I Have Some Questions...

Erik Berglund
  • 5.0 (42)
  • Management
  • Updated Daily

Most people know the headline of a leader’s story. Few know the path it took to get there. This podcast goes beyond titles, book launches and business wins, to explore the lived journey behind the thought leader. Through deep, unhurried conversations, we uncover the moments that shaped them—the doubts, pivots, convictions, and quiet breakthroughs that built their body of work. Each episode features authors, coaches, executives, and bold thinkers who have forged their own path. Instead of rehearsed talking points, they’re invited into a space where thoughtful questions unlock something more human. The result is a layered conversation that reveals not just what they preach, but how they became the kind of person who can teach it.Because we believe the best stories aren’t always told—they’re revealed. And when brilliant people are given the right questions and the room to answer them fully, what emerges is insight you can feel, frameworks you can apply, and a deeper understanding of what it truly takes to lead, create, and contribute at a meaningful level. 

  1. 4h ago

    203: Eric Wiklendt: "What No One Talks About in M&As About Adding Value"

    In this conversation, Erik sits down with Eric Wiklendt, Managing Director at Speycide Equity, to pull back the curtain on private equity, acquisitions, and the future of manufacturing. Eric explains how investors think about value creation, why stable cash flow beats optimistic projections, and what separates companies that are attractive acquisition targets from those that struggle to command premium valuations. Along the way, they explore the differences between private equity and venture capital, the frameworks Speycide uses to evaluate businesses, and why AI, robotics, and Industry 4.0 technologies may reshape the future of North American manufacturing. 👤 About the Guest Eric Wiklendt is the Managing Director of Speycide Equity, a private equity firm focused on acquiring and growing middle-market manufacturing businesses. Before joining Speycide, Eric served as President and CEO of Kellex Heat Transfer Systems and held leadership roles at Eaton and Hilti. His background spans mergers and acquisitions, manufacturing operations, strategy, and value creation across industrial businesses. Today, he helps identify, acquire, improve, and grow manufacturing companies through what Speycide calls a "Fix and Build" strategy. 🧭 Conversation Highlights The Difference Between Corporate Acquisitions and Private Equity. Eric explains that corporate buyers and private equity firms may both acquire businesses, but they play entirely different games. Corporate acquirers typically buy businesses under the assumption of long-term ownership and focus heavily on strategic synergies. Private equity firms operate within defined hold periods and focus on creating measurable value that can be realized within a specific timeframe. The result is a fundamentally different approach to evaluating opportunities, structuring deals, and defining success. The Hidden Framework Behind Value Creation. Eric walks through Speycide's internal system for evaluating businesses: Portco Value Creation System (PVCS)Multiple Accretion Framework (MAF)Process Assessment Framework (PAF)Human Capital Assessment Framework (HCAF)Together, these frameworks help answer a single question: With demographic trends creating long-term workforce shortages, he sees AI, robotics, automation, digital twins, and advanced manufacturing technologies becoming essential infrastructure rather than optional upgrades. In his view, the factories of the future won't eliminate people—they'll shift people into maintaining and improving the systems that run production. 💡 Key Takeaways Private equity and venture capital operate under fundamentally different return models.Great investors underwrite controllable outcomes, not optimistic possibilities.Stable, repeatable cash flow is one of the most valuable characteristics a business can possess.Process quality often matters more than individual talent.Human capital remains the hardest variable to predict in any business.A single source of truth creates alignment and better decision-making.❓ Questions That Mattered What makes private equity different from corporate acquisitions?How should founders think about building a company that someone wants to buy?Why do investors care so much about process and cash flow?How do you evaluate human capital before acquiring a business?What makes a company scalable?🗣️ Notable Quotes "We don't pay for anecdotes and assertions. We pay for cash flow." "What we really want are companies with $100 million revenue, with systems, processes, and people." "Culture tells people what to do when nobody is telling them what to do." "Together everybody achieves more." 🔗 Links & Resources Follow Eric Wiklendt on LinkedInCheck out Speyside’s Website: speysideequity.com

    203: Eric Wiklendt: "What No One Talks About in M&As About Adding Value"

  2. 1d ago

    202: Vincent Azzolina: "What Does It Take to Lead in a Zero-Mistake Environment?"

