The Unparalleled Success of Intuit in Deploying Generative AI at Scale

In the realm of generative AI, one company has firmly established itself as a frontrunner in terms of speed and efficiency in deploying this advanced technology at scale: Intuit. While numerous enterprise companies have ventured into the field, Intuit has managed to stand out by introducing an LLM-drive assistant, Intuit Assist, across all its products. This assistant, which includes TurboTax, QuickBooks, Credit Karma, and MailChimp, has revolutionized the way users interact with these platforms.

Intuit’s initiative in this domain extends beyond the development of Intuit Assist. In June, the company unveiled its very own Gen AI operating system, which serves as the orchestrator for the large language model (LLM) activity throughout the entire organization. This comprehensive vision, I must emphasize, was conceived well before any other major company had even begun to explore the potential of generative AI.

A recent interview with Alon Amit, Intuit’s VP of Product Management, shed light on the pivotal aspect of any company’s journey towards achieving Gen AI success: the establishment of a best-practice data management layer. According to Amit, Intuit dedicated several years to refining this data layer, ensuring the seamless integration, accuracy, governance, and non-replication of data. Only after this meticulous process did the LLMs gain the ability to access the data, enabling personalized interactions with Intuit’s vast customer base of 100 million small business owners and consumers.

During the interview, Amit showcased a single slide portraying Intuit’s data layer, bestowing upon us a glimpse into how a well-constructed data layer should appear, at least according to the company’s perspective. I highly encourage enterprise data leaders to watch the video linked above, as Amit expertly walks us through the essential areas Intuit is diligently working on, including those that require further refinement and optimization in the coming years. (The interview was conducted as part of our AI Unleashed event, the full video of which is provided above)

The Key Elements of the Data Management Layer

Allow me to outline the significant highlights that stood out to me during the interview, as they underscore Intuit’s data management success and its commitment to achieving excellence in the realm of generative AI:

1. The Data Map Registry

Intuit developed a meticulous universal repository, known as the Data Map Registry, which encompasses every single real-time and batch data asset generated within the company. This repository ensures the comprehensive governance of assets, including the clear identification of asset owners and their intended purposes. Admittedly, this process is an ongoing endeavor, but Intuit aims to achieve near-perfection, targeting a success rate of “very close to a hundred percent” by the end of the upcoming year.

2. Culture of Treating Data as a Product

Capitalizing on the foundation established by the Data Map Registry, Intuit cultivated a culture among its product managers, developers, engineers, and other stakeholders that goes beyond perceiving data solely within shipped products as a product itself. Rather, any generated data, irrespective of its end-use, is considered a product in its own right. This shift in mindset signifies Intuit’s unwavering commitment to leveraging data as a valuable asset throughout the organization.

3. Uniform Governance of Data Schema Changes

Any modifications to data schemas, whether they pertain to click-stream data or third-party data integrated into Intuit’s data ecosystem, undergo a uniform governance process. This approach ensures that the modifications do not disrupt downstream data systems critical for supporting generative AI capabilities. The left-side of the chart visualizes the inflow of data, including Intuit’s own “domain events” such as the real-time flow of data generated by the developers via an application. This inflow is automatically consolidated within Intuit’s data lake, furnishing a harmonious and efficient data ecosystem.

4. Governed Data Derivation

The concept of derivation encapsulates any transformations applied to data beyond its original source. This includes computations for analytics, extraction of features for AI models, and attributes for marketing campaigns, to name a few. By maintaining a robust data registry, developers receive prompt notifications whenever they attempt to derive a feature that already exists within the system, thus mitigating the duplication of efforts and data redundancy.

5. The Future of Real-Time Data Derivation

While Intuit’s achievements in data management and generative AI are certainly impressive, the company acknowledges that its quest for perfection remains ongoing. Anticipating the year 2024, Intuit has set its sights on building real-time paved paths for data derivation. This would enable developers to seamlessly observe and analyze the actions taken by users in near real-time, enhancing the overall user experience and further refining Intuit’s generative AI capabilities.

Intuit’s groundbreaking approach to deploying generative AI at scale is truly remarkable. Through its meticulous data management techniques and best-practice data layer, Intuit has revolutionized the way businesses and consumers interact with its products. By treating data as a valuable product, Intuit has fostered a culture of data-centered innovation, propelling the company to the forefront of generative AI adoption. Furthermore, the company’s commitment to continuous improvement is evident in its pursuit of real-time data derivation, an initiative set to enhance the precision and responsiveness of its generative AI systems. As the field of generative AI continues to evolve, Intuit serves as an exemplary model for other companies seeking to harness the true potential of this transformative technology.


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