How Organizations Are Responding Videos

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Many companies aren't ready for AI yet, but that doesn't have to slow you down.

Organizations move slowly, and for good reasons. They need policies, they need approvals, they need alignment, training, security reviews, and budget cycles. And all of that takes time.

Meanwhile, this technology is moving very quickly, much faster than the social and adoption curves inside most companies.

So you end up with this gap where the tools evolve weekly, but the organization adapts yearly. So that's not a failure, but it is how large systems work.

Individuals, on the other hand, can move immediately. You don't need a policy to rewrite an email, or to summarize a meeting, or clarify a thought, offload a tedious task, or explore a new workflow.

Companies wait for structure. People can start with habits.

And that creates a meaningful advantage for anyone who's willing to move a little faster than the environment around them.

Your company will catch up, but you don't have to wait for that. You can become more capable today.

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The adoption percentage in this transcript is Mike’s directional estimate, not a universal benchmark.

Most companies today are not AI driven, AI native, AI first.

They're a handful of people using AI tools in a few corners of the business. And that's the first stage of a long maturity curve.

Adoption moves through layers. It starts with individuals, and they use AI to summarize notes and draft an email, or experiment with built-in vendor tools.

And 2025 is really where we've seen the beginning of that. The point where people are just starting to get AI enabled tools in their day to day stack.

And these early steps are about proof of concept, small MVPs, quick experiments, and just getting something working.

And then once multiple people start using AI, consistency becomes more important.

And so we see teams standardizing on how they use tools, shared prompts, templates, and workflow integration.

So they start coordinating their use of AI across projects.

And then the next level of structure is a full department. And that's often IT or marketing, which are moving the fastest right now.

Different teams within those departments start to align around broader systems and shared data.

And then it goes to enterprise wide integration. It's full end to end automation across business processes.

And that's kind of the current technical end state for AI adoption today, before we start talking about any future AGI territory, which at the moment remains science fiction.

And right now, maybe 15 to 20% of organizations even have the foundational infrastructure for that upper tier.

Everyone's either experimenting with fragmented projects, or actively working to build that foundation.

And AI maturity is not just about tools. It's about repeatability, reliability, and integration.

It's not a switch. It's a staircase.

So figure out where your organization stands, and focus on the next step, not the entire climb.

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The adoption timeframes in this transcript are Mike’s directional estimates, not universal benchmarks.

Integrating AI across the company is not like installing software. It’s a rebuild of how the business actually works. Even with clear intent and full leadership support, and like everybody’s all in, it’s still at least an 18 to 36 month journey, and realistically more like five plus years.

AI exposes every structural weakness an organization already has. To automate a process you first have to understand it end to end, the data, the decisions, the exceptions, and most companies don’t. You need the foundations first, eliminate data silos, unify formats, break tool fragmentation. Deal with legacy systems and vendor lock in, that make integration slow and brittle.

Build security and governance, because once systems start sharing data and models, risk increases dramatically. You need policies and permissions and audit trails and accountability. Someone has to own every automated decision. You can’t just blame the AI, and all of that is cross functional, and it is expensive and it’s political, and it has to be stable before automation can scale.

And then comes training. The tools evolve faster than the habits. We all still have pre AI ways of working, and changing those habits across an entire organization takes time. And some people will resist, and they will have to see real value before they adopt. Implementation must pair with enablement, training documentation, feedback loops, shadow runs. Otherwise new workflows collapse under normal work pressure.

Enterprise AI is not a dashboard or an app, it is foundational infrastructure. If you treat it that way you’ll gain durable capability, and if you treat it like a plug in you’ll get chaos.

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AI doesn’t spread evenly inside a company. It moves through departments in a predictable order starting where work is measurable structured and repetitive.

Customer service often moves first. It’s the most directly measurable function. Faster responses lower ticket volume higher satisfaction and teams already work through digital systems chat email CRM so integrating AI for triage summarization and support is straightforward and platforms like Salesforce Service Cloud have made that transition more seamless.

