How to build an AI-First culture

There’s a big difference between being an AI-Forward company and being an AI-First company. 

AI-Forward companies treat AI as a way to free up a little bit of time. They’re looking for simple automations—cleaning up a spreadsheet, drafting a social post, summarizing a meeting. These things matter, but they’re incremental. They make the operation a little faster, a little cheaper, a little more convenient.

AI-First are asking how AI technology can fundamentally change the way they work and the value they deliver to their customers and clients. For them, AI isn’t an accessory to the process. It is the process.

Making that shift requires a different mindset. You can’t get there by memorizing prompts or plugging in a new tool. You need a way of seeing where AI belongs in the work, what kind of leverage it creates, and how everyone in your organization can move from dabbling at the edges to using AI to building entirely new capabilities.

That’s what this article is about. We’ll look at three models that can guide the shift from AI Forward to AI First:

• The 4x2 Model of Work, which shows how AI copilots or delegates across different kinds of tasks.
  
• The AI Double Helix—internal efficiency and external value—that explains how AI should be leveraged for value-creation as well as productivity gains.
  
• And the Galaxy Brain Ladder, which maps the journey from casual users to power users to builders.
  

The 4x2 Model of Work

To understand what it means to work in an AI-first culture, it helps to step back and look at the work itself. Not the tools, not the org chart—the actual kinds of work we do every day.

Most of what happens inside any business falls into four categories:

• Design — creating something new from a blank page. Developing an idea or vision. Designing a campaign concept, rethinking a team structure, sketching out a new product.
  
• Problem-Solving — navigating obstacles to reach a defined goal. Debugging a broken campaign, handling a tricky client situation, figuring out why conversions are down.
  
• Decision-Making — the invisible architecture of strategy. Deciding where to invest marketing budgets or how to structure your campaigns. Using data or personal wisdom to inform the path forward.
  
• Building — the work of doing. Shipping campaigns, running reports, executing the plan once the decisions are made.
  

On top of those four categories, there are three ways any task can be approached:

• Solo — you do it yourself, start to finish.
  
• Copiloted — you share responsibility with someone (or something).
  
• Delegated — you hand it off completely.
  

That gives us a 4x3 matrix: four kinds of work, three ways of working. For most of modern business history, the default mode has been solo. 

AI changes that. It shrinks the need to do things fully alone. You begin to shift into a 4x2 world—where copiloting and delegation are the norm.

• For Decision-Making, AI can surface patterns in data you’d never have spotted, run simulations, or pull context from multiple sources so you’re not choosing between options blind.
  
• For Problem-Solving, AI can act like a thought partner, walking you through frameworks, challenging your assumptions, or combing through dense documentation to find the signal you need.
  
• For Design, AI helps transform a blank page into a launchpad—generating directions to explore, visualizing concepts, or building prototypes you can refine.
  
• For Builders, AI takes on the grunt work. Automating steps, catching errors, checking consistency—so you can ship faster with less risk of mistakes.
  

Once you see your work this way, the role of AI comes into focus. The point isn’t just speed. It’s leverage. In a 4x2 world, very little needs to be done alone, and the time you save is only half the story. The other half is the scope of what becomes possible when you let copiloting and delegation take center stage.

The AI Double Helix

If the 4x2 Model helps you see where AI belongs in the work, the AI Double Helix framework explains why it matters.

AI creates leverage on two intertwined strands: internal efficiency and external value.

• Internal efficiency is about doing the work faster, cheaper, and with fewer errors. Automating reports. Cleaning up data. Drafting first passes of copy. These are the obvious wins, and for many AI-Forward companies, this is where the journey stops.
  
• External value is about using AI to deliver something new to customers or clients. Running campaigns you never had the resources to build. Personalizing an experience at a scale that was impossible. Creating tools that make your product more useful.
  

The key is that these strands twist together. Efficiency makes room for experimentation. Value creation makes the efficiency worthwhile. 

The trap most companies fall into is thinking AI is only about productivity. Shave a few minutes here, cut a cost there. That’s fine, but it won’t change your trajectory. The real shift to AI-first happens when you pull on both strands of the helix at once—when operational efficiency and customer value spiral together to create new forms of leverage.

