Leaders are racing to deploy generative AI, but 87% of pilots fail without a strong enterprise data foundation (RAND Corporation, 2024). Organizations generate massive volumes of raw data, but because it lives across fragmented applications, they lack the unified, governed data layer necessary to feed predictive models and real-time AI triggers for impactful results.
True business transformation requires an outcome-first approach, having the foresight of your end goal in mind to unlock the right data for the right result. By using an integrated framework with technologies such as Snowflake and customer data platforms (CDPs), organizations can ingest, unify, and optimize enterprise data into a centralized foundation. This foundation supports AI and unlocks intelligence across marketing, sales, and customer success to drive sustainable B2B growth.
Why 87% of AI Pilots Fail
The dirty secret of enterprise AI is that your models are only as good as the infrastructure supporting them. When an AI pilot stalls, leaders often blame the algorithm or the software vendor. But the real culprit often points to a fragmented data layer. Without the right data, context, and instructions safely encoded into your data architecture, any AI or agentic workflow you deploy will fail to yield meaningful business results.
To successfully ground an AI tool or autonomous agent, it requires three core components:
- The Right Data: AI is only as effective as the data it can access. Based on the outcome you’re trying to achieve, does your AI have access to the right data, or is critical information trapped in siloed systems?
- Context: This dictates who the data is for and how it should be interpreted. For example, the same customer data might be used differently by a seller in the field than a digital marketer. Context maps those differences.
- Instructions: The rules that govern what the AI actually does with an input or API trigger, determining what the response looks like, and how data is processed on a schedule.
Without this precise grounding with the right data, context, and instructions, your AI outputs will remain vanilla, generic, and completely unaligned with your specific buyers or use cases. Making it difficult for marketing, sales, and customer success to actually get the value they need out of the technology.
The Strategy for an Integrated, Enterprise Data Foundation
Building a modern enterprise data foundation means shifting your mindset to focus on business outcomes first. Once you define the goals across your growth engine, you can work backward to unlock the exact data sources required to achieve them.
This isn’t about chasing data perfection. Leaders may feel discouraged that their data will never be “ready for AI”. Pursuing a destination of “readiness” or “perfection” could be endless, and organizations need to be able to start unlocking value from their data and AI solutions now. Therefore, this outcome-based mindset toward the data layer helps to prioritize which sources are the most important to unlock and integrate first, and then moving onto the next important source. Taking this phased approach enables teams to start making progress on their integrated data layer while also making progress with AI.
Sercante l Trilliad takes a similar phased approach for creating an AI roadmap. For more details, read Jenna Packard’s blog, The AI Roadmap for Enterprise: A Framework for Identifying Value-Driven AI Use Cases.
Approaching Your Data: Do you have the right software in place?
Achieving this requires balancing the structural “muscle” of a massive data warehouse with the agile “brains” of an activation layer. For high-volume, multi-threaded processing, a platform like Snowflake is built to handle billions of records seamlessly. Attempting to force a customer relationship management (CRM) platform or standalone CDP to process raw, unrefined data at that scale can cause extreme system inefficiencies and operational bottlenecks. The ideal architectural pattern uses the warehouse to do the heavy data rationalization lifting, flowing only refined, actionable customer data to your operational platforms.
Therefore, when thinking about the data that you want to unlock for your AI use case, consider the systems that you have. Bring in IT to get their expertise and align with them on the intended goal. Do you have the right software in place? If not, would it be beneficial to bring in an implementation expert to fast-track your setup, ensure the right configuration with your data and tech stack, and accelerate time-to-value?
Knowing the systems you have, aligning with IT, and making a plan forward to ensure you have the right software in place to support your goals for an integrated data layer and AI is a critical step in your strategy.
Establishing Data Governance: How are you going to control access?
Data governance requires precise access control so that sales, marketing, and customer success teams interact only with the data they are cleared to see. Within a clean architecture, organizations can create a primary master database. From there, you can spin up derived databases scoped specifically for separate departments or business units without duplicating records or creating isolated data silos.