    Vince Azzolina shares how his career pivoted from CFO to COO at Myers Wells Service, moving from behind-the-scenes finance to hands-on operational leadership. They discuss incentives, the shift from cost-per-barrel to hourly pay to reduce unsafe driving, and how safety and engagement became core to profitability and retention. 👤 About the Guest Vince Azzolina is COO of Myers Wells Service, an oil and gas trucking provider headquartered in Western Pennsylvania. He leads financial performance and strategy while overseeing business operations. Previously, he worked at Volvo North America running purchasing for Volvo and Mack in North America. 🧭 Conversation Highlights Why his CFO-to-COO transition happened in the 2019 timeframe, driven by ownership wanting deeper operational engagementHow rate model changes and a safety bonus program helped reduce speeding and accident severityWhat it takes to lead through high-pressure, demanding customer environments and equipment availability challengesHow technology and AI are being explored to ease admin load, improve maintenance, and strengthen safety systems💡 Key Takeaways Aligned incentives can drive performance, but they must be designed to avoid unintended safety pressure.Safety culture is both a values system and an economic strategy through reduced accidents, insurance exposure, and turnover.In a service business, people are the primary asset, so training, communication, and trust are operational strategies.AI and new tech are only useful when they remove real operational pain, like Excel-heavy reporting and integration gaps.❓ Questions That Mattered How do you grow as a leader when you shift from CFO comfort with data to COO needs for trust and delegation?What incentive design reduces unsafe behavior without undermining delivery performance?Is safety truly more profitable, and where does the economics show up beyond lawsuits and insurance?What does “predictive” safety look like when human factors like fatigue are involved?🗣️ Notable Quotes "There are a lot of things that aren't our fault but are still our responsibility.""If you're a safe company, you're a profitable company.""Our people are our asset.""I've become a big believer in engagement and being present.""I don't need to be meddling in this. I trust them.""I would love to look back three years from now and say we figured out how to use AI to make our people's lives easier.""We want people to retire here.""Safety is the first topic of discussion with every customer and the first topic of discussion inside our company."🔗 Links & Resources Follow Vincent Azzolina's LinkedInCheck out Myers Well Service’s Website: myerswellservice.com

    202: Vincent Azzolina: "What Does It Take to Lead in a Zero-Mistake Environment?"

  3. 2d ago

    201: "Vision First Or Tools First: The Hidden Step Before Any AI Implementation" ft. Justin Coats

    Erik and Justin unpack Paul Rotzer’s AI maturity “eight pillars” and use it to frame what leaders should do after the hype: step back, explore what AI can do, choose a priority problem, then build vision, strategy, governance, literacy, and measurement in that order. 🧭 Conversation Highlights AI adoption is a forcing function: excitement about what AI can do often triggers the harder question of what the business should actually solve first.Justin argues for a mindset shift from “pick the perfect problem” to acknowledging an unsolved problem set, learning AI capability, then applying it deliberately.Governance quickly turns practical as teams hit token limits, spend tracking gaps, and model availability changes during real adoption.Early value often starts in communication and as an individual thought partner, but real ROI comes when leaders provide guardrails, education, and space to iterate.💡 Key Takeaways Start with exploration, but don’t skip selection: the ability to do many things can paralyze you if you do not stack-rank what matters.Vision and literacy shape what happens next. Without a clear “why AI,” tool knowledge alone will not translate into durable adoption.Adoption is iterative: one department, one strategic goal, then replicate. “Go slow to go fast” beats trying to tackle everything at once.Token cost governance is becoming unavoidable. Plan for consumption monitoring, model selection, and policy before limits surprise you.❓ Questions That Mattered When leaders see AI capabilities expand, how do they choose problems instead of chasing shiny possibilities?Do you treat AI literacy and vision as prerequisites, or as learning that happens after you start piloting?Where should a company look first for a beachhead of value across teams like revenue, ops, finance, and internal product?Will tokenomics and pricing changes push more companies toward hybrid models like hybrid seat plus usage, or on-prem and open source?🗣️ Notable Quotes “If you can do everything, you can do nothing.”“Stop spinning, choose one problem set, learn about AI in general and what it's possible and capable of doing, then choose that problem.”“Tokenomics part is going to become part of your governance.”“You’re going to pay for the tokens that we consume.”🔗 Links & Resources Listen To Other Episodes Co-Hosted With JustinRead the referenced Article

    201: "Vision First Or Tools First: The Hidden Step Before Any AI Implementation" ft. Justin Coats

