AI and Work Videos — Page 2

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So why hasn’t AI made productivity skyrocket overnight? It’s made creating emails, research, code cheap, but the hard part is connecting the dots, coordination across teams and systems and decisions.

More output floods in. There’s more drafts, more ideas, more analysis, more code. But someone has to prioritize it, someone has to integrate it, someone has to decide what matters.

And that coordination is expensive. It slows everything down if you don’t have the right system.

So you have an engineering team, right, and they’re using AI to turn out code. They’re likely producing much more code than they were before, but now you have more code to manage.

And if reviewing, merging, testing, and shipping it still moves at the old pace, you haven’t removed your bottleneck, you’ve shifted it downstream.

So if two different companies are using the exact same AI tools, and one thrives while the other stalls, the difference is who coordinates better.

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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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So when everyone has powerful tools, what actually sets you apart?

When execution is easy, your competitive advantage shifts to the systems that organize and coordinate that execution.

If anyone can produce research and analysis and code, capability gaps shrink, and the advantage moves to how you integrate workflow systems, feedback loops, and decision-making processes.

Those systems are harder to copy, and that’s where the competitive advantage is.

If we think about two competing startups, they’re both using Claude Code. They’re both using the same skills. Who wins?

It’s the one with the better process.

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Technology changes constantly, and the people who succeed are the ones who learn the fastest. That is the ultimate competitive advantage. AI lets you experiment faster. You can try ideas, test approaches, ask questions, iterate.

But learning only happens if you actually try something. See what happens and learn from it. And here’s the easiest way to see this for yourself. Think of something you don’t know how to do, something useful or not.

Open a model, explain what you’re trying to accomplish, give it some context, and start working through it. Do that consistently, and you’ll learn faster than the environment changes. And learning is a huge advantage.

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Demis Hassabis, who’s the CEO of Google DeepMind, recently stated that we are in the foothills of the Singularity. Jensen Huang, who’s the CEO of Nvidia, has stated that he thinks they’ve already achieved AGI. And AGI, if you’re not aware, is artificial general intelligence. People love to talk about AGI. It’s simple to understand. It’s the benchmark which we have: an AI that’s just as good as a person is at doing whatever.

But I think the reason that most people really like to talk about AGI is because it’s kind of scary. It’s once we have AGI, then what? Right, if I have an AI that’s as good as a person at doing a job well, then why would you hire the person for 100 times the price when you could have AI do it? And so it’s terrifying, the idea that we’ll have AGI that is as good as us at doing effectively anything. So that’s scary.

I just don’t think it matters. Like, what we have with the current technology with AI is already so powerful and transformative that whether or not we have AGI is almost beside the point.

I haven’t made videos for a month because I’ve been busy, and most of what I’m doing at work is business process consulting, business process automation, and modernization. So we are talking to companies about how to reorganize their business processes within their organization to effectively take advantage of AI. It’s what do we need to do to change the way we do work to take advantage of this technology, to be able to do more work faster and cheaper?

And this is the change that most large organizations are going through now. These are the things that they are working on. The scope and scale is massive. It’s effectively every business process in every organization can be affected by AI.

And so whether or not we get to AGI, whether or not we already have AGI, if you’re waiting for AGI to figure out the then what moment, you’re too late. We’re already at the then what moment.

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The more I work with AI, the less convinced I am that the intelligence of the model is currently the bottleneck.

The models have improved incredibly quickly, right? A few years ago, it felt kind of like a high school student, and today they feel more like pretty capable professionals. And we can debate how smart the models are, but it’s obvious how quickly they’ve improved. And it makes me wonder how much additional intelligence will actually change the equation.

If I think about why experienced people are valuable, it’s easy to say experience, but what does that really mean? Some of it lives in people’s heads. A lot of it lives in people’s heads. But a surprising amount of it actually gets written down, like architecture decisions and design documents, emails, meeting notes, code, project plans, lessons learned, and retros.

People naturally accumulate knowledge by doing the work, and AI doesn’t. But that doesn’t mean that AI has to learn it the same way that we do.

If every design decision, trade-off, meeting, project success story, and failure, if all those things become searchable and referenceable, how much smarter do the models actually need to be?

The work we’re gonna be doing over the next decade, the integration of AI into business processes and into people’s lives, is taking decades of accumulated organizational knowledge and making it available to models, not just making the models smarter and smarter.

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I spend all day, like all day, working with AI, and I still haven’t seen an AI that could replace any of my team members.

What I’ve instead seen is people becoming dramatically more capable because of AI. Not replaced, but amplified.

And the conversation around AI often gets framed as humans versus AI, but there’s a much more reasonable possibility, which is AI and humans integrating and working together.

