The Bridgecast with Scott Kinka

Latest Episodes:

Beyond Compliance: Building a Framework for Trustworthy AI May 19, 2026

Show notes

In this episode of The Bridgecast, host Scott Kinka welcomes Reggie Townsend, Vice President of AI Ethics, Governance, and Social Impact at SAS, for a wide-ranging conversation about what it truly means to build AI that organizations — and society — can trust.
Reggie brings something rare to the AI ethics conversation: a commercial backbone. After years at Motorola, IBM, and Sun Microsystems before joining SAS eleven years ago, he approaches responsible AI not as an academic exercise but as a practical business imperative. His core thesis? Doing well and doing good are not mutually exclusive — and organizations that treat ethics as purely defensive are leaving enormous strategic value on the table.

What you will learn:

Why "responsible AI" is being replaced by "trustworthy AI" — and why the language shift matters more than most people realize
How to apply SAS's four-pillar AI governance framework (culture, operations, regulations, oversight) inside your organization today
The hidden cost of treating AI governance like a compliance exercise instead of a strategic leadership function
Why AI is not word processing — and what automated decision-making really means for your business risk
How to think about productivity metrics, workforce pipeline, and the human cost of hollowing out junior talent in the AI era
What CIOs should do right now to build a durable governance model across fragmented global regulations

Reggie Townsend is the Vice President of AI Ethics, Governance, and Social Impact at SAS, a data and AI company with 50 years in business. He previously served on the National AI Advisory Committee, advising the U.S. Department of Commerce, and currently sits on the board of Equal AI. With a career spanning Motorola, IBM, and Sun Microsystems before SAS, Reggie brings a practically grounded lens to AI ethics — one that insists organizations can simultaneously drive profit and protect people. He leads teams across ethics, governance, regulatory strategy, accessibility, and social impact at one of the world's most quietly consequential AI companies.

Episode Highlights:

[15:27] The Language Shift That Changes Everything

At SAS, the term isn't "responsible AI" — it's "trustworthy AI." And Reggie explains why that distinction matters far more than it first appears. "No one is going to sign up for irresponsible," he says — which means the term has become politically loaded before the real conversation even starts, entangled in doomer narratives and Luddite debates. Trustworthy AI, by contrast, cuts straight to the question that actually matters: will people invest their trust in this technology? This framing is backed by governments worldwide — the EU, the UN, and both recent U.S. administrations have all gravitated toward it. More importantly, it reframes the business conversation entirely. Trust isn't a constraint on AI deployment. It's the precondition for adoption at scale. Organizations that are still fighting over whether their AI is "responsible" may be having the wrong conversation entirely.

[24:02] The Hollow Pipeline Problem

One of the sharpest observations in this episode concerns what happens when organizations race to maximize AI-driven output at the expense of building their talent pipeline. Reggie warns that senior employees who become heavy AI users can now produce at levels that make junior hiring seem unnecessary — but then asks, "What happens when those senior people leave?" The institutional knowledge disappears. And there's no pipeline to replace it. He extends this to the macro level: if GDP is just a measure of output, AI agents producing more may look like a win. But if the people who used to produce are left behind, "we will have missed the boat by a long shot." The implication for leaders is direct: AI workforce strategy isn't just about productivity gains. It's about what kind of organization you intend to be five to ten years from now — and whether you'll have the human foundation to sustain it.

[19:12] The Four-Pillar Governance Framework That Actually Works

When businesses ask what good AI governance looks like in practice, Reggie doesn't point to a policy template. He points to SAS's "quad" framework: Culture, Operations, Regulations, and Oversight. Culture asks how normative behaviors in the organization need to shift to absorb AI disruption — and what the psychological impact on employees will be. Operations addresses workflows and how work will actually get done going forward. Regulations covers both compliance obligations and aspirational standards in a currently fragmented global landscape. Oversight asks where accountability sits, who monitors AI decisions, and how fast the organization should allow change to move. His advice for getting started: "Don't go try to solve for cancer." Instead, identify repeatable, laborious tasks — what he calls "pearls" — AI-ify those first, build early wins, develop the organizational muscle, and then expand. It's a framework that respects both business reality and human complexity, and it's one any organization can start using today.

Episode Resources:

Reggie Townsend on LinkedIn
SAS Website
Scott Kinka on LinkedIn
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

Connectivity, Data, and People: The New IT Ecosystem May 05, 2026

Show notes

In this live episode of The Bridgecast, recorded at the Lumen Lounge during the Channel Partners Conference, host Scott Kinka welcomes Danny Benedetti, Vice President of Partner Solutions, and Jim Ortbals, Senior Vice President of Global Partner Solutions at Lumen. Together, they dive into the evolution of one of the industry's most significant players—from the legacy of CenturyLink and Level 3 to becoming a purpose-built infrastructure provider for the AI era.
The discussion moves beyond traditional connectivity to explore how organizations can "cloudify" their networks to scale bandwidth on demand. With Jim joining the conversation on his first day in the role, the episode offers a unique look at leadership during rapid change and the strategic importance of building a "digital workforce" through AI agents. Whether you are managing complex M&A integrations or preparing your infrastructure for agentic AI, this episode provides a roadmap for translating technology into business outcomes.
What you will learn:

How to shift from static to dynamic networking to support the AI economy
The leadership superpowers required to guide teams through constant industry change
Strategies for managing the human fear and systemic risks inherent in M&A integrations
Why internal AI agents are creating immediate ROI by building a digital workforce
The importance of humility and servant leadership in a high-stakes tech environment
Why network control must move from the provider into the hands of the customer

