The Manufacturers Network

Manufacturing companies don’t usually fail because of bad products. They fail because growth exposes leadership gaps nobody wanted to deal with.

In this episode of the Manufacturers Network Podcast, Lisa Ryan talks with leadership advisor and Trajectify founder Mike Krupit about what really happens when organizations grow faster than their people, systems, and communication.

Mike shares lessons from decades in Silicon Valley startups and explains why retention problems are often clarity problems, leadership problems, and culture problems disguised as “people issues.”

The conversation covers:

• Why companies outgrow key employees and founders
• How poor communication quietly destroys trust
• The difference between employees who “get it, want it, and are capable”
• Why psychological safety matters on the shop floor
• What leaders miss when they blame workers for disengagement
• How to manage up when leadership refuses to change
• Why “this is how we’ve always done it” is dangerous in manufacturing
• The connection between AI, workforce change, and leadership adaptability
• Practical ways manufacturers can rethink scheduling, culture, and retention

One of the biggest takeaways from this episode:

You cannot build a future-ready company with leadership habits designed for the past.

Mike also shares actionable ideas leaders can use immediately to evaluate whether they truly have the right people in the right seats before growth turns into chaos.

• What assumptions are we making that may no longer be true?
• Are we measuring outputs clearly enough for people to succeed?
• Do employees feel safe trying new ideas or making mistakes?
• Are leaders communicating enough, especially across shifts and teams?
• Is the organization growing faster than the people leading it?
• What is one thing leadership could change tomorrow?

LinkedIn: Mike Krupit on LinkedIn

Website: Trajectify

Website: Grategy / Lisa Ryan, CSP

If you enjoyed this episode, share it with a manufacturing leader, HR professional, or operations executive who’s trying to build a stronger workplace culture while navigating growth and change.

E-commerce looks easy… right up until it isn’t. More orders. More customers. More growth. And then everything starts to crack.

In this episode of The Manufacturers Network Podcast, I sit down with Ethan Giffin to talk about what really happens when e-commerce businesses try to scale. Not the highlight reel. The operational reality behind it.

We get into fulfillment pressure, inventory accuracy, customer expectations, and why so many companies focus on growth before they’ve built the foundation to support it.

Because here’s the truth…You don’t rise to the level of your marketing. You fall to the level of your operations.

• Why scaling e-commerce creates operational challenges most teams underestimate
• The pressure fulfillment puts on systems, people, and decision-making
• Where inventory accuracy becomes a make-or-break issue
• How customer expectations are reshaping operations
• Why growth exposes weaknesses instead of fixing them
• The role your team plays when systems fall short
• What leaders need to get right before pushing for scale

Ethan Giffin brings a practical lens to e-commerce. No hype. Just how things actually work when volume increases.

What stood out in this conversation is how quickly things get complicated.

At low volume, you can get away with inefficiencies.

At scale, those same issues turn into daily problems.

Orders increase.

Returns increase.

Customer expectations tighten.

And suddenly, your operation isn’t just fulfilling orders… it’s reacting all day long.

Even with automation in place, the reality doesn’t change. When something breaks, it’s your people stepping in to fix it.

That’s where a lot of companies get surprised.

They invest in systems… but forget to build the operational discipline behind them.

E-commerce is an operations business

Marketing brings the orders in. Operations decides whether customers come back.

Speed raises the stakes

The faster you promise delivery, the less room you have for error.

Inventory accuracy drives everything

If your numbers are off, every decision downstream gets harder.

Growth magnifies what’s already broken

What works at 100 orders a day rarely works at 1,000.

People are still the safety net

When systems fail, your team is the one holding it together.

• The realities of fulfillment as order volume increases
• Where inventory issues start and how they impact everything else
• The pressure created by faster delivery expectations
• How small operational gaps turn into major customer problems

Ethan Giffin works in the e-commerce space, focused on helping businesses navigate the operational side of growth, from fulfillment to inventory management to scaling systems that actually hold up under pressure.

He brings a straightforward, practical perspective to challenges that are easy to underestimate and hard to fix once they show up.

Selling online is the easy part.

Delivering on that promise… consistently… under pressure… is where most companies struggle.

And when things go wrong, they don’t get fixed by software.

They get fixed by people.

Gratitude is a strategy.

Here’s what nobody says out loud. You can automate a facility, install the dashboards, optimize the flow… and still lose the people who make it all work.

In this episode of The Manufacturers Network Podcast, I sit down with Rylan Pyciak to talk about what’s really happening inside modern supply chains. Not the polished version. The real one.

We get into the tension between automation and human judgment, why most leaders are spending money in the wrong places, and how culture quietly becomes the difference between a system that scales and one that stalls.

Because the truth is simple. If your people don’t feel ownership, your systems don’t matter.

• Why “fully automated” operations still depend heavily on people
• The difference between scale, speed, and true orchestration
• How to build accountability into your operation without micromanaging
• Where most manufacturers waste money on technology
• Why culture is the #1 failure point in M&A activity
• How community involvement impacts retention more than most leaders realize
• What leaders miss when trying to modernize legacy industries

Rylan’s background spans everything from large-scale fulfillment environments to third-party logistics and fragmented legacy industries. That perspective shows up fast.

He breaks down what it actually takes to scale operations without losing control. And more importantly, without losing your people.

One thing that stood out right away… Even in highly automated environments, you’re still managing thousands of employees across multiple shifts. That creates a different challenge. Not just operations. Consistency, communication, and culture across a 24/7 workforce.

And that’s where most leaders underestimate the work.

Accountability beats intelligence

You don’t need a room full of geniuses. You need a team that owns results, learns from mistakes, and adjusts quickly.

Start simple before you get fancy

Some of the best operational insights still come from a whiteboard on the floor. Not a screen. Not a dashboard. Just people talking about what broke and why.

Automation should remove frustration

If you want buy-in, start with the work people hate doing. That’s where automation earns trust.

Culture is not optional in M&A

Too many leaders treat people integration like a detail. It’s not. It’s the difference between success and failure.

Top-down vision. Bottom-up execution

Leaders define where you’re going. The people closest to the work figure out how to get there. Skip that and everything slows down.

Community drives retention

When companies invest outside their walls, employees feel it. And they stay longer because of it.

• The reality behind large-scale automated facilities and workforce demands
• Why expensive tech often replaces simple, effective communication tools
• The mindset shift from “spend money” to “solve the right problem”
• What actually happens after an acquisition when culture is ignored

Rylan Pyciak is a supply chain and operations leader focused on building scalable, resilient systems across complex environments. His work spans automation, logistics networks, and integrating fragmented industries into high-performing operations.

He brings a practical, no-nonsense approach to solving problems most leaders overcomplicate.

You can build the smartest system in the world.

But if you’ve designed it without your people in mind, it’s fragile. You just don’t see it yet.

Gratitude is a strategy.

And in a world that’s moving faster every day, it might be the one thing that keeps your people grounded enough to stay.

