Artificial Intelligence

• Turn AI transformation ambition into adoption that sticks
• Operationalize the business case, outcomes, and partnerships
• Partner closely with technical leads to ensure adoption
• Build change narratives for affected stakeholders
• Own the initiative plan and track progress against goals
• Produce clear, honest status for stakeholders and governance forums

Director, AI Change Adoption

• Champion Agile principles and practices across AI and data-focused teams, driving adoption and continuous improvement
• Coach product owners, engineers, and business stakeholders on Agile frameworks such as Scrum, Kanban, or SAFe
• Facilitate core Agile ceremonies (sprint planning, stand-ups, reviews, retrospectives) to ensure effective team execution
• Foster a culture of transparency, collaboration, and iterative delivery across cross-functional teams
• Partner with technical teams (data science, ML engineering) and business leaders to translate AI use cases into actionable backlogs and sprint goals
• Guide teams in delivering AI/ML solutions iteratively, accounting for experimentation, model training, and validation cycles
• Align delivery plans with business objectives, ensuring AI initiatives provide measurable value
• Support prioritization of work based on business impact, technical dependencies, and data readiness
• Enable collaboration across diverse teams, including data scientists, engineers, product managers, and domain experts
• Remove impediments and escalate risks impacting delivery timelines or outcomes
• Act as a liaison between technical and non-technical stakeholders to ensure shared understanding
• Define and track Agile and AI-specific performance metrics (e.g., velocity, cycle time, model performance indicators)
• Ensure AI delivery adheres to governance, compliance, and ethical standards (e.g., bias, data privacy)
• Facilitate retrospectives and apply learnings to improve delivery effectiveness and team maturity
• Promote data-driven decision-making and experimentation
• Support enterprise Agile transformation efforts, particularly within AI and digital initiatives
• Influence leaders and stakeholders to adopt Agile ways of working and AI-enabled delivery models
• Drive mindset shifts toward iterative development, experimentation, and continuous learning

AI Agilist

• Lead and develop the platform team, setting direction and driving execution across strategic initiatives
• Lead architecture execution across initiatives, ensuring high-quality delivery
• Design the application’s technical infrastructure
• Build a framework and execution plan to onboard segment- and division-specific applications into the platform
• Deliver self-service report and application management capabilities
• Improve enterprise platform CI/CD, maintainability, and long-term platform operability
• Modernize insight delivery by enabling the transition of key reporting workloads from traditional BI tools to AI-first experiences

Senior Manager, Internal Enterprise Analytics – AI Experience Gateway

• Own the full AI engagement arc from discovery through sustained workflow adoption
• Drive requirements and delivery precision to track against plans and surface risks early
• Champion continuous quality through testing and feedback loops during AI engagements
• Hold the R&D feedback loop accountable to ensure field learnings flow back
• Build playbooks to identify reusable patterns and scalable frameworks for the team
• Own engagement economics and track cost structure across active engagements
• This is a field-first role with expected travel to partner locations

AI Engagement Manager

• Actively partner with brand and business teams to audit workflows and identify AI opportunities
• Evaluate existing or vendor-proposed AI tools for integration
• Build and test AI-driven agents and intelligent automation workflows using approved APIs
• Embed with brand teams during early deployment phases and troubleshoot solutions
• Own the AI COE value narrative by building and maintaining a leadership-ready dashboard

AI Operations & Enablement Lead – Marketing Center of Excellence

• Develop and fine-tune LLM-powered applications for internal teams and pharmaceutical clients.
• Build AI-powered agents to assist in areas like drug commercialization, market intelligence, competitive analysis, and procurement.
• Design and implement RAG-based (Retrieval-Augmented Generation) solutions using enterprise CRM and knowledge data.
• Collaborate with software engineers and domain experts to deploy AI models into production.
• Work with APIs (e.g., OpenAI, Anthropic, Google Gemini, Cohere, Hugging Face) to integrate generative AI capabilities into applications.
• Experiment with multimodal AI models for text, image, and audio processing in pharmaceutical use cases.
• Optimize AI models for efficiency, scalability, and compliance with pharmaceutical industry regulations.
• Contribute to new AI-driven products and prototypes for the pharma industry.
• Demonstrate a commitment to diversity, equity, and inclusion through continuous development, modeling inclusive behaviors, and proactively managing bias.
• All other duties as assigned.

