Senior UX Researcher - Enterprise AI Adoption & Workforce Transformation

Infinitive Inc•Mclean, VA

2d•$124,000 - $169,000

About The Position

Infinitive is seeking an experienced Senior UX Researcher – Enterprise AI Adoption & Workforce Transformation to support a large U.S. financial institution in accelerating the adoption and effective use of artificial intelligence across its workforce. This role is focused on understanding how associates actually work, where they encounter friction, and how AI tools can help them become more effective, productive, and confident in their day-to-day responsibilities. The ideal candidate is a highly curious, hands-on researcher who enjoys observing people in their working environments, asking thoughtful questions, facilitating discussions, and uncovering opportunities that may not be immediately obvious to users or leadership. Working across multiple Lines of Business (LOBs), functions, and associate skill levels, you will conduct observational studies, interviews, focus groups, and usability assessments to understand how associates interact with existing processes, enterprise applications, and emerging AI capabilities. You will go beyond collecting feedback. You will translate research findings into clear, prioritized, actionable recommendations that help technology, product, training, and change management teams improve the associate experience and accelerate the adoption of AI. This is an opportunity to help shape how AI becomes a practical, trusted, and valuable part of everyday work at one of the country's largest financial institutions.

Requirements

6+ years of experience in UX research, user research, human factors, design research, organizational research, or a closely related discipline.
Demonstrated experience planning and conducting observational research, contextual inquiries, workflow studies, interviews, and usability assessments in enterprise environments.
Strong experience designing and facilitating focus groups, workshops, and qualitative user studies involving diverse participant groups.
Proven ability to uncover underlying problems, distinguish symptoms from root causes, and translate findings into actionable recommendations.
Experience studying how employees interact with enterprise applications, technology-enabled workflows, or digital workplace tools.
Strong qualitative research skills, complemented by practical experience with surveys, basic quantitative analysis, and behavioral data.
Ability to work across multiple business functions, stakeholder groups, and organizational levels.
Excellent facilitation, active listening, critical thinking, synthesis, and executive communication skills.
Experience developing clear research deliverables such as journey maps, workflow assessments, usability findings, opportunity maps, and prioritized recommendations.
Ability to operate independently, manage multiple research initiatives, and adapt to changing program priorities.
Understanding of research ethics, confidentiality, informed consent, and responsible handling of employee information.
Bachelor's degree in Human-Computer Interaction, Psychology, Anthropology, Sociology, Design, or a related discipline, or equivalent practical experience.

Nice To Haves

Experience researching enterprise AI adoption, generative AI assistants, copilots, or AI-enabled productivity tools.
Experience supporting technology adoption or workforce transformation initiatives within a large financial institution or other highly regulated enterprise.
Familiarity with how generative AI can support knowledge work, document analysis, information retrieval, writing, summarization, research, and process improvement.
Understanding of AI-related user behaviors, including trust, confidence, prompt effectiveness, output validation, and appropriate human oversight.
Experience with organizational change management, associate enablement, learning programs, or digital transformation.
Experience conducting research across multiple Lines of Business or large, geographically distributed employee populations.
Familiarity with research and collaboration platforms such as Dovetail, UserTesting, Qualtrics, Microsoft Forms, Miro, Figma, or similar tools.
Ability to analyze available enterprise AI usage telemetry and combine behavioral data with direct research observations.
Consulting experience, particularly in client-facing roles requiring strong stakeholder engagement and executive communication.

Responsibilities

Conduct hands-on observational research, including contextual inquiries, workflow shadowing, and task-based studies, to understand how associates perform their daily responsibilities.
Identify repetitive activities, manual processes, information discovery challenges, knowledge gaps, and workflow inefficiencies that may present opportunities for AI-enabled improvements.
Evaluate how associates currently use AI assistants, copilots, enterprise search, knowledge tools, and other available technologies.
Understand variations in workflows, technology proficiency, and AI adoption across different LOBs, teams, and job functions.
Distinguish between challenges that can be addressed through improved training, better tool capabilities, workflow redesign, or broader organizational changes.
Design, organize, and facilitate focus groups, user interviews, workshops, and structured user studies across diverse associate populations.
Engage associates ranging from AI beginners to experienced users to understand differences in needs, confidence, behaviors, and expectations.
Create a comfortable, productive research environment that encourages candid feedback and surfaces unmet needs, frustrations, and practical opportunities.
Conduct usability testing and task-based evaluations of AI tools, prototypes, and existing enterprise workflows.
Develop surveys and qualitative research instruments to measure adoption barriers, associate sentiment, usability, confidence, and perceived value.
Partner with LOB leaders and program stakeholders to identify research participants and ensure appropriate representation across teams and roles.
Analyze associate workflows to identify where AI can help reduce effort, improve quality, accelerate information discovery, and support better decision-making.
Identify obstacles to adoption, including limited AI knowledge, unclear use cases, lack of trust, ineffective prompting practices, difficult user experiences, or workflow integration challenges.
Observe how associates formulate questions and prompts, evaluate AI-generated responses, and incorporate AI outputs into their work.
Identify opportunities to improve AI proficiency through targeted coaching, practical examples, reusable prompts, and role-specific training.
Recommend ways to make AI capabilities more accessible, intuitive, relevant, and useful within existing business processes.
Recognize where human judgment, oversight, data protection, and established banking controls remain essential.
Synthesize observational findings, interviews, focus groups, surveys, and available usage analytics into clear, evidence-based insights.
Translate research into practical recommendations for product enhancements, AI use cases, associate enablement, workflow improvements, and adoption strategies.
Prioritize findings based on associate impact, business value, adoption potential, feasibility, and implementation effort.
Create workflow maps, user journeys, associate personas, opportunity assessments, and concise research summaries.
Identify quick wins that can improve the associate experience immediately, alongside longer-term opportunities for AI-enabled transformation.
Partner with technology, product, training, and change management teams to turn recommendations into measurable actions.
Follow up on implemented improvements to assess whether they address the original associate challenges.
Establish research approaches and baseline measures to understand current workflows, AI proficiency, and adoption maturity.
Combine qualitative findings with available quantitative measures, including tool usage, task completion time, user satisfaction, friction points, and self-reported productivity.
Identify opportunities to evaluate time savings, reduced manual effort, improved task quality, and increased associate confidence.
Conduct follow-up studies to evaluate changes in behavior and effectiveness following training, product improvements, or process changes.
Help identify differences between tool availability, actual adoption, effective usage, and measurable business value.
Develop practical insights that enable program leadership to understand where AI adoption is working, where additional support is needed, and what to prioritize next.
Work closely with AI adoption leaders, LOB stakeholders, product managers, UX designers, technology teams, learning and development, and change management partners.
Present research findings and recommendations to business sponsors and senior leaders in concise, actionable formats.
Facilitate working sessions to align stakeholders around research findings, priorities, and next steps.
Establish repeatable methods for gathering feedback and identifying emerging associate needs as AI capabilities evolve.
Maintain appropriate research documentation, participant consent, confidentiality, and compliance with enterprise policies for employee information and sensitive banking data.