Learn to work WITH AI, not just use AI tools. The thinking behind the tech — so you build systems that fit your actual work.
Most AI training teaches you to use tools. Type a prompt, get an output, repeat. That’s useful for about a week — until the tool changes, the interface updates, or you realize the outputs don’t actually fit your work.
Connected Intelligence is different. It teaches you how to think about AI so that any tool, any platform, any update still makes sense. You learn the principles behind the technology so you can build systems that work for you — not just follow someone else’s template.
This isn’t “AI for beginners.” It’s AI for people who want to actually understand what they’re building.
Click a tier to explore the curriculum. Start where you are. Go as deep as you need.
Build the mental models for working with AI. Understand how AI thinks, how to communicate with it effectively, and how to design workflows around your actual needs.
Who it’s for: Professionals who use AI tools but want to understand the thinking behind them.
Format: Self-paced modules + community access on Skool.
Price: $197/year founding member (first 20 students) • $297/year after.
Go beyond individual prompts. Learn to build multi-step AI workflows, create persistent context systems, and design agent architectures that match your work.
Who it’s for: Knowledge workers and operators ready to build real AI infrastructure.
Format: Guided cohort + hands-on projects + community.
Price: $697 — Coming soon.
Design AI-native operating systems for yourself or your team. Multi-agent coordination, governance frameworks, knowledge management, and the organizational design that makes it all work.
Who it’s for: Leaders and technical professionals building AI into their operating model.
Format: Small cohort + direct access to Daniel + custom implementation support.
Price: $2,497 — Coming soon.
Each module builds on the last. Every one gives you a new capability you can use immediately.
Mental models for understanding what AI is (and isn’t). Why prompts work the way they do. The difference between using AI and working with AI.
How to talk to AI effectively. Context, constraints, and specificity. Why your prompts aren’t working and how to fix them.
Map your actual work. Identify what’s automatable, what’s augmentable, and what should stay human. Build your first AI-assisted workflow.
Give AI the context it needs to actually help. Persistent instructions, knowledge bases, and the art of teaching AI about you.
Go from one-off prompts to a persistent AI assistant. Define its role, give it memory, set its boundaries. Hands-on-keyboard from start to finish.
Bring it all together. Your personal AI operating model — designed around who you are, how you think, and what your work actually requires.
Move from single-agent work to multi-step systems. Build infrastructure that lasts.
Chain prompts, build sequences, and create repeatable processes. Move beyond one-shot interactions to workflows that compound results.
Instruction files, CLAUDE.md architecture, and memory design. Build the long-term context layer that makes AI actually useful over time.
Organize your information so AI can actually use it. File structures, naming conventions, and retrieval patterns that scale.
Move from one general AI to role-specific agents. Define scopes, personalities, and guardrails. Specialization beats generalization.
Connect AI to your existing tools and platforms. APIs, MCP integrations, and the connective tissue between your AI and your work.
Measure, iterate, and improve your AI systems. Identify bottlenecks, reduce friction, and build feedback loops that make the system smarter over time.
Design AI-native operating systems. This is where individuals become organizations.
Redesign your operating model around AI capabilities. The C-suite metaphor, role definition, and the organizational patterns that make multi-agent systems work.
Handoff protocols, shared context, and agent-to-agent communication. How to build a team of agents that work together without stepping on each other.
What AI can and can’t do. Approval workflows, permission systems, and the safety rails that make autonomous agents trustworthy.
Your organization isn’t a chart anymore. It’s a living network of humans, agents, data, and permissions that converts intent into outcomes.
Build systems that learn and improve over time. Institutional memory, discovery logs, and the infrastructure that makes organizational learning automatic.
ROI, impact metrics, and the outcomes that don’t show up on dashboards. How to know if your AI system is actually making things better.
The doing isn’t the work anymore.— Daniel Walters
The thinking is the work.
Tier 1 (Foundations) launches soon. Get on the list to be first in — and get early-bird pricing when we open enrollment.
Connected Intelligence isn’t just a course — it’s a community. All students get access to the Connected Intelligence Skool group where you can ask questions, share what you’re building, and learn from other people doing the same work.
The best learning happens when you’re building alongside people who get it.
Join the Community on Skool →