AI Development Intern – 10-week Program

• Architect end-to-end AI solutions across the Workday ecosystem
• Act as a trusted technical advisor to C-suite and senior technical leaders
• Lead technical delivery for AI workstreams
• Partner closely with Workday Product and Engineering teams
• Design and validate patterns for how Workday AI interacts with third-party systems and external LLMs

Senior Technical Delivery Consultant – AI Practice

• Lead end-to-end development for our AI platform and customer experiences, from initial roadmap to rollout.
• Partner with engineers to design prompt strategies, evaluation frameworks, and guardrails—balancing latency, cost, and accuracy.
• Serve as the technical translator between engineering and the broader organization, establishing AI best practices and platform standards.
• Drive systematic improvement in answer quality, customer satisfaction, and automation rates through rigorous experimentation.
• Work with partners at OpenAI to evaluate and deploy the next generation of voice models and workflow automation.

Manager, AI Agents and Platform

• Design, build, and refine AI-powered workflows that support research synthesis, deliverable drafting, proposal coordination, market analysis, and knowledge management.
• Develop and maintain a library of prompt templates calibrated to GEN Advisory’s specific work.
• Manage the practice’s AI knowledge base: organize past project materials, methodologies, and reference content so they are accessible to AI platforms as context for future work.
• Identify bottlenecks in current workflows where AI assistance would have the highest impact; prototype solutions and bring them to the team for feedback.
• Apply AI tools to operations functions: utilization tracking, assignment pipeline reporting, revenue data synthesis, and team reporting, working in coordination with the Director of Operations.
• Conduct market research, demographic analysis, and comparable transaction research to support active client engagements.
• Assist with financial modeling, development pro formas, lease comparisons, portfolio cost analyses, all under direction of senior advisors.

AI Analyst – Government, Education, Non-Profit Advisory

• Set the multi-year strategy for ML, data, and BI
• Build and scale the Data Science / AI, Data Engineering, and BI Engineering organization
• Manage managers and tech leads
• Run performance management, career development, and retention
• Operate as a peer to VP-level leaders across various functions
• Own the architecture, deployment, and operation of the production ML

VP of AI, ML and Data Strategy

• Bring a consultative approach to every engagement, taking the time to understand a team's workflows, challenges, and goals before identifying where AI can make a real difference
• Partner with leaders across all major functions to surface workflow friction, knowledge gaps, and high-value opportunities where AI tools or training can make a meaningful difference
• Own and drive the enterprise AI enablement roadmap, scoping, prioritizing, and executing a portfolio of initiatives from discovery through adoption and measurement
• Define and maintain company-wide AI policy and guardrails, ensuring Crain is adopting AI in a way that is responsible, secure, and aligned with our values and obligations
• Lead the effort to capture and centralize Crain's institutional knowledge (decisions, processes, data, and expertise) into AI-powered knowledge bases that make the whole organization smarter: HR resources, customer research, sales and marketing materials, process documentation, and more
• Partner with IT Services on the company-wide rollout of Claude Enterprise, driving adoption and ensuring teams are set up to get real value from the platform within our Microsoft 365 environment
• Identify and implement agentic workflows and automation that reduce manual overhead across key business functions
• Build a structured approach to connecting Crain's core data sources (Naviga, Salesforce, ArcXP, Smartsheet, Fireflies, AWS, and others) as inputs into a coherent, AI-accessible model of how the business operates
• Implement and manage a company-wide AI micro-learning platform, rolling it out in deliberate phases starting where it will have the most impact, tracking adoption and outcomes at each stage to learn, iterate, and expand intelligently across the organization
• Stay close to advancements in the AI space, continuously evaluating new tools, models, and capabilities; find regular, structured ways to surface and communicate relevant opportunities to stakeholders across Crain so we're always making informed decisions about what to explore, pilot, or adopt
• Maintain a clear focus on cost efficiency and ROI across all AI initiatives; every tool evaluated, every workflow built, and every platform adopted should be held to a standard of delivering measurable value for the business

