Maya Enterprise adds the identity and orchestration layer missing from transformation programmes. Before, during, and after AI workflow change, Maya shows who is ready, who needs support, which roles fit, what managers should do next, and how enterprise AI should work with each human.

Your transformation plan maps the future state. Maya maps the people who must get there.

Protect AI transformation ROI before the human failure cost appears.

TRUSTED BY CERTIFIED CAREER COACHES AND HR PROFESSIONALS GLOBALLY

Built for leaders responsible for delivering AI workforce transformation and growth.

The financial case is straightforward

£10M

people failure cost in a 1,000-person restructuring where 20% of the affected workforce disengages or departs

£35K

baseline replacement cost per mid-level employee who leaves during a transformation programme

96%

proportion of employees who intend to adapt to a new workflow but do not follow through without structured support

70%

of large-scale AI transformation programmes fail to deliver intended outcomes, primary cause: people not technology

Where WORK-SELF sits in your transformation architecture

Strategy layer

Target operating model definition. Determine which workflows AI replaces or augments.

Maya human-context layer

Maya maps each employee's readiness, work preferences, transition fit, and how agents should collaborate within the workflow.

Reskilling layer

LMS / L&D platforms deliver learning content to validated populations with confirmed readiness to engage.

Skills execution layer

Talent redeployment uses skills data, informed by WORK-SELF identity profiles.

WORK-SELF does not replace the platforms you already have. It is the intelligence and AI orchestration layer that makes each of them more effective because redeployment built on identity fit outperforms redeployment built on skills fit alone.

Maya tells enterprise AI how to work with your people.

As AI agents enter core workflows, employees should not become the manual orchestration layer. Maya creates a permissioned Work Contract for each human-AI workflow: what AI can do, what the human must decide, when to interrupt, what to show, and what context is allowed.

Work Contract

Who owns what between human,
AI, manager, and Maya.

Context Capsule

The minimum necessary task, role, policy, and work-preference context shared with approved agents.

Review Map

What the human must read, skim,
ignore, or decide.

Autonomy Dial

Manual, copilot, delegated,
or autopilot depending on risk and trust.

Operational in 30 days.

Book an enterprise demo, align on your workforce cohort, and deploy WORK-SELF across your organisation through a guided implementation experience built for HR and People teams.

Week 1: Load target operating model

Load affected cohorts, role architecture, HRIS data, transformation timeline, and target operating model.

Week 2-3: Maya provides workforce intelligence

Employees complete the Workforce Reinvention Assessment. Maya generates readiness, fit, support, and transition profiles.

Week 4: Operationalise 90-day transition plan

CHRO and transformation leaders receive cohort intelligence, risk map, internal mobility recommendations, and 90-day intervention plan.

Meridian Capital Partners: Workforce Reinvention Intelligence in Financial Services

Case Study

This Maya Enterprise simulation shows how a 1,200-person UK investment management and corporate advisory firm could de-risk AI-driven transformation before announcing workflow change to its people. Across a 312-employee cohort in Research, Compliance, and Client Advisory, Maya deployed its identity-aware intelligence architecture to assess reinvention readiness, identify high-risk disengagement and departure signals, surface internal mobility opportunities, and generate personalised 30/60/90-day transition plans for every affected employee. The simulation identified 34% of the cohort as High or Critical Risk, addressed £4.2M in estimated people failure exposure, and became operational within 28 days. Beyond workforce diagnostics, the case study points toward Maya’s broader role as a Human-AI orchestration layer: helping enterprises understand not only which workflows AI will transform, but how each employee should be supported, redeployed, coached, and paired with AI systems so transformation lands in the workforce rather than breaking against it.

WORK-SELF, built from 25+ years of experience in AI workflow automation and talent development

Wolf Magdelinic spent 20 years at PwC, CIBC, and Deloitte designing the target operating models that organisations use when they restructure. He then co-founded Overbond, an AI workflow automation platform deployed at Wells Fargo, Mizuho, and other Tier 1 financial institutions with a 17-claim US patent, and exited in 2024.

He built WORK-SELF because in every transformation programme he worked on, the operational architecture lacked the human layer transition system. The cost of that gap was visible in every programme. The product to address it at scale did not exist.

WORK-SELF is the platform he would have recommended to every CEO, COO, and CHRO he worked alongside. It exists because the problem is structural, the cost is measurable, and the intelligence layer to prevent it is now buildable with agentic AI in a way it was not 2 years ago.

Wolf Magdelinic Portrait

Wolf Magdelinic

Co-founder and CEO of WORK-SELF
Former: PwC · CIBC · Deloitte · Overbond AI (exit 2024)

Common questions from transformation leads

Your AI transformation is already creating human transition risk.

Maya shows where the risk is, what to do next, and how to make humans and AI work together without losing trust, talent, or ROI.