AI Business Partner Boston - Massachusetts; Carmel - Indiana; Chicago - Illinois; Lambertvi

AI Business Partner Role Overview

The AI Business Partner translates business priorities into practical AI solutions that improve how work gets done. Sitting at the intersection of business strategy, domain expertise, and applied AI, this role identifies high‑value opportunities, builds working prototypes and pilots, and partners cross‑functionally to scale successful solutions. It is ideal for someone who can move fluidly from ambiguity to action—working with stakeholders to define problems, rapidly testing ideas using modern generative AI tools, and helping the organization adopt AI in ways that are useful, responsible, and aligned with business needs.

This is a hybrid role based in any of our U.S. offices—including New York City, Boston, Chicago, Carmel, or San Francisco—or remotely within the U.S., depending on team and business needs.

AI Opportunity Identification & Prioritization

• Identify high‑value opportunities to apply generative AI across workflows, deliverables, and business processes.
• Translate strategic priorities and business challenges into clearly scoped AI use cases, pilots, and solution concepts.
• Assess opportunities based on business value, feasibility, user needs, adoption potential, and risk.
• Partner with leaders and subject matter experts to prioritize initiatives that can drive meaningful efficiency, quality, or innovation gains.

Prototyping & Solution Development

• Build and iterate on working prototypes, pilots, and lightweight AI‑enabled tools using modern development environments, LLM APIs, agentic workflows, and related technologies.
• Design prompts, workflows, and tool interactions that guide AI behavior toward accurate, useful, and contextually relevant outcomes.
• Test solutions quickly, incorporate user feedback, and refine prototypes based on real‑world usage and evolving requirements.
• Move ideas from concept to demonstrable value, balancing speed, practicality, and quality.

Cross‑Functional Delivery & Scaling

• Partner closely with AI technical teams, engineering, product, and business stakeholders to transition validated prototypes into scalable, production‑ready solutions where appropriate.
• Help define requirements, document learnings, and support handoffs that enable successful implementation and long‑term sustainability.
• Work across technical and non‑technical teams to align on priorities, solution design, success metrics, and rollout approach.
• Use strong judgment to determine when to continue iterating independently and when to engage specialized technical partners.

Adoption, Enablement & Change Management

• Support successful adoption of AI solutions by creating practical guidance, training, onboarding materials, and usage playbooks.
• Communicate AI concepts, capabilities, and limitations in clear, accessible language for non‑technical audiences.
• Help build AI fluency across the organization by enabling teams to use tools responsibly and effectively in their day‑to‑day work.
• Foster a culture of experimentation, continuous learning, and practical innovation.

Governance, Quality & Responsible AI

• Help ensure AI solutions are designed and applied in ways that are responsible, compliant, and appropriate for a regulated healthcare communications environment.
• Incorporate considerations related to privacy, safety, quality, transparency, and human oversight into solution design and rollout.
• Partner with relevant stakeholders to align AI use with organizational standards, governance expectations, and evolving best practices.
• Promote thoughtful experimentation while maintaining appropriate safeguards and quality controls.

Domain Expertise Application

• Apply strong understanding of healthcare communications, including advertising, activation, communications, and medical domains, to shape relevant and impactful AI solutions.
• Ensure solutions reflect real‑world workflows, stakeholder needs, and the nuances of regulated client environments.
• Translate domain knowledge into practical requirements that improve usability, relevance, and business fit.

• Bachelor’s degree in Computer Science, Data Science, engineering, or equivalent practical experience.
• Experience in AI…