You’ve probably got “AI” written in big, bold letters on your 2026 roadmap. If you’re like the 120+ leaders we surveyed this year, you’re also navigating the gap between ambition and execution.
The challenge is real. According to our 2025 AI Readiness Report, while 67% of organizations are increasing AI spending, only 21% are seeing measurable ROI.
The gap exists because, for years, our ambition has outpaced our infrastructure.
We’re asking for more—more alignment, more insight, more confidence—than our current systems can deliver. Now, the pressure of AI is making that gap impossible to ignore. Use cases are surfacing quickly, but most systems weren’t built to support intelligence at scale.
Where Your AI Plans Are Breaking Down
You don’t need a technical audit to see where the architecture is failing. The friction shows up in your daily workflows.
- Pipelines break: Logic is buried in brittle code, so a single change can break the entire flow.
- Definitions drift: “Revenue” means one thing in Salesforce and something else in your ERP, making trust impossible.
- Pilots stall: Promising AI ideas die in the sandbox because they can’t access real-time context in production.
These infrastructure problems stem from how your systems are layered and connected. You can’t pilot your way out of an architecture problem.
The Three Layers Every AI Strategy Needs
Modernizing isn’t about just storing more data or processing it faster. It’s about creating a connected, layered system where each part can evolve without breaking the whole.
Most organizations want the top layer—the intelligent automation—without realizing it relies entirely on the two layers beneath it.
1. The Scalable, Modular Platform Layer (System of Reference)
This is the base of the stack. It’s not enough to have a data warehouse; you need a platform (like Snowflake, Databricks, or BigQuery) that supports distributed workloads and governed sharing. This is your “System of Reference”—where raw and processed data live at scale. If you’re still running on tightly coupled ETL pipelines or legacy servers, you’re blocked.
2. The Identity and Activation Layer (System of Activation)
This is where most transformation efforts fail. It’s where raw data gets context. This layer—powered by Salesforce Data 360 (formerly Data Cloud)—handles identity resolution and semantic models. It connects directly to your platform layer (often via Zero-Copy integration) to turn raw storage into a real-time signal, defining what “customer” or “churn” means once, so it applies everywhere.
3. The AI Orchestration Layer (System of Intelligence)
This is the payoff—where Agentforce, prompt builders, and automation tools live. But this layer doesn’t create context; it consumes it. It relies entirely on the unified definitions provided by Data 360 to make decisions. When the foundation provides a clear signal, this layer is where data finally transforms into action that drives revenue.
How to Build This Without Stalling Your Business
Here’s the paradox: you need this layered architecture to succeed, but trying to build the “perfect” version of it all at once will paralyze you.
The solution is to establish the architectural pattern, then apply it one use case at a time.
You don’t need to migrate all of your data to the new layers immediately. You just need the layers to exist so you can route your first high-value use case through them.
1. Select the Use Case: Identify one critical capability to start, such as predicting renewal risks or automating tier-one support. Pick something that combines clear ROI with executive visibility.
2. Audit the Layers: Look strictly at the data required for that one use case. Do you have the platform capability to process it in real time? Is the identity defined in Data 360? Map every dependency before you build.
3. Execute the Cycle:
- Unify: Connect and standardize only the data required for this specific pilot.
- Deploy: Launch the AI capability on this stabilized slice of the stack.
- Scale: Apply this architectural pattern to the next use case.
When you build this way, you validate the architecture with real business value rather than waiting for a multi-year project to finish.
Your 2026 Planning Resources
End-of-year planning brings a specific kind of pressure: you need a strategy that’s bold enough to get funded, but realistic enough to actually work.
To make that easier, we pulled together a simple three-part toolkit. It’s designed to replace guesswork with clarity, keeping your plan grounded in what your business actually needs.
Here is what’s inside:
- The Reality Check (Research Report): We break down exactly why AI is underperforming for most organizations—and what a stronger data foundation actually requires to fix it.
- The Proof (Video Walkthrough): See how unified data translates into measurable revenue. We walk through real examples from clients like Datasite and PracticeTek to show you what “good” looks like in practice.
- The Plan (Complimentary Data Strategy Lab): A one-hour working session where we apply these insights to your architecture. We’ll help you pinpoint issues, align priorities, and define the steps to build a durable strategy for 2026.
Organizations with a clear data roadmap are 2.7x more likely to report positive ROI from AI. Let’s make sure your 2026 plan is built on solid ground.
