Know Before You Ask

On Friday morning’s Coffee with Digital Trailblazers, Isaac Sacolick’s 191st episode, we spent an hour on why AI adoption stalls. The panelists all made excellent points: redesign the work, win the middle, measure adoption, not activity. All good advice and mostly aimed at the people doing the work.

Listening, it occurred to me that we were skipping the first step. What about the board? What about senior management? What about the CIO?

In thirty years of running technology organizations, I never asked anyone to do something I wasn’t willing to do myself. “Do as I say, not as I do” isn’t leadership. It’s a memo.

Yet that’s exactly what most companies are doing with AI. Board members read about it. The CIO saw a demo. And now everyone else is told to embrace AI and magically transform the company.

We used to call it airline magazine syndrome: you read an article at 35,000 feet and land thinking you’re an expert. Today it’s social media syndrome. You watched a three-minute video and suddenly you have an AI strategy.

Here’s the problem. If you haven’t touched it, seen it, and felt it, you don’t understand it. And AI isn’t one tool that does everything. Generative models, machine learning, agents, and a dozen other things are each good at some jobs and terrible at others. On Monday I wrote about the Department of Know. This is what it looks like from the top of the org chart.

The order matters. There are four steps:

1. 
   The CIO goes first. Start with yourself. Not a briefing, not a vendor demo. Hands on the keyboard until you can speak intelligently about what these tools are and what they aren’t.
2. The middle layer goes next. Give your managers the same chance to play. They will make or break every rollout you attempt, and right now most are on the sidelines.
3. The board and senior management get their hands on it, then set strategy. Same keyboard, same rule. Now the expected outcomes are grounded in something real, not an airline magazine.
4. Then the strategy goes to the rank and file.

A note on step 4. The sandbox is open to everyone from day one, and I’ll keep saying so. Curiosity needs no permission slip. The sequence governs who sets the strategy and the mandates. If the people setting them have never used the tools, you get a mandate nobody can follow.

I’ve been saying a version of this for fifteen years. You don’t earn the seat by holding it. You earn it by knowing the business and knowing the technology, in that order, and never asking people to go where you haven’t gone yourself.

I practice what I preach. Over a week and a half ago I started working with Muse, The AI agent that now handles a few regular chores for me, including posting my weekly LinkedIn invitation for our SIM Members in Transition session. This Friday morning Muse looked at my calendar, saw that I am at a conference next Thursday morning. It asked me whether I still wanted the post to go out. When I suggested we check with my co-host, it drafted and sent him an email. Muse later reported back that it had received no reply and was going ahead with the post. 

Nobody mandated that. There was no strategy deck. I played with it until I understood what it could do. This happens to line up with the first of my Three Golden Rules : I always want to be the first to know. This applies to agents too.

That’s the whole point. Know before you ask.

Captain Joe

Follow me on X @JPuglisiLLC

The Department of Know

This past Friday morning I appeared on Isaac Sacolick's "Coffee with Digital Trailblazers." The topic was "Vibe, Buy, or Automate? Deciding How to Build AI Agents." There were several of the finest minds in the business on the panel, another 25 or 30 sharp people in the audience, and everybody was making excellent, valid points.

Listening to all of it, it hit me that the conversation itself was the message. Oh, my word, this is a complex problem. Nobody on that call was wrong. And that's exactly the trouble.

When everybody's right and the problem is still confusing, you don't have a tooling problem. You have a knowing problem.

So, I told the group that we, the people who lead technology, have to become the Department of Know (K-N-O-W)—not the Department of No (N-O).

Our job as technology leaders is to be the guiding light that looks at each proposed use and says one of three things:

1. Vibe It: Yes, AI makes sense here, but it can be done with a short, well-defined program. Vibe-create it in a small blast zone and move on.

2. Automate It: The process is stable and boring. Use deterministic automation. Boring wins.

3. Engineer It: Whoa, this is going to need a whole array of agents collaborating to create new value. Let's engineer the living daylights out of it, get the data right, put the guardrails in, and keep a human in the loop.

