The Amplitude of Tech

Amplix

Welcome to The Amplitude of Tech podcast, produced by Amplix, a leading technology advisory firm, where we bring the voices of technology thought leaders, subject matter experts, and enterprise IT decision makers to you to talk about today’s transformative technology and how it can create opportunities for increased success.

  1. 7h ago

    Practical Lessons on AI Orchestration and Managed Network Services from XTIUM's Frank Scanga

    Enterprise networks are quietly getting smarter, but the people who understand them best are heading for retirement, and the pipeline behind them is running dry. In this episode, Shawn sits down with Frank Scanga, Executive Vice Chairman of XTIUM, to unpack how networks have evolved from simple transport to AI enabled, self monitoring infrastructure, and why that shift is colliding with a looming network engineer shortage. They cover the difference between AI first level one support and the human judgment still needed at level two and three, why token orchestration is becoming its own discipline (with a "least cost routing" parallel to old school telecom), the security risks of agentic AI acting on your network, and how to know when your