About the Guests:

Danny Benedetti is the Vice President of Partner Solutions at Lumen Technologies, where he has spent 16 years leading channel strategy through multiple industry shifts. He focuses on helping partners deliver value to enterprise customers in increasingly distributed IT environments.
Jim Ortbals serves as the Senior Vice President of Global Partner Solutions at Lumen. A veteran of the service provider and cybersecurity space, Jim has held leadership roles at Cisco, VMware, Zscaler, and BeyondTrust before joining Lumen to lead their global channel organization.
To find out how Bridgepointe Technologies helps businesses make IT decisions faster with world-class engineering support and ongoing guidance, head to https://bridgepointetechnologies.com/

Episode Highlights:

[05:41] Leading Through the "Hard"

Danny Benedetti explains that leading through change is about more than logistics; it's about caring for the people you lead. When change creates fear, leaders must steady the boat and help their teams see the opportunity on the other side. Danny shares a core philosophy he uses with his team: "Everything you want is on the other side of hard."

[09:00] Cloudifying the Network

Lumen is moving away from static, telco-controlled networks to a model where the network scales on demand. This "cloudification" allows enterprises to dial bandwidth up or down and move workloads to hyperscalers seamlessly via tools like the Multi-Cloud Gateway. As Danny puts it, Lumen is becoming the "backbone for the AI economy" by ensuring infrastructure can scale with the needs of the enterprise.

[14:48] The Human Element of Integration

Managing corporate mergers isn't just about systems; it's about human fear. Danny notes that people often worry about their own place in a new organization rather than what's best for the technology. Jim adds that from a systemic perspective, security must be a primary focus to ensure integrations don't create new attack vectors while chasing productivity.

Episode Resources:

Danny Benedetti on LinkedIn
Jim Ortbals on LinkedIn
Lumen Technologies Website
Scott Kinka on LinkedIn
Bridgepointe Technologies Website
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

From Plumbing to AI: The Evolution of Managed Services Apr 21, 2026

Show notes

In this episode of The Bridgecast, host Scott Kinka welcomes Frank Scanga, Executive Vice Chairman at XTIUM—the powerhouse formed by the combination of ATSG and Evolve IP. With over three decades of experience in the managed services and cloud space, Frank offers a candid look at how technology leadership must evolve from "wrench turning" to driving actual business outcomes.
The conversation dives deep into the current state of AI, where Frank reveals a startling statistic: 70% of AI projects fail because organizations take a technology-first rather than a business-outcome approach. From winning top honors in Gartner's Magic Quadrant for Managed Network Services to building an "AI Manager" platform to orchestrate LLMs, Frank explains why the "S" in MSP—Service—remains the most critical component of the tech stack.

What you will learn:

Why 70% of AI projects fail and how to pivot toward business outcomes
The definition of a "full-stack" MSP and why point products are no longer enough
How XTIUM uses machine learning and AI for business impact modeling
Why the Windows 10 end-of-life is driving a massive resurgence in Desktop as a Service (DaaS)
The one question every business should ask an MSP: "How do you perform in a failure?"
Why curiosity and listening to customers are the keys to long-term leadership

Frank Scanga is the Executive Vice Chairman at XTIUM and a longtime leader in the managed services and cloud industry. A New York native and Fordham University graduate, Frank founded Maxpoint (later ATSG) in 1994 and has scaled the organization to support 1,700 clients across complex IT environments. Today, he helps drive XTIUM’s "security-first, AI-enabled" approach to IT, focusing on providing high-level business value by automating and managing the foundational technology stack.
To find out how Bridgepointe Technologies helps businesses make IT decisions faster with world-class engineering support and ongoing guidance, head to https://bridgepointetechnologies.com/

Episode Highlights:

[11:17] The 70% Problem: Tech-First Is the Wrong First

Frank opens the episode with a number that stops listeners cold: 70% of AI projects are failing. And the culprit isn't the technology—it's the mindset. "They're taking a technology approach to it and not so much a business outcome approach," Frank explains. This isn't new. It's the same pattern he's watched play out since the dot-com boom, through the cloud rush, and now into AI. CIOs who lead with bits and bytes get tuned out in the boardroom. The ones who thrive frame everything around business impact.
The analogy Frank uses is pointed: "It's a service; it should be up and running, period. You turn the lights on, you expect the lights to be on." The moment a CIO starts talking about infrastructure for its own sake, they've already lost the room. The lesson applies directly to AI: a "technology-first" AI strategy isn't a strategy—it's a procurement plan. The organizations winning with AI are the ones asking "what problem does this solve for the business?" before they write a single line of code or sign a single vendor contract.

[13:25] Building the MSP of AI: Ten Years in the Making

While most organizations are just beginning to think about AI governance, XTIUM has been quietly building it for nearly a decade. Their observability platform uses machine learning to distinguish "white noise" from "bad noise" in managed networks—and goes further with business impact modeling that ties network events directly to business outcomes. They built AI into their DaaS cloud management platform as well.
But the most forward-looking development is internal: XTIUM built an "AI Manager" to govern how their own teams deploy and use AI—because they experienced firsthand what happens when AI usage becomes sporadic and ungoverned. "We realized our own AI deployment was sporadic, so we centralized it," Frank explains. Now, they're preparing to offer that platform to customers. The positioning is characteristically direct: "Someone's going to manage the robots—why not us?" That's the XTIUM thesis in six words. And given their Gartner ranking—#1 in managed network services, automation/AI, and security in MNS—it's not just a tagline.

