Neurons Lab Logo

Neurons Lab

AI Adoption Manager

Posted 2 Days Ago
In-Office or Remote
Hiring Remotely in Georgia, USA
Senior level
In-Office or Remote
Hiring Remotely in Georgia, USA
Senior level
Leads enterprise AI adoption programs from assessment through workshops, hands-on enablement, adoption measurement, and team self-sufficiency. Builds client relationships, develops internal AI champions, creates reusable prompts and templates, ensures compliance with data-governance and responsible-AI policies, reports business impact and ROI, and identifies expansion opportunities. The role requires practical AI fluency, strong facilitation, change-management expertise, customer success capabilities, and executive communication skills.
The summary above was generated by AI
Objective

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.

About the project

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.

KPIs
  • 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

Areas of responsibility
  • 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

Skills
  • 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

Knowledge
  • 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

Experience
  • 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

What We Offer
  • Competitive compensation — a monthly base plus expansion revenue upside

  • Fully remote — outcomes over attendance

  • Unlimited PTO

  • Full-time contractor engagement with a fast-growing AI consultancy at the forefront of enterprise transformation

Similar Jobs

2 Days Ago
In-Office or Remote
2 Locations
215K-358K Annually
Senior level
215K-358K Annually
Senior level
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Leads measurement and product marketing for eight enterprise AI platforms. Builds shared KPI, taxonomy, scorecard, data-quality, and value-realization frameworks; translates usage and outcome data into executive insights and investment guidance. Oversees positioning, internal launches, campaigns, enablement, adoption, and audience segmentation. Partners across product, engineering, data, communications, and business teams while building and managing teams responsible for analytics, marketing, and communications.
Top Skills: Ai PlatformsBusiness IntelligenceDashboardsData ContractsData FabricData VisualizationEvent TaxonomyExperimentationKnowledge GraphsKpi FrameworksOkr PlatformsProduct AnalyticsTelemetry
2 Days Ago
In-Office or Remote
2 Locations
163K-272K Annually
Senior level
163K-272K Annually
Senior level
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Leads product strategy, roadmap, lifecycle ownership, adoption, and measurable outcomes for a greenfield enterprise AI platform. Defines platform boundaries, governance controls, evaluation, registration, and cost-tracking capabilities while translating evolving risk, privacy, security, and GxP requirements into usable product features. Partners with engineering, design, legal, compliance, risk, security, and global agent-building teams to prioritize investments, guide delivery, communicate direction, and drive platform adoption.
Top Skills: AgileAIAi AgentsAi GovernanceGxpLeanModel Evaluation
3 Days Ago
In-Office or Remote
2 Locations
177K-294K Annually
Senior level
177K-294K Annually
Senior level
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Build, test, and ship production-grade full-stack software and AI-powered systems for Pfizer’s Medical Affairs portfolio. Integrate LLMs, agentic AI, and RAG pipelines; develop scalable systems from prototypes through production; optimize prompts for cost, latency, and quality; integrate enterprise platforms; and monitor AI/ML performance. Collaborate with architecture, product, UX, and data engineering teams while maintaining strong code quality, documentation, testing, and verification practices.
Top Skills: Agentic AiAgileApi-First DesignAWSAzureCi/CdGdprGxpHipaaLlmopsLlmsMlopsRagSalesforce Life Sciences CloudSalesforce Marketing CloudScrumVector DatabasesVeeva Crm

What you need to know about the NYC Tech Scene

As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.

Key Facts About NYC Tech

  • Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
  • Key Industries: Artificial intelligence, Fintech
  • Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
  • Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account