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Over the past six weeks, we've covered a lot of ground. What agents are. Why data connectivity matters. How workflows get orchestrated. What machine learning adds. Whether your business is ready. And why the window to get ahead is still open — but won't stay open forever. Today I want to tie it all together. Because the companies that succeed with agentic AI don't get lucky. They build on the right foundation. Every time. Here are the five things that foundation requires. 1.

I'll keep this one short because sometimes a story tells itself. A few years back I brought my son into the office for bring your kid to work day. At the time I had two office suites in an industrial complex — a front office where my admin worked, a back office where I worked, a small kitchenette, and a bathroom. Nothing fancy. Functional. I got him set up in the back with some schoolwork, told him to stay put, asked my admin to keep an eye on him, and headed out to meet with

I want to say something that might surprise you coming from someone who talks about AI every week. Most companies have not deployed agentic AI yet. That's not a criticism. It's actually good news — because it means you're not behind. The window is still open. The early mover advantage is still available. But here's the thing about windows. They close. Where Most Businesses Actually Are Right Now When I talk to business leaders across Pittsburgh and Western Pennsylvania, I fin

I don't make cold calls often. Once a year, maybe, I make it a point to pick up the phone and reach out to some new companies. Just to get a feel for the market. To introduce myself. To ask one simple question — can we help you, and is there a chance we can help you in the future? That's it. That's the whole ask. What I've learned from those calls would fill a book. Not about recruiting. About people. The rudeness is staggering. I'm talking about people who hang up mid-senten

By now you understand what agents are. You know they need connected data. You know multiple agents can work together. You know machine learning makes the whole system smarter over time. So the question now is: are you actually ready? Because deploying AI agents before your business is prepared doesn't accelerate you. It creates expensive chaos. Here are five honest questions to ask before you start. 1. Do you know which problem you're solving? "We want to use AI" is not a str

I want to ask you something. When your recruiter sends you a candidate, what do they say? I'll tell you what most of them say. "This person is incredible." "One in a million." "Perfect fit." "Best candidate I've seen in years." Every. Single. Time. I've heard it. You've heard it. And if you've been in business long enough, you know exactly what that means — nothing. It means the recruiter wants their candidate in front of you. It means they want the fee. And it means they'll

Every recruiter will tell you the same thing. Never take the counter offer. There are stats, scripts, and entire training programs built around this belief. You'll hear things like "98% of people who accept counter offers are gone within a year." You'll hear that the counter offer is just a Band-Aid. That your employer is only doing it to protect themselves. That the reasons you were looking haven't gone away — they've just been buried under a raise. Most of the time, that's

We've talked about agents. We've talked about workflows. We've talked about data. Now let's talk about what separates a good AI system from one that keeps getting better without you having to rebuild it every year. That's machine learning. And most people confuse it with AI in general. They're not the same thing. The Difference Between AI and Machine Learning AI executes. It takes instructions and acts on them. An agent screens a resume because you told it what to look for. A

I once walked into a senior leadership meeting at a large industrial manufacturer and proposed something that made the room laugh. I suggested we interview all 20 candidates in one day. Not over several weeks. Not spread across multiple trips and scheduling nightmares. One day. One conference room. All the decision makers at the table at the same time. They thought I was out of my mind. I didn't care. Here's the context. This company needed to hire five district sales manager

You see the invoice. $35,000. And your first thought is — that's a lot of money for finding one person. I get it. I really do. But let me show you what that fee actually looks like from where I'm sitting. Before you ever saw that candidate, I spent weeks sourcing. I called people who weren't looking. I screened dozens of resumes. I had conversations with candidates at 7am and 7pm because that's when they could talk privately. I coached them through your interview process. I m

We've covered what an AI agent is. We've covered why your data has to be connected for agents to work. Now let's talk about what happens when you stop thinking about one agent — and start thinking about a team of them. One Agent Has Limits A single agent can handle a defined task well. Screen resumes. Monitor a pipeline. Generate a weekly report. Pull data from connected systems and flag anomalies. Useful. Absolutely. But most real business processes aren't one task. They're

I've read thousands of job descriptions over 30 years. Most of them are awful. Not because the companies writing them are bad at their jobs. But because nobody taught them what a job description is actually supposed to do. Here's a hint: it's not a legal document. It's not an internal HR form. It's a sales pitch to someone who doesn't need a job. What Most Job Descriptions Look Like Fifteen bullet points of requirements. Five years minimum experience. A master's degree prefer

I've been a recruiter for over 30 years. And I still run into companies that think my job is to post their opening on job boards and forward resumes. Let me be direct: that's not recruiting. That's administrative work with a markup attached. Here's what a real retained search actually looks like — and why the difference matters for the quality of hire you end up with. It Starts Before the Search Before I make a single call, I spend time understanding your business. Not just t

Around 2015, a Fortune 500 company came to me for a quote on a retained CTO search. I knew this role. I had filled it multiple times. I had the reputation, the network, and the track record to back it up. So I put together what I thought was a fair, competitive quote — a strong number, honest for the work involved, without the massive overhead of a large firm behind me. I lost the bid. My contact called me with the reason. I hadn't been beaten on quality. I hadn't been beaten

Many companies rush to deploy AI agents, expecting immediate improvements in customer service, sales, or operations. Yet, a common mistake slows down or even derails these efforts. They focus on the AI agent itself, without ensuring the underlying data systems are connected and accessible. This oversight limits what the AI can do and wastes resources. An AI agent depends entirely on the information it can access. If your customer data sits in one system, your sales pipeline i

Last week we talked about the difference between talking to AI and actually deploying it. Today let's go one level deeper — because there's a mistake I see companies make the moment they decide to get serious about agents. They focus on the agent. And they ignore the data. An Agent Without Data Is Just an Expensive Clipboard Think about the last time you hired someone new. On day one, you didn't hand them a phone and say "figure it out." You gave them access to your systems.

Finding the right executive or professional talent is critical. The choice between retained and contingency search can shape your hiring success. I will break down both methods. You will get clear facts, practical tips, and a straightforward guide to decide what fits your needs. Retained vs Contingency Search: The Basics Retained search means you pay upfront. You hire a recruiter exclusively. They focus on your role until filled. This method suits senior or niche roles. It gu

The U.S. Department of Labor estimates that a bad hire costs approximately 30% of that employee's first-year salary. For a $200,000 executive, that's $60,000. But that number is conservative. Dramatically conservative. Because it only accounts for what you can measure easily. Let's talk about what it doesn't count. The Costs Nobody Puts in a Spreadsheet When a senior hire doesn't work out, the direct costs are real but manageable. Recruiting fees. Severance. The time your HR

For a $200,000 executive, that's $60,000. That number is wrong. It's way higher. It doesn't count the team disruption. The momentum you lost. The client relationships that got damaged. The second search you now have to run. Bad hires almost always trace back to three things: speed over diligence, skills over fit, or the wrong recruiter. I've spent 30 years making the right hires, not the fast ones. The difference shows up three years later.

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