AI First Imperative: What Does It Mean to Be AI First
I’ve been practicing an AI First mindset for a few months now. It wasn’t something I defined from the start—it just emerged naturally from how I worked. Whether I was tackling a complex project, debugging a technical issue, or drafting a message, AI became a central part of my thinking loop.
Published Jun 2, 2025
“AI is not accurate.”
“AI hallucinates.”
Well—so do humans.
After years building, mentoring, working with startups and judging in funding programs, I’ve seen it over and over again: we hallucinate too. We imagine product-market fit. We assume customer needs. We fall in love with our own ideas. Sometimes we’re right. Often, we’re not.
But here’s the twist: hallucination isn’t a flaw—it’s a feature. It’s fuel for creativity. Without it, there’d be no misfits, no rebels, no troublemakers—no one daring to build what doesn’t yet exist.
In myths, genies grant wishes. But the curse is built in:
You get what you ask for—even if it destroys you.
The genie doesn’t question your assumptions.
It doesn’t clarify your intent.
It just delivers. No matter the consequences.
You get what you ask for—even if it destroys you.
The genie doesn’t question your assumptions.
It doesn’t clarify your intent.
It just delivers. No matter the consequences.
That’s the danger—not just false information, but false direction.
This is the problem the Lean Startup tried to solve. Every product begins as a wish—a hypothesis. But instead of trusting the wish, it proposes a loop to test it:
If it works, keep going. If not, pivot—fast.
The sooner you find what’s wrong, the sooner you can get it right.
The sooner you find what’s wrong, the sooner you can get it right.
The genie curse has a cousin: the curse of knowledge—when you’re so deep in an idea, you forget what it’s like not to know. That’s how I felt writing this. I didn’t just want to explain AI First—I wanted it to stick.
So I did what any AI First practitioner would do:
I asked the machine.
I asked the machine.
What emerged was this:
AI First: Don’t just move fast—start smart.
Most people use AI like a car—to go faster.
I use it like a compass.
Before I move, I ask:
Where am I?, What’s the terrain?, What’s the smartest route?It’s not about speed. It’s about clarity.
Not just accelerating execution—but sharpening intent.
Not just accelerating execution—but sharpening intent.
At first, I called AI a “strategic amplifier.” Technically accurate—but abstract.
So I tested metaphors.
So I tested metaphors.
The genie metaphor worked. It captured AI’s power—but also its danger. The idea was sticky, but not trustworthy. I needed something better—familiar, grounded, directional.
That’s when the compass appeared.
Not magic. Strategy.
Not wishes. Navigation.
Not magic. Strategy.
Not wishes. Navigation.
That shift clarified everything: AI First isn’t about working faster.
It’s about working smarter—by thinking with the machine before acting.
It’s about working smarter—by thinking with the machine before acting.
It’s a mindset.
A process.
A process.
The conversation didn’t begin with a prompt like:
“Write a post about AI First.”
It started with questions—shaped through conversations like:
“What are the best books on communicating complex ideas?”
“How do you pitch a concept so it sticks?”
“What makes an abstract idea like AI First feel intuitive?”
Each question refined the intent. Each answer sharpened the metaphor.
Because that’s what AI First really means:
Start with the question—before the task.
Start with direction—before acceleration.
Start with the compass.
Start with the question—before the task.
Start with direction—before acceleration.
Start with the compass.
Once the intent is clear, execution becomes deliberate. You’re not just acting—you’re iterating with purpose. Like in Lean Startup, AI First doesn’t skip straight to building. It starts upstream, in the planning conversations that shape what gets built.
When I talk about using AI as a compass rather than just acceleration, the question naturally becomes: where do you start? How do you implement an AI First mindset in a way that's secure, governed, and actually practical for your organization?
This is where Amazon Q Business becomes your go-to AI-first toolbox.
Most organizations face a fundamental tension: they recognize AI's transformative potential but can't sacrifice security and governance. Public AI tools like ChatGPT or Claude are powerful, but they weren't designed with enterprise data security as a foundational requirement. Your company's confidential documents, customer data, and proprietary processes need protection that general-purpose AI simply can't provide.
Amazon Q Business solves this by being built security-first from the ground up. It understands and respects identities, roles, and permissions, ensuring personalized interactions based on user access levels. If a user is not permitted to access certain data without Amazon Q, they cannot access it using Amazon Q either. This isn't security bolted on afterward—it's security woven into the fabric of how the system operates.
Here's what makes Amazon Q Business different: it becomes intelligent about your business by connecting to your data sources. Amazon Q Business supports connectors for over 40 enterprise systems including Amazon S3, Microsoft SharePoint, Confluence, Salesforce, Jira, Gmail, Google Drive, Microsoft Teams, Box, Dropbox, and many more.
This means you can ask questions like:
- "What were the key decisions from last quarter's board meetings?" (pulling from SharePoint)
- "Show me all customer feedback about our new product feature" (combining Salesforce, Zendesk, and email data)
- "What's our current project status across all teams?" (integrating Jira, Confluence, and Slack)
The AI doesn't just search—it synthesizes information across all these sources while respecting the permissions you already have in place.
But here's where it gets really interesting. Remember how I mentioned that AI First isn't just about faster execution—it's about starting with the right questions? Amazon Q Business Apps takes this concept and makes it scalable across your organization.
You can rapidly create generative AI-powered, secure, reusable, and customizable apps by describing them in natural language. Amazon Q Apps intelligently captures the context, nuances, and specifics of your conversation, translating them into a customized app tailored to your needs.
Let's say you have a conversation with Amazon Q Business about analyzing weekly sales performance. Instead of repeating that same conversation every week, Amazon Q Apps can capture that workflow and turn it into a reusable application that anyone in your sales team can use.
What makes this enterprise-ready? All data sources, associated user permissions, and guardrails are preserved when using Amazon Q Apps. Administrators can leverage controls to give additional credibility to user apps with app verification, create customized library labels to meet customer-specific organizational requirements, and enable or disable the Amazon Q Apps creation and run features.
This governance layer means your IT team stays in control while empowering business users to build AI-powered solutions. It's the sweet spot between innovation and security that most organizations struggle to find.
Think of Amazon Q Business as your training wheels for AI First thinking. You start by asking better questions of your existing data. As you develop comfort and see results, you begin creating apps that capture and scale your most valuable AI workflows.
This isn't about replacing your existing tools—it's about adding an intelligent layer that helps you navigate them more strategically. Your Confluence pages, Salesforce records, and email threads become nodes in a larger intelligence network that you can query conversationally.
The beauty of this approach is that you don't need to transform everything at once. Pick one high-volume, information-heavy process in your organization. Maybe it's employee onboarding, where new hires constantly ask the same questions about policies and procedures. Or perhaps it's sales enablement, where your team needs quick access to competitive intelligence scattered across different systems.
Connect those data sources to Amazon Q Business. Start asking questions. See how the AI synthesizes information you previously had to hunt for manually. Then, once you find a valuable pattern, capture it as an Amazon Q App that others can use.
This is AI First in practice: starting with clarity about what you need to know, using AI to navigate toward better insights, then scaling that intelligence across your organization.
The compass analogy isn't just metaphorical—Amazon Q Business literally helps you orient yourself in your organization's information landscape, find the most direct path to answers, and create navigational tools others can follow.
Your next step? Identify one information-heavy challenge in your organization and ask yourself: "What questions do I wish I could ask across all our systems?" That's your starting point for an AI First transformation.