[26:50] The One Question That Exposes Every MSP

When Scott asks what question a company should ask when evaluating an MSP, Frank doesn't talk about certifications, headcount, or pricing. He gives a single, deceptively simple answer: "How do you perform in a failure?" It's a question that cuts through vendor theater instantly.
"Technology fails all the time," Frank says. "The 'S' is the big part of 'MSP'—it's a services business." What matters isn't how smooth things run when everything's working; it's what happens at 2 a.m. when something breaks. Frank has kept his first customer for over 32 years. His first employee is still with him. That's not a marketing claim—it's the output of a culture that treats trust as the core product, not the technology stack underneath it. "If you trust that we are taking care of the infrastructure, you can focus on your business." Everything else is commentary.

Episode Resources:

Frank Scanga on LinkedIn
Scott Kinka on LinkedIn
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

Beyond the Hype: Building Ethical AI for Business Apr 08, 2026

Show notes

In this episode of The Bridgecast, host Scott Kinka welcomes Dr. Eva-Marie Muller-Stuler, a literal rock star in the world of data science and AI ethics. As the former leader of IBM’s first AI center of excellence and a top advisor to the European Parliament, Dr. Eva-Marie brings a wealth of experience in turning emerging technologies into measurable business outcomes. She challenges the current corporate obsession with "agentic AI" and "autonomous workers" by highlighting the foundational work required in data governance and strategy.
Dr. Eva-Marie shares a seminal moment from her career where a major telecom company offered her an intern's worth of raw data without an MOU, illustrating just how far the industry has come—and how many ethical pitfalls remain. The discussion dives deep into "data archaeology," explaining why data for AI requires a much more rigorous standard than data for traditional analytics.
What you will learn:

Why AI must always start with a business need rather than a technical capability
The fundamental difference between data for analytics and data for AI
How to identify and mitigate "hidden proxies" that create bias in machine learning models
The framework for choosing between "buying" versus "building" AI solutions
Why the loss of senior expertise in the workforce is a major threat to AI monitoring and safety
The reality of current AI regulations and why most models are not yet truly GDPR compliant

Dr. Eva-Marie Muller-Stuler is the Founder and Chief AI Officer at The Hummingbird Group, a firm dedicated to responsible, high-performance AI deployment. With over 25 years of experience helping global organizations turn emerging technologies into measurable business outcomes, she has led major AI initiatives at KPMG, Ernst & Young, and IBM—where she built their first AI center of excellence. She has also advised the United Nations, UNESCO, and the European Parliament on AI governance and risk mitigation. Recognized as a Top 10 Most Influential Woman in Technology, World’s Best Data Scientist, and Top Brilliant Women in AI Ethics, Dr. Eva is a leading voice on the intersection of technical rigor, ethical AI, and real-world business outcomes. She is based in Dubai and is currently writing a book on how to correctly build AI systems.
Episode Highlights:

[04:34] The Data Wild West That Changed Everything

Dr. Eva’s pivotal career moment came in 2013 at KPMG, when her team was building “always-on machines for decision support.” With almost no data governance norms in place, they contacted one of the world’s largest telecom companies and asked for all their customer data—who called whom, from where, which bills were paid. The company’s response was essentially: help yourself. They sent an intern with flash drives to pick it up. No MOU. No payment. No legal framework.
Sitting with the data, Eva and her team had a sobering realization: they didn’t need AI models at all. They could simply filter behavioral patterns to identify individuals’ religion, health status, and financial situation—with no model required. “We had so much knowledge about individual people,” she recalls. That moment became the catalyst that drove her into AI governance work with the UK government, EU, United Nations, and NGOs worldwide. Even today—with GDPR and the EU AI Act in place—most large language models are not compliant, and enforcement remains dangerously weak. The wild west isn’t over. It’s just better dressed.

[14:30] The Champions Program: Nike's Secret to GitHub Copilot Adoption

When Nike rolled out GitHub Copilot, leadership assumed flipping the switch would be enough. It wasn't. Adoption stalled. What unlocked it? Training, targeted use-case discovery, and — most critically — a "champions program" that activated the engineers who were already experimenting on their own. Elaine connects this directly to the shadow IT problem: fear of displacement drives people to go rogue. Organizations that surface those early adopters and give them a sanctioned role in the rollout don't just solve a security problem — they create a self-sustaining adoption engine. Her formula: top-down mandate gives permission, bottom-up champions give momentum. "Build community structures that understand and operate. Then, all of a sudden, you're not just pushing a literacy program; it's creating itself. That's sustainable."

[19:15] Tribal Knowledge Is the Real AI Advantage

Elaine lands one of the episode's most counterintuitive insights: it's not Gen Z leading AI adoption in the enterprise — it's the seasoned operators with years of context. AI tools are only as powerful as the prompts fed into them, and great prompts require deep domain knowledge. "Intelligence lives outside the tool," she says. "You can't just apply an agent and think you're diving into the workflow. You have to learn about all the tribal knowledge gained over the years." This reframes the entire upskilling conversation. The goal isn't to teach experienced workers to be tech-savvy; it's to help them see that what they already know is the most valuable input to any AI system — and then invest in developing that capability intentionally.
Episode Resources:

Dr. Eva-Marie Muller-Stuler on LinkedIn
Scott Kinka on LinkedIn
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

