Semantic understanding, not just vectors: How Intuit’s data architecture powers agentic AI with measurable ROI

Intuit — the financial software giant behind products like TurboTax and QuickBooks — is making significant strides using generative AI to enhance its offerings for small business customers.

In a tech landscape flooded with AI promises, Intuit has built an agent-based AI architecture that’s delivering tangible business outcomes for small businesses. The company has deployed what it calls “done for you” experiences that autonomously handle entire workflows and deliver quantifiable business impact.

Intuit has been building out its own AI layer, which it calls a generative AI operating system (GenOS). The company detailed some of the ways it is using gen AI to improve personalization at VB Transform 2024. In Sept. 2024, Intuit added agentic AI workflows, an effort that has improved operations for both the company and its users.

According to new Intuit data, QuickBooks Online customers are getting paid an average of five days faster, with overdue invoices 10% more likely to be paid in full. For small businesses where cash flow is king, these aren’t just incremental improvements — they’re potentially business-saving innovations.

The technical trinity: How Intuit’s data architecture enables true agentic AI

What separates Intuit’s approach from competitors is its sophisticated data architecture designed specifically to enable agent-based AI experiences. 

The company has built what CDO Ashok Srivastava calls “a trinity” of data systems:

1. Data lake: The foundational repository for all data.
2. Customer data cloud (CDC): A specialized serving layer for AI experiences.
3. “Event bus“: A streaming data system enabling real-time operations.

“CDC provides a serving layer for AI experiences, then the data lake is kind of the repository for all such data,” Srivastava told VentureBeat. “The agent is going to be interacting with data, and it has a set of data that it could look at in order to pull information.”

Going beyond vector embeddings to power agentic AI

The Intuit architecture diverges from the typical vector database approach many enterprises are hastily implementing. While vector databases and embeddings are important for powering AI models, Intuit recognizes that true semantic understanding requires a more holistic approach. 

“Where the key issue continues to be is essentially in ensuring that we have a good, logical and semantic understanding of the data,” said Srivastava. 

To achieve this semantic understanding, Intuit is building out a semantic data layer on top of its core data infrastructure. The semantic data layer helps provide context and meaning around the data, beyond just the raw data itself or its vector representations. It allows Intuit’s AI agents to better comprehend the relationships and connections between different data sources and elements.

By building this semantic data layer, Intuit is able to augment the capabilities of its vector-based systems with a deeper, more contextual understanding of data. This allows AI agents to make more informed and meaningful decisions for customers.

Beyond basic automation: How agentic AI completes entire business processes autonomously

Unlike enterprises implementing AI for basic workflow automation or customer service chatbots, Intuit has focused on creating fully agentic “done for you” experiences. These are applications that handle complex, multi-step tasks while requiring only final human approval.

For QuickBooks users, the agentic system analyzes client payment history and invoice status to automatically draft personalized reminder messages, allowing business owners to simply review and approve before sending. The system’s ability to personalize based on relationship context and payment patterns has directly contributed to measurably faster payments.

Intuit is applying identical agentic principles internally, developing autonomous procurement systems and HR assistants.

 “We have the ability to have an internal agentic procurement process that employees can use to purchase supplies and book travel,” Srivastava explained, demonstrating how the company is eating its own AI dog food.

Designed for the reasoning model era

What potentially gives Intuit a competitive advantage over other enterprise AI implementations is how the system was designed with foresight about the emergence of advanced reasoning models like DeepSeek.

“We built gen runtime in anticipation of reasoning models coming up,” Ashok revealed. “We’re not behind the eight ball … we’re ahead of it. We built the capabilities assuming that reasoning would exist.”

This forward-thinking design means Intuit can rapidly incorporate new reasoning capabilities into their agentic experiences as they emerge, without requiring architectural overhauls. According to Srivastava, Intuit’s engineering teams are already using these capabilities to enable agents to reason across a large number of tools and data in ways that weren’t previously possible.

Shifting from AI hype to business impact

Perhaps most significantly, Intuit’s approach shows a clear focus on business outcomes rather than technological showmanship.

“There’s a lot of work and a lot of fanfare going on these days on AI itself, that it’s going to revolutionize the world, and all of that, which I think is good,” said Srivastava. “But I think what’s a lot better is to show that it’s actually helping real people do better.”

The company believes deeper reasoning capabilities will enable even more comprehensive “done for you” experiences that cover more customer needs with greater depth. Each experience combines multiple atomic experiences or discrete operations that together create a complete workflow solution.

What this means for enterprises adopting AI

For enterprises looking to implement AI effectively, Intuit’s approach offers several valuable lessons for enterprises:

• Focus on outcomes over technology: Rather than showcasing AI for its own sake target specific business pain points with measurable improvement goals.
• Build with future models in mind: Design architecture that can incorporate emerging reasoning capabilities without requiring a complete rebuild.
• Address data challenges first: Before rushing to implement agents, ensure your data foundation can support semantic understanding and cross-system reasoning.
• Create complete experiences: Look beyond simple automation to create end-to-end “done for you” workflows that deliver complete solutions.

As agentic AI continues to mature, enterprises that follow Intuit’s example by focusing on complete solutions rather than isolated AI features may find themselves achieving similar concrete business results rather than simply generating tech buzz.