AI Adoption Manager

• Workshop and training design and delivery, with strong live facilitation

• Change management and adoption, grounded in instructional design and adult learning

• Practical, daily AI fluency (Claude, ChatGPT, agentic workflows, prompt engineering) and the ability to rebuild an expert's workflow as an AI-assisted one for non-technical users

• Customer success - trusted client relationships, healthy adoption, and usage turned into demonstrated value

• Strategic program design and clear executive, cross-functional communication, with comfort in ambiguity

• Modern AI tools, agentic workflows, and prompt engineering, applied practically and daily

• Change management and adoption psychology - what makes change stick from within

• AI governance and responsible-use frameworks - data classification, acceptable-use, and responsible-AI policy, enough to keep enablement inside client guardrails

• Enablement/training business or consulting background

• 5+ years in change management, enablement, digital-transformation consulting, or enterprise software rollout, including at least one full-cycle deployment

• Top-tier management-consulting experience (e.g. McKinsey, BCG, Bain) is a strong plus

• Customer success experience strongly desired - owning client relationships, adoption health, and value realization

• Hands-on experience driving technology or process adoption inside organizations

• Track record designing and delivering workshops/training sessions personally

• Experience running assessments, feedback sessions, and executive updates

• Fluent English required

Lead AI adoption across the client teams - from first assessment, through workshops and hands-on enablement, to self-sufficient daily AI use, with adoption reported as measured business impact.

Neurons Lab delivers AI education and adoption programs for enterprise clients, mainly in financial services (banking, insurance, and capital markets). This role leads those programs inside the customer's own teams - taking each new client from first assessment, through workshops and hands-on enablement, to confident daily AI use.

The AI Adoption Manager is the face of the program with the client. Embedded in the customer's business teams, the role runs the full education and adoption cycle and carries the customer success side of the engagement: building the relationship, keeping adoption healthy, reporting outcomes to client stakeholders, and surfacing where the account can grow. Because most clients operate in regulated financial-services environments, every program runs inside the client's data-governance, compliance, and responsible-AI guardrails.

• Post-workshop AI adoption per team (primary KPI)

• Measured business impact per team - time saved, cycle-time, or effort reduced against a baseline captured before enablement

• Number of active champions identified, developed, and made visible to client leadership

• Cadence adherence - recurring sessions held on rhythm, response times measured in hours, not days

• Teams released as self-sufficient; qualified opportunities passed to the technical tracks

• Engagement growth - follow-up workshops, recurring enablement, new scopes originating from business team engagement

• Assess and prioritize - map each team's workflows and current AI usage, turn their real pain points into a prioritized enablement plan, and rule out use cases where the payoff isn't real

• Deliver enablement end to end - design and run workshops (personally and with external trainers) that target each team's own use cases and produce walk-away skills, prompts, and tools they use the next day

• Build reusable assets - maintain a shared library of approved prompts, skills, and templates teams can reuse without you in the room

• Grow champions and adoption - develop champions inside each team, surface and remove adoption blockers, and hold a steady cadence with the business teams

• Keep it inside the guardrails - align every plan with the client's data-governance, acceptable-use, and responsible-AI policies, working with IT, security, and legal

• Measure and report value - baseline each team, track adoption against targets, and report progress, risks, and ROI to client sponsors

• Drive to self-sufficiency and expansion - hand teams over once they sustain AI use on their own, pass engineering-grade work to the technical track, and surface new scopes for the account