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

The Agile Shall Inherit the Earth

Digital transformation is not a project. You will always be transforming, and that requires dexterity.

Not a buzzword. Not a tech skill. Digital dexterity is a leadership muscle: the ability to navigate through
the fog, move across boundaries, and stay focused on outcomes even when the ground keeps shifting. It’s a kind of superpower, adaptability in motion, that separates those who react from those who lead.

You don’t identify it in people by using a checklist. You watch how people behave when things aren’t well defined. Do they lean in? Ask better questions? Connect all the dots that others don’t even see? Are they comfortable being uncomfortable? That’s the sign, the tell.

If you're a digital trailblazer, or trying to be, stop waiting for permission. Volunteer for the mess. Pair up with people outside your lane. Learn by doing. Stretch until it hurts a little. That’s where real dexterity forms. The people who grow fastest are the ones who embrace the unknown, not just tolerate it.

Organizations don’t only need more training programs. They must also build environments where learning is embedded in the work. Shadowing, rotations, rapid pilots. These aren’t side projects, they’re leadership accelerators. Want to develop transformation leaders? Give them something to transform.

Change isn’t the challenge. The challenge is building leaders who know how to move through it.

Captain Joe 

Follow me on Twitter @JPuglisiLLC

The Horizon is Closer Than It Appears

For years, we’ve treated Horizon 3 innovation like a moonshot. These were far-off bets we hope will pay off someday, safely removed from the core business. It was a comforting illusion: we could acknowledge the future without having to engage with it. But like most illusions, it didn’t age well.

Today, the horizon isn’t ten years out. It’s next quarter, perhaps even next week. Emerging technologies don’t wait patiently in the lab. They break out, scale fast, and change customer expectations overnight. What was once the bleeding edge is quickly becoming table stakes.

The classic Three Horizons model gave us a sense of order, a neat way to sort ideas by distance. But as
Steve Blank recently pointed out, that mental model no longer applies. All three horizons operate in parallel now. Innovation is no longer a linear path. It’s a simultaneous juggling act between now, next, and what’s emerging.

This shift requires a different mindset.

First, we need to stop treating Horizon 3 as something defined by the calendar. It’s not “what we’ll care about in 2030” it’s what’s just outside your operational comfort zone today. For example, AI is already deeply embedded across industries. If that’s Horizon 1 now, Horizon 3 might be quantum computing or autonomous decision frameworks. What matters is not the timeline, but the potential for disruption.

Next, it’s crucial to focus on capabilities and not just technology. The trap too many organizations fall into is tech-first thinking: chasing blockchain or metaverse hype without asking what business value they’re trying to unlock. A more grounded approach is to ask: What do we need to be able to do that we can’t do today? That question leads to meaningful innovation, whether it's real-time personalization, trusted data provenance, or radically adaptive systems.

Finally, Horizon 3 shouldn't exist in a vacuum. The skunkworks approach of separate teams, with their own goals, and no connection with the rest of the organization is where good ideas are born and then die. These efforts must be loosely coupled and strategically aligned with the business. That means executive sponsorship, visibility throughout, and a well-defined means to bring mature ideas into the mainstream.

That’s the thing about Horizon 3: it doesn’t wait. The signals are already there if you’re paying attention. The challenge isn’t to predict the future, but rather to be ready when it shows up. It's not unlike the well-known advice in hockey to "skate to where the puck is going to be."

The bridge isn’t just for crossing from now to next. It's where you go so you can see what’s coming. And from here, the future looks a lot closer than we thought.

Chief Chief Officer

Every major technology innovation marks the dawn of a new era and with it the promise that business and technology will never be the same again.  And it seems to follow that we need a new 'chief' to lead us into this promised land.

The latest earth-shattering wave of innovation is, of course, AI.  So, as with BI, Cloud and others before them, we are now seeing cries for a Chief AI Officer.  Ours is the only discipline which can't decide on what the top spot should be called.  Of course, it took decades before technology rose to the C-level at all, but when we did, we immediately confused everyone by using CIO and CTO almost interchangeably. 

The CEO, CFO and COO titles are by far the most consistent. CMO and CRO are later entrants to the c-suite but clearly represent the leader of the marketing and sales organizations, respectively. HR recently got into it by adopting CPO (Chief People Officer) presumably so they too could have a three-letter acronym. 

There have been many other novel chief somethings invented along the way such as Chief Experience Officer, Chief Sustainability Officer, Chief Diversity Officer and others.  However, these Chiefs are focused on an outcome and not on the use of a specific toolset. 

There is only one head of technology in any organization, just as there is only one CEO. This has been referred to in some circles as the President of Technology having overall responsibility for the use of technology throughout the business. This is a C-level position, and the CIO (or CTO) should be focused on the business first and, being the person on the senior management team most knowledgeable about technology, how to best leverage technology to execute the mission of the company. 

Part of that role involves building the right team with all the necessary skills and experience to efficiently and effectively design, build and operate the technology that supports the business. This team must include or have access to expertise in relevant technologies. AI is critically important, and the emergence of generative AI is clearly a watershed moment in technology, but it is still only a tool to be used wisely and not an outcome in and of itself. 

There is an exception to my CIO rule. The CISO role does rise to the C level on its own for two reasons. First, security should not report to the CIO. There are conflicting priorities, and it is unfair to hold the CISO accountable when the "boss" can override decisions. Second, like sustainability or diversity, security is an outcome and not merely a set of tools and techniques. 

A company with a Chief AI Officer, Chief BI Officer, Chief Cloud Officer or other C whatever Officers, will probably need a Chief Chief officer, one Chief to bind them all. 

Captain Joe 

Follow me on Twitter @JPuglisiLLC