You can't do that with a policy memo. And you definitely can't do it by handing people the tools and saying, "Here, create value, save time." Give them a sandbox, sure. Let them play, let them get familiar. But a sandbox doesn't generate purpose. Purpose comes from somebody who knows which tool fits which job, carefully evaluating case by case.

Here is the curriculum—the three questions I brought to the show that you should teach your people to ask before they build anything:

1. Can you state the job concisely? Vibe coding is cheap when the ask is crisp and brutally expensive when you're exploring as you go. If you can't name the exceptions the business actually runs, you don't have an agent project—you have a guessing project with a credit card attached.

2. Is the process stable, or still being invented? If it's stable (rules known, inputs clean, exceptions rare), automate it. Agents earn their keep where judgment lives: places where you deal with messy inputs, exceptions, and decisions that need context and data lineage.

3. Who owns the swarm when it grows? Every agent needs three things on day one: an owner, a cost line, and a kill switch. An agent without an owner is just shadow IT with a better vocabulary.
   

Our host, Isaac Sacolick, published a sharp 2x2 matrix: buy vs. build, rules vs. judgment. My footnote: the "buy judgment" quadrant is where the swarm breeds fastest, because buying feels like someone else's problem. It isn't. 

Here are two quick examples from my own work last week:

• Task 1: I keep a spreadsheet with attendees of the weekly Thursday morning SIM MIT session. Updating it by hand is tedious, and writing macros isn't my strength. But I didn't need an agent; I needed Gemini to write a simple macro. I pasted the code into the sheet, and now it automatically sorts and highlights rows on every entry. Deterministic problem, simple tool.

• Task 2: In contrast, I gave Muse access to LinkedIn and instructed it to post my weekly meeting invitation every Friday around 8:15 AM, advance the date to the following week, and skip holidays like Thanksgiving. It posted a test on Thursday. Then, on Friday morning, it recognized it had already run, skipped the duplicate post, and confirmed it was set for next week. That requires context and judgment.

That's the whole sermon. The technology leaders who win the agent era won't be the ones with the most tools. They'll be the ones who built the Department of Know—the people everyone comes to with an idea, a need, or a concept for guidance. And they will teach their organizations to ask these three questions for themselves.

Start with a sandbox. Follow with office hours. End with people who know.

Captain Joe

Follow me on X @JPuglisiLLC

An EMP elevates Transformation to Reinvention

I've been saying for years that every major technology wave brings with it a new set of acronyms and a new generation of enthusiasts eager to deploy them. ERP. CRM. SCM. We built careers orchestrating the implementation of these systems. We transformed companies, changing the way employees worked, how customers engaged, how products moved through supply chains. We were proud of it, and rightly so.

But I want to propose something different today. A new frame. A new acronym. Allow me to introduce the concept of an EMP, an Enterprise Management Platform.

Here's why it matters, and why it's categorically different from everything that came before.

When we implemented ERP, we were transforming a company. We were changing how people worked.
Humans were still there, in greater numbers than before, in fact, just working differently, more efficiently, supported by technology. The technology served the workforce. The workforce ran the business. That's been the paradigm for decades.

AI in general, and agentics in particular, changes that equation entirely. We are no longer talking about orchestrating a transformation of select processes, or even an enterprise-wide transformation. We are talking about orchestrating the operation of the business itself through agents and robots. Technology doesn't support the workforce in this model. In many cases, technology becomes the workforce.

Think about what it meant to implement an ERP. You had a business. You had people running that business. You were asked to improve how they operated. Success was measured by adoption, efficiency gains, cost reduction. You were, in essence, a very sophisticated tool-giver. And a good one at that.

Now think about what the EMP requires. You're not being asked to improve how people work. You're being asked to envision an organization where the agents are the workers. Your procurement agent negotiates. Your finance agent approves. Your logistics agent schedules. Your customer service agent resolves. The CIO, or whoever owns this portfolio going forward, is no longer primarily a technology implementer. They are an organizational architect. They are designing and operating a digital enterprise.

That is a fundamentally different job description.

