobility, and succession decisions. iMocha validates skills and assigns proficiency on a structured 1 to 5 scale — backed by rubrics that define what each level looks like in practice — and passes that data into the Worker Profile inside Workday. When a talent manager or HRBP looks at an employee profile in Workday HCM, they see not just what skills are listed but how capable that employee actually is at each one. That is the precision that makes skills-based decisions reliable.

Career pathing accuracy depends on two things: knowing what skills an employee currently has at what proficiency level, and knowing what skills each target role actually requires. When both are grounded in validated data rather than manager perception, career paths become objective and credible. iMocha addresses both — building proficiency-rated skill profiles for every employee and maintaining accurate role-to-skill mappings. It also surfaces adjacent skills: complementary capabilities an employee already has that qualify them for roles they might not have been considered for. Career recommendations inside Workday HCM become something employees and managers trust, because they are grounded in evidence rather than opinion.

Prioritizing skills requires understanding two things simultaneously — what your organization needs strategically, and what your workforce can actually demonstrate today. Workday HCM holds your workforce and role structure; what converts that into actionable priorities is a gap analysis layer that compares validated current skills against target skill requirements for each role. iMocha runs this analysis continuously, surfacing skill shortfalls at the individual, team, function, and organizational level. CHROs and talent leaders get a clear picture of where the most critical gaps sit — so L&D budgets, hiring decisions, and reskilling programs are focused on what matters most to the business rather than what is simply most popular.

Yes. iMocha is built to extend what Workday HCM already does, not replace any part of it. It reads employee context from Workday Employee Data — which continues to serve as your system of record without any changes — and feeds validated skills into the Worker Profile and enriched taxonomy into the Skills Cloud. Your HRBPs, talent managers, and recruiters continue working in the Workday interfaces they already use. iMocha also connects with the LMS and LXP platforms in your existing ecosystem — Coursera, LinkedIn Learning, Degreed, Cornerstone — so skill gap-driven learning recommendations flow through infrastructure you already have in place. The integration adds intelligence without adding disruption.

A skills-first analytics capability means moving from headcount reporting to capability reporting — understanding not just who you have, but what they can do, where the gaps are, and what needs to happen next. iMocha's Skills Intelligence Cloud delivers this within the Workday HCM environment: real-time visibility into skills supply and demand, gap severity at the individual and organizational level, proficiency benchmarks against role requirements, and reskilling priority rankings by function or business unit. Because the underlying data comes from validated, multi-signal skills evidence rather than self-declared profiles, the analytics are reliable enough to drive actual workforce decisions — on mobility, succession, hiring, and learning investment.

There are five distinct input signals, and the most effective approach uses all of them. First, AI-driven inference from data already in Workday HCM — learning completions, certifications, Work Data, and resumes — surfaces skills without any additional burden on employees. Second, proctored assessments from a library of over 10,000 skills spanning technology, domain, cognitive, and communication capabilities across multiple languages give a structured, objective measure of proficiency. Third, Tara — iMocha's conversational AI interviewer — delivers adaptive, on-demand evaluation of applied and contextual capability that assessments alone cannot capture. Manager reviews and employee self-ratings add the final layer of context. The output from all five signals flows as validated skills into the Worker Profile inside Workday.

Yes, and this is one of the more powerful levers available for both workforce planning and career development. Adjacent skills are complementary capabilities that sit close to an employee's core skill set — skills that make them applicable across multiple roles and functions beyond the ones they were originally hired for. An employee with strong data engineering skills, for example, may have adjacent capability in analytics, automation, and cloud infrastructure that qualifies them for roles that would never surface through a title-based search. iMocha maps adjacent skills at both the taxonomy and individual level using AI, surfacing hidden talent pools and enabling cross-functional mobility within the Workday HCM environment. For employees, this translates into visible career paths that go beyond linear progression within a single function.

Workday HCM gives you the HCM foundation — the workforce data, role structures, the Skills Cloud to maintain your skills taxonomy, and the Worker Profile to track employee-level skills. What converts that into a skills-first engine is the layer on top: validated, proficiency-rated skills for every employee, a living skills architecture that stays aligned to roles as they evolve, and AI that connects skills data to hiring, mobility, learning, and succession in a continuous rather than periodic way. Most enterprises have the platform and the intent. What is typically missing is the skills intelligence layer that turns Workday data into something decision-ready. iMocha provides exactly that layer — reading from Employee Data and feeding validated skills into the Worker Profile and enriched skills into the Skills Cloud — so the investment already made starts delivering on its full potential.

Faster than most expect. A validated skills baseline for a focused population — typically one function, region, or business unit — is achievable within 4 to 8 weeks. Enterprise-wide coverage typically follows within 6 to 12 months. The speed comes from AI-driven skills inference: iMocha reads what Workday HCM already holds — role and function data, learning completions, Work Data, and certifications — and infers skills from that without requiring employees to fill out self-assessments or commissioning a months-long consulting engagement. Validation through assessments and AI interviews then confirms those inferred skills with proficiency levels. The result is a skills baseline built on existing data, validated by AI, and live inside the Worker Profile without disrupting your current HCM roadmap.

