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In any large organization, progress often stalls where siloed teams meet. Complex processes like analyst Requests for Information (RFIs), legal reviews, or sales proposals become black holes for productivity, requiring a manual, soul-crushing grind to hunt down information from dozens of experts in different departments. This cross-functional friction is a universal tax on innovation. But a new model is emerging, where purpose-built AI acts not just as an assistant, but as a central orchestrator, transforming these bottlenecks into accelerators.
As Databricks’ Head of Global Analyst Relations, Sonya Vargas spearheaded a powerful case study in unlocking RFI productivity with its Analyst Relations Intelligent Assistant AI. ARIA is an internal tool that accelerated Vargas' team’s workflow and attracted attention throughout the company, becoming a blueprint for what successful agentic AI workflows can do.
The initial temptation with AI is often to create a simple Q&A bot that sits on a subset of the organization's knowledge base, Vargas noted. But the Databricks team learned that true value isn't unlocked until the AI evolves into a reliable knowledge engine. The tool's leap in capability wasn’t magic; it was the result of connecting it to high-quality internal analyst data sources that represent the company's ground truth.
Orchestrating expertise: "As a lone AR professional for four years, the number of reports we could participate in was limited. I often had to decline participation because if we couldn't do it well, it was not worth taking on the task," said Vargas. "My focus was exclusively on opportunities that offered the highest ROI, where we could fully dedicate resources, people, and time. Consequently, the number of reports we could engage with was very restricted. There's a long tail of reports I've had to defer year after year, always due to bandwidth limitations. Now, thanks to ARIA, the number of new reports we can participate in has grown exponentially."
With this knowledge engine in place, the impact on cross-functional work was immediate and profound. An RFI with a one-month deadline, which would have previously required weeks of "chasing people just to get a first draft," was completed in minutes. Through programs like these and a culture of deep innovation throughout the company, Databricks earned a spot in the 2025 Gartner Magic Quadrant, recognized as a "leader of leaders" for Data Science and Machine Learning Platforms.
How it works: The AI acts as the primary orchestrator. Instead of a human project manager manually pulling information from product, engineering, and marketing, the AI instantly synthesizes the required knowledge from its connected sources. The process is turned from a blocker into an afterthought. As Vargas noted, "It still blows my mind every time, because it replaces weeks of chasing people."
From weeks to minutes: "The bot's first task is reformatting: we upload the questionnaire, and it transforms all sub-questions into actual questions and answers, making it easier for the bot to reason and generate responses. While this was a significant advancement, the biggest game-changer for me was the ability to upload an entire RFI—what's called 'batch inference' from a product perspective—and receive all responses in minutes," said Vargas. "It used to take weeks just to get a solid first draft of responses before subject matter experts could even begin providing feedback. Now, it happens in minutes. It's truly revolutionary for the role of analyst relations."
Human curated, deeply integrated: "The decisive step was creating keyword mappings, teaching ARIA that a question about governance must discuss Databricks' Unity Catalog," said Vargas. This layer of human curation teaches the AI to think like the organization, connecting concepts and retrieving information with the nuanced understanding of an internal expert. It transforms the AI from a simple tool into a reliable extension of the collective team brain.
While ARIA was built for analyst relations, its success quickly attracted attention from other departments. Teams in Legal, Sales, and other functions saw its potential to solve their own document-heavy, cross-functional problems.
The platform effect: "This is obviously purpose-built for analyst relations, but ARIA has so many other applications. I wasn't surprised when I posted about it on LinkedIn that the analyst relations community would be thrilled by the prospect of using it themselves, recognizing how it could change their work and significantly scale their programs. People from Legal, Contract Review, teams in different regions started reaching out to me about using ARIA. There are so many different individuals and teams grappling with similar challenges and trying to figure out how to do almost the same type of work. While the audience and questions might differ slightly, the core need is much the same."
Beyond point solutions: This "use case explosion" proves the broader principle: a well-designed AI knowledge engine is not a point solution but a platform. It provides a scalable way to solve any problem rooted in fragmented institutional knowledge. Recognizing this, Databricks is now working to package the blueprint as a "Solution Accelerator" for its customers. "I want to change the game for all the AR people," Vargas said, but the implications are clearly bigger.
A tool this powerful inevitably brings up fears of job replacement. However, this model suggests a redefinition of roles, not an elimination of them. The AI handles the laborious 90% of the work: the hunting, gathering, and initial assembly of information. This frees up human experts to focus on the critical last 10%: validation, strategic nuance, and adding insights that "only exist in people's heads."
Redefining the Human Role: "We're not taking the human out of the loop. We will always need human-led evaluation and validation," Vargas insisted. The mandate for the modern professional becomes sharper: contribute value that the machine cannot. "It's not about AI replacing humans, but it could very well replace those who resist AI transformation. There's so much goodness in what AI can do for our jobs, enabling us to focus on strategy: how we evolve, enter new markets, extract better insights from analysts. It allows us to spend more time with them in actual conversation, thinking about the strategic vision of the company and product direction. In so many areas where our time and resources are frankly limited, if ARIA can satisfy things like RFIs, there's immense opportunity for us to engage in truly quality time."
The ultimate lesson from ARIA is that the next frontier for AI in the enterprise lies in its ability to orchestrate complex processes that span an entire organization. By building curated knowledge engines, companies can finally solve the chronic problem of cross-team friction, unlocking new levels of velocity and allowing their experts to focus on the high-value work that truly drives the business forward.