Lisa Ryan welcomes Paul Sippil, forensic 401K consultant and self-described 401K vigilante. Over 17 years, Paul has analyzed thousands of public retirement plan filings and documented more than 1,300 cases of apparent excessive fees. His central argument is simple and uncomfortable: most American employers, including manufacturers, have never seen the true cost of their 401K plan, and the structure of the industry is specifically designed to keep it that way.

From Recovering CPA to 401K Vigilante

Paul describes himself as a recovering CPA who knew from his very first day as an auditor that the path wasn't right for him. His career shifted into financial advising focused on estate and succession planning for business owners until a conversation with a colleague revealed that retirement plan tax forms were publicly available online. That discovery changed everything.

When Paul began calling business owners to alert them to the fees he was seeing in their own public filings, he expected to be a hero. Instead, he got a response that told him everything he needed to know about the industry: I'm not paying anything and my friend handles that. The disconnect between what employers believed and reality was so profound that Paul realized he could build an entire career consulting in just this one area and he did.

The Invisible Fee Problem

The reason most employers have no idea what their 401K plan actually costs is simple: there is no invoice. Fees don't arrive as a bill. They flow silently through the structure of the plan itself: embedded in mutual fund management fees, deducted directly from participant accounts, or buried in arrangements between advisors and record keepers that employers never see or negotiate.

The major fee categories Paul walks through include:

• Mutual fund fees: unavoidable, but can be minimized by choosing index or passively managed funds
• Record-keeping and administration fees: charged by providers like Principal, Voya, Fidelity, John Hancock, Paychex, and ADP, often as a percentage of total plan assets
• Financial advisory fees: either embedded as broker commissions within fund costs, or charged as a separate percentage of assets by registered investment advisors
• Custodial fees: charged by institutions like Charles Schwab that hold the assets

The compounding problem: because fees are typically percentage-based, they grow automatically as the plan grows — with no increase in services provided.

The Case That Says It All

Paul shares a story that captures everything wrong with this industry in one example. A company with just three employees paid out over $49,000 in broker commissions between 2019 and 2024, to an advisor they didn't even know they had. When the HR director called to find out who the broker was, a quick Google search revealed the broker had been dead since 2014.

When Paul raised this publicly, an industry administrator pushed back saying "everybody's gotta get paid." Paul's response: you first have to be living. The story isn't just an anomaly, it's a window into an industry structurally incentivized for advisors to do nothing, stay invisible, and hope clients never think to ask questions.

What Manufacturers Are Leaving on the Table

The financial stakes for manufacturers are significant, particularly for business owners who often hold the largest account balances within their own plans. Paul walks through several practical opportunities most plan sponsors don't know they have:

• Negotiate record-keeping fees. Simply asking, no financial sophistication required, can reduce percentage-based fees that providers don't automatically lower as plan assets grow. On a $3 million plan, reducing the fee by 0.1% saves $3,000 every year, permanently.
• Negotiate or eliminate advisory fees. In many cases, advisors aren't performing active management; they're waiting for participant phone calls. Participants direct their own investments from a list of fund choices. The advisor isn't making buy-sell decisions for anyone.
• Demand dollar-denominated answers. Fee disclosures required since 2012 exist, but they're often expressed as percentages, buried in documents few people know to look for, and disconnected from any real-time cost experience. Asking "how much have you made off our plan in actual dollars, for each of the last three years?" is a question every plan sponsor should ask every provider.
• Consider paying fees at the employer level. Employers can choose to pay plan fees directly rather than passing them to participants. The benefit: employer-paid fees are tax-deductible business expenses, and the business owner, who typically holds the largest share of the plan balance, stops paying non-deductible fees out of a tax-advantaged account.

The Department of Labor has published research showing that an extra 1% in annual fees costs a 35-year-old participant with a $25,000 balance approximately $64,000 over 30 years; roughly 28% of their ending balance. For participants with larger balances, the damage is proportionally greater.

Why the Industry Doesn't Change on Its Own

Paul draws a sharp distinction between price transparency - being able to see fees - and price literacy, having enough context to know whether what you're seeing is reasonable. Even when fees are disclosed, there's no easy way to comparison-shop the way you would for a car or a cell phone. Providers don't make their pricing easy to compare. RFP processes often result in manufacturers switching from one expensive provider to another without meaningfully reducing costs.

The root cause, as Paul frames it, echoes economist Milton Friedman's four ways to spend money: the people making purchasing decisions about the plan often hold a small fraction of the assets, while the people whose money is actually at stake — participants and business owners — have little or no say. That misalignment is what keeps the market from behaving like a competitive one.

Paul is actively working with the Department of Labor to push for guidance — not mandates — that would require providers to send actual invoices reflecting fees in plain dollar terms. His view: if employers received invoices the way they receive bills from attorneys or accountants, the industry would change overnight.

Actionable Takeaways for Listeners

• Find out the name of every provider involved in your plan. Record keeper, administrator, advisor, custodian — list them all. More people may be getting paid than you realize.
• Ask every provider the same question in plain English: How much money have you made from our plan, in dollars, for each of the last three years?
• Don't accept a percentage as an answer. Push for actual dollar amounts. Fee disclosure documents exist but are often deliberately opaque.
• Negotiate. You don't need a financial background to pick up the phone and ask your record keeper to reduce your percentage fee. The worst they can say is no.
• Ask your advisor how many hours they've spent servicing your account, who they spoke to, and what they discussed. If the answer is vague, the fee is worth questioning.
• Consider whether you need an advisor at all. In participant-directed plans, advisors often aren't making any investment decisions. If services aren't being provided, fees shouldn't be either.
• Look into flat-fee providers. Companies like Ascensus and Employee Fiduciary offer low-cost, transparent, flat-fee structures that are often comparable in service quality to far more expensive alternatives.
• Think about paying plan fees at the employer level. It creates cost sensitivity, generates a tax deduction, and protects the tax-advantaged growth inside participant accounts.

Connect with Paul Sippil: paulsippil.com : resources, contact info, and more 📧 [email protected]

Lisa Ryan welcomes Friddy Hoegner, founder of Scope Recruiting and a former procurement and supply chain leader. Friddy helps manufacturing and supply chain companies build the teams that actually keep operations moving and he does it from a perspective most recruiters simply don't have: he's lived the job himself.

From Global Supply Chain to the Recruiting Desk

Friddy's career began in Germany with ABB in a global rotational supply chain program, where he eventually became a global commodity manager following ABB's acquisition of Thomas & Betts. He later moved into a supply chain manager role with a furniture manufacturer in North Carolina before co-founding Scope Recruiting in 2017 with his wife, who had already identified a critical gap in the market: recruiting firms that specialized in supply chain were staffed almost entirely by people with HR backgrounds, not supply chain experience.

That insight became Scope's founding principle. Rather than teaching supply chain professionals how to recruit, Friddy and his wife hired people with supply chain backgrounds and taught them the recruiting side of the business. The result is a firm that clients and candidates alike describe as refreshingly different, because the recruiters actually understand what the job requires.