The traditional approach — self-assessment surveys and manager ratings — breaks down at enterprise scale because both are time-consuming and subjective. A more scalable approach draws on what Workday HCM already holds: role and function data from Employee Data, learning and certification records, and Work Data. iMocha applies AI to infer current skills and proficiency from these sources, then compares them against target skill profiles for each role. The gap analysis runs continuously rather than as a periodic project, and surfaces shortfalls at the individual, team, and organizational level inside the Workday environment your HRBPs already work in. No additional surveys. No manual consolidation.

The challenge with a skills library built primarily on self-declaration is uneven coverage, overstated proficiency, and the same skill named differently across business units. Making it decision-ready requires two things: a clean, well-structured taxonomy that is maintained over time, and individual skill records that are validated rather than self-reported. iMocha enriches the Skills Cloud with a structured 4-level taxonomy — aligned to global frameworks like O*NET and Lightcast — that gives your skills library consistent definitions, proficiency rubrics, and adjacent skill mappings. It then validates individual employee skills through assessments, AI interviews, and multi-signal inference, passing proficiency-rated results into each employee's Worker Profile. The taxonomy becomes reliable and the employee-level data behind it becomes evidence-backed.

Most skills architectures are outdated within a year because they are built as a consulting output — a taxonomy document with no system behind it to keep it current. The alternative is to anchor the architecture directly to your Workday HCM role framework and use AI to maintain it as roles, technologies, and business priorities shift. iMocha builds a 4-level taxonomy — Domain, Sub-Domain, Skills Cluster, and individual Skills with proficiency rubrics and adjacent skill mappings — drawing on globally recognized frameworks including O*NET, Lightcast, and the Singapore Skills Framework. Because iMocha maintains this architecture continuously, it stays aligned with Workday HCM as your organization evolves, rather than requiring periodic consulting interventions to refresh it.

Posting open roles surfaces candidates who happen to be looking. Skills-based matching surfaces candidates who are actually qualified — including those who are not actively considering a move but whose validated skills make them a strong fit. This requires the Worker Profile to hold proficiency-rated skills for every employee, and for open roles to be defined in skills terms rather than just titles and years of experience. iMocha feeds this foundation — and adjacent skills mapping surfaces non-obvious internal candidates who are one or two skills away from a role and could get there with targeted development. Your existing HRBP and recruiter workflows in Workday HCM remain the same; the difference is the quality and confidence of the internal candidates they surface.

Incomplete role-to-skill mappings produce either empty career paths or paths so generic that neither employees nor managers trust them — and without knowing what skills actually matter for the next role, development conversations go nowhere. iMocha addresses this by completing role-to-skill mappings through AI-driven inference rather than manual workshops, and by building validated current skill profiles for every employee so the system can calculate the real distance between where someone is and where they want to go. Adjacent skills extend this further — revealing cross-functional paths that are achievable based on what an employee already has. Career paths in Workday HCM shift from static org charts to dynamic, evidence-based recommendations.

Personalized learning requires knowing three things about each employee: what skills they actually have, what their target role requires, and the precise gap between them. Without that, learning recommendations default to generic role-based catalogs. iMocha generates individual-level skill gap data from multiple signals — assessments, AI interviews, learning completions, Work Data, and certifications — and maps those gaps to specific content from Workday Learning and connected LXPs like Degreed or Cornerstone, including providers like Coursera, LinkedIn Learning, Udemy, and Pluralsight. After learning is completed, Tara and iMocha assessments validate whether the skill was actually acquired — closing the loop between learning investment and demonstrable capability.

ROI shows up across four areas Workday HCM already tracks. Time-to-hire and quality-of-hire improve when candidates are screened against validated skill requirements rather than resume keywords. Internal mobility rate and retention improve when employees are matched to opportunities based on skills rather than tenure. L&D efficiency improves when learning is tied to validated skill gaps rather than broad catalogs, so budget goes where it has the highest impact. Workforce planning accuracy improves when decisions are based on real-time skills intelligence rather than headcount projections. The most rigorous approach is to baseline each of these metrics in Workday before the integration goes live and track quarter-on-quarter movement after.

The Skills Cloud and Worker Profile are the right places for skills to live inside Workday HCM — Skills Cloud as the taxonomy and skills repository, Worker Profile as the record of each employee's validated capabilities. The question is what fills them. The Skills Cloud needs a well-structured, maintained taxonomy to serve as a reliable reference layer. The Worker Profile needs validated, proficiency-rated, continuously refreshed employee skills to power mobility, career pathing, succession, and workforce planning. Neither populates itself. Generating that data at scale requires AI-driven inference across learning completions, Work Data, certifications, and resumes; multi-source validation through assessments and AI interviews; skills decay logic that prompts reassessment on a structured cycle; and taxonomy maintenance as roles and skills evolve. iMocha is purpose-built for this layer — enriching the Skills Cloud and feeding validated skills into the Worker Profile — so the Workday HCM investment you have already made becomes genuinely decision-ready.