## Overview ADP, one of the world's largest Human Capital Management (HCM) companies, presented their generative AI strategy at a Databricks event. ADP provides HR, payroll, benefits, talent management, and time tracking solutions to over one million clients across 140 countries, with their systems processing payments for more than 41 million global workers. Notably, one in six US workers are employed by companies using ADP systems, giving the company access to an extraordinarily large and valuable dataset about the world of work. The presentation was delivered by Fernando Schwarz, VP of AI at ADP and head of the Core AI team, who discussed how the company is leveraging Databricks as a strategic partner in their generative AI initiatives. It's important to note that this presentation represents ADP's ongoing journey rather than a completed project with measured results—the company acknowledges they are "still sort of working on this" and are in the process of scaling their capabilities. ## The ADP Assist Initiative The flagship generative AI product being developed is called "ADP Assist," designed to make ADP's platforms more interactive and user-friendly. The company's guiding philosophy follows a pyramid framework with three tiers: Easy (baseline usability), Smart (intelligent features), and Human (natural, human-like interactions). The goal is to progress through these tiers, though Schwarz acknowledges they "haven't made it human yet but it's getting there." Key objectives for ADP Assist include: - Making payroll processes easier for clients - Providing smarter analytics capabilities - Delivering human-like insights to companies - Enabling intent-based search functionality - Maintaining security, ethics, and responsibility throughout ## Core Challenges in LLMOps Schwarz outlined four major challenges that ADP faces in deploying generative AI at enterprise scale, which represent common concerns in the LLMOps space: ### Quality Assurance Given that ADP's products deal with critical areas like payroll and tax advice, ensuring high-quality, accurate responses is paramount. Schwarz explicitly referenced recent media coverage of companies deploying ChatGPT-like tools that have made headlines "for the wrong reasons" due to hallucinations and inaccurate outputs. The company is deeply focused on implementing proper guardrails to prevent such issues. They are working with Databricks' Mosaic team specifically on quality measurement and achievement, though specific metrics or methodologies were not detailed in the presentation. ### Intellectual Property Protection With a million clients trusting ADP with their data, IP protection is a critical concern. The company must navigate the complex challenge of exposing data to AI systems while ensuring there are no IP issues. This is particularly challenging in generative AI contexts where training data and retrieved content could potentially leak or be misattributed. ### Data Access and Structure ADP is building RAG (Retrieval Augmented Generation) systems that require accessing and structuring unstructured content. The challenge involves formatting data appropriately, creating the right datasets, and structuring information in ways that LLMs can effectively utilize to produce quality results. This represents a significant data engineering challenge at ADP's scale. ### Cost Control and ROI Cost management has become increasingly important as the company moves from proof-of-concept to production. Schwarz candidly noted that about a year prior to the presentation, executives were more focused on proving capabilities worked, with cost being a secondary concern. Now that generative AI has proven viable, the organization is taking "deep breaths" when seeing bills and focusing on cost containment. The natural progression has been from enthusiasm and POC development to making implementations financially viable. ## Technical Architecture and Platform Strategy ADP's AI strategy centers on building two foundational platforms: ### One Data Platform This central data platform leverages Databricks capabilities extensively: - **Delta Lake**: Broadly used within the One Data platform for data storage and management - **Unity Catalog**: Provides governance, observability, and catalog capabilities - The platform serves as the foundation upon which the AI platform is built ### One AI Platform The central AI platform includes multiple components, many tied to Databricks capabilities: - **Model Serving**: For deploying and serving AI models at scale - **MLOps (LLMOps)**: For managing the lifecycle of machine learning and LLM models - **Vector Search**: For enabling semantic search capabilities essential for RAG implementations - **RAG Studio**: ADP is working with Databricks' POCs in RAG Studio to enable scalable retrieval-augmented generation One notable advantage Schwarz highlighted is that enterprise platforms like Databricks help solve the painful permissioning challenges that plague enterprise AI deployments. Having a unified platform that handles permissions makes scaling significantly easier. ## Governance and Control Governance is described as "top of mind" for ADP's clients and leadership. A key concern is preventing "rogue teams building their own thing" that becomes difficult to manage. Unity Catalog is specifically mentioned as providing a solution for governing vector indexes and ensuring consistent oversight across the organization. The company is also leveraging the Mosaic team at Databricks for fine-tuning capabilities. This includes the potential to fine-tune open-source models in-house, though Schwarz noted they are "just getting started" with this approach. ## Cost Optimization Strategy The transition from experimentation to production has brought cost optimization to the forefront. ADP's approach to controlling costs includes: - Spinning up smaller, more specialized models instead of large general-purpose ones - Fine-tuning models for specific use cases - Hosting models in-house rather than relying on expensive external API calls - Moving away from "off-the-shelf" models with high per-token costs This represents a common maturation pattern in enterprise LLMOps: initial experimentation with powerful but expensive commercial APIs, followed by optimization through fine-tuned, smaller, or self-hosted models once value is proven. ## Data Strategy and Commercial Ambitions Beyond internal operations, ADP has a broader data strategy with four pillars: - **Elevate brand through thought leadership**: Positioning as leaders in safe, cutting-edge technology - **Improve business operations**: Driving efficiencies in sales, marketing, and customer support (including AI-powered service centers) - **Reimagine product**: Infusing existing products with generative AI capabilities - **Commercialize data**: Leveraging anonymized, aggregated workforce data to provide insights about the world of work The commercial data angle is particularly interesting—ADP sees significant value in the aggregated, anonymized patterns they can derive from processing payments and HR data for such a large portion of the workforce. ## Critical Assessment While ADP's presentation provides valuable insights into enterprise LLMOps challenges and strategies, it's important to note several limitations: The presentation is primarily forward-looking and strategic rather than retrospective. No concrete metrics, success rates, or quantified results were shared. The initiatives described are works in progress, with phrases like "still sort of working on this" and "just getting started" appearing throughout. The presentation also serves as a Databricks partner testimonial, so the framing naturally emphasizes the value of Databricks tools. While the use cases and challenges described appear genuine, the specific tool recommendations should be evaluated in that context. Additionally, the quality assurance and hallucination prevention approaches mentioned are acknowledged as priorities but not detailed technically. For an organization dealing with payroll and tax advice, the specific guardrails and validation mechanisms would be critical implementation details not covered here. ## Organizational Context ADP has established a Core AI team led by Fernando Schwarz (VP of AI) that is building the One AI platform. This team operates as a Center of Excellence, partnering with various business units across the organization. The team is actively scaling and hiring, indicating significant organizational investment in these capabilities. The approach of having a central AI platform team that partners with business units while maintaining governance and shared infrastructure represents a common and generally effective enterprise AI organizational pattern, balancing innovation velocity with control and consistency.