The Hiring Mistake That's Killing Retention

Friddy's most consistent finding across years of working with manufacturers is that retention problems almost always start with the hiring process; specifically, with a failure to define who you're actually looking for before you start looking.

The pattern is predictable: a hiring manager submits a generic job description, different stakeholders have entirely different ideas about what the role should accomplish, and the organization moves forward without alignment. The hiring manager wants procurement expertise. The director of operations wants logistics. No one compared notes. The wrong person gets hired. And months later, the company wonders why they have a turnover problem.

Friddy's solution is to work with all stakeholders upfront to build an ideal candidate profile; a detailed picture of the skills, experience, and behaviors the role actually requires, along with a clear definition of what success looks like at 6 months and 12 months. From that profile comes a scorecard, submitted with every candidate, that creates a consistent and less biased basis for evaluation.

Why ChatGPT Can't Save a Bad Interview Process

Candidates today can walk into any interview with a ChatGPT-prepared answer to every standard question. Friddy argues this makes the ideal candidate profile and structured interview process more important than ever, not less. When you know the job deeply, you can ask follow-up questions that no AI can prepare someone for. The first answer is rehearsed. The second and third follow-up questions reveal whether someone actually knows what they're talking about.

This is precisely where Scope's supply chain background pays off. Generic recruiters can be fooled by a polished surface. Recruiters who've done the job can dig into the weeds and expose the gap between what someone says and what they actually know how to do.

Hiring for an AI-Disrupted Future

As automation and AI reshape manufacturing operations, Friddy cautions against hiring for narrow, specific skill sets that may be obsolete in two to three years. The manufacturers best positioned for the future aren't hiring for what they need today; they're hiring for adaptability, mental aptitude, and a demonstrated willingness to embrace change.

Friddy points to a striking example from his time at ABB: a football field-sized factory in Germany producing miniature circuit breakers at the same cost as factories in Indonesia and Argentina, because there were almost no people on the floor. A handful of engineers and maintenance staff. That future, he argues, is closer than most manufacturers realize, which means the people you hire now need to be able to pivot as the environment shifts around them.

Competing for Talent When You Can't Win on Salary

The majority of Scope's clients are mid-market and family-owned manufacturers who will never outspend a Fortune 500 company on compensation or benefits. Friddy's advice: stop trying to compete on salary and start competing on impact.

The candidates most valuable to smaller manufacturers are often the ones who've grown frustrated with corporate bureaucracy: the pace, the layers of approval, the distance from real decision-making. Smaller companies can offer something larger ones genuinely cannot: the ability to talk directly to the owner, change something meaningful in a day, and actually see the results of your work. That's a powerful draw for the right candidates, and it costs nothing to offer.

Why the Best Candidates Aren't on Job Boards

Friddy is direct about the limits of posting and praying. The top 10% of talent in any field are rarely browsing job boards. Many have never applied for a job in their lives; they get recruited from one role to the next. Reaching them requires dedicated outreach to passive candidates: contacting 100, hearing back from 15 to 20, submitting 3 to 4. It's time-intensive, unglamorous work — but it's how you find the people who are genuinely performing in their current roles rather than actively looking to leave.

Red Flags That Signal the Wrong Hire

Two interview warning signs Friddy's team watches for consistently:

• The "my way or the highway" attitude. Candidates who are so confident in how they've done things before that they can't adapt to a new environment. You can hear it in interviews: an overconfidence that signals they'll push their previous playbook regardless of context, without the sensitivity required to build buy-in.
• The inability to get specific. Candidates who speak in broad strokes about what "we" accomplished but struggle to articulate their own role, the steps they took, or the details of how it actually worked. Behavior-based questions that require specifics will surface this every time.

Friddy's Mic-Drop Closing Tip

When you've done the work of defining your ideal candidate profile and you find someone who checks every box, don't hesitate. Don't manufacture reasons to interview three more candidates. The right people are rare, and the hiring process itself can cost you them.

Actionable Takeaways for Listeners

• Align all stakeholders before you write a job description. Disconnected expectations between hiring managers and leadership are one of the most common and costly hiring mistakes in manufacturing.
• Build an ideal candidate profile, not just a job posting. Define what success looks like at 6 and 12 months before you evaluate a single resume.
• Use a scorecard for every candidate. Consistency reduces bias and helps you make better decisions — especially when interviews are spread over days or weeks.
• Ask behavior-based questions and follow up twice. The first answer is rehearsed. The follow-up questions reveal whether the knowledge is real.
• Hire for adaptability, not just current skills. In a rapidly automating environment, the ability to pivot matters more than a narrow set of technical expertise.
• Lead with impact, not salary, when competing against larger companies. Speed of decision-making, access to leadership, and the ability to drive real change are advantages small manufacturers genuinely have.
• When you find the right person — move.

Connect with Friddy Hoegner: scoperecruiting.com: hiring resources, candidate scorecards, and contact info

Lisa Ryan welcomes Rick Elmore, founder and CEO of Simply Noted, a 100% bootstrapped, handwritten mail automation company powered by patented robotics and AI-driven personalization. A former NFL athlete turned 9-patent tech founder, Rick scaled Simply Noted to $10 million-plus in revenue by combining manufacturing, automation, and disciplined sales systems, all without a single dollar of outside investment.

From the NFL to the Factory Floor

Rick's path to building a robotics company is anything but conventional. Drafted to the Green Bay Packers in 2011, he played for six teams in three years before facing what he describes as an identity crisis at 25. He took the transferable skills of an elite athlete: grit, discipline, competitiveness, and the willingness to embrace a process, and applied them first to corporate sales, where he became a consistent top 1% performer, and then to entrepreneurship.

The spark came during his MBA program when a marketing professor closed a three-hour lecture with a simple observation: handwritten notes get opened 99% of the time and the mailbox is empty. Rick tested the idea with a pen plotter, 500 targeted prospects, and sold $300,000 in six weeks on a $50,000 quota. The entrepreneurial seizure, as he calls it, had arrived.

Building What Didn't Exist

What followed was eight years of over-engineering everything. Because no off-the-shelf solution existed, Rick had to build it from scratch: robots, software, algorithms, and all. Key milestones include:

• Going through 14 engineering firms over more than a year, using each proposal to sharpen the next, before committing to a single partner
• Spending three years building the technology in chunks, funded entirely by customer revenue, with Thursday-night engineering sessions running from 2 PM to 10 PM
• Developing intelligent handwriting algorithms that understand context — an "E" at the start of a word is drawn differently than an "E" in the middle or at the end
• Building 220 custom handwriting robots in a 10,000 square foot facility, holding real pens, replaced twice a day by human attendants
• Earning 9 patents along the way — and openly sharing why he now thinks they may not have been worth it

Personalization at Scale: What Simply Noted Actually Does

Simply Noted has made handwritten mail as automatable and trackable as email. Clients can start simple: a spreadsheet with first name, last name, and address. or go deep with full CRM integration, LLM-powered personalized messaging, QR code tracking, delivery notifications, and trigger-based workflows. Examples include:

• A lead moving to "closed" in Salesforce automatically triggers a personalized handwritten thank-you note
• A complaint in a ticketing system pulls the complaint data, drafts a custom apology via an LLM, and sends a pen-written note to keep bad reviews offline
• E-commerce brands sending anniversary notes on the date of a customer's first purchase — completely automated
• QR codes on notes that, when scanned, automatically alert a sales rep via text message to follow up in real time

Why the Mailbox Is the Last Uncluttered Channel

Ads are ignored. Inboxes are buried. Social feeds are sponsored noise. But the physical mailbox is nearly empty, and something that looks genuinely handwritten stops people cold. Rick shares how a single handwritten note from a window contractor led Lisa's family to refer over $100,000 in business. That story, he says, is exactly why relationship-based businesses: real estate, home services, financial services, nonprofits, e-commerce see the strongest results.

