Straight answers on AI adoption

Yes. The Foundational AI Bootcamp is built for business professionals, not engineers, and no coding is required. If you can use email and a web browser, you can learn to use AI well.

Six weekly workshops take your team from prompting to skills to a working agent, and every exercise uses work they already do.

The use cases span every department. In Wharton's 2025 AI Adoption Report, the most common enterprise uses were data analysis, content creation, and research synthesis.

Tools are not the bottleneck. Capability is. Implementation means deploying AI tools. Adoption means people use them every day. Enablement means building the capability that makes adoption stick and scale.

Most companies buy tools and skip the capability to use them, which is why adoption stalls.

Yes, if you want adoption that lasts beyond the training. An AI champion is the employee who becomes your company's AI point person. They identify use cases, train colleagues, troubleshoot problems, and keep adoption moving after the formal training ends.

The bootcamp shows you who your AI champions are: the people who light up in sessions and start finding use cases on their own. The leadership strategy session that closes the program names that person and gives them a company-wide roadmap to own.

There is a clear progression, and companies that skip phases tend to struggle.

The Foundational AI Bootcamp starts in Phase I. By Week 5, each participant has a working agent running on their own recurring work. Teams that jump straight to automation tend to struggle because they lack the mental models to direct AI.

We work to make ourselves unnecessary. We build your internal capability so you can run AI without us. We are direct about what AI can and cannot do. And the skills transfer across tools, so the training holds when the tools change.

Six weekly live workshops take your team from prompting to skills to a working agent. A leadership strategy session closes the program. Every exercise is built on work your people already do, and it works with ChatGPT, Claude, Gemini, or your preferred AI tool.

Your team walks away with a custom context skill that tells AI their role, company, and priorities; a skill library in their own voice; one working agent running on a schedule against their own recurring work; a capstone each participant presents to the team; and an executive AI roadmap. See the session-by-session curriculum.

A month of continued support, then whatever the roadmap calls for next. Session recordings, office hours, and a follow-up strategy call continue for a month after the program ends. The leadership session also leaves you with a company-wide roadmap and a named internal AI champion.

Which of these you need, and when, is a roadmap decision. None of them is a package you have to buy.

Live, hands-on training in one AI specialization, with weekly 60-minute sessions in a small cohort. Two tracks run now.

AI for Automation is five sessions for semi-technical professionals and team leaders. It covers agentic AI concepts, n8n fundamentals, agents in n8n, RAG and knowledge retrieval, and a final project taken to production. AI for Marketing is six sessions for marketing leaders and specialists. It covers strategy, lead sourcing, content creation in your own voice, AI search optimization, chatbots and analytics, and automation.

Both finish with something you keep using: a working n8n automation built from your own use case, or a content system and AI search playbook your team runs. If your team needs a different track, such as AI for IT providers, sales reps, or finance teams in locked-down environments, tell us on the Advanced Bootcamps page.

Because generic tutorials do not translate to your workflows and data, and without deadlines and a cohort, adoption stalls. People learn to use AI on the work in front of them, and that is what the bootcamp is built around.

The capstone is one real workflow from each participant's own job, built across the six weeks. You leave with working skills and a running agent, not a list of ideas.

The tools will change, which is why we teach principles, not buttons. Your people learn how to learn AI: prompting, what models can and cannot do, spotting use cases, and building workflows. When the next model ships, they adapt because they understand the fundamentals.

The training works with ChatGPT, Claude, Gemini, or whichever tool your company standardizes on. The same holds for what we build. An AI Brain runs in your own cloud, and the model and vendor behind it can be changed.

Sometimes you do, and that is what AI agent builds and the AI Brain are for. The question is where custom work can take on defined tasks without weakening control, quality, or accountability.

Many companies that think they need custom AI first need better prompting, skills, and workflows with the tools they already have. The AI labs spend billions on research, so where an off-the-shelf tool does the job, use it. When the work repeats in a recognizable way, depends on information scattered across your systems, and needs clear limits on what AI may do, an agent build or an AI Brain is the right answer.

Not every workflow needs an agent. Deciding where one is worth building is part of the work, and that starts with a strategy call.

Start by naming the fear. There are three kinds, and each needs a different answer.

Fear creates resistance, and resistance kills adoption. Week 1 of the bootcamp is built to address all three in the open.

The gap is real, but AI helps your lower performers catch up the most. The Harvard and BCG study of 758 consultants found below-average performers improved 43% with AI, versus 17% for above-average performers. AI levels the field for the people who learn to use it.

Treat it as a real concern. It is the #1 thing that slows adoption. Week 1 of the bootcamp addresses it head-on, with concrete examples of AI handling tedious work while people do the thinking.

What works: train everyone together, frame AI as augmentation from the start, and let people experience it first-hand. In BCG's AI at Work 2025, employees with five or more hours of hands-on AI training showed higher adoption and lower anxiety. In the Wharton data, 89% of leaders who use AI say it augments their work rather than replacing it.

An AI Brain is a living model of your company, built from the systems you already use: email, files, CRM, accounting, and projects. A language model brings general intelligence. An AI Brain supplies what it cannot: your company's knowledge, operating history, and rules, organized into a current view of the business and available to your people and your agents under clear permissions.

It organizes everything around what matters: customers and commitments, projects and deadlines, decisions and why they were made, and your rules and permissions. With that in place, AI can do more than draft documents. It can watch the work, answer questions, flag risks, and take approved action. Read more about the AI Brain.

Each of those does one job well. The Brain sits across all of them.

You own it. The Brain runs in your own cloud account, you own the code, and you can revoke our access at any time. If you stop, the Brain and its code stay with you.

There is no new app to roll out. Your people use it inside the AI tools they already work in, with their existing work sign-in. We do not train models on your data, and the model and the vendor behind it can be changed.

Access is scoped to the work. Each person and each agent gets access based on role and purpose. If no rule allows a request, it is refused and the refusal is recorded. Reaching one customer, workflow, or data type does not mean reaching the rest of the company.

Every agent has an identity and a named owner, so you can say which agent acted, what information it used, and what authority it held. Every answer shows its sources, and consequential actions, approvals, and corrections inside the Brain are logged. Authority is separated by kind. One agent may draft an email without permission to send it. Another may send routine updates while escalating anything that changes a contract or commits money.

It starts with one problem, not the whole company. The first engagement is one workflow, named users, and a measured result, plugged into the systems your team already uses. It has a defined end, and the choice is yours: go further, change course, hold, or stop.

Once that first workflow proves useful, the Brain extends to more of the business and keeps itself current as work happens. Then your team and your AI agents work from one shared view of the company, within the permissions you set. Typical starting points are a process that pulls from several systems, a decision that lives with one person, or a report rebuilt by hand every month. Most teams start with the Foundational Bootcamp first, so their people can build on company data with the right permissions in place.