AI in Operations: Automating the Hidden Work Behind User Research

In every research team, there’s a layer of operational work that doesn’t make it into presentations—the hidden logistics that keep projects moving: screening participants, scheduling sessions, sending confirmations, logging data. It’s essential but often invisible, consuming valuable hours that could be spent uncovering insights. This is where AI in operations is quietly transforming how research gets done.

At Chai, we’ve built the Research Ops AI Worker, an Agentic AI designed to handle the repetitive side of research operations like automating calls, verifying participant data, scheduling sessions, sending confirmations, and even generating structured summaries. In short, it takes care of the process so researchers can focus on the purpose.

The Invisible Weight of Research Operations

Ask any researcher, and they’ll tell you: recruiting, screening, and scheduling participants can take up an enormous share of their workload. According to the 2024 User Interviews “State of User Research” report, researchers spend nearly 35–40% of their total time managing these logistics instead of analyzing findings or uncovering insights. The more studies an organization runs, the more this operational overhead compounds, leading to delayed timelines and researcher burnout.

The irony is that while companies are investing heavily in AI for analytics, design, or decision-making, the operational layer often remains manual. According to one recent analysis by Mckinsey, companies that fully embed generative AI into operational workflows can add substantial value — including an estimated US $4.4 trillion in productivity growth potential over time.

In research teams, that means turning hours of administrative work into minutes of orchestration. Automating this hidden layer doesn’t replace the researcher—it liberates them.

Why AI in Operations Matters Now

AI adoption has moved from the frontlines—like chatbots, copilots, and analytics—into the operational core. According to a report by the Boston Consulting Group, more than half of all measurable business value from AI now comes from core operational functions such as logistics, research, and supply chain management 

This shift signals a new era where automation is no longer an isolated tool—it’s becoming a structural capability embedded into how organizations operate day to day.

For research teams, the timing couldn’t be better. Shorter product cycles, hybrid teams, and globally distributed participants mean researchers have to manage more studies with fewer resources. AI in operations enables that scalability—taking on the repetitive work of screening, scheduling, and follow-ups so teams can focus on analysis and synthesis instead.

By embedding AI directly into research workflows—participant screening, scheduling, and data management—companies can scale insights faster and more consistently. As Chai’s Chief AI Officer Roger Human explains in the demo, “We’ve seen firsthand how much time goes into finding and screening participants for user research. Our goal was to give that time back.”

Inside the Research Ops AI Worker

Watch the demo below to see how the Research Ops AI Worker handles a full screening call—from adaptive conversation to automatic scheduling and reporting.

The demo shows a live, human-like call between the AI Worker and a potential research participant. The AI opens the conversation naturally, confirms basic details like name and age, and adapts instantly when the participant changes their incentive preference from Amazon to Apple. It checks for availability, schedules the session, and automatically sends a confirmation email.

But what’s happening behind the scenes is even more transformative. While the call takes place, the AI Worker logs all data into a spreadsheet in real time—capturing participant info, responses, and scheduling details. Immediately after, it populates a slide deck summarizing the screening data.

This is what AI in operations looks like in practice: not an assistant waiting for commands, but an intelligent teammate executing end-to-end tasks across multiple systems.

More importantly, it’s not limited to research. The same architecture could be applied to HR onboarding, procurement, or customer feedback operations—any process where structured conversations generate data that needs to flow into tools like Sheets, Notion, Airtable, CRM systems or any system it needs to be plugged into.

From Task Automation to Agentic Collaboration

Traditional automation follows a script. It’s efficient at handling repetitive tasks but rigid when the unexpected happens. Agentic AI changes that dynamic, since it observes, reasons, and acts, adapting in real time just like a human teammate.

The Research Ops AI Worker goes beyond simple form-filling or preprogrammed workflows. It listens, interprets context, and adjusts mid-conversation—whether a participant changes their incentive preference or reschedules a session on the spot.

This evolution reflects a broader movement in enterprise technology: the rise of intelligent systems that collaborate instead of just execute. As Bain & Company notes, “Agentic AI is a structural shift in enterprise tech, reshaping companies with agents that can reason, coordinate, and execute complex workflows.”

That’s precisely what Chai’s AI Workers are designed to achieve. They function as workflow partners (not passive tools) learning, acting, and communicating across systems to simplify operations. The Research Ops AI Worker bridges the gap between data and decision-making, orchestrating what used to take dozens of manual steps into one continuous flow.

It's designed to amplify researchers and help them add more value ,more easy and faster. As Roger demonstrates in the demo, the agent’s ability to reason through changing inputs and autonomously complete multi-step tasks illustrates what Agentic AI in operations truly means: adaptive, reliable, and human-centered collaboration.

What This Means for Businesses

While the demo focuses on UX research, the implications extend far beyond it. Every enterprise function has a “hidden operational layer” of repetitive work—screening candidates, following up with customers, scheduling deliveries, logging data. AI in operations unlocks massive value by automating these layers.

In a recent survey by Thomson Reuters, organizations that report successful AI implementations cite improved efficiency (78 %), faster response times (56 %), and enhanced decision-making (55 %) as the top benefits.

For Chai’s clients, that’s the promise of Agentic AI: systems designed to act as digital teammates, not just tools. From research to logistics, AI in operations represents the quiet revolution transforming how work gets done across industries.

The future is about workflows becoming intelligent enough to think, adapt, and collaborate. That’s how organizations turn operational efficiency into innovation.

Building the Human-Centered Future of Operations

The Research Ops AI Worker is a glimpse of what’s possible when AI becomes part of the operational fabric. It shows that automation doesn’t need to feel mechanical, it can be intelligent, conversational, and trustworthy.

By embedding Agentic AI into the systems teams already use, companies can amplify human potential while eliminating the administrative drag that slows progress.

AI in operations is happening right now, in live calls, in spreadsheets, in workflows—quietly redefining what it means to work smarter.

To see more agentic AI use cases, or know more about what we do, you can visit us at https://heychai.ai.