The Athlete Mindset Applied to Entrepreneurship

Rick draws a direct line between fifteen years of athletic training and his ability to build slowly, stay disciplined, and not quit when things got hard. He pushes back against the social media myth of overnight success, pointing out that most "overnight" stories hide decades of accumulated domain expertise. Compounding success over time, he argues, is as fundamental in business as it is in sport.

Actionable Takeaways for Listeners

• Start with a crawl-walk-run approach. Request a sample kit, test a simple send, then integrate; don't try to fully automate on day one.
• Relationship building works best consistently over time. A one-time campaign won't move the needle. The businesses getting the most value from Simply Noted are using it month after month, year after year.
• The mailbox is your competitive advantage. If everyone you compete with is fighting for inbox and ad space, stepping into a nearly empty channel is an asymmetric opportunity.
• Personalization doesn't require complexity. Even a simple mail merge with first names on a templated message outperforms nearly any digital equivalent in open rates and memorability.
• Patents are lawsuit coupons. Protect your business by being so difficult to copy that competitors exhaust themselves trying — not by relying on legal protection alone.

Connect with Rick Elmore: LinkedIn: Rick Elmore simplynoted.com to request a free sample kit directly from the homepage

In this insightful and practical episode, Lisa Ryan welcomes Sebastian Chedal, founder of Fountain City and co-founder of TestFox.ai. Sebastian helps executives implement AI strategies that actually work, focusing on one critical question: How do you join the 20% of AI initiatives that succeed instead of the 80% that fail? With 60% of his work in manufacturing and industrial sectors, Sebastian brings a grounded, practical perspective where implementation matters more than hype.

Sebastian's journey began in 1998 when he started Fountain City in the Netherlands. Over more than two decades, his work has evolved through network security, website and app development, creative projects, and ultimately into digital transformation with a focus on AI implementation—predominantly in manufacturing.

As a self-described generalist at heart with diverse interests, Sebastian has founded five businesses total (two non-profits that didn't make it), giving him an entrepreneurial track record that includes both successes and failures. This real-world experience informs his practical, results-oriented approach to AI implementation. Fountain City has been the anchor and core of his professional life, adapting and evolving as technology has transformed over the past 26 years.

Sebastian draws a powerful parallel between today's AI landscape and the mid-1990s internet era, when people would ask, "What's a website? I don't need a website. Why would I need a website?" People didn't understand the benefits, how it worked, or how much effort it would take to implement.

Like many technological innovations, AI has finally reached a threshold catalytic point where it becomes truly useful, effective, and mainstream. The real breakthrough with large language models (LLMs)—what most people refer to when discussing AI today—is the ability to create qualitative automations, not just deterministic ones.

Deterministic automation (traditional): If this number is above this number, do this thing—straightforward logic gates we've had for decades.

Qualitative automation (AI-powered): Integration of nuanced, context-dependent decisions into automation processes, opening entirely new categories of automation.

This capability works at multiple levels:

1. Workflow automation: Eliminating time-consuming, mundane work like data transformation and entry that used to require hours or intern labor
2. Strategic support: Brainstorming, strategic planning, code planning, and design patterns
3. Knowledge work: Tasks requiring judgment, context, and understanding rather than simple calculations

The last year in particular has brought proposals and curiosity from people wanting to understand what it actually takes to put these systems in place—but the hype also leads to overestimation of capabilities and underestimation of implementation effort.

Sebastian outlines several critical dimensions of AI readiness that organizations must address:

The wrong approach: "We need to make sure 30% of our processes are run by AI by the end of the year."

This mandate isn't inspiring and doesn't give teams something meaningful to rally behind, even if it's the directive from stakeholders or management.

The right approach: Transform mandates into meaningful vision:

1. "We're bringing in AI to help you do less of the time-consuming work that distracts you from the real work you want to be doing"
2. "We're implementing AI to help with knowledge retention and dissemination so the experts' answers reach more people's hands"
3. Focus on removing bottlenecks and freeing up people's time

1. Define what you're trying to achieve with AI long-term
2. Break it down into concrete steps with measurable ROI
3. Ensure each step has an actual, achievable outcome
4. Keep the focus as narrow as possible—projects that try to do too many things with AI often fail

Start before you even have a project: Capture as much data as possible everywhere you can:

1. Record calls
2. Transcribe videos and podcasts
3. Store blogs and written content
4. Organize existing documentation

Without good data, you end up with generic inputs for AI systems. With rich data, you can plug services directly into it and create genuinely useful, customized solutions. Data can be organized, synthesized, or analyzed—but you must have it first.

This is especially critical in manufacturing, where Sebastian frequently sees companies with ambitious AI project ideas but:

1. Processes exist only in someone's head
2. The CRM system is "Bob's phone with all his contacts"
3. Sales approaches vary completely by individual with no standardization
4. No formal documentation of workflows or decision trees

The three-tiered AI implementation process:

Tier 1 - Data: Identify what data is needed and ensure it's being captured

Tier 2 - Process: Document, create, and formalize processes (healthy for business regardless, supporting legacy, growth, and consistency)

Tier 3 - AI Integration: Once processes are well-defined (ideally as flowcharts or UML diagrams showing logic trees), integrate the actual LLM components

Critical insight: Don't just throw AI everywhere. Use traditional automation for deterministic tasks and AI only where you need qualitative assessment. Using AI for math or deterministic systems can lead to significant issues.

One of the most popular AI applications Sebastian sees in manufacturing is knowledge retention and dissemination. When you have people who've been with the organization for 20, 30, or 40 years with critical knowledge in their heads—or in "Mikey's CRM on his phone"—losing them creates devastating gaps.

The right approach depends on where the data currently exists:

Email archives: One manufacturing client is leveraging 20-25 years of email conversations, using AI to analyze and create a knowledge springboard

Data synthesis: Use AI with structured inputs to generate data, then have subject matter experts review, correct, and essentially train the system on what's accurate

Existing documentation: Documentation may already contain answers but be inaccessible or unsearchable—AI can make it queryable and useful

Structured interviews:

1. Formal interviews recorded on calls
2. Self-guided prompts where the expert records video or voice responses on their own schedule
3. Adapt the method to how each person thinks and works best—make the process as smooth as possible

The key mindset shift: AI isn't here to replace jobs—it's here to ensure that the work someone has been doing for 30 years can continue for the next 30 years, preserving institutional knowledge and expertise.

