AI and Work Videos

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AI is changing work faster than most people can process, in every industry, not just tech.

I'm the CTO of a digital agency that builds large custom websites, enterprise-grade software, and increasingly, AI solutions for mostly Fortune 1000 clients. I've been building software for 30 years, and I'm watching this shift happen in real time.

It's not subtle. It's not theoretical. It's reshaping how work actually gets done. Developers, analysts, PMS, designers, everyone, everyone's job is changing, in ways that most people haven't caught up to yet.

In this series I'm gonna show you how I use AI, how my team uses AI how enterprises are adopting it, and what that means for your career going forward. If that interests you follow along. I'll be posting videos a few times a week.

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AI isn't wiping out white collar work. It's compressing it. And the shift's already underway in real businesses that we work with.

AI is not replacing entire professions, but it is replacing the slow parts: the drafts, summaries, research, Q&A, scaffolding, documentation, prep work.

When you remove all that, you don't need 5 people for a project anymore. Maybe you need two.

You have PMS that previously needed a BA to create requirements. They can use AI to take a first pass of that. Is it final? No. Is it enough to get started? Yes.

A dev that needed QA support, you can use AI to build their own test scaffolding. Now a strategist who needed a writer can spin up multiple outlines in minutes.

And again, this doesn't remove the need for people doing any of those jobs, but it does make it dramatically faster, easier, removes the busy work, and improves the quality.

So this is the real transformation. White collar work is not disappearing, but it is condensing. It's fewer people needed to build more output, and there's higher expectations on everyone.

And then you've got AI kind of quietly filling in all the details around the edges.

So to thrive, you need to learn to orchestrate AI across your entire workflow, and not just use it for one off tasks.

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The biggest impact of AI right now isn't about replacing people. It's about reducing the number of hours it takes to get work done.

If you look at most workdays, a lot of the time we spend is not the actual job. It's the friction around the job. You're waiting on replies, searching for context, drafting something that needs to get rewritten five times, sitting in meetings where the first 20 minutes are spent figuring out what's going on.

AI removes a lot of that friction.

And when you remove friction, the amount of time required to produce the same output naturally shrinks. Not the role, but the time attached to the role.

And this creates quiet structural changes inside organizations. Workflows tighten up. People get more done with fewer hours. Hiring slows. Backfilling slows. And the shape of work shifts, even if the org chart doesn't.

This is what happens when a technology that accelerates clarity, writing, coordination, and decision-making is implemented.

The part that matters is seeing where your own friction is, and how quickly you can offload it.

AI doesn't eliminate people first. It eliminates wasted hours.

And recognizing that early gives you a real advantage.

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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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If you're trying to figure out use cases for AI, you're going the wrong direction.

Most people approach AI by asking, what can AI do here? How can I use AI? Can AI help me? Executives do it, managers do it, teams do it. It's logical, but it's the wrong lens.

The better question is, where is the bottleneck? Where are you waiting? Where are people confused? Where does work slow down? Where do things get lost? Where is clarity missing? Where is rework high? Where do people spend time on things that don't actually matter?

AI is not about sprinkling capabilities across your workload. It's about removing friction from the places that slow everything else down.

When you find a bottleneck, you'll find leverage. And when you apply AI to leverage, the impact is disproportionate.

So stop searching for use cases. Start identifying bottlenecks, and the AI fits naturally once you understand where the friction is.

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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 isn’t an overnight revolution

It’s a decade long shift.

Systems change, and it is already underway, but it’s unevenly distributed.

AI is removing friction inside roles faster than companies can redesign them. That raises expectations for output while shrinking teams.

Quality and velocity go up, but headcount doesn’t.

For individuals, that creates both leverage and pressure. The tools that save time also raise the bar for competence.

Every role is widening. There’s more scope, more automation, and less margin for delay.

Time to market becomes the driver.

For organizations, this means rethinking foundations and training. Most don’t yet have enterprise-grade infrastructure or interoperability between systems.

Adoption will stay patchy, creating both job losses and new opportunities.

Every company serious about AI will need to reinvest in training, process analysis, and responsible automation.

It is a long journey. The human part, learning, adapting, deploying, takes time.

For workers, the takeaway is simple: adapt now. Use AI daily. Offload repetition. Build fluency before your organization mandates it.

AI will enter every workflow before it redefines every role. That gives you time to adapt, but not time to wait.

The next decade belongs to professionals who combine human judgment with machine scale execution.

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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 is making everyone louder, not just faster. Writing emails, doing research, writing code, those things all took time and effort. And the production of that content is what’s been the bottleneck in knowledge work for a very long time.

But now that first draft is basically free. So all the ideas that used to get filtered out by effort don’t get filtered out anymore. Every team can generate five proposals instead of one, and every person can produce a strategy in an afternoon.