This mean for those of us who have spent our careers implementing ERPs, managing CRM rollouts, optimizing supply chains? It means we need to elevate our thinking. The era of the technical specialist, the person who is valuable because they know SAP modules or Salesforce configurations, is giving way to the era of the digital enterprise architect. Someone who can envision a company that runs on agents and robots, design the oversight structures that keep it honest, and preserve space for the human creativity that no model can replicate.

That's the EMP. And if you're a technology leader today, you'd better start getting comfortable with it.

The horizon isn't ten years out. It's next quarter.

Captain Joe Follow me on Twitter @JPuglisiLLC

Three Golden Rules for AI Agent Implementation

Last week on Isaac Sacolick's LinkedIn Live show "Coffee with Digital Trailblazers" the topic was AI agents at work and how IT and HR should collaborate to drive adoption. It occurred to me that my three golden rules apply perfectly to this new world of AI agents. Let me explain.

Big tech is expected to spend nearly $500 billion on AI infrastructure by 2026, up from $100 billion annually before ChatGPT arrived on the scene. Microsoft, Google, Meta, Amazon, and Nvidia are all making big bets. Meanwhile, over 50 large SaaS and security companies are rolling out AI agents and related capabilities. There's a lot of noise and excitement, but here's what's missing from most of the conversation: How do you actually make this work in your organization?

Anyone who has been a member of my staff will tell you that to get along with me you only need to adhere to my golden rules. These are very simple, straightforward and quite reasonable. Nothing too complicated to remember or follow, and they have served me well for many years. Now it turns out they work beautifully for managing adoption of AI agents too.

First golden rule: When something, anything goes wrong I always want to be the first to know. Phone me, text me, send a telegram or message on a carrier pigeon. There is nothing worse for me than to hear about a systems related issue from someone outside of my department.

The same principle applies to AI agents. According to recent reports, 87% of enterprises have Microsoft Copilot enabled, more than half the agents access sensitive data, 90% are over-permissioned, and they move 16 times more data than humans. That's a lot of activity happening in the shadows. You need visibility before your CFO finds out from Twitter that your AI agent just did something embarrassing, costly, or worse.

Deploy AI agents with transparent monitoring. Know what they're doing, what data they're touching, and what decisions they're making. My focus is always on understanding what happened, fixing it, and then devising a means of ensuring it can never happen again. The only fatal mistake you can make is trying to hide a problem from me or in this case, not knowing there's a problem until it's too late.

The second golden rule: Stay on task but see the big picture. There is an old expression that goes you can't see the forest for the trees. I remember the time I waited patiently behind a waitress who was diligently refilling the large coffee urn in the lobby of a hotel where I was staying. She was so focused on her task that she couldn't see I was standing right there with an empty cup badly in need of a refill.

So, whatever we are doing, no matter how "critical" we think it may be, we should always be certain it will not somehow adversely affect the operations of the company. Executives need to evaluate how AI enhances their products or service.  It is good practice to lift your head and take in the big picture from time to time. It will also keep you from walking into a telephone pole.

The third golden rule: Your opinion matters. It struck me that I embrace the concept that the entire department is a team working together towards common goals. No one works for me. Everyone works with me. So too, agents are a part of the team and not totally independent. This is perhaps the most critical rule when it comes to AI agents. The agent shouldn't make you a lemming following it over the cliff. Encourage your teams to question AI recommendations with sound, fact-based arguments.

The best AI implementations treat the agent as another team member, not an infallible oracle. Your human judgment still matters. Your experience still counts. Your opinion which is based on years of knowing your customers, your processes and your business is the secret sauce AI alone can't replicate.

So how should you drive adoption? Stop thinking about "enticing" employees and start thinking about solving real problems. If your AI agent needs a 40-page manual, you've already lost. Remember when I tried Spotify? No training required. Performance was flawless. That's your bar. Make the agent intuitive enough that people discover value organically.

And whatever you do, don't form a task force or alliance that creates PowerPoints nobody reads. Create real cross-functional partnerships where IT provides the infrastructure and guardrails, HR ensures alignment with workflow realities, business units define actual problems worth solving, and everyone has permission to say, "this agent isn't working."

As I learned in my time playing Shark Tank with investments, we had to separate high potential candidates from glitzy flash in the pan ideas. The same principle applies here. Focus on AI agents that solve real problems, not the ones with the best demo.