I haven’t seen AI replace developers or architects or project managers, or business analysts, marketers, any role that I work with regularly.

But what I have seen is people becoming more productive, more capable, taking on larger problems, expanding their skill sets.

The future isn’t AI taking over everything that people do. The future is people becoming much more capable because they use AI effectively.

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So I started this week talking about how quickly AI is getting smarter, and I’m ending the week with how intelligent people really are.

I spent a lot of time this past week with my team on multiple client projects and software delivery challenges. And it’s not about how much AI can do, but it’s about how much judgment people apply subtly without even really thinking about it.

AI is incredible at generating code and content and synthesizing information and finding patterns in things. But this week, as I watched people recognize incomplete requirements, spot contradictions before they became problems, balance competing priorities, navigate organizational politics, and ask questions that completely changed the direction of a conversation, I noticed that none of those things looked particularly remarkable while they were happening.

The more experienced someone is, the more invisible their expertise becomes.

At the beginning of this week, I wondered whether AI was approaching the capability of a mid-career professional. And by the end of this week, I wasn’t thinking nearly as much about AI. I was thinking about how much of professional expertise is really judgment.

The more I work with AI, the less impressed I am with memorized knowledge, and the more impressed I am with human judgment and nuance and understanding. And those things are a lot harder to automate than people think.

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Last week, my team at Sagepath Reply and I ran an AI enablement workshop with one of our clients using Optimizely Opal. Opal is Optimizely’s agentic marketing platform that sits on top of the Optimizely ecosystem and integrates really well with their tools.

We don’t start by talking about AI at all. We start by opening a FigJam board and listing what the teams responsible for campaigns, trade shows, email marketing, website content, and product launches do. Then we break those responsibilities down into smaller, individual tasks underneath them, like writing emails, building landing pages, or preparing for trade shows.

Even within those, there are smaller tasks. If you’re building a landing page, you need to create copy and assets, consider the audience, personalize, and analyze click-through rate and marketing uplift.

Once that’s mostly mapped out, we start asking questions. Which of these things are repetitive? Which happen constantly? Which are tedious? Those become our first candidates for automation because nobody in the room is trying to replace marketers. We’re trying to look at the process of marketing, break it down, analyze it, and then rebuild that process in a more efficient way.

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So when we’re running these AI enablement sessions, once we’ve identified the first candidates for automation, we start building Opal agents to handle them. And things like landing pages, campaign emails, website updates, they’re good candidates because they’re pretty structured, they’re repeatable, and they never really stop coming.

And so as we start talking about automating those tasks, people don’t really ask, okay, well what work is left for me? It’s pretty obvious what work is left for them. What they start asking is, what work’s never really been possible before, that is now?

So what if competitor research wasn’t something you did once a quarter, or once a year, or never? What if voice of customer analysis was always current, instead of based on the last survey that you did, which was who knows how long ago? What if every campaign started with current market research, instead of whatever you were able to get together, in the schedule that was allotted for it?

Before AI, a lot of that work wasn’t economical. You couldn’t justify spending days researching every competitor before every campaign or continuously analyzing customer feedback. The time just wasn’t there.

AI changes that equation. Work that used to be too expensive or too slow, or too tedious becomes practical.

And so, AI is not just helping marketing teams finish the same work faster. It’s changing what a marketing team is capable of doing.

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So by the end of the workshop, we’d already started building some automations, and what stuck with me was how the conversation changed. Instead of asking, how do we save time, people started asking, what should we do with the time we get back? And the answer is not really work less, sorry. It was run more campaigns, experiment more often, spend more time understanding your customers, continuously analyze competitors instead of just reviewing them every few months, invest more time in figuring out what’s actually working instead of just simply producing more marketing assets.

And it’s a completely different way of running a marketing organization. AI changes the economics of work, and as that happens, the work that creates the most value changes too. And that’s why I keep saying AI automates tasks, not roles. The tasks change, the role gets redesigned, and I think that’s one of the biggest opportunities that AI creates.

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Someone I talked with this past week described an interesting use case involving feature flags. They have this recurring process where several different feature flags, that are split across a few different systems, need to be turned on or off. And accuracy matters because if you miss some of them, it means the process wasn’t completed correctly.

So he built a tool to handle it, and nobody’s job disappeared. The person responsible for the task still owns the outcome. The process just became faster, more reliable, and easier to maintain.

And I think that’s a useful way to look for AI use cases. You find a point of friction. You look for a task that happens repeatedly, follows understandable rules, and produces a result you can verify. And then you automate that task. You make the person doing the job more capable, you learn from it, and then you find the next point of friction.

You don’t have to start by transforming your entire company. You can start by making one frustrating part of someone’s job work better.

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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.