Innovation Lessons from Nike and Amex Mar 24, 2026

Show notes

In this episode of The Bridgecast, host Scott Kinka welcomes Elaine Barsoom — Venture Partner at Silicon Foundry and a recognized leader in enterprise AI and corporate innovation — for a wide-ranging conversation on what it actually takes to build an AI strategy that sticks.
With 20 years of experience bridging strategy and next-generation technology across Nike, American Express, and multiple sectors including retail, fintech, and hospitality, Elaine brings a rare lens: the view from inside the machine. Her defining insight? Most organizations are asking the wrong question. Instead of "How do we use AI?", the right question is "What problem are we trying to solve, and can AI help?" — a subtle but transformative shift that changes everything about governance, adoption, and ROI.
What you will learn:

Why most AI mandates are set up to fail — and how to reframe the right question
How to map current-state workflows and redesign around human needs before selecting tools
Why experienced employees with tribal knowledge outperform younger workers when using AI
How Nike's GitHub Copilot rollout almost stalled — and what a champions program did to fix it
The top-down mandate + bottom-up build model for sustainable AI adoption
Why most AI governance frameworks are "theater" and what to do instead
Real-world CX wins: Lowe's Milo, Wingstop's Smart Kitchen, and what they teach about ROI
The bold prediction that AI agents will soon outnumber humans in enterprise org charts

Elaine Barsoom is a Venture Partner at Silicon Foundry and a globally recognized leader in AI strategy and corporate innovation. She spent 20 years helping Fortune 500 organizations marry strategy with next-generation technologies, most recently serving as Global Head of Tech Innovation, Partnerships, and Strategy at Nike. Her career also includes pivotal leadership roles at American Express, where she built one of the company's first e-commerce ventures in the years following September 11th. A first-generation daughter of Egyptian immigrants and a systems thinker at her core, Elaine now works with mid-market organizations and leaders to navigate AI transformation with rigor, empathy, and measurable results.
Episode Highlights:

[09:52] Ask the Right Question First

Elaine's most foundational piece of advice for AI-overwhelmed CIOs reframes the entire conversation. At Nike, before touching any tool or vendor, the team asked: "What problem are we trying to solve, and can AI help?" — not "How do we use AI?" This single reframe changes how friction points get identified, how workflows get redesigned, and ultimately whether AI gets used at all. Elaine is unambiguous: "Just putting a tool on top of a tool just incurs more technical debt. Don't just do AI for AI's sake." She also challenges the very structure of governance conversations, arguing that IT leaders must walk in with systems and frameworks already in place — not reinvent the wheel for each vendor conversation. A pre-agreed risk framework between IT and legal isn't bureaucracy; it's the only way to move fast and stay protected.

[14:30] The Champions Program: Nike's Secret to GitHub Copilot Adoption

[19:15] Tribal Knowledge Is the Real AI Advantage

Elaine Barsoom on LinkedIn
Scott Kinka on LinkedIn
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

The CIO’s Guide to AI Governance and Risk Mar 10, 2026

Show notes

In this episode of The Bridgecast, host Scott Kinka welcomes Paul Lekas, Executive Vice President of Global Public Policy and Government Affairs at the Software and Information Industry Association (SIIA), to explore the intersection of tech policy, national security, and operational strategy. With a career spanning three presidential administrations and senior roles at the Department of Defense, Paul offers a rare, high-level view of the legislative forces currently shaping the AI landscape.
The conversation dives into the growing tension between federal and state governments, where over 1,500 AI-related bills were introduced last year alone. Paul explains why the SIIA is advocating for federal oversight of AI models to prevent a "confusing field" of 50 different sets of rules that could hinder economic growth and innovation. For mid-market leaders, he provides a pragmatic framework for AI governance: map your data, categorize your systems by risk, and ensure your engineers are in constant dialogue with your legal team.

What you will learn:

The distinction between AI "developers" and "deployers" and why it matters for your liability
Why the federal government should have sole authority for AI model oversight
How to identify "high-risk" AI scenarios in healthcare, finance, and employment
Practical steps for data mapping and conducting AI impact assessments
Why the court system—not new legislation—is currently the best place to settle AI copyright disputes
The emerging policy focus on AI infrastructure and data center licensing

Paul Lekas is the Executive Vice President of Global Public Policy and Government Affairs at the SIIA, representing nearly 400 companies across the technology and information sectors. A graduate of Harvard Law, Paul has a distinguished background in public service, having co-authored recommendations for the National Security Commission on Artificial Intelligence and served as senior counsel at the Department of Defense. His work now focuses on bridging the gap between fast-moving innovation and the educational needs of government enforcers and lawmakers.
To find out how Bridgepointe Technologies helps businesses make IT decisions faster with world-class engineering support and ongoing guidance, head to https://bridgepointetechnologies.com/

Episode Highlights:

[14:38] Build Your AI Governance Stack—Before Regulators Build It for You

Paul’s advice for CIOs is the clearest playbook you’ll hear: any company using AI needs a governance mechanism where engineers, lawyers, and business stakeholders are in active conversation with each other—not in silos. That means mapping your data, mapping your AI, and categorizing your systems by risk level. Under currently enacted laws, “high-risk” AI consistently shows up in the same categories: healthcare, employment eligibility, and access to financial services. If your AI systems touch any of those areas, focus your audit practices and explainability requirements there first, because that’s where legal liability is most likely to materialize. The companies building this infrastructure now aren’t just complying—they’re creating the organizational muscle that will let them innovate faster and more confidently when the regulatory environment hardens further.

