Turning AI Promise Into Performance

By Mike Rudinsky, General Manager of Galvanize
Posted on 2/2/2026

Organizations don’t struggle to adopt new technology because the tools don’t work. They struggle because the distance between strategy and skilled execution is often underestimated.

We’ve seen this before.

During the cloud era, companies invested enormous sums in new infrastructure, systems, and tools. Over the course of the 2010s, billions of dollars were spent modernizing platforms with a clear, measurable value proposition and relatively well-defined implementation roadmaps. It was reasonable to move deliberately. Many organizations did.

Only later did a common realization emerge: despite the investment, teams didn’t yet have the skills required to operate effectively in the new cloud paradigm. Strategy and infrastructure were in place, but skilled execution lagged behind.
That gap is the bridge Galvanize was built to support.

AI changes the equation.

With AI, the value proposition is less defined, even as the urgency to act is greater. Organizations don’t have the same luxury of waiting. Advantage will accrue quickly to those who start testing, learning, and adapting early, while others remain on the sidelines.

AI also depends on human adoption much sooner than cloud ever did. Value isn’t unlocked after implementation. It emerges through iteration, as people experiment, refine workflows, and discover what augmentation actually looks like in their day-to-day work. That makes learning harder to design upfront, but far more critical to embed early, while the building phase is still in motion.

We’ve Been Here Before

For more than a decade, Galvanize has worked with organizations navigating moments of major change. We’ve supported teams through shifts in tools, platforms, and ways of working, from cloud adoption to software development practices like Extreme Programming.

In every case, the lesson has been the same: technology changes quickly, but behavior changes more slowly. And without behavior change, value doesn’t materialize.

We’ve helped organizations modernize by learning forward, not by stopping work to redesign everything at once. Sustained, applied learning allowed teams to evolve incrementally while continuing to deliver.

What’s different about AI is not just its power, but its reach. AI touches nearly every role, every function, and every workflow in an organization. This isn’t a transformation for a single team or department. It’s an organization-wide shift in how work gets done.

The Missing Piece in AI Adoption

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

Most AI initiatives begin with strategy. Leaders evaluate tools, assess risk, and define where AI could have the greatest impact. That work is essential. But adopting AI requires participation and collaboration by those who own specific workflows. They are best positioned to, in the context of AI, learn new ways to approach problems, decisions, and work. Applying AI safely and consistently is a teachable skill organizations have to build deliberately.

AI creates significant value when it changes how work actually gets done. Use cases emerge through experimentation and learning has to happen during execution, not after plans are finalized.

Why Training Can’t Be an Afterthought

Too often, training is treated as something that happens after decisions are made and tools are selected. When that happens, organizations hit predictable limits:

• Leaders understand the opportunity, but teams don’t know how to act on it
• AI feels like something happening to people rather than with them
• Fear and resistance slow adoption, even when the strategy is sound
• Recommendations remain theoretical instead of operational

When learning is embedded alongside experimentation, this dynamic changes.

As more people across the organization develop practical AI skills, value no longer depends on perfect central coordination. Teams begin identifying opportunities, testing improvements, and unlocking efficiency within their own workflows.

When leaders, users, and builders learn together:

• Strategy is tested against real work early
• The best business cases emerge from actual workflows
• Governance becomes practical instead of abstract
• People see AI as a valuable, practical tool that supports their work and improves how they perform
• Buy-in is built through participation and transparency

This Is the Work We’ve Been Preparing For

We design and deliver training that’s grounded in real work, not hypothetical use cases. We embed learning into production environments to reinforce new skills and habits. And we focus on behavior change, not just knowledge transfer.

With AI, this approach becomes essential. Learning can’t be static or deferred. It has to be embedded, adaptive, and closely aligned to what teams are discovering in real time.
That’s why the organizations that succeed with AI will be the ones that invest in their people as intentionally as they invest in the technology itself.

That’s the work we do at Galvanize. And it’s the work this moment demands.