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.
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You approved the technology investment. The operational plan is sound. What erodes transformation ROI is not the system, it is the 20% of your workforce that disengages, misses placements, or leaves during the change. WORK-SELF is the layer that protects that investment.
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You are accountable for the people side of a transformation your organisation has already committed to. WORK-SELF helps when attrition rises and redeployment underperforms. We give you the intelligence, the intervention layer, and the board-ready reporting to answer questions before they are asked.
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Your employees need more than reskilling plans. They need clarity on who they are and where they fit in the organisation you are building. WORK-SELF gives your transformation team identity-aware employee intelligence, measurable transition outcomes, and a human capital layer that most transformation programmes do not have.
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
Co-founder and CEO of WORK-SELF
Former: PwC · CIBC · Deloitte · Overbond AI (exit 2024)
Common questions from transformation leads
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No. WORK-SELF sits above your existing skills and HRIS infrastructure as an identity intelligence layer. Maya's profiles inform redeployment decisions made in Workday or Gloat; they do not replace them. Most clients find the two work better together than either does alone.
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The assessment is self-directed and available immediately. A workforce of 1,000 employees can complete assessments within two to three weeks with standard internal communications support. Maya is operational from day one of assessment completion. The dashboard is live by week four.
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Thirty minutes with the co-founders. You bring your transformation programme parameters (headcount affected, timeline, current platform stack). We show you what the workforce readiness assessment would produce for your population, what the attrition risk model looks like, and what a pilot would cost and deliver. No commitment required to proceed.
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Maya does not expose raw identity data as a manager surveillance tool. The enterprise receives transition readiness, support needs, role fit, and aggregated risk intelligence. Employees receive personal guidance and control over their work context. Approved AI agents receive only the context required to collaborate safely and effectively.
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No. Maya sits above them as the human-context and transition-intelligence layer.
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Yes, through permissioned Context Capsules and Work Contracts, starting with pilot workflows.
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Support needs, transition progress, and role-fit signals - not private reflections or raw psychological data.
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Through consent boundaries, purpose limitation, audit trails, and employee-visible context controls.
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.