AI as a Rising Tide: Stacking Tools to Elevate Everyone
AI is creeping into our daily work—whether we’re ready for it or not. Tools like Copilot for M365 and Gemini for Google Workspace are dropping into our inboxes, meeting chats, and documents, helping us summarize, draft, and ideate. For most people, this is where AI lives: inside familiar workflows, offering useful nudges but not fundamentally changing how organizations operate.
But then you see something like UC San Diego’s TritonGPT, and you realize AI can do a lot more than just help an individual work faster. It can fundamentally change how an entire institution interacts with information.
This is what I call AI stacking—leveraging multiple layers of AI to elevate users at both the individual and enterprise level. It’s the difference between someone using Copilot to summarize a report versus an AI-powered system that understands institutional data, automates reporting, and provides real-time insights to leadership.
The AI Stacking Strategy: Individual Tools vs. Enterprise AI
UC San Diego’s TritonGPT isn’t just a chatbot—it’s a custom-built AI ecosystem that brings together structured and unstructured institutional data. It helps employees, students, and administrators get answers, analyze trends, and streamline tasks by integrating AI directly with the university’s enterprise data systems.
This is the key difference:
Standalone AI tools (Copilot, Gemini, ChatGPT Enterprise, etc.)
Help individual users with writing, summarization, and task automation
Work well in day-to-day productivity apps
Are general-purpose and don’t integrate deeply with organization-specific data
Enterprise AI systems (TritonGPT, Azure OpenAI, custom AI assistants)
Connect directly to institutional data, policies, and workflows
Act as an intelligence layer across departments, supporting better decision-making
Require governance, security, and training to ensure effective adoption
Much like “a rising tide lifts all ships,” an enterprise AI strategy can elevate everyone—if done right.
The Real Challenge: Change, AI Literacy, and Support
Deploying AI at scale isn’t just a tech project—it’s an organizational change effort.
UC San Diego didn’t just build TritonGPT and expect people to figure it out. They also invested in AI literacy programs to help faculty, staff, and students understand how to use it effectively. This is a crucial step that too many organizations skip.
AI doesn’t just need to work—it needs to be understood and trusted.
Users don’t just need access—they need support and training.
Leaders don’t just need dashboards—they need confidence in AI-driven insights.
Think about it: if someone handed you an AI-powered financial forecasting tool but never explained how it works, what data it pulls from, or how to interpret its recommendations, would you trust it? Probably not.
This is why AI adoption is as much about change management as it is about technology.
How Organizations Can Prepare for Enterprise AI
If you’re in an organization looking at AI beyond standalone tools, here are some key lessons from this example:
Stack Your AI Tools Wisely – Let productivity AI (Copilot, Gemini) handle individual tasks, but invest in an enterprise-wide AI system to centralize intelligence and make data-driven insights accessible.
Make AI Literacy a Priority – AI doesn’t replace human judgment—it enhances it. Make sure people know how to use AI responsibly, effectively, and with a critical eye.
Plan for Support & Governance – AI needs ongoing support, refinement, and ethical oversight. Just like any major technology shift, it requires a strategy—not just enthusiasm.
AI can be transformational—but only if people know how to use it and trust it.
So, here’s the real question: Is your organization stacking AI tools for maximum impact? Or is everyone just experimenting on their own?