[10:09] Federal Preemption: Why Model-Level Oversight Belongs in Washington

SIIA’s position is direct: frontier AI model oversight—the major models like ChatGPT, Gemini, and Claude—should be the exclusive domain of the federal government, not fragmented across 50 separate state frameworks. Paul’s reasoning is practical rather than ideological. Evaluating national security risks, unintended model behavior, and public safety events requires specialized expertise and institutional resources that no individual state government can reasonably be expected to replicate. The alternative—fifty separate state-level investigations into frontier model risk—would be “incredibly complicated and resource-intensive” and would almost certainly not produce better outcomes. This doesn’t mean no regulation. It means regulation in the right place, by the right institutions, with the right expertise. Paul is careful to distinguish this from deployer-level regulation, where state and sector-specific oversight can play a meaningful role.

[27:37] Data Mapping Is Your First Line of Defense

For a mid-market business without a dedicated policy or legal team, Paul’s starting point is deceptively straightforward: know what data you have and know what AI you’re using. From that inventory, work with legal to conduct impact assessments, update terms and conditions, and identify near-term compliance obligations. If you operate across borders, think carefully about data localization requirements and consider building modular approaches to your services that can flex as local rules diverge. Paul frames it simply: if you’re already handling PII, medical records, or financial data with care, you’re most of the way there. “You’re already taking care of certain data; keep doing that, and then layer the AI piece on top.” The companies that will be caught off guard are the ones that haven’t done this inventory before regulators arrive asking for it.
Episode Resources:

Paul Lekas on LinkedIn
Scott Kinka on LinkedIn
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

Zero Trust, Zero Passwords: Mobile Mentor's Blueprint for Hybrid Work Security Feb 24, 2026

Show notes

In this episode of The Bridgecast, host Scott Kinka sits down with Denis O'Shea Founder and CEO of Mobile Mentor, Microsoft Global Partner of the Year, for a wide-ranging conversation on endpoint management, identity security, Zero Trust architecture, and what it really takes to build a workforce that is secure, productive, and ready for the AI era.
Denis's journey is anything but linear. Born in Ireland, he built cellular networks for Nokia in Finland and New Zealand before a fateful dressing-down from a European telco CEO: "Why are your customers only making calls and sending texts?" sent him on a two-decade mission to close the gap between technology investment and human adoption. From deploying Microsoft Intune when it was, in Denis's own words, 'a dog,' to now managing the world's largest Intune tenant and sitting on Microsoft's engineering advisory board, Denis has earned the right to tell IT leaders exactly what to do next.
What you will learn:

Why 97% of cyber breaches start with compromised credentials and what to do about it today
The "allergic reaction" test for hiring elite technical engineers
How to use "security by stealth" to drive adoption without user pushback
Why organizations only use 44% of their software licenses and how to consolidate
The hidden security risks of executive MacBooks and unmanaged home devices
A structured approach to AI implementation that avoids the "52-tool" trap

Denis O'Shea is the Founder and CEO of Mobile Mentor, a global technology company recognized as Microsoft's Global Partner of the Year. Over two decades, Mobile Mentor has helped more than one million people across the US, Australia, and New Zealand become more secure, more productive, and better equipped for hybrid work. Denis began his career with Nokia, helping set up some of the world's earliest cellular networks, and has spent the 20 years since proving that the biggest barrier to technology transformation is never the technology; it's the human experience. He lives in Nashville, Tennessee, and has not typed a password in three and a half years.
Episode Highlights:

[03:33] Go Passwordless Now: 97% of Breaches Start with Compromised Credentials

Denis O'Shea makes a compelling case that passwords are obsolete technology invented in 1961, yet organizations continue forcing employees to take massive security risks every time they type one. Since 97% of cyber breaches originate from compromised credentials, moving to passwordless authentication should be your security North Star. The good news is it's not as difficult as you think—you've already solved this on smartphones, and the same biometric and hardware-based authentication methods work on Windows and Mac devices. Start by mandating TPM 2.0 on all new hardware, adding infrared cameras for facial recognition, rolling out phish-resistant MFA, and tying everything together with conditional access policies. O'Shea personally hasn't typed a password in three and a half years, making him nearly phish-proof as a human since he literally doesn't know his own password. For IT leaders, this shift eliminates your largest attack surface while improving user experience—a rare win-win that justifies the investment immediately.

[11:04] Set a North Star and Align Your Organization Around It

Denis O'Shea's career demonstrates the power of a clear, inspiring vision: when Microsoft asked him to help launch the immature Intune product in 2015, all his engineers said no—but he committed to becoming "the best in the world at deploying Microsoft Intune," knowing the product would mature and market dynamics would favor consolidation. That single North Star aligned the entire organization, attracted the right talent, and ultimately led to Microsoft Global Partner of the Year, advisory board seats, and managing the world's largest Microsoft tenant. A North Star works because it gives teams something bigger than themselves to pursue, makes resource allocation decisions obvious, and attracts people who share that vision. When you're hiring, designing strategies, or deciding where to invest, a clear North Star prevents drift and keeps teams focused on what actually matters. For IT leaders building modern security and workplace strategies, this means defining your endpoint management, security, and digital workplace vision—then measuring every decision, hire, and tool against that vision. O'Shea's experience shows that commitment to a single direction, combined with the right people, consistently outperforms scattered efforts across too many competing priorities.

