AI Enablement Lead

AI Enablement Lead

$144,000 - $180,000 + bonus. This role also requires a minimum of 2 days a week onsite in the IANS Boston office. 

As IANS accelerates its investment in AI, we are focused on moving beyond answers toward outcomes, using AI and agentic workflows to deliver faster, more scalable, and more impactful outputs for both clients and internal teams.

IANS is seeking a highly collaborative and execution-oriented AI Enablement Lead, to drive enterprise-wide AI adoption as part of the AI Enablement function. This role is responsible for translating proven AI workflows and agentic outputs into repeatable, scalable practices across IANS—ensuring that the best ideas developed in the Agentic Capabilities & Output Studio reach the teams and clients who need them.

This is an execution and change management role that also requires direct, hands-on technical implementation — building working tools, not only coordinating others' work. The AI Enablement Lead operates with significant autonomy inside a centrally governed function, working directly with functional leaders across Research, Sales, Client Services, and Product to prioritize, build, 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?

• Partner with the Chief Product Officer and functional leaders to maintain a ranked pipeline of AI use cases tied to client and business impact.
• Apply clear criteria to identify which initiatives to accelerate, pause, or stop—actively preventing duplication and low-value experimentation.
• Serve as the primary coordination point between the Agentic Capabilities & Output Studio and the broader organization.

• Build, deploy, and own AI workflows, dashboards, and tools across functions, from first working prototype through enterprise-wide rollout.
• Develop adoption playbooks, training materials, and onboarding resources that make new workflows accessible and repeatable for non-technical users.
• Design and build the measurement layer itself — usage instrumentation, health-scoring models, live dashboards, and scheduled reporting — and use it to track adoption, surface at-risk accounts, and continuously improve deployed workflows.
• Continuously observe how deployed systems, pipelines, and agents behave in production, and inject enhancements and automation wherever they add leverage — treating live systems as something to watch, tune, and improve, not just to launch.

• Lead change management efforts to build AI fluency and confidence across the organization, moving teams beyond experimentation toward consistent, productive use.
• Serve as a trusted internal resource on what AI tools can and cannot do in practice—grounded in evidence, not hype.
• Champion a culture of urgency, focus, and accountability around AI adoption without sacrificing quality or governance.

• Work closely with the Agentic Lead, Product, Technology, and Research teams to ensure workflows handed off for scaling are well-documented, tested, and fit for purpose.
• Collaborate with functional leaders to embed AI-enabled outputs into recurring client workflows, increasing the depth and stickiness of IANS's value proposition.
• Contribute observations and adoption learnings that inform broader AI strategy, governance, and roadmap decisions.

• Demonstrated experience driving adoption of new tools, workflows, or practices across a cross-functional organization.
• Strong project management and prioritization skills, with the ability to manage multiple workstreams and stakeholders simultaneously.
• Excellent communication and facilitation skills, with the ability to translate technical capabilities into practical, accessible guidance for non-technical audiences.
• Comfortably operating in fast-moving, ambiguous environments with a bias toward action and measurable outcomes.
• Strong judgment around what is scalable and impactful versus merely interesting—and the confidence to advocate for focus.

• Experience working in or alongside product, research, or content-driven organizations.
• Hands-on fluency with AI tools and LLMs, plus enough technical range to build working solutions independently — writing SQL against a data warehouse, assembling dashboards, authoring agent and automation skills, and reasoning about connectors, identity, and data pipelines.
• Background in learning & development, internal consulting, or operational transformation roles.
• Exposure to change management frameworks or adoption measurement approaches.