Furthermore, governance has evolved past managing human users. With tools like Snowflake Horizon Catalog, enterprises can centralize data governance features explicitly designed as guardrails for AI agents and Large Language Models (LLMs). This ensures that generative AI outputs and retrieval-augmented generation queries cannot inadvertently reach into a database and expose restricted enterprise data.
Marketing, Sales & Customer Success: Accessing the Data They Need
To help business leaders take advantage of this data architecture, natural language interfaces allow them to explore data, uncover insights, and ask questions without relying on technical teams. Data-native AI-coding agents like Snowflake CoCo are making it more accessible to enable cross-functional teams to interact directly with the data warehouse using conversational prompts. Rather than waiting weeks for an SQL developer to write complex queries, a marketer or sales director can use natural language to build data tables, establish joins, and run deep analysis. Similarly, Snowflake CoWork (formerly Snowflake Intelligence) is an AI-powered personal work agent built for enterprise knowledge workers, allowing users to ask complex questions like, “What is the cross-channel campaign performance over the last quarter?” and instantly generate automated reports based on real-time ingestion. All this while respecting your existing Row-Level Security (RLS), Role-Based Access Control (RBAC), and budget governance.
When creating your data strategy, after thinking through your outcomes, the specific data that you’re after, and the systems you have in place, consider: how will you get the data into the hands of marketing, sales, and customer success for them to be able to take action and activate the data in their analytics and across the customer journey?
Driving Impact with Activated Data: The Indianapolis Colts
When an organization successfully prioritizes its enterprise data foundation to focus on a specific outcome, AI can move from experimentation to measurable business impact.
For example The Indianapolis Colts, a National Football League (NFL) team, aims to treat every fan like an MVP with personalized, high-touch engagements. However, a lack of a cohesive data layer across systems like Ticketmaster, Snowflake, their CRM, and their marketing automation platform created silos of fan data.
This data disconnect severely limited visibility, blocking marketing from running automated, persona-based nurture journeys and forcing their internal teams to manually piece together customer data across systems. Furthermore, without real-time insights, the sales team lacked the visibility to effectively prioritize outreach, leading to inefficient sales efforts of up to twelve attempts per fan. The Colts required a scalable, integrated solution to unlock their data silos, optimize sales and marketing operations, and deliver a connected, high-value fan experience.
Partnering with Sercante | Trilliad, the Colts connected their data across Snowflake, their CDP, CRM, and marketing automation platform. With the right data flowing to the right systems, their sales and marketing teams could deliver the enhanced, personalized experiences fans expected. Read the full case study here.
- Warehouse Optimization: Configured Snowflake for zero-copy integration with Ticketmaster data.
- CDP Harmonization: Resolved identities and calculated fan insights to build distinct segments.
- System Activation: Pushed unified profiles into Sales and Marketing Cloud for targeted outreach.
- Proven Business Results: Reclaimed the time that was originally spent on manual data reconciliation and inefficient outreach, while unlocking millions of fan’s data for marketing and sales to deliver prioritized and more personalized engagements that create lasting customer loyalty.
When organizations position themselves to access the data they need for proper activation, like the Indianapolis Colts did, it sets them up for the next phase, triggering real-time agentic experiences, such as automatically answering a fan’s question about where the best parking is according to their seat in the stadium and then sending a coupon for the closest concessions to help make the most of their game-day experience.
To help visualize what an agentic fan experience like this could look like, watch this 5-minute demo: The Ultimate Fan Experience on Agentforce.
Getting Started with Your Enterprise Data Foundation
For organizations to effectively deploy AI that impacts the bottom line, it requires prioritizing an integrated enterprise data foundation. By leaning into powerful data systems like Snowflake and implementing the governance necessary, it allows for the right data to be unlocked for the intended outcomes and makes it easily accessible across marketing, sales, and customer success to be activated across your growth engine.
To fast-track your approach to creating your integrated data foundation, reach out to the Sercante l Trilliad team. Share your goals with their experts, and they can help develop an effective vision map that clearly charts your next steps to access the right data for impactful AI.