Sebastian has studied resistance extensively and emphasizes that education is the most important antidote to fear.

The less people understand about how AI works and what it does, the more they glorify it and see it as a threat. The more they understand it, the more they realize how to control and use it as a tool—just like a calculator helps a scientist do faster math equations.

The dangerous pattern: People who refuse to learn AI out of fear are actually at the biggest risk of replacement. If you didn't want to use computers when they were being invented because you feared job loss, but then jobs required computer skills, you couldn't get hired—and the thing you feared came true because you created it.

The reality: Jobs of the future will be influenced and affected by AI. There is a need to adapt, learn, and shift in certain domains. But understanding AI greatly reduces the risk of flat-out replacement.

The people honestly at biggest threat are those who are afraid and don't want to learn.

Timeframe: Something between 3 months to 1 year is ideal for AI sub-projects. Rarely do projects over 2 years make sense in the current AI landscape.

Breaking down big goals: You might have a long-term vision (like creating a virtual sales engineer who knows everything about your B2B products and can speed up customized quotes), but break it into achievable steps:

Step 1 (3 months): System answers Q&A emails after webinars. Initially, humans answer while training the knowledge base.

Step 2 (3 months): Launch internal/external product replacement lookup tool for specifications—could be conversational or simple search.

Step 3 (6 months): Expand to training new engineers with best practices and accumulated knowledge.

Step 4 (1 year): Eventually reach the goal of quote assistance for customized solutions supervised by engineers.

Each step has a concrete outcome and measurable value, building toward the ultimate vision.

Sebastian emphasizes that scaffolding is crucial whether you're writing code or improving sales and marketing processes. Think of it like painting:

First: Fill the canvas with broad strokes and broad fields of color Then: Work on the detail

Many projects fail because teams focus intensely on detail execution without doing the scaffolding first. Define the logic flow and overall structure before diving into specifics.

Important AI characteristic: AI is a "pleasing system"—it always wants to please you in the way it thinks will please you. If you ask it to criticize your work, it will criticize because it thinks that's what you want. You must have the right methodology and approach in both project planning and implementation steps to work effectively with AI systems.

The zero-tolerance trap: Some companies have zero AI tolerance, telling teams they can't use AI due to legal risks, copyright concerns, IP protection, or process concerns.

Why it's dangerous: People bypass the restrictions anyway—using different browsers, personal laptops, or working from home. The water flows around your barriers.

The solution: Establish at minimum a governance policy framework:

1. How should people use AI?
2. How can they use it safely?
3. What's allowed and not allowed?

Create control without being so tight that people work around you. Balance protection with practicality.

AI projects are fundamentally transformation projects, which means they're change management projects. Several dynamics emerge:

Underlying issues surface: An AI project might be "the straw that breaks the camel's back" where a department that's felt ignored for two years suddenly expresses frustration—which has nothing to do with AI but is triggered by another major change.

Education and experience gaps: The difference in AI familiarity between upper management and the floor can create challenges. Sometimes upper management hasn't used AI yet (though they may be shy to admit it), while certain departments or individuals race ahead.

Assessment needs: Understanding where your business sits on the AI maturity spectrum is critical:

1. Pre-AI with no idea how it works?
2. Casual familiarity?
3. Advanced users in some departments?

Sebastian recommends an AI readiness self-assessment where you rank yourself against different areas. Following the principle that "a chain is only as strong as its weakest link":

If your team has tremendous AI hunger and passion, racing ahead with learning and implementation, but you have zero governance in place—governance is your weakest link. Address it to catch up with where you're steaming ahead in other areas.

Critical insight: Even if you don't immediately address disparities, knowing they exist is valuable. You know what you don't know, instead of not knowing what you don't know—which is usually where projects fail.

Most project failures over 20+ years happen because someone didn't realize something they didn't know they needed to know.

Sebastian emphasizes that 2025 saw knowledge retention as a particularly popular application, with multiple manufacturing companies working to:

1. Capture expertise before retirements
2. Distribute knowledge more widely for training
3. Remove bottlenecks created by having all answers in one person's head
4. Create systems that preserve institutional memory

The correlation Sebastian draws to social media adoption is instructive: companies initially had either zero tolerance or wild-west approaches until they developed policies and governance. Now it's part of the standard business lexicon. AI will follow a similar trajectory.

If you're at the very beginning: Focus on learning, awareness, and discussion. Recognize AI's importance and that it's not going away. Make time for this—it matters.

If you're further along: Conduct an AI readiness assessment to understand where you stand across all dimensions.

If you're ready to implement: Create a roadmap with proper planning, breaking projects into those 3-month to 1-year achievable milestones with clear outcomes.

Step one for everyone: Figure out where you're at, then have a good overview assessment of your business across all AI readiness dimensions.

1. Start Capturing Data Everywhere Now
2. Before you even have an AI project, begin recording calls, transcribing videos, storing blogs, and organizing documentation. Data is the foundation—without it, you only get generic AI outputs.
3. Transform Mandates into Meaningful Vision
4. Don't just say "30% of processes must use AI by year-end." Frame it as "freeing you from time-consuming work" or "getting expert knowledge into everyone's hands."
5. Document Your Processes First
6. If your processes exist only in people's heads or vary by individual, formalize them before attempting AI implementation. Create flowcharts showing logic trees and workflows.
7. Keep Projects Narrow and Time-Bound
8. Target 3 months to 1 year for AI sub-projects. Break big visions into concrete, measurable steps with clear ROI. Avoid projects trying to do too many things at once.
9. Focus on Scaffolding Before Details
10. Like painting with broad strokes before adding detail, establish your overall structure and logic flow before diving into execution specifics.
11. Combat Fear with Education
12. The less people understand AI, the more they fear it. The more they understand, the more they see it as a controllable tool. Make education a priority across all levels.
13. Use AI Only Where You Need Qualitative Assessment
14. Don't throw AI everywhere. Use traditional automation for deterministic tasks (math, simple logic gates). Reserve AI for contexts requiring judgment, nuance, and qualitative decisions.
15. Establish AI Governance Early
16. Create clear policies on...

In this energetic and information-packed episode, Lisa Ryan welcomes Tony Gunn, who leads global operations at his new venture TGM Global Services after a successful five-year run with MTD CNC. Tony has spent two decades on shop floors and in boardrooms around the world, traveling approximately 300 days a year to over 60 countries, giving him an unparalleled front-row seat to the technologies, trends, and people shaping modern manufacturing.

Tony shares his remarkable journey from mopping floors on weekends for minimum wage and learning to use basic presses, to mastering CNC machining through the mentorship of industry veterans who taught him line-by-line programming. His story exemplifies the power of workplace mentorship and the importance of taking skilled workers under your wing—lessons that continue to guide his mission today.

Tony lives by a powerful principle: "If you're the smartest person in the room, you're in the wrong room." He thrives on being the "dumbest person in the room," learning from experts across the manufacturing spectrum—from garage shops with three or four machines to CEOs of the world's largest manufacturing companies. This humility and hunger for knowledge informs everything he does in media and content creation.