The golden rules have served me well for many years. They've helped me manage complex technical environments, build effective teams, and deliver results for the business. Now they can help us navigate this new world of AI agents. Perhaps they can help you too.

Captain Joe
Follow me on Twitter @JPuglisiLLC

Stop Building Faster Horses

During a recent Coffee with Trailblazers, we discussed how many companies think of AI as a means to inventing a faster horse when they should be developing cars. 

You know the story. Henry Ford famously said that if he'd asked people what they wanted, they would have said a faster horse. But Ford built something entirely different that solved the same problem in a revolutionary way. Today, I'm watching the same pattern play out with AI. So, here's my take.

If your AI strategy centers solely on "efficiency gains," you're already losing. While your competitors debate AI ethics, their customers are using ChatGPT to solve problems you should be solving for them. The companies winning are the ones making AI invisible to users.

Here's what I've learned from more than thirty years of watching technology reshape business: the real money isn't in the obvious applications. It's in the combinations, extensions, reinventions, and entirely new categories that emerge when you stop thinking about what new technology can do and start thinking about what it makes possible. There are four types of innovation that actually matter. 

The first is what I call unexpected combinations. This comes when industries collide in ways that create entirely new value. For example, if Waymo and Airbnb had a baby in might look like a self-driving Tesla. One that allows its owner to leverage their vehicles when they're not using them. This isn't just ridesharing; it's asset optimization at scale. The pattern here isn't complicated. You take two existing business models that never intersected before, add AI as the connective tissue, and suddenly you've created value that neither could achieve alone. What if your CRM got together with your SCM? What if your HR system combined with your customer service platform? Don't use AI to simply further automate these systems. It should enable them to speak to each other in ways that create entirely new value propositions.

The second type is product line extensions. This involves taking what you already do and amplifying it into spaces you could never reach before. This is where most companies get it wrong. They think AI is about replacing what they do. Smart companies realize it's about extending what they do. For instance, tax consulting where for decades, you had two choices. You could employ expensive human expertise or cheap software that missed nuances. AI enables personalized tax strategy that scales. Your tax consultant doesn't show up in March or April to prepare your return. Your agent becomes your year-round financial coach, analyzing every transaction and proactively suggesting ways to minimize exposure. The lesson? Don't ask what AI can do for your existing products. Ask what your expertise could become if it could scale infinitely and personalize completely.

Then there's complete reinvention. Here's where it gets interesting. What if your ERP system wasn't a collection of modules but a collection of AI agents? Imagine your procurement agent negotiating with your inventory agent, while your finance agent approves the deal and your logistics agent schedules delivery. This isn't automation but rather orchestration. The product disappears and becomes a conversation among intelligent agents, each representing different aspects of your business. Instead of screens and reports the interface is natural language requests and intelligent responses. We're not just digitizing existing processes; we're reimagining what those processes could be if they were designed from scratch for a world where information flows freely and decisions happen at machine speed.

Finally, there are net new categories. These include markets that don't exist yet. The biggest opportunity isn't B2B or B2C, it is going to be entirely new businesses that exist purely to meet your needs. Personal health coaches that know your genetic markers, your daily habits, your stress patterns, and your goals. They don't just give advice; they orchestrate your entire health ecosystem. These aren't enhanced versions of existing services. They're entirely new categories that couldn't exist before AI made them possible. The market didn't exist because the capability didn't exist.

Now here's the bottom line. Every leader should stop asking "How can AI make us more efficient?" and start asking "What becomes possible when intelligence is no longer the bottleneck?" When little Mary 

asked why her grandmother cut the roast in half, she uncovered a process designed for constraints that no longer existed. Today's AI capabilities could make most of our business constraints obsolete if we are willing to question why we're still cutting the roast.

The companies that win won't be the ones with the best AI. They'll be the ones that use AI to create value that was impossible before. They'll stop building faster horses and start building cars. The future isn't about human versus machine. It is about human creativity amplified by machine intelligence. Revenue from AI should not come from replacing humans. It should come from amplifying human potential at machine speed.

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Captain Joe
Follow me on Twitter @JPuglisiLLC