[20:34] Security by Stealth: The Vanderbilt Playbook

Your organization is likely paying for technology it doesn't use; across hundreds of assessments, Denis O'Shea found enterprises only utilize 44% of their Microsoft licenses while simultaneously maintaining overlapping security and productivity tools from multiple vendors. This fragmentation creates a compounding problem: poor integration, manual workarounds, limited automation, and zero ability for AI to reason across siloed data sources. Instead of spreading resources thin across 52 overlapping security tools (the industry average), consolidate around one or two strategic platforms to maximize integration, automation, and data accessibility. A practical first step is auditing your current stack and identifying which tools duplicate functionality, then mapping a consolidation roadmap that prioritizes depth over breadth. O'Shea's clients who moved from "best of breed" to "best of platform" strategies reported significantly improved security posture, faster incident response, and measurable cost savings from license optimization. For procurement officers and CTOs, this consolidation strategy directly addresses budget constraints by extracting maximum value from existing investments.
Episode Resources:

Denis O'Shea on LinkedIn
Scott Kinka on LinkedIn
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

Begin Before You're Ready: Military Precision Meets Business Transformation Feb 10, 2026

Show notes

In this episode of The Bridgecast, host Scott Kinka welcomes Joel Neeb, Chief Transformation and Business Operations Officer at 8x8, for a deep dive into organizational change and the reality of the AI-driven market. Joel brings a unique perspective forged in the cockpit of an F-15 and refined through high-stakes leadership roles at VMware and Afterburner. He challenges the notion that AI is a "parlor trick" and instead presents it as the ultimate catalyst for businesses to reinvent themselves—or risk becoming obsolete to upstarts they don't even know exist yet.
Joel shares the harrowing yet inspiring story of his Stage IV cancer diagnosis at age 33 and how surviving a 15% survival rate gave him the courage to walk away from a guaranteed military retirement to conquer the business world. This conversation bridges the gap between military tactical excellence and modern business operations, offering IT leaders a practical framework for navigating the "revolutionary change" required in the next three years.
What you will learn:

How to implement the "Visualize, Optimize, Automate" framework before touching AI
Why the military's brief-execute-debrief cycle is the blueprint for business agility
The "chair flying" technique to accelerate team performance without burning budget
How to position communications data as your competitive moat in the AI era
Why revolutionary budgeting beats evolutionary optimization every single time

How IT leaders can become architects of transformation, not just infrastructure keepers

About the Guests:
Joel Neeb is the Chief Transformation and Business Operations Officer at 8x8, a global leader in cloud communications and contact center solutions. Before 8x8, Joel spent 15 years as a US Air Force Lieutenant Colonel and F-15 Mission Commander, flying 3,000 missions and training the top 0.5% of pilots in the world. After surviving Stage IV cancer in 2010 (given just an 18-month prognosis), Joel left the military five years before retirement to pursue his passion for transformation in the business world. He completed his MBA and joined Afterburner, a consultancy focused on corporate execution, before joining VMware's Office of the CEO where he worked directly with Pat Gelsinger to lead the company's shift from product sales to outcome-based SaaS models. Joel is the author of Survivor's Obligation and posts daily on LinkedIn, where he answers every message and engages in dialogue with leaders navigating transformation.
To find out how Bridgepointe Technologies helps businesses make IT decisions faster with world-class engineering support and ongoing guidance, head to https://bridgepointetechnologies.com/

Episode Highlights:

[05:42] You Have Seven Missions to Solo

Joel reveals the most powerful early lesson from flight school: you begin before you feel ready. After just seven training flights, the instructor climbs out of the cockpit, leaves one engine running, and says "Good luck." When Joel grabbed his instructor's shoulder and said he wasn't ready, the instructor replied, "I know you don't, but you are." This principle applies everywhere in business. We wait for the "perfect" moment, the complete data set, the executive buy-in—but transformation requires movement before comfort arrives. Joel explains: "The secret was, he was right. He had done the things to make sure I was ready. We often get stuck waiting for the 'perfect' opportunity, but you have to move before you feel ready." The companies that thrive in the AI era won't be the ones who wait for certainty. They'll be the ones who launched when they had 70% of the answers, not 100%.

[08:15] Chair Flying: Programming Your Brain for Success

Every night before a mission, Joel would go home, stick a plunger on the floor, close his eyes, and visualize the entire sortie—every maneuver, every action, every decision point. This practice, called "chair flying," was the determining factor in performance the next day. Joel notes that flight instructors still ask struggling students: "Did you chair fly last night?" The business world hasn't adopted this yet, but it should. Visualization isn't mysticism—it's deliberate mental programming. Whether you're preparing for a board presentation, a customer negotiation, or rolling out a new AI initiative, the act of mentally rehearsing every scenario dramatically increases your odds of success. As Joel puts it: "That act of visualizing success was a determining factor in my performance the next day." In an era where execution speed matters more than perfect strategy, mental preparation is the edge most leaders overlook.

[20:50] Visualize, Optimize, Automate—In That Order

Joel delivers the framework every IT leader needs: Visualize your current processes, Optimize them ruthlessly, then Automate the bottlenecks. Most companies skip straight to automation—and that's how you fail. "If you put AI on an inefficient process, you just become inefficient faster," Joel warns. "You're paving over the cow path." He uses the example of how 8x8 approached AI implementation: they first mapped every business procedure (sending emails, updating Salesforce), then eliminated convoluted steps, and only then introduced AI to handle the manual work. The result? Automated meeting summaries, instant CRM updates, and a custom-built sales training simulator that cost pennies compared to the $200,000 off-the-shelf version. The lesson: AI isn't magic. It's a tool that amplifies what you feed it. Feed it broken processes, and you get faster failure. Feed it optimized workflows, and you get compounding leverage.