His approach to sharing stories and technology stems from remembering his own starting point—when he was just learning to turn raw material into something of value. He's passionate about explaining concepts at a level that empowers everyone, avoiding the industry jargon and acronyms that can leave people behind. He never forgets the experts who gave their time to an amateur, and now pays that forward by putting others under his wing.

Tony candidly admits that even though it's his full-time job to know as much about the manufacturing industry as possible and share it with as many people as he can, he still can't keep up with how fast everything is moving. He can only imagine how difficult it must be for shop owners and operators whose day-to-day activities involve actually running their businesses.

From a global perspective, Tony sees shops still running machines that are 15, 20, 30, even 40 years old—machines that run good parts but can't complete a part on one machine, requiring five machines and much longer cycle times compared to modern technology. He draws a powerful contrast from his visit to the American Precision Museum in Vermont: 200 years ago, they were making micron parts, but it took two weeks. Today, it takes two minutes.

The conversation centers on what manufacturers are most hungry to understand and solve right now. Tony identifies the labor shortage as a critical issue that companies are trying to address through multiple strategies:

Inspiring the next generation through STEM - While crucial, this is years in the making and can't be the only solution

Adapting technology in the midterm - Companies must figure out which technologies are most affordable and provide the best ROI to minimize labor shortages while competing globally

Various forms of automation - From traditional robots and cobots to pallet systems and bar feeds, companies are finding ways to have one machinist run 10 machines instead of one, with processes running 24/7

Digital transformation - Tools like Datanomics and Fulcrum that take traditionally tribal knowledge and display it on screens, giving operators and management real-time visibility into what's actually happening on the shop floor—eliminating the need for all-day meetings filled with 80% truths and 20% fabrication

Tony emphasizes that knowing actual uptime, real capabilities, bottlenecks, and opportunities for improvement allows companies to create better platforms for making quality products cost-effectively in a globally competitive market. While there's conversation about reshoring and nearshoring, manufacturing will always be global, and U.S. manufacturers must figure out how to compete with regions that can mass-produce with millions or billions of people ready to work.

When asked about the smartest ROI for shops just getting started, Tony acknowledges this is complex because every situation is different—whether you're an expert machinist starting your own shop or someone still learning, whether you're doing production runs or one-off jobs, what parts you're making, and your size constraints all matter.

However, he shares powerful insights from his friends at EBITDA Growth Systems about "wizards on the machine"—highly skilled machinists who get frustrated feeling underpaid or undervalued, quit to start their own shops, and discover they can make any part imaginable but struggle with the front end and back end of business.

The front end challenge: Understanding how to quote properly. Many new shop owners underquote to win more bids, working 100-hour weeks without making profit because they're selling themselves short.

The back end challenge: Communication, customer service, and lead times. You can make a mistake on a part if you communicate two or three days in advance—that communication keeps clients. But calling the day of delivery saying you're a week behind will lose clients fast.

Tony's theoretical approach if he were starting a shop today? Go completely automated with one or two people, where machines essentially run themselves—even automated tool loading/unloading and part removal. Take on the debt believing in yourself, invest in the quickest ROI (keeping machines running and chips flying), and either sell what you need to sell or create your own proprietary part to avoid constant bidding. Keep those spindles turning to create profitability.

When discussing how to create a culture where machinists don't want to leave, Tony delivers a nuanced and honest perspective. If you poll disgruntled employees about why they're unhappy, you'll get 10% in dozens of different categories—it's not one simple answer.

While most people say "pay them more," Tony has been paid more and wasn't happy, so money isn't everything. Yes, most machinists deserve more money. Yes, the gap between shop floor workers and CEOs has increased dramatically. But at the end of the day, Tony believes most people are happiest when they:

1. Feel they have purpose in their lives
2. Feel wanted and desired in their company
3. Know what they bring to the table is valued
4. Feel appreciated when they go to work

These things honor the human soul more than just money. Money and materialistic things come and go—money doesn't buy happiness, though it can buy things that make us happy. But in the end, those are still just things.

Tony suggests bringing back elements like:

1. Offering respect and appreciation
2. Competitive pay that allows people to care for families
3. Retirement benefits and partnerships that many companies have dropped
4. Clean air and proper filtration in shops
5. Reasonable break times
6. Purpose and meaning in daily work

Every person wants something different—some just want breathable air and a good lunch sometimes. But the easiest common denominators are having purpose in life and having enough money to live comfortably.

Having visited more than 60 countries, Tony has powerful insights into what U.S. manufacturers should be paying attention to globally.

Tony recently visited Brazil and was blown away by SENAI, a company in business since 1942 that has trained over 80 million students (putting through about 2.8 million per year). They have 60-80 locations throughout the country, and for rural areas, they send buses and boats to provide 6-9 month trainings.

The game-changing aspect: It's machine shop funded. One percent of the profit that every shop in the country makes goes toward the education system, so students get free manufacturing education. They have facilities of 500,000 square meters with hundreds of the best machines available.

Location specialization: Many locations are designated based on what's being made in that area. Brazil is known for aerospace, so certain areas of São Paulo focus completely on training kids for aerospace parts. Other sections focus on mold and die, plastic injection, or medical devices—similar to Switzerland's regional specialization in watches, medical devices, etc.

The result: No labor shortage. No skills gap.

Tony emphasizes a critical difference between many U.S. trade schools and international programs: while manual machining has value for understanding machine vibration and what can/can't be programmed, many U.S. schools limit students because of fear they might crash expensive equipment.

Students often don't get exposure to 5-axis machines, 8-axis machines, or turning centers with 4 turrets and 2 spindles. They learn remedial processes, so when they enter a shop, they can do remedial work but can't immediately help make the complex parts that need to be made.

In contrast, places like Brazil and Switzerland have students working on the technology of today in their trade schools. When they graduate, they can immediately drive impact and make good parts on complicated machines. In Switzerland, 300,000 students aged 11-18 attend an exhibition dedicated to helping them select careers, and by age 20, they have a degree and are productive in the workforce.

While China is known as the "king of reverse engineering," Tony points out they've also started improving on that engineering and now have genuinely great technology. More significantly, they've purchased more automation than any other country in the world, allowing them to automate high productivity in machine shops better than anyone else right now.

The U.S. got left behind in automation adoption over the last 20 years. American manufacturers need to look to countries that have adopted automation far more quickly and successfully.

Tony's passion for putting machinists on camera and sharing their stories stems from two powerful motivations:

When a machinist gets on camera and shares their somewhat unbiased or agnostic view about how technology is working for them, it's the best way for viewers to receive information without feeling like they're being sold something. While salespeople should know their technology better than anyone (and there's value in learning from them), everyone knows they're being sold something.

When you hear from the person on the floor working the equipment every single day—someone who's had the trials, mistakes, and failures, and has worked to create success—that has unique value. People want to receive information from someone running the parts daily.