Director, Enterprise AI Enablement

• Supports the deployment, orchestration, standardization, and continuous improvement of enterprise Agentic AI workflows and AI-enabled business processes
• Coordinates multi-agent workflows across enterprise systems, business functions, and AI platforms to improve operational efficiency, user experience, and process automation
• Promotes the effective use of repeatable AI processes, orchestration methodologies, governance standards, and enterprise best practices
• Collaborates with business stakeholders, technical teams, and platform teams to identify automation opportunities, improve workflows, and enable scalable AI solutions across the enterprise
• Participates in the evaluation, deployment, and operational support of Agentic AI capabilities, including workflow orchestration, AI task coordination, process monitoring, and AI governance adherence
• Coordinates and supports multi-agent AI workflows across enterprise tasks, systems, and business processes
• Assists in defining and deploying repeatable orchestration standards, methods, and best practices for Agentic AI solutions
• Collaborates with business process owners to identify automation and workflow optimization opportunities
• Supports implementation and configuration of AI-driven workflows using enterprise AI and automation platforms
• Monitors workflow performance, operational metrics, and orchestration effectiveness to identify improvement opportunities
• Participates in evaluating emerging AI orchestration technologies and enterprise automation capabilities
• Assists in documenting workflow logic, orchestration patterns, governance controls, and operational procedures
• Promotes adoption of enterprise AI standards, common processes, and reusable workflow patterns
• Coordinates with technical teams, product managers, and platform teams to support deployment and scaling of Agentic AI capabilities
• Supports integration activities across enterprise systems, knowledge sources, APIs, and workflow platforms
• Participates in operational reviews, lessons learned, and continuous improvement activities related to AI-enabled processes
• Helps establish governance and compliance alignment for AI workflow execution and enterprise usage standards
• Provides education, guidance, and operational support to users adopting AI-enabled workflows and orchestration capabilities

Mid-Level AI Workflow Orchestration Specialist

• Shape how AI is responsibly developed and deployed across the enterprise
• Develop and run a process to operationalize governance for AI
• Contribute to the development of a holistic perspective of AI controls
• Work closely with other risk functions for alignment

AI Governance Lead Consultant

• Own complete AI transformation projects end-to-end
• Manage direct reports through formal performance management
• Serve as primary client relationship owner
• Drive solution development through use case identification
• Proactively drive business development activities
• Oversee practice improvement initiatives
• Facilitate client workshops and capability building activities
• Manage project risks, issues, and changes proactively
• Provide formal coaching to team members

AI Solutions Consulting Lead

• Set the direction of the venture and lead its execution.
• Refine the ICP, pricing model, and product positioning across freight brokerages, 3PLs, carriers, and warehouse operators
• Lead pilots with mid-sized to enterprise freight brokerages, convert them to paid engagements, and build the go-to-market motion targeting Heads of Logistics, Supply Chain, and Operations
• Partner with the FutureSight product and engineering team to ship V1 and iterate on user feedback, with a focus on TMS and load board integrations, conversational AI quality, and autonomous workflow execution
• Lead the seed raise, supported by FutureSight's network and traction
• Recruit and lead the founding team, and establish the cultural foundation of the company

Co-Founder & CEO – AI Communication Agents for Freight & Logistics

• Set the direction of the venture and lead its execution
• Refine the ICP, pricing model, and product positioning across the AEC vertical wedge
• Lead pilots with mid-market AEC firms, convert them to paid engagements, and build the go-to-market motion through AEC networks, marketing associations, and industry partnerships
• Partner with the FutureSight product and engineering team to ship V1 and iterate on user feedback, with a focus on compliance extraction, semantic mapping, and SME workflows
• Lead the seed raise, supported by FutureSight's network and traction
• Recruit and lead the founding team, and establish the cultural foundation of the company

Co-Founder & CEO – AI Proposal Intelligence for AEC

• Set the direction of the venture and lead its execution.
• Refine the ICP, pricing model, and product positioning.
• Lead pilots with operations-heavy commerce, retail, or supply chain organizations, convert them to paid engagements, and build the go-to-market motion.
• Partner with the FutureSight product and engineering team to ship V1 and iterate on user feedback.
• Lead the seed raise, supported by FutureSight's network and traction.
• Recruit and lead the founding team, and establish the cultural foundation of the company.