  4. 3d ago

    200: "Need Vs Want: The Abundance Shift That Changes Everything" ft. Alli Murphy

    Erik and Alli Murphy unpack why “Operation Off Duty” caught so much attention, and how the simple act of asking for permission to rest creates real communication, less resentment, and more alignment in everyday life. 🧭 Conversation Highlights Alli explains the reel’s resonance: a curiosity hook plus an honest moment about needing permission because self-permission feels harder.Erik reframes the concept as giving people a vehicle to say what they actually want in relationships, including the quiet desire to do nothing sometimes.They connect “want vs. need” and the value of taking a step back to ask what you really want right now, not just what you think you should need.Alli shares a practical way to run the play: declare it, share it, set the timeline, and make the goal doing nothing for the specified window.💡 Key Takeaways People often need permission to rest, not because they are unwilling, but because they feel obligated to justify it.The value of Operation Off Duty is that it turns an unspoken desire into something you can communicate without blame.Want is an observation and an opportunity, while need can be a scarcity filter that narrows what feels possible.A simple playbook works: tell your household, clarify duration, and explicitly define what “off duty” means.❓ Questions That Mattered Why did Operation Off Duty land so hard with people: permission, curiosity, or the goofiness?How do couples actually surface what they want in the moment, especially when “we should be together” gets in the way?Am I asking for what I need, or am I avoiding what I want because I think I’m not supposed to want it?What’s the first step for someone who wants to try it: how do you roll it out with the people who matter?🗣️ Notable Quotes “We have permission to do these things, but it was me sharing the honest story about how I would walk around and be like, ‘I want permission from my husband so that I don't have to give permission to’“It almost gives you permission to have permission. That’s very meta.”“The goal all of a sudden is to do nothing… and that made it more palatable.”“Hey, TJ, I just learned about this ridiculous idea… I wanna have an operation off duty.”🔗 Links & Resources Listen To Other Episodes Co-Hosted With AlliCheck out Alli's Skool Community

    200: "Need Vs Want: The Abundance Shift That Changes Everything" ft. Alli Murphy

  5. 6d ago

    199: "How Do We Actually Get ROI From AI?" (reflections on Lauren Cappell)

    🧠 Erik’s Take After speaking with Lauren Cappell, Erik walked away with a realization that extends far beyond the legal profession: the biggest barriers to successful AI adoption aren’t technical—they’re human. While most organizations are focused on finding the right tool, Lauren challenged a deeper question: Do leaders actually understand the work they’re trying to automate? The conversation highlighted how AI is exposing weaknesses that have existed inside organizations for years—unclear processes, underdeveloped systems, and leadership teams that haven’t fully mapped how value is created. AI doesn’t eliminate those problems. It magnifies them. 🎯 Top Insights from the Interview 1. You Can't Automate What You Don't Understand Before AI can improve a process, leaders need clarity on what actually happens inside that process. Most work isn't a straight line. It's a web of decisions, judgment calls, handoffs, and exceptions. Mapping that reality is difficult—but it's the prerequisite for meaningful automation. 2. AI Adoption Is a Leadership Challenge The organizations that succeed with AI won't necessarily have the best technology. They'll have leaders willing to:  Invest time understanding workflows  Train their people to use AI effectively  Build systems for oversight and quality control  Commit resources to ongoing learning Technology isn't the limiting factor. Leadership engagement often is. 3. Efficiency Can Create a Talent Problem Many professions develop expertise through repetitive, foundational work. When AI removes that work, organizations gain efficiency—but may lose an important training ground for future experts. The challenge becomes identifying:  What skills actually need to be developed  Which activities are valuable learning experiences  How simulations or alternative training methods can accelerate mastery 4. Human Oversight Isn't Going Away One of the biggest misconceptions about AI is that it removes people from the process. In reality, many workflows require human intervention at critical checkpoints. Leaders still need people who understand the work well enough to recognize mistakes, redirect efforts, and improve systems over time. 5. The Next Step Matters More Than the Perfect Plan Companies waiting for the perfect AI solution may be making the biggest mistake of all. The organizations gaining advantage today are experimenting, learning, adapting, and building capability one step at a time. Five years from now, experience will compound. 🧩 The Personal Layer One idea particularly resonated with Erik: the realization that he had been viewing AI adoption primarily as a process problem. His focus had been on understanding workflows well enough to automate them. Lauren pushed that thinking further. Before organizations can automate effectively, leaders must invest in creating people who are capable of working alongside AI. That means training, literacy, experimentation, and ongoing development. The real bottleneck may not be process mapping. It may be whether leaders are willing to make the investment required to build an AI-capable workforce. 🧰 From Insight to Action If you're exploring AI inside your organization, consider these questions:  Can your team clearly explain how a critical process works today?  Where does judgment—not just execution—create value?  Which repetitive tasks are teaching future experts important skills?  How much time are you investing in AI literacy and training?  What is the next practical step you could take instead of waiting for a perfect solution? The goal isn't to automate everything. The goal is to become the kind of organization that learns how to leverage AI better every year. 🗣️ Notable Quotes "You can't automate what you don't already know how to do." "The real hurdle isn't technology. It's leadership." "If AI does all the grunt work, how do we develop the next generation of experts?" "The companies figuring out AI today will be far ahead of the companies waiting for AI to figure itself out." "The next step is the most important one." 🔗 Links & Resources Listen to Lauren Cappell's Episode

