Maya gives transformation leaders a live view of workforce readiness and a governed operating model for human and AI work. See who is ready, where roles and workflows are changing, where support is required, and where clearer decision rights are needed before the programme scales.
Protect transformation value before workforce friction becomes costly.
Built for the leaders accountable for making AI transformation work.
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Maya gives each leader the workforce intelligence, operating controls and evidence they need to make different parts of the transformation succeed.
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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.
Result: See where workforce friction and unclear decision rights will erode the operating value of the AI investment. -
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.
Result: Move from broad change plans to cohort-specific readiness, role fit and intervention decisions. -
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.
Result: Connect the target operating model to the people, workflows and decision rights required to make it real.
The financial case is straightforward
£10M
potential people related cost in a 1,000 person transformation where 20% of the affected workforce disengages or leaves.
£35K
typical replacement cost for a mid level employee leaving during transformation.
96%
of employees intend to adapt to new ways of working, but intention does not guarantee follow through without structured support.
70%
of large scale transformation programmes fail to deliver their intended outcomes, with people and adoption a major factor.
Where Maya fits in your transformation stack
Strategy layer
Target operating model, workforce priorities and transformation decisions.
Maya human-context and orchestration layer
Maps workforce readiness, role fit and work preferences, then defines how people and AI agents share work through context, ownership, review and escalation.
Reskilling layer
Your existing learning, skills and mobility platforms deliver reskilling and redeployment actions informed by Maya.
Enterprise execution layer
Your HRIS, CRM, ERP, service platforms, AI agents and workflows remain the systems that execute the work.
Your existing platforms stay in place. Maya connects workforce intelligence to the systems and AI agents changing how work gets done, so workforce decisions and agent actions operate from the same human context.
Maya tells enterprise AI how to work with your people.
As AI agents enter core workflows, employees should not become the manual control layer. Maya defines who owns the work, what the agent may do, what context it receives, where human review is required and when the workflow should escalate.
Work Contract
Defines who owns what between the employee, manager and AI agent.
Context Capsule
Gives the agent only the task, role, policy and working context it needs.
Review Map
Defines what a person must review, decide, ignore or escalate.
Autonomy Dial
Sets how much the agent can do independently based on risk, trust and workflow rules.
Operational in 30 days.
Start with one affected cohort and one priority workflow. In 30 days, Maya establishes the baseline, maps workforce readiness, defines how people and AI share responsibility, and gives leaders the evidence to decide what should scale next.
Week 1: Define the operating model
Load the affected cohort, role architecture, transformation timeline and systems in scope. Agree the workflow, decision rights and measures of success.
Week 2-3: Generate workforce and workflow intelligence
Employees complete the Workforce Reinvention Assessment while Maya maps readiness, transition fit, support needs and where human and AI responsibilities need to be clearer.
Week 4: Turn insight into an operating plan
Transformation leaders receive the cohort intelligence, risk map, Work Contract recommendations and a 90 day action plan covering intervention, deployment and scale.
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.
Built from enterprise transformation and AI execution experience
Wolf Magdelinic spent over two decades across PwC, CIBC and Deloitte working on operating model and transformation programmes. He later co founded Overbond, an AI workflow platform deployed within major financial institutions.
WORK-SELF was built around a recurring gap in those programmes: organisations could redesign the technology and the workflow, but lacked a structured way to understand how people would adapt and how human accountability should work as AI took on more of the work.
Maya Enterprise brings those two disciplines together: workforce transition and governed human and AI orchestration.
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.
Turn workforce transition risk into a plan you can act on.
Maya shows where people and AI need clearer roles, where support is required, and what to change before the transformation scales.