By Mike Rudinsky, General Manager of Galvanize
Posted on 12/2/2025
AI is changing the way work gets done, but the challenge isn’t simply choosing the right tools or rolling out a new platform. It’s helping people understand what AI means for their roles, their workflows, and their confidence at work.
For more than a decade, we’ve trained builders: software engineers, data scientists, and product practitioners. But AI has expanded the scope. Training builders alone is no longer enough. AI touches every corner of an organization, which means literacy, fluency, and governance must be shared across leaders, users, and builders together.
Because AI is unfamiliar, fast-changing, and behaves differently across tools and tasks, people need human-led learning to interpret its outputs and understand how to use it in their own work.
The Shift: AI Is for Everyone, Not Just Builders
AI brings enormous potential, but also a level of uncertainty about job security, relevance, and the future of work that earlier technologies didn’t. Many people don’t know where AI helps, where it creates risk, or how it affects the work they do every day. That’s why AI learning must begin with clarity rather than complexity. Before teams design new workflows or adopt new tools, they first need a shared understanding of what AI is, how it behaves, and where it can be trusted.
The New Literacy
AI literacy has become the new baseline skill in the workplace. It’s not about turning everyone into a prompt expert, it’s about creating shared language and realistic expectations. When employees understand what AI can and can’t do, they become more confident and more willing to explore.
This is why we begin with literacy taught through hands-on interaction. Learners spot AI outputs, simulate how a model predicts words, and map where AI could support their own work. These experiences reduce uncertainty and give people the grounding they need to move forward with curiosity rather than hesitation.
Practical AI Upskilling
AI upskilling is fundamentally human work. We don’t start by teaching tools; we start with real problems, real workflows, and the friction people feel in their day-to-day tasks. From there, instructors guide learners as they experiment, try things out, and make sense of unfamiliar AI outputs.
Our instructors act as educators, coaches, and architects. They read how teams think in real time, ask targeted questions that surface assumptions, and connect every concept back to the pressures and constraints of actual work. They help people understand why a model responded the way it did, when to trust it, when not to, and how to adjust their habits accordingly.
The result is active, scenario-based learning that feels immediately relevant, builds confidence instead of reliance, and turns AI from an abstract idea into a practical, shared way of working.
Leaders, Users, Builders: A Coordinated Strategy
AI only delivers value when leaders, users, and builders learn in the right sequence and build complementary skills. Leaders set strategy and governance. Users redesign workflows and apply AI in daily tasks. Builders create the systems that make those workflows possible, and they benefit from deeper, more specialized training to support advanced applications — the kind of training we’ve always delivered. Helping builders go deeper is core to who we are. When each group develops the right level of literacy and fluency for their roles, AI adoption accelerates because teams are aligned rather than operating in silos.
Workflow Redesign Over Tool Training
AI tools become far more powerful when teams first rethink how their work gets done and then apply the tools where they create the most impact. Workflows are the backbone of any organization; they’re how strategy turns into action. Every role has workflows, whether it’s preparing a client update, reviewing data, coordinating a handoff, or making a decision under pressure.
In our programs, learners prototype AI-enabled workflows, iterate on prompts, run verification drills, map human versus AI responsibilities, and draft playbooks that can be scaled. These are guided scenarios, not company-specific projects, giving participants patterns they can bring back to their own work.
When people see how AI fits into the flow of their day, they stop asking “What can this tool do?” and start asking “What would it look like to do this work better?”
Responsible, Ethical, and Safe Use
Governance is important from the beginning, and it becomes even more critical as teams start putting AI into practice. Bias, privacy, and data handling must be addressed early, not after adoption. That’s why governance grows alongside fluency, helping people use AI responsibly as they build confidence and apply it to more complex tasks.
What Organizations Gain When Everyone Is Fluent
When leaders, users, and builders move through literacy, fluency, and governance together, AI stops being a set of disconnected tools and becomes part of how the organization works. Teams communicate more clearly, automate more effectively, and create more space for high-value human work.
AI doesn’t replace human expertise; it amplifies it. And the organizations that thrive in this next phase will be the ones that invest in their people as deeply as they invest in the technology itself.
That is the work we do at Galvanize, building AI fluency across leaders, users, and builders so teams can move forward together.
Let’s Collaborate
Galvanize helps organizations bridge the gap between strategy and execution by building the technical capabilities of their people. Our model — Collaborate, Translate, Innovate, Validate — ensures learning is directly tied to performance. Talk to us about scaling capability within your organization.