Manufacturing has been an invisible industry for far too long. Tony's company slogan is "We machine the unseen that moves the world." People are happy to have their phones, cars, and planes, enjoying all the things made through manufacturing, welding, and trades—yet these industries don't get respect.

Manufacturing was demonized as the second opportunity if you couldn't get into university. It was considered for misfits, losers, dropouts, and GED educations. That's simply not the case.

As AI starts taking more simplistic jobs of people sitting at computers coding in cubicles, it currently cannot replace people working with their hands. Plumbers, electricians, and machinists represent the future for anyone who wants a good, stable career for a lifetime.

Tony wants to give these professionals a voice and opportunity to share their stories because he's tired of them not being respected. He was tired of not being respected himself, but he has a "big freaking mouth" and keeps talking about it until hopefully earning that respect. You can't demand or force respect—you earn it. In doing so, he tries to give others the confidence to do the same and let them know how important they are.

Tony is tremendously excited about his new transition to TGM Global Services, which gives him more opportunity, flexibility, and creativity to do what he's always wanted. His YouTube channel is approaching 400,000 subscribers (aiming for a million within the year), LinkedIn is thriving, and Instagram is growing in markets like Brazil and India.

The partnerships he's forming for 2026 involve great people and products, with opportunities to showcase technology the world has never seen before—like one of only three fourth-generation systems in the world (the others being in France and Sweden) that can even help fight cancer.

Beyond personal excitement, Tony sees a resurgence in the industry. Respect is starting to come back. He has friends who've quit their day-to-day jobs to pursue inspiring the next generation—traveling to middle schools and high schools, giving keynotes, conveying messages not just to students but to parents (because you have to convince parents this is a good idea, not just students).

As a perpetual optimist (sometimes annoyingly so), Tony is hopeful and grateful to play a small part in this amazing industry that's given him so much over the last 25-plus years.

1. Embrace the "Dumbest Person in the Room" Mindset
2. Surround yourself with experts and admit when you don't know something. The best learning happens when you're comfortable saying "I don't know that answer, thank you for teaching me."
3. Remember Your Starting Point
4. When explaining technology or processes, consider those who are just learning. Avoid overwhelming people with acronyms and jargon—empower them with clear, accessible information.
5. Prioritize Digital Transformation for Real Visibility
6. Implement systems like machine monitoring and shop management software even if you only have one or two machines. Knowing the real truth about uptime, capabilities, and bottlenecks is invaluable for growth.
7. Master the Front End and Back End of Business
8. Being a wizard on the machine isn't enough. Learn to quote properly (don't undersell yourself), communicate proactively with customers, and manage lead times transparently to retain clients.
9. Start with Strategic Automation
10. If starting today, consider taking on debt to invest in automation that keeps machines running 24/7. The quickest ROI comes from keeping spindles turning and chips flying with minimal labor requirements.
11. Build Culture Beyond Compensation
12. While competitive pay matters, focus equally on creating purpose, appreciation, and respect in your workplace. People stay where they feel valued and know their contributions matter.
13. Invest in Modern Training Equipment
14. Don't limit your team or apprentices to outdated manual equipment out of fear. Exposure to 5-axis machines, turning centers, and modern automation prepares people to create immediate value.
15. Learn from Global Best Practices
16. Study how countries like Brazil fund education through shop contributions (1% of profits), how they align training with regional specializations, and how China has embraced automation at scale.
17. Adopt Automation to Compete Globally
18. The U.S. got left behind in automation over the last 20 years. Look to countries that have adopted it more successfully and implement proven solutions to remain competitive.
19. Give Your Team a Voice
20. Share your machinists' stories and perspectives. Let them showcase their expertise on video, social media, or industry platforms. Their authentic experiences are more valuable than any sales pitch.
21. Pay Attention to Technology Even When You Can't Keep Up
22. Even experts struggle to keep pace with technological change. Make time to follow industry media, attend trade shows, and stay connected with your network to avoid falling behind.
23. Mentor the Next Generation
24. Remember the people who took you under their wing and pay it forward. Take time to teach, support, and encourage those just starting their manufacturing journeys—they'll never forget it.

Want to continue the conversation or follow Tony's global manufacturing adventures? Connect with him on:

1. LinkedIn: For business updates and day-to-day activities (Tony respects LinkedIn as a business platform and keeps content...

In this thought-provoking episode, Lisa Ryan welcomes Bruce Vojak, a leading authority on strategic innovation with a unique combination of deep and broad experience. As a business advisor, board member, senior fellow with The Conference Board, and author of two highly regarded books on innovation published by Stanford University Press, Bruce helps mature companies in mature industries survive and thrive in an increasingly volatile, complex, and ambiguous world.

Bruce shares his journey from engineer and techie to innovation strategist, sparked by his fascination with remarkable innovators—not their processes or cultures, but the people themselves. This curiosity led him to decades of research exploring the question: "How do they know what to do?" His work focuses specifically on mature manufacturing companies, making his insights particularly relevant for today's industrial leaders.

Bruce clarifies a common misconception: innovation isn't just creativity or something new—it must have financial impact and marketplace value. While many manufacturers focus on lean implementations, Six Sigma, or equipment upgrades, true innovation changes the basis of competition in an industry. It creates advantages or protects against disadvantages in transformative ways.

He illustrates this with compelling examples:

1. The Carrot Evolution: From knife peeling to safety peelers, then to Oxo's ergonomic design and finally pre-peeled baby carrots that increased overall consumption
2. Moneyball: How the Oakland Athletics revolutionized baseball team optimization using sabermetrics instead of gut feelings

The lesson? Innovation exists in every industry, you just need to start looking for it by asking questions you didn't think you needed to ask.

For manufacturers at the maturity stage of their lifecycle, the biggest danger is retreating to familiar ways of doing things without questioning unarticulated assumptions. Bruce emphasizes that the real risk isn't making big innovation investments—it's failing to ask the right questions at all.

He frames innovation investment through two financial lenses:

1. Insurance: Protection against being blindsided by market changes
2. Options: Opportunities for future growth beyond the "bond-like" steady returns of optimized manufacturing operations

Both require relatively small initial investments, often just time and attention, but provide critical protection and opportunity.

With AI and other technologies transforming business at lightning speed, Bruce advises companies to focus on three critical elements:

1. Internal Alignment: Both strategic and tactical
2. Strategic: Are we really going to invest in innovation?
3. Tactical: What about this specific idea or problem?
4. Alignment failures can derail innovation even at individual contributor levels
5. Simple Processes: Especially for small and mid-sized companies
6. Don't need elaborate systems
7. Focus on incremental, poker-like bets
8. Emphasize learning cycles over "failing fast"
9. Innovation Exemplars: People who see patterns before others
10. Not just idea generators, but individuals who navigate organizations to get buy-in
11. Examples include Tom Osborne at Procter & Gamble (feminine hygiene products) and Nancy Doss (Oil of Olay transformation)
12. Often "the most important people you've never heard of"—not always the CEO or owner

Bruce and Lisa explore the tension between fear of change and the need for psychological safety. Key insights include:

1. Learning Over Failing: Reframe "fail fast" as "learn quickly"—small bets teach you about applications and opportunities you'd never consider
2. Bridging Generations: Combine the "tradition" of experienced workers with the "tradition" of digital-native younger employees to unlock discovery potential
3. Managing Resistance: Sometimes alignment requires marginalizing those who simply won't get on board, but winning people over winsomely should always be the first approach

The conversation emphasizes that even in an automated, technology-driven world, people and culture remain at the heart of successful innovation.