  6. Jul 16

    198: Lauren Cappell: "Time Kills Deals: How Faster Legal Work Creates Real Revenue"

    Lauren Cappell and Erik unpack why realizing ROI from AI adoption in law is harder than it sounds, especially under legacy business models. Lauren argues the real shift is not just efficiency, but value creation: new kinds of work, faster cycles, better workflows, and better pricing that aligns incentives. They also dig into the training and QA demands required to ensure AI outputs are trusted. 👤 About the Guest Lauren Cappell is a strategist at the intersection of enterprise, law, and artificial intelligence. With leadership experience at Amazon, Thomson Reuters, and BlackBerry, plus startup roles, she helps organizations translate AI capability into measurable business value. Her mission is to eliminate busy work and replace it with systems that elevate human potential. 🧭 Conversation Highlights Billable hours create tension because AI can reduce the time needed to complete a task, but it also enables work that was previously uneconomic or impossible.In law, ROI shows up differently across in-house teams, large firms, and smaller firms, because incentives and constraints vary.AI services that offer usage based billing to legal teams are the right move right now: they can assist in driving adoption and ensure aligned incentives between the AI service and the buyer/user.Adoption depends on training, human in the loop QA, learning how to work with AI outputs, and rethinking the way we work, not just training on how to use individual AI tools.💡 Key Takeaways AI ROI has to be tied to reduced delivery cost and/or increased value, and it often requires work redesign rather than speed alone.Billable hours are not the whole story, because much of legal work happens outside large firms and without the same pricing constraints.Usage based billing can align incentives , but it increases the importance of effective training and successful “usage” definitions.The primary limiting factor to seeing value from AI investments is people and process: automation requires you to know the workflow well enough to de-risk it and to QA output quality.❓ Questions That Mattered How does a billable hours model persist when AI reduces the time needed for certain tasks?Where does ROI show up first across in-house teams, large firms, and smaller firms?Why is adoption often slower than expected, even for sophisticated AI users?What does it mean in practice to deploy agents or automation while staying accountable for quality and decision making?🗣️ Notable Quotes “Time kills all deals.”“If you’re waiting for your clients to ask you about AI usage or to challenge you on it, I don’t think that’s a great position to be in.”🔗 Links & Resources Follow Lauren Cappell's LinkedInCheck out Lauren’s personal website: deathofbusywork.com

    198: Lauren Cappell: "Time Kills Deals: How Faster Legal Work Creates Real Revenue"

  7. Jul 15

    197: Jason Robinovitz: "What Happens When Students Outsource Their Thinking To LLMs?"