Think about what that looks like in practice: a small agency that uses AI to automate its reporting (efficiency), and then reinvests that saved time into building custom creative for clients that would’ve been out of reach before (value). Or a retailer that uses AI to manage inventory more precisely (efficiency), and then applies those same tools to deliver a smoother shopping experience for customers (value).

The Galaxy Brain Ladder

If the 4x2 Model shows where AI fits into the work, and the Double Helix shows why it matters, the Galaxy Brain Ladder explains how people (and teams) progress from dabbling to building real capability. Each rung marks a shift in habits, judgment, and scope—not just more tools.

The Galaxy Brain Ladder looks like this:

Level 1: Novice — You benefit from AI without noticing.

Level 2: Explorer — You use AI on a few tasks; useful, siloed.

Level 3: Operator — AI is your default starting point; you add structure and checks.

Level 4: Builder — You ship assets and analyses at a new pace and scope.

Level 5: Innovator — You develop unique systems that run, measure, and improve.

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Level 1 — Novice

What it looks like day-to-day
You benefit from AI because it’s embedded in the tools you already use: Salesforce flags a lead as “high likelihood to convert,” Gmail finishes your sentence, or your ecommerce dashboard recommends an upsell strategy. You know these features are AI-driven, and you may even appreciate them, but you don’t go looking for ways to apply AI beyond what’s handed to you. It’s background help, not something you actively drive.

Strengths
Zero friction. You’re comfortable with AI-enhanced products and often willing to accept their suggestions.

Limits / risks
No intentional leverage. Because you rely on AI passively, you mistake “smart features” for strategic leverage. The danger is assuming you’re already keeping up with AI adoption, while competitors are using it to transform what they can deliver.

Move up by
Try one deliberate experiment per week: ask an LLM to summarize a client brief, outline a slidedeck, or translate a messy meeting into action items. Treat it as a trial, not a commitment.

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Level 2 — Explorer

What it looks like day-to-day
You open ChatGPT (or similar) for specific tasks you’ve already associated with AI: email composition, spreadsheet formula help, blog post drafts. Useful, but narrow. AI is an occasional assistant.

Strengths
You capture easy wins—fewer blank-page moments, faster admin work, better first drafts.

Limits / risks
You stop at convenience. Work quality is still gated by your personal capacity. Outputs vary because prompts and expectations are ad hoc.

Move up by

• Upgrade to a paid version of ChatGPT if you haven’t already—access to projects, custom instructions, and advanced tools unlocks real leverage.
  
• Create a ChatGPT project folder for something you’re working on. Upload files, meeting transcripts, or datasets. Make it a habit: whenever you touch that project, bring it into ChatGPT and explore how the tool can push the work forward.
  
• Experiment with ChatGPT’s Deep Research feature. Pick a domain you care about—your industry’s competitive landscape, the science behind a new diet trend, or the offseason moves of your favorite team. See how AI can surface narratives and patterns you wouldn’t have spotted alone.
  
• Upload a Deep Research report to Notebook.lm and turn it into a quick summary podcast. If not Deep Research, choose an academic report you’d love to read but haven't found the time. 

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Level 3 — Operator

What it looks like day-to-day
AI is your first instinct. You keep a session open. You bring every non-trivial problem to the model: planning, analysis, QA, messaging, scenario drafts. You upload files, use projects or custom GPTs, and give the model context about your brand, clients, and constraints. You know when to copilot and when to delegate (4x2 in action).

Strengths
Consistent leverage. Fewer stalls. Better framing of problems. You get to stronger “first right answers” faster and can evaluate more alternatives.

Limits / risks
Overconfidence in fluent answers. If you don’t specify data sources, guardrails, or acceptance tests, you can scale errors faster than before.

Operators are realizing operational efficiencies at work—they spend less time writing emails, reading long reports, and are capable at automating simple tasks. However, they are not yet using AI to transform the value they provide to their customers or other stakeholders. They are only benefiting from one side of the AI Double Helix.

It’s also easy to plateau here. Operator-level habits feel powerful—but if you stay too comfortable, you stop pushing the frontier of what’s possible.