[24:35] We Are All Blockbuster Video Today

Joel drops the most uncomfortable truth in the episode: every company today is Blockbuster Video. Blockbuster had the chance to buy Netflix for $50 million in 1999. Even if they had, they likely would have strangled it to protect their brick-and-mortar cash cow. Netflix won because they disrupted themselves repeatedly—from DVD logistics to streaming to Hollywood production studio. Joel's warning is stark: "We have to be willing to disrupt everything we do in this moment because the sad part of the story is we are all Blockbuster Video today. We all have to figure out how to reinvent ourselves the way Netflix did, or we will become obsolete—and not obsolete to our competitors, but obsolete to some upstart we don't even know exists right now." This isn't fear-mongering. It's reality. The companies winning in AI aren't the ones protecting legacy revenue. They're the ones cannibalizing their own business models before someone else does. If you're optimizing for comfort instead of survival, you've already lost.
Episode Resources:

Joel Neeb on LinkedIn
Scott Kinka on LinkedIn
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

2025 Year End Recap: The Return to Office Debate, AI Predictions & Season 4 Preview Jan 27, 2026

Show notes

In this continuation of The Bridgecast's year-end wrap-up, producer Gene Volpe and host Scott Kinka shift from stats and buzzwords to the human questions that will define 2026: Is the office dead? How do we train the next generation remotely? And what does leadership look like when five generations share the same workforce?
This episode pulls from some of 2025's most thought-provoking moments. Gary Sorrentino, Global CIO of Zoom, predicts that by 2030, a new wave of leadership will emerge—one that throws away "the older guy's book" and readdresses hybrid work based on what's right for people and outcomes. Chris MacFarland, Chief Development Officer at Comcast, warns about the "magical thing" that gets lost when onboarding and training happen through screens. Aaron Darcy from Lumen challenges CIOs to stop being operators and start being transformation agents. Chad Townes from AT&T reveals the secret to aligning thousands of employees: custom metrics that tie back to one collective mission. And Neil Foard delivers the thesis that ties it all together: "We have Paleolithic emotions, medieval institutions, and space-age technology."
Scott doesn't just recap—he adds layers. He explains why hybrid work has "crossed the chasm" and become permanent, why communication expectations across five generations are a leadership failure (not a generational one), and why the role of the CIO has fundamentally changed from operator to transformation agent.
The episode closes with Scott's 2026 predictions, his ChatGPT "Wrapped" (which includes an 8-bit image of his life: a computer, a microphone, and a Phillies baseball), and a preview of Season 4's "Design List"—a framework focused not on tools, but on the decisions organizations make about how they work, scale, and compete.
This isn't nostalgia. It's a roadmap.
What you will learn:

Why 2030 will bring a new wave of leadership that redefines hybrid work
What "water cooler moments" really represent—and how to recreate them intentionally
Why the traditional office isn't dead, but the traditional use of the office is
How to set communication standards across five generations in the workforce
Why CIOs must become transformation agents, not just operators
The "Big Rocks" theory and how to align projects with top-level business goals
Neil Foard's framework: Paleolithic emotions, medieval institutions, space-age technology
Scott's predictions for 2026 and the "Design List" framework for Season 4

To find out how Bridgepointe Technologies helps businesses make IT decisions faster with world-class engineering support and ongoing guidance, head to https://bridgepointetechnologies.com/

Episode Highlights:

[01:07] Gary Sorrentino: By 2030, Leadership Will Readdress Hybrid Work

Gary Sorrentino predicts a fundamental shift by 2030 when Boomers and upper-end Gen Xers retire. "We're in a state where there are still gray-haired senior managers out there who are thinking, 'For 25 years, I've been a leader of this type of group, and that's the way I know how to lead, so everybody should come in.'" The next wave of leadership, he argues, will ask, "What's right for the people that work for me, and what's right for the outcome of my company?" Gen Zs and Gen Alphas—who are 12 years old now—don't want to work in "Mom and Dad's company" anymore. They want quality of life and a different way of working. Scott agrees wholeheartedly: "Hybrid is not a stopgap to the place where we'll ultimately land; it is the place where we've landed."

[02:08] Scott Kinka: The Office Isn't Dead, But Its Traditional Use Is

Scott clarifies the nuance: "The office isn't dead, but I think the traditional use of the office is more than likely dead." Hospitals, medical facilities, and manufacturing will always require physical presence. But for traditional information work—the kind Scott and most technology people do—the old concept of the office is largely dead. "We've crossed the chasm. Hybrid is not a stopgap; it is the place where we've landed. Businesses have to plan around hybrid work, and IT has to architect for it. It's about empowering and securing the end user, not the location."

[10:37] Aaron Darcy: You Can't Just Be an Operator Anymore

Aaron Darcy from Lumen delivers a reality check for CIOs: "Even for CIOs today, with AI, multi-cloud, and the explosive growth in data, you really can't just be an 'operator.' You have to think about transformation. If you're not comfortable with that, you have to surround yourself with people who are." Scott couldn't agree more: "The role of the IT leader has changed; you are now a transformation agent. If you're a CIO, the role involves sitting next to the CEO, CRO, and CFO. If you just want to punch the clock and watch a screen, you're not a CIO. The job is to lead the business through transformation because we've entered an age where most transformation is technology-led."

[13:04] Chad Townes: A Thousand Little Missions Tied to One Mission

Chad Townes from AT&T shares his secret to aligning massive, complex teams: "The biggest thing I found successful is building custom metrics. Everybody wants to be measured to know if they're doing a good job. But the metric isn't the same for every group. You have to change the metric so it ties back to the central theme but remains specific enough that the group knows their achievement lends to the collective effort." Scott ties this to Bridgepointe's "Big Rocks" theory: "Everything is driven by top-level initiatives. Whether you are two or 47 levels down, your KPIs should trace back to the five main goals of the CEO or CIO. If you can't trace a project back to those goals, is it really important?"