It often moves in parallel. Developers and operations teams already work in structured systems version control CI CD tickets monitoring. That makes AI integration natural for code completion documentation testing and incident response. Tools like Github Copilot or Cursor extend capability without adding friction. They become collaborators not replacement.

Then comes marketing. The marketers are used to experimenting with new tools. The martech stack exploded long before AI email automation personalization AB testing customer data platforms. Now that major systems like Optimizely or Salesforce have embedded AI directly marketers can produce campaigns assets and reports faster without rebuilding their workflows.

But beyond that adoption slows. Finance HR legal and operations face heavier regulation and fragmented systems. They will get there but slower and more carefully. Culture matters too. Technical and service teams expect change. Governance driven teams manage it carefully and both are right for their context.

AI maturity tracks readiness not hype. It moves from service to technical to creative to operational. If you want traction start where your data is clean your systems connect and your teams already trust automation.

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Last week we hosted an event called Reply Day. We invited a group of executives in energy, manufacturing, financial services, retail to the Porsche Driving Experience, and yes, we drove cars around a track, which is obviously very fun, but that wasn’t really the point. The real reason they came was to talk about what’s changing inside their organizations and how fast it’s changing.

So we spent the day discussing how AI is reshaping customer discovery, how evaluation of products and services is shifting, how staffing models are compressing, how automation and robotics are expanding, and what leaders actually need to do to respond responsibly.

So I taught a joint session with Optimizely on how AI is changing the way customers discover and evaluate digital experiences, and if your content isn’t structured for AI-mediated discovery and not just human browsing, you’re really gonna feel that impact.

We’ve been fortunate to do this work at scale at Sagepath Reply. We were recently named Optimizely’s North American Solution Partner of the Year, and we also recently won Kentico’s Site of the Year for one of our clients, Oppenheimer. And while I’m very proud of those awards and the work my team put into them, the real story is that executives are not ignoring these changes in technology and customer behavior. They’re trying to understand them clearly so they can adapt their organizations without breaking them.

So over the next few videos, I’ll break down some of the themes we covered, what’s changing in customer behavior, team structure, and digital architecture, because the conversation that’s happening in those rooms is very different than what we see in the headlines.

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It’s pretty well established that personalization drives revenue, and increases it.

But there’s a tension that leaders wrestle with, is that the more precisely you tailor experiences, the more customers wonder how you know what you know.

And AI increases the ability to infer intent, but it also increases that opacity.

Customers are expecting relevance, but they also expect ethical boundaries. And if personalization feels invasive, performance drops. If governance slows innovation, then growth stalls.

So personalization is not just a marketing lever, it is a governance problem. And the companies that figure that balance out early are going to outperform the companies who don’t.

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AI capability isn’t the same as AI readiness.

As AI gets more powerful, governance becomes the key constraint.

The organizations that can manage risk well, deploy faster and scale better.

And it’s not really about the tools.

It’s about ensuring security, reliability, and compliance, because if you can’t audit it and monitor it, then you can’t scale it.

Governance doesn’t slow you down, it’s what lets you move fast safely.

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AI exposes how poorly documented most organizations actually are. A lot of what makes a company work lives in people’s heads. It’s in old conversations, Slack messages, emails, decisions that everybody remembers, but nobody ever wrote down.

And then we give a task to AI and expect it to understand how our organization works, but it can’t read your mind. If you don’t give it the goal, the relevant history, your standards, your terminology, examples of good work, constraints it needs to follow, it’s gonna fill in those gaps, and you’re probably not gonna like what it fills them in with.

The same documentation that gives AI useful context also makes work easier to teach, transfer, maintain, and improve.

So when AI produces something that doesn’t fit your organization, it’s likely not the model that’s the problem. You should ask whether your organization ever documented what good actually looks like.