Move up by

• Add structured checks: define success criteria before generation; ask the model to self-critique against them.
  
• Develop your first script: Take a Google spreadsheet you already use and ask ChatGPT to write a Google Sheets script that automatically generates reports or charts. Be explicit: “I’m not a developer, I need step-by-step instructions for Google Scripts.” Expect errors, and treat them as part of the process. Copy-paste error messages or screenshots back into ChatGPT and ask for fixes until it works.
  
• Push into light tooling. Experiment with a no-code agent builder like n8n or MindStudio. Start simple—like a Chrome extension that summarizes blog content. You’ll learn far more from the act of building than from the finished product. Both platforms have extensive YouTube tutorials to guide you.
  

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Level 4 — Builder

What it looks like day-to-day
You use AI to ship things you previously needed specialists for—or couldn’t do at all. Examples:

• Spin up landing pages and test variations.
  
• Produce creative variants and storyboards; turn long clips into platform-native edits.
  
• Run statistical checks, cohort cuts, and diagnostic analyses.
  
• Write step-by-step implementation guides; troubleshoot tracking; generate working snippets for GTM/GA4/Shopify.
  
• Compile competitive landscapes with sourced evidence and a POV.
  

Strengths
Throughput jumps. Cycle time collapses. You unlock external value (the second strand of the Helix): new services, faster delivery, sharper insights.

Limits / risks
Quality drift if you scale without standards. IP and data-handling mistakes if you don’t set policies. The work “works,” but it may not be production-grade without reviews.

Move up by

• Get hands-on with APIs. Create an OpenAI API key and ask ChatGPT to write a Google Sheets script that connects directly to it. Start small: download a customer list of a few hundred rows and ask the model to categorize customers into regional DMAs based on their zip codes. Add the DMA as a new column. Troubleshoot until it works—then step back and analyze. (“Wow, I knew I had a lot of customers in Texas, but I didn’t realize most were clustered around San Antonio.”) From there, experiment with new categorizations: by margin, by lifetime value, or even by inferred demographics.
  
• Cross-train with multiple LLMs. Subscribe to a second model like Claude or Gemini and compare results side by side. Use one model to coach your use of the other—for example, ask ChatGPT: “I’m writing a Google Sheets script in Claude Sonnet 4 and keep running into this issue. Can you write me a new prompt that will help Claude resolve it?” You’re now managing an ecosystem of AI assistants that strengthen each other.
  
• Build for fun. Pick a personal hobby or curiosity and make it your playground. Launch something with Lovable or spin up a simple web app on Vercel. Keep the stakes low but the interest high. It won’t be perfect (and it will be harder than you expect), but the process will teach you more than any tutorial. By wrestling with real builds, you’ll internalize how to move from “AI-assisted work” to “AI-powered systems.”
  

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Level 5 — Innovator

What it looks like day-to-day
You’re no longer asking, “How can AI help me with this task?” Instead, you’re asking, “How would this process look if it were designed with AI at the core?” Innovators treat AI not as a helper but as the foundation of new operating models.

That could mean:

• Creating internal systems that continuously monitor, measure, and improve campaigns without needing human handoffs.
  
• Building AI-powered customer tools—like self-service dashboards, personalization engines, or decision-support assistants—that expand what your company can actually deliver.
  
• Designing workflows where efficiency gains (automation, speed) and external value (better client outcomes, richer customer experiences) are inseparable.
  

Your day-to-day isn’t just using AI; it’s shaping how the organization itself uses AI.

Strengths
You create new leverage loops. The systems you design compound over time—getting smarter, faster, and more valuable with use. You’re not just more productive; you change the value equation for your company and your customers.

Limits / risks

• Vision can outpace execution. Without guardrails, the systems you design may be fragile, overcomplicated, or difficult for others to adopt.
• It’s fun to sit around and talk about all the great things you can build—what matters is whether you have the discipline to make them come to life.
• Risk of “pet projects” that wow in demos but don’t tie back to strategy.