[16:29] Neil Foard: Paleolithic Emotions, Medieval Institutions, Space-Age Tech

Neil Foard delivers the thesis that ties the entire year together: "We have Paleolithic emotions, medieval institutions, and space-age technology, yet we still make decisions based on emotions. Let's take advantage of space-age technology to pull us back to the campfire and do what we know is true: People buy from people they like and trust." Scott breaks it down: "We have Paleolithic emotions—we're mammals reacting to stimuli. We have medieval institutions—hierarchical structures where we measure productivity by how hard someone looks like they're working. And then we have space-age tech that helps us think faster and build autonomous structures. It's the most interesting time to be in tech because all three things coexist."
Episode Resources:

Scott Kinka on LinkedIn
Gene Volpe on LinkedIn
Bridgepointe Technologies Website
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

The Bridgecast Wrapped: 2025's Top AI Insights, Predictions & Behind-the-Scenes Jan 20, 2026

Show notes

In this special year-end episode of The Bridgecast, the tables turn as producer Gene Volpe steps in front of the camera to interview host Scott Kinka. It's the fourth annual wrap-up show, and this one delivers more than nostalgia—it's a strategic debrief on what 91,000 downloads and 26 episodes taught us about the state of enterprise technology in 2025.
Gene walks through the numbers: 26 episodes, 29 companies represented, 34 executives interviewed, 11 live tapings, and over 91,000 downloads. But the real story isn't in the metrics—it's in what those conversations revealed. AI dominated the year (no surprise), but security ranked sixth on the buzzword list. Scott explains why: "We're beyond scaring people. Security needs to be an undercurrent, not a topic."
The episode revisits three standout moments from 2025:

Tim Sanders on the "butter analogy": AI isn't a silver bullet with an end-to-end use case—it's butter that makes every workflow faster and more enriched. Scott unpacks why this reframes the conversation from "AI replacing humans" to "AI supercharging humans."
Tony Moroney on AI washing: Boards are pressuring executives to "do something with AI," leading to blind adoption without business outcomes. Scott adds a critical layer: IT leaders are rarely invited into line-of-business conversations, so how can they solve problems they don't even know exist?
Michael Quince on the "mom test": If your AI solution feels confusing or impersonal, it's back to the drawing board. Scott translates this for non-technical audiences: "A stands for artificial. You're already interacting with AI every day—you just don't realize it."

Scott also reflects on live events, the return of human interaction post-pandemic, and why 2026 will demand more from IT leaders than ever before. This isn't just a recap—it's a roadmap for navigating the next wave of transformation.
What you will learn:

Why AI ranked #1 but security ranked #6 on the year's top buzzwords
The "butter analogy" that reframes how to think about AI in workflows
Why IT leaders need to shift from plumbers to general contractors
How shadow AI can be an enabler instead of a threat
Why asking "why" repeatedly is the most powerful leadership tool
Scott's predictions for 2026 and what enterprises are getting wrong about resilience

[08:52] Tim Sanders: The Original Online Networker

Scott geeks out about Tim Sanders, one of the original Yahoo executives and author of Love Is the Killer App (2003). "This is LinkedIn before LinkedIn," Scott says. The book held up when he reread it before the interview—proof that the humanity of technology matters more than the tech itself. Tim's career spans the first Victoria's Secret online show, live streaming innovation, and a philosophy that relationships, not algorithms, drive success. The episode is a masterclass in why leading with love and logic still works in the AI era.

[16:41] Why Security Ranked Sixth, Not Second

Gene is shocked that security ranked sixth on the year's top buzzwords. Scott's response is revealing: "We're beyond scaring people. Security used to be sold through fear, uncertainty, and doubt. I think we're done with that." Security needs to be an undercurrent—embedded in every conversation, not treated as a separate topic. The shift from "fear-based selling" to "resilience-based building" is one of the year's most important themes, and Scott believes 2026 will accelerate this transformation.

[19:35] Tony Moroney: AI Washing is the New Greenwashing

Boards are pressuring executives to "do something with AI," leading to adoption without strategy. Tony Moroney from The Digital Explorer calls this out: "Think about what you need to do for your business to be relevant and competitive in the future. Then figure out what role AI plays in supporting that." Scott adds a critical insight: IT leaders are rarely invited into line-of-business conversations, so they're being asked to solve problems they don't understand. The solution? IT needs to decide whether they're plumbers or general contractors.

[22:56] The Butter Analogy That Changed Everything

Tim Sanders delivers one of the year's most memorable metaphors: "There is no end-to-end use case in the world for butter. You can't eat a stick of butter for breakfast. But tell any chef they can't cook with butter and they'll go on strike. Butter makes everything better." Scott breaks it down: Stop thinking about AI as autonomous replacement systems. Think about AI as the thing that supercharges your humans. "Be the butter," Scott says, summarizing the philosophy perfectly.

[26:29] Can Your Mom Use It? The AI Empathy Test

Michael Quince from NICE introduces the "mom test": If your AI feels confusing, impersonal, or untrustworthy, it's back to the drawing board. Scott takes this further, explaining to his hypothetical mom that she already uses AI every day. "You talk to Alexa. You call your bank and speak to an automated attendant. AI isn't changing how you interact with your bank—it's changing how the bank programs the infrastructure to understand your needs at scale." The point: AI is artificial intelligence, not alien intelligence.

Episode Resources:

Scott Kinka on LinkedIn
Gene Volpe on LinkedIn
Bridgepointe Technologies Website
The Bridgecast on Apple Podcasts
The Bridgecast on Spotify
The Bridgecast on YouTube

The Bridgecast is handcrafted by our friends over at: fame.so

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