    Jason Robinovitz explores what education needs to become in an AI-saturated world. He argues that AI will increase opportunities, but only if schools keep teaching students to think for themselves. He shares practical classroom tactics to reduce cheating, why incentives in education often fail learning, and which human skills, like critical thinking and soft skills, will matter most as hiring shifts. 👤 About the Guest Jason Robinovitz is CEO, COO, and General Counsel at Score at the Top Learning Centers, Score Academy, and JRA Educational Consulting. The family-owned organization was founded in 1980 and operates across South Florida. A former medical malpractice attorney, he joined the business in 2008 and brings a legal, systems-minded approach to education.  🧭 Conversation Highlights AI will not remove the need for education, but it will force schools to verify thinking through process integrity and human judgment.AI detectors are unreliable, so educators need better proof methods like Google Docs version history and proctored, handwritten exams.Education incentives can drift toward standardized-test outcomes rather than learning, which drives both cheating and hollow credentials.Jason emphasizes the essential skills employers want: soft skills and critical thinking, supported by reading, mental math, and discomfort-building practice.💡 Key Takeaways The most valuable student skill in an AI world is thinking for yourself, not outsourcing decisions to LLMs.Cheating controls should prioritize process visibility and classroom verification, not detector trust.Employers will increasingly hire for soft skills and critical thinking demonstrated under real conditions, not just grades.Tech in education should augment teachers, especially for feedback, and focus on building “understanding” rather than automating judgment.❓ Questions That Mattered What does education need to teach when students can outsource work to AI in minutes?How can educators ensure evidence of thinking without relying on weak AI-detection tools?Will prestige-based hiring proxies break down, and what would replace them?How do we help critical thinking grow under stress, not just in ideal conditions?🗣️ Notable Quotes “In my opinion, the most important thing that students can learn today is the ability to think for themselves.”“Most AI detectors suck. They are full of false positives. They can’t be relied upon.”“Education is going to become incredibly important, but probably not in the ways that most people are thinking about it.”🔗 Links & Resources Follow Jason Robinovitz's LinkedIn Follow Jason Robinovitz’s Substack: News From The TopCheck out Score Academy’s website: Score-Academy.com Check out JRA Educational Consulting: jraEducationalConsulting.com Check out Score At the Top’s Website: ScoreAtTheTop.com

    197: Jason Robinovitz: "What Happens When Students Outsource Their Thinking To LLMs?"

  8. Jul 14

    196: "Agent Collaboration Should Look Like Co-Working, Not Hand-Offs" ft. Justin Coats

    Erik and Justin talk through why AI agents get stuck when teams cannot articulate how work is done, and why the answer is iterative agent deployment with guardrails, sandbox testing, and ongoing process refinement. 🧭 Conversation Highlights Most professionals struggle to explain the steps of their work, which blocks automation and agent adoption.Agents feel hard to hand off to because people fear losing control, making mistakes, or looking foolish.A practical path forward is iterative process creation: give an agent the goal, tools, and guardrails, then tighten the workflow based on outcomes.To make iteration safe, teams need preview or sandbox testing plus limits on real-world actions and token budgets to avoid runaway usage.💡 Key Takeaways AI adoption is shifting from “AI literacy” to “how to build and govern agents,” so companies need shared understanding beyond IT.Iterative deployment works better than trying to hard-code every step upfront, but it requires verification checkpoints and process feedback loops.Sandbox or preview environments are critical for low-risk learning before enabling agents to take real actions.Token spend should be treated as governance: set budgets and limits per user/team, and track usage in a way leaders can understand.❓ Questions That Mattered How do we automate work we cannot clearly describe step-by-step without stalling adoption?What guardrails let humans feel safe handing tasks to agents, including security, safeguards, and action approvals?Is there a practical way to test agent workflows in “failure-free” conditions before going live?When iteration causes back-and-forth, how should companies think about token efficiency and budget controls?🗣️ Notable Quotes “It’s hard to automate what you don’t already know how to do.”“The majority of people have a really hard time articulating precisely how they perform a task.”“Understanding the tool and technology, and knowing what it can and cannot do, really creates a safer environment.”“It’s taking our typical structure. You watch how they work and adapt to that.”🔗 Links & Resources Listen To Other Episodes Co-Hosted With Justin

    196: "Agent Collaboration Should Look Like Co-Working, Not Hand-Offs" ft. Justin Coats
See All (201)

5
out of 5
42 Ratings

About

Most people know the headline of a leader’s story. Few know the path it took to get there. This podcast goes beyond titles, book launches and business wins, to explore the lived journey behind the thought leader. Through deep, unhurried conversations, we uncover the moments that shaped them—the doubts, pivots, convictions, and quiet breakthroughs that built their body of work. Each episode features authors, coaches, executives, and bold thinkers who have forged their own path. Instead of rehearsed talking points, they’re invited into a space where thoughtful questions unlock something more human. The result is a layered conversation that reveals not just what they preach, but how they became the kind of person who can teach it.Because we believe the best stories aren’t always told—they’re revealed. And when brilliant people are given the right questions and the room to answer them fully, what emerges is insight you can feel, frameworks you can apply, and a deeper understanding of what it truly takes to lead, create, and contribute at a meaningful level. 

Information

  • Creator
    Erik Berglund
  • Years Active
    2025 - 2026
  • Episodes
    201
  • Rating
    Explicit
  • Copyright
    © 2026 I Have Some Questions...
  • Show Website
    I Have Some Questions...