Staying here (without stalling)

• Design a system, not a task. Instead of automating a single report, build an AI-driven reporting pipeline that refreshes, analyzes, and distributes insights weekly—without you touching it. Document how it works so others can rely on it.
  
• Turn efficiency into value. Ask: “How can this same automation create a better customer experience?” For example, if you built an AI tool that categorizes customer data for ops, extend it into a client-facing dashboard that shows trends in real time.
  
• Test at the edges. Run experiments that look more like product development than process tweaks. Use APIs, multi-model orchestration, or lightweight apps to prototype something that could eventually become a new service line.
  
• Teach the system to others. The true marker of an Innovator isn’t just building—it’s institutionalizing. Package what you’ve built into a repeatable playbook, template, or internal product so your whole team can benefit.

Using the Ladder to Shift Culture

• Diagnose honestly. Different teams—and people—sit on different rungs. That’s fine. Don’t mandate Level 5 everywhere.
  
• Advance by one rung at a time. A small, durable step beats a flashy leap that doesn’t stick.
  
• Tie every step to the Double Helix. When efficiency frees time, point that time at customer value. Make the connection explicit.
  
• Embed the 4x2. Ask in every review: Is this Solo, Copiloted, or Delegated? If Solo, why? What would make Copilot or Delegate safe?

Building an AI-First Culture

The shift from AI-Forward to AI-First won’t happen by accident. It takes intention, structure, and the willingness to move one step beyond your current comfort zone. The frameworks here—the 4x2 Model, the Double Helix, and the Galaxy Brain Ladder—are meant to give shape to that journey. They’re not blueprints for a single right answer, but lenses to help you see where AI belongs, how it creates real leverage, and what it looks like to grow from tinkering with tools to transforming what your company can deliver.

Becoming an AI-First organization isn’t about chasing every new feature. It’s about compounding advantage: using efficiency to buy time, using that time to create new value, and institutionalizing the habits that make those gains stick. If you can do that, AI stops being a sidekick in your workflow and starts becoming a core part of how you work, how you compete, and how you win.

Immediate Next Steps: Here’s Your Prompt!

Reading this post is one thing, but acting on it is another. If you've come this far, I assume that you have enough history with ChatGPT to make this following exercise useful.

Copy and paste the prompt below directly into ChatGPT (or your preferred model). It will help you diagnose where you currently sit on the Galaxy Brain Ladder and generate a practical set of action items tailored to you.

Prompt to use:

Here is an article that describes how to become more proficient using AI tools: https://www.adventureppc.com/blog/how-to-build-an-ai-first-culture .  In the article, Patrick Gilbert of AdVenture Media presents three frameworks for leveling up AI skills and developing a shared culture around AI skills throughout an organization. The article discusses a 4x3 model for work, an AI Double Helix framework to describe the different ways AI can impact businesses, and an AI Galaxy Brain framework for helping individuals understand where they currently are in their AI journey, with advice on how to level up.

Here's what I want you to do:

Step 1: Review that blog post and make note of the different frameworks. Summarize the key points for me. Patrick's examples mostly tie to digital marketing and PPC problems, so please create new examples that are more relevant to my specific business (if necessary).

I’m committed to advancing my AI skills and need your support to help me navigate this topic that often feels overwhelming.‍Please present practical, concrete ideas and also suggest ways I can hold myself accountable to my own education (for example, through routines, metrics, or structured experiments).  

‍‍Step 2: I want your advice: Based on everything you know about me, can you tell me where you think I currently sit under the five levels of the AI Galaxy Brain framework described in this blog post? Be brutally honest.

Can you also help me game plan specific action items that I can take—drawing from other work, habits, and projects I’ve shared with you in the past—that will allow me to experiment with some of the new ideas outlined in this blog post?  

Step 3: Review the Double Helix concept. Please suggest ways I can think about using AI to unlock operational efficiencies in my business. Then provide advice about how I can use AI to deliver more value to my customers, clients, or other stakeholders.

Step 4: In the future, as we work on projects together, make a point to tie things back to these models whenever possible, including identifying which aspect of the 4x2 Model we are currently working on. I want you to take an active role in coaching me throughout my AI journey as we work on future projects together.

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