And Herbert Simon described this problem decades ago. When information becomes abundant, attention is what becomes scarce. Because more output doesn’t automatically lead to better decisions. You’re not just choosing between two options anymore. You’re choosing between 20 very convincing options.

So the companies that win in an AI world probably won’t be the ones generating the most content. They’ll be the ones that get very good at deciding what actually deserves attention.

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Creating convincing information used to be expensive. If you wanted something to look legitimate, a report or research summary, an article, it took time and expertise to put it together.

But now a single person can generate hundreds of possible articles with fake analysis, synthetic images, and even very realistic voices and video. And they can test different versions of that same story until one spreads.

And we already know how information behaves in networks. Studies of social media have shown that false news travels further and faster than true news. And it’s not because of bots. It’s because people are more likely to share things that are surprising or emotionally charged.

So when you lower the cost of producing convincing content, that dynamic scales. And the problem isn’t just misinformation.

It’s that when everyone can produce convincing looking information at scale, people stop judging information based on the content alone. Instead they ask, or they should be asking where did this come from. Who produced it, and is the source reliable. Because when credibility becomes easy to imitate, trust is the thing that actually matters.

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Even though AI makes it easier for people to produce good ideas, think about how LLMs work. They’re trained on massive amounts of human writing, books, articles, research papers, code.

So when a lot of people ask the same type of question, they often get variations of the same answer, and that makes the average quality of ideas go up. But the range of ideas can shrink.

And there is some early research showing this. When people use generative AI in creative tasks, their individual output often improves, but the ideas across the group become similar. Everyone performs better, but everyone starts clustering around the same patterns.

And you can already see it. If you ask 10 startups how to launch a product, or ask 10 marketing teams how to run a campaign, a lot of the answers start to sound familiar. So if everyone’s drawing from the same playbook, being different can be your competitive advantage.

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How do you know something’s real? Not does it sound convincing, or does it look professional, right? A report can look legitimate, and a voice can sound authentic, and a photo can look like evidence, but none of that tells you where it came from.

Economists have studied a version of this problem for decades. George Akerlof called it the market for lemons. The idea is you’re selling a car, you generally know if it’s a good car or a lemon.

But if you’re buying a car, it’s hard to tell. So you’re not willing to pay as much, because you assume it’s probably a lemon. And that forces prices down, which prices good cars out of the market.

And that same dynamic can happen with information. When convincing content becomes cheap to produce, people stop trusting the content itself, and they start relying on signals like brand and reputation, and verification.

Who published this? Where did it originate? Has this source been reliable before? And organizations that build that real credibility end up with something that’s very difficult to fake.

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Technology moves fast, and the institutions around it don’t. AI is an example of that, but it’s far from the only one.

AI can write reports, generate code, produce images, analyze data. But the systems that govern how we use technology, laws and standards, internal policies, professional norms, take years to adapt.

And these major innovations are called general purpose technologies. Things like electricity, computers, or the internet.

And the biggest changes don’t come from the technology alone. They come from everything that has to reorganize around it: companies and workflows, regulations and governance.

And that adjustment takes time. It’s already happening, but it’s not a quick process. It’s a systemic change. Governments are creating new laws, companies are building new policies, and industries are trying to figure out what responsible use means.

So the messy part we’re in right now, unfortunately, is normal. Every general purpose technology creates a period where the capability exists, but the institutions that stabilize it haven’t caught up yet.

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AI makes writing, analysis, design, coding cheap. Things that used to take hours or days can now happen in minutes.

And when the cost of producing something collapses, the systems behave differently.

You see it in the noise explosion. When ideas are cheap to produce, organizations get flooded with them, and then the bottleneck becomes attention.

You see it in misinformation. When convincing content becomes cheap to generate, credibility gets harder to judge.

You see it in idea convergence. AI can raise the average quality of ideas, but it can also pull people toward the same answers.

You see it in trust systems. When content itself can’t be trusted, people rely more on signals like reputation.

You see it in institutional lag. Technology moves quickly, but the rules and systems around it take time to catch up.

And none of this means that AI is bad for organizations. It means the advantage used to come from the ability to produce, but increasingly it is coming from the ability to filter, decide, coordinate, and build trust. Because when execution becomes cheap, the systems around that execution become your advantage.

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So if the hard part isn’t doing the work, then deciding what work to do becomes the hard part.

AI makes producing work faster, cheaper, easier, which moves the bottleneck. The constraint becomes about organizing, not just executing.

Because when the cost of making something drops, everyone makes more, right? There’s more ideas, more code, more reports. But now you have a flood of output, and who decides what gets shipped, who integrates it, who makes sure it works across teams?

And that’s where the bottleneck goes as coordination. That’s where the leverage point is.

For example, cloud computing made launching software cheaper, but the winners weren’t just the ones with servers. They were the ones with faster decisions and better products, and tighter feedback loops.

So if you’re thinking AI will give you an edge just by producing more, you’re wrong. The edge comes from how you organize all that output.