Bruce shares powerful examples of innovation exemplars in action:

1. West Tech Automation Solutions: A mid-sized manufacturer that accepted a radical request to collaborate with competitors in a virtual room, creating new business models and ongoing revenue streams
2. Steve Jobs and Apple: Taking off-the-shelf technology and transforming it through design perspective and deep understanding of human needs

These examples prove that you don't need to be a Silicon Valley entrepreneur to innovate—mature manufacturers can achieve remarkable results by putting their minds to it.

Bruce's approach begins with assessment:

1. Start with informal confidential conversations to discuss your situation
2. Conduct formal assessments with leadership teams or functional groups to identify hangups and efficiencies
3. Work through workshops and ongoing advisory relationships
4. Focus on both owners/presidents and the innovation exemplars within the organization

His guarantee? You'll learn whether there's something to act on or defend against—delivering both the "option" for growth and "insurance" against disruption.

1. Redefine Innovation for Your Organization
2. Move beyond incremental improvements like lean and Six Sigma. Ask: What could change the basis of competition in our industry? What advantages can we create or disadvantages can we avoid?
3. Question Your Assumptions
4. Identify and challenge your unarticulated assumptions. Ask questions you didn't think you needed to ask—that's where breakthrough opportunities hide.
5. Start with Small Bets
6. Don't make massive innovation investments. Use incremental, poker-like bets that minimize risk while maximizing learning opportunities.
7. Find Your Innovation Exemplars
8. Identify the people in your organization who see patterns before others and can navigate the company to get ideas implemented. They're often not in the C-suite.
9. Secure Strategic and Tactical Alignment
10. Get clear organizational commitment at two levels: (a) Will we invest in innovation at all? and (b) What about this specific idea? Address misalignment quickly and directly.
11. Create Learning Cycles, Not Failure Cycles
12. Replace "fail fast" with "learn quickly." Each small experiment should teach you something valuable, even if the original hypothesis doesn't pan out.
13. Bridge Generational Traditions
14. Combine the wisdom and experience of long-tenured employees with the fresh perspectives and technological fluency of younger workers to unlock discovery potential.
15. Build Psychological Safety
16. Create an environment where people can experiment without fear of being thrown under the bus. Focus on learning lessons and moving forward together.
17. Think of Innovation as Insurance and Options
18. View innovation investments as (a) insurance against market disruption and (b) financial options for future growth—both are critical for long-term sustainability.
19. Start with an Assessment
20. Begin your innovation journey with an honest assessment of your current capabilities, hangups, and opportunities. Reach out for expert guidance to identify where to focus first.

Ready to explore how innovation can transform your manufacturing business? Connect with Bruce on LinkedIn at Bruce Vojak or visit his website at Breakthrough Innovation Advisors to schedule a confidential consultation.

Bruce is also the author of two highly regarded books on innovation published by Stanford University Press, offering deep insights into the people and practices that drive transformative change in mature industries.

For more episodes, insights, and actionable strategies for manufacturers, stay tuned to the Manufacturers Network Podcast with Lisa Ryan.

In this insightful episode, Lisa Ryan welcomes Dr. Joshua Tarbutton—Chairman and Chief Innovator at Bravo Team, an engineering firm specializing in custom automation solutions for manufacturers facing tough challenges. The conversation tracks Joshua Tarbutton's journey from childhood curiosity with Light Brights and exposure to structural engineering via his father, through military service, academia, and ultimately into entrepreneurship and innovation in manufacturing.

The episode tackles the urgent push for automation in manufacturing, driven by rising costs, supply chain instability, and workforce challenges. Joshua Tarbutton reflects on how fear and control can impede leadership decisions, and points out the importance of moving beyond blame and understanding the deeper social and economic forces at play.

A major theme is reskilling the workforce in response to automation. Joshua Tarbutton highlights the pressures at the lower end of the labor pool—jobs that are tough to automate and have high turnover—and notes the necessity of upskilling those in roles most likely to be displaced by technology. He emphasizes a need for earlier cultivation of manufacturing interest and skills in young people, advocating for more proactive outreach beyond "manufacturing month."

For companies lacking robust R&D departments, Joshua Tarbutton suggests an experiment-focused, risk-decreasing approach—start small, test hypotheses, and find the right experts to guide implementation. He cautions leaders to seek out genuinely knowledgeable advisors rather than relying solely on titles.

AI and large language models are discussed as powerful tools for manufacturers at every scale. Joshua Tarbutton sees AI as both a knowledge accelerator and a supportive "smart friend," especially for leadership looking to execute better and maintain margins.

Both speakers explore workplace culture, emphasizing that even in an automated world, people and teams remain the heart of innovation. Creating environments where it's safe to fail and learn, and supporting open, honest communication across teams and departments, are crucial for successful transformation.

Joshua Tarbutton closes by outlining Bravo Team's approach: solving tough, high-value problems for clients through clever engineering and collaboration, supporting innovation from machine design to full product development.

1. Automate Strategically:
2. Don't rush into automation out of fear—carefully assess timing, ROI, and reskill your workforce to maximize benefit and minimize disruption.
3. Invest in People Early:
4. Start cultivating interest and skill in manufacturing at a young age. Partner with schools and programs for real hands-on exposure beyond industry holidays.
5. De-Risk Innovation:
6. Before committing big budgets, run small, targeted experiments to prove out new ideas. This minimizes financial and technical risk in automation and R&D projects.
7. Find the Right Experts:
8. The right solutions depend on the right people, not just credentials. Seek out advisors and partners who prioritize transparency and a proven track record.
9. Leverage AI for Competitive Edge:
10. Use AI and language models to access knowledge, troubleshoot challenges, and support leadership decisions. Treat AI as a resource for innovation and execution.
11. Prioritize Workplace Culture:
12. Foster psychological safety for your teams to test, fail, and learn. Support open communication between financial and technical roles to keep projects aligned and teams motivated.
13. Ownership and Honest Conversations:
14. Break down silos and create space for honest dialogue around budgets, strategy, and goals—even if this means rethinking how information is shared across your organization.
15. Maintain Clarity in Project Management:
16. Set clear definitions of success and checkpoints for teams. Celebrate progress and create regular "vistas" so employees feel recognized and motivated.

Want to learn more or see if Bravo Team can help your business tackle tough challenges? Visit Bravo Team Tech for videos and case studies, check out Joshua Tarbutton's personal website, or explore innovations like BravoWalk, the team's engineered dog collar solution.

For more episodes, insights, and actionable strategies, stay tuned to the Manufacturers Network Podcast with Lisa Ryan.