Co-Founder & CEO – AI Reverse Logistics Orchestration Platform

• Own the enterprise AI feasibility function , providing authoritative guidance on Model selection (traditional ML, GenAI, agentic AI)
• Approved platforms, tools, and services
• Architectural patterns and trade‑offs
• Act as the primary technical advisor to business and technology teams evaluating AI use cases.
• Ensure teams are guided toward enterprise-approved solutions that accelerate delivery and reduce long‑term operational risk.
• Identify delivery blockers early (data readiness, platform onboarding, governance dependencies) and drive resolution.
• Establish and maintain AI playbooks, standards, and best‑practice frameworks for internal teams.
• Sponsor and guide enterprise‑wide workshops, training, and knowledge‑sharing initiatives.
• Provide consultative guidance on AI solutions and architecture, partnering with product, engineering, and business teams to shape use cases, design patterns, and implementation approaches.
• Act as a trusted advisor to teams evaluating AI feasibility, trade‑offs, and architectural alignment with enterprise standards.
• Build and lead an AI Center of Excellence to drive talent strategy, mentorship, and continuous capability development.
• Provide leadership and oversight for enterprise MLOps practices, including CI/CD, model registry, and automated rollback.
• Ensure AI systems meet regulatory, security, and privacy standards in collaboration with risk and compliance stakeholders.
• Define and review SLAs, KPIs, and observability standards for AI services, ensuring operational excellence and accountability.
• Set the architectural vision for a scalable, modular AI ecosystem spanning data ingestion, feature stores, training infrastructure, and inference.
• Champion standards for model governance, including versioning, data lineage, explainability, and auditability.
• Evaluate emerging AI technologies and approaches, and define adoption roadmaps aligned with business value and risk tolerance.
• Own the strategic evolution of the on‑prem AI platform stack (e.g., Airflow, Elasticsearch) and its operating model.
• Ensure delivery of self‑service platforms, APIs, and tooling that enable teams to innovate efficiently and safely.
• Partner with cloud, DevOps, and security leaders to balance performance, cost efficiency, scalability, and compliance.

AI/ML Deployment and Enablement Lead

• Own execution of the AI Review Framework
• Ensure governance processes are consistent, efficient, and audit-ready
• Execute AI governance activities through the AIS / Security Governance Team
• Perform AI risk classification (low, mid, high)
• Assess: Business impact, Customer and regulatory risk, Data sensitivity
• Prepare governance recommendations for CIO, CLO, and AIS Committee
• Own vendor AI governance execution, including: AI usage detection across vendor portfolio, FactSheet collection and review
• Ensure vendor AI is properly routed through the AIS Program review process
• Partner with Vendor Management on third-party risk alignment
• Coordinate twice-annual AI system validations
• Track: Model updates, Control gaps and remediation, Ensure FactSheet updates trigger re-reviews
• Work with member carriers to guide teams through AI governance requirements
• Ensure consistent application of policies and standards
• Support adoption of shared AI governance practices
• Maintain AI inventory, Governance documentation and decision logs
• Prepare materials for AIS Committee, Leadership reporting
• Partner with AI / Data Science teams, Legal and Compliance, Vendor Management, and coordinate with Cyber / Privacy teams as needed

Senior Manager, AI Risk & Governance

• Own delivery, performance, and risk outcomes for a large, complex Claims Applied AI portfolio spanning multiple teams, domains, and value streams
• Drive measurable business value across the end-to-end claims journey by developing, testing, deploying, and scaling Predictive, Generative, and Agentic AI solutions
• Be the senior business partner for Claims leaders
• Build deep partnerships within portfolio and value stream frameworks
• Lead and develop senior leaders and teams
• Provide portfolio-level technical direction and oversight
• Ensure disciplined architecture and delivery trade-offs across quality, grounding, latency, cost, scalability, and regulatory risk
• Establish and enforce evaluation, monitoring, and production readiness across solution types
• Set governance expectations for unstructured data and retrieval across Claims
• Accountable for AI governance and compliance-by-design across the Claims portfolio
• Influence technology integration and platform strategy
• Champion reuse and scalability by partnering with AI platform owners and peers
• Provide thought leadership and change leadership
• Oversee portfolio planning, dependencies, resourcing, and financial stewardship

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