AI Enablement Lead, IANS Research

• The AI Enablement Lead is a technically fluent operator who can translate between the worlds of engineering and the business—someone who understands how AI systems work, has a demonstrated track record of driving measurable value through tools and workflow transformation, and knows how to bring people along through change
• This is not a purely technical role, but it requires enough depth to be credible with engineering teams and enough organizational instinct to move an entire company
• The ability to build trust across technical and non-technical stakeholders—and to drive urgency without creating resistance—is as important as any specific technical credential
• Technical fluency with AI and LLM-based tools—enough to configure, evaluate, and troubleshoot AI-enabled workflows and to engage credibly with engineering teams on integration and tooling decisions
• Demonstrated track record of driving measurable adoption of new tools or workflows at organizational scale—from identifying opportunities through rollout, training, and sustained change
• Experience working cross-functionally with both technical and non-technical stakeholders, with the ability to translate between the two
• Strong change management skills: able to build urgency, navigate resistance, and sustain momentum across functions with different priorities and appetites for change
• Strong communication skills—able to make complex AI concepts accessible to non-technical audiences and credible to technical ones
• Comfort operating with significant autonomy in a fast-moving, ambiguous environment

Desirable

• The AI Enablement Lead operates with significant autonomy inside a centrally governed function, working directly with engineering, product, research, sales, and client services to identify, deploy, and embed AI-enabled workflows into day-to-day operations
• The AI Enablement function exists to answer a single question: How do we scale what works—quickly, responsibly, and in ways that deliver measurable value to clients and the organization?
• You'll partner with engineering and IT leadership to accelerate AI tool adoption across the organization—bringing enough technical fluency to engage substantively on tooling decisions, integration approaches, and workflow design
• Serve as a credible interlocutor with engineering teams—able to ask the right questions, push back on timelines, and identify where AI tooling is being underutilized, without needing to be the one writing the code
• Bridge the Agentic Studio’s experimental outputs and engineering’s production systems—translating prototypes and validated workflows into clear requirements for scaling
• Stay current on AI tooling, LLM capabilities, and workflow automation trends well enough to inform prioritization and challenge the status quo
• Identify the highest-impact AI workflow opportunities across the organization in partnership with functional leaders
• Own end-to-end rollout of approved workflows—from scoping and tool configuration through change management, training, and adoption measurement
• Build and maintain a library of repeatable workflow templates, prompt libraries, and deployment playbooks grounded in IANS data and IP
• Serve as the primary interface between the Agentic Studio’s experimentation and the broader organization’s readiness to adopt
• Work directly with leaders in research, sales, client services, and marketing to understand their workflows, surface AI opportunities, and prioritize initiatives based on client value and operational ROI
• Lead training programs and hands-on enablement sessions tailored to different functions and technical levels
• Eliminate redundant or uncoordinated AI efforts across the organization by serving as the central governing function for AI workflow adoption
• Establish lightweight governance mechanisms for AI tool procurement, usage standards, and output quality review
• Define and track KPIs for AI adoption, workflow impact, and time/cost savings across each function
• Report progress to the CPO and executive team with a bias toward concrete outcomes—not activity metrics
• Build the internal case for continued AI investment by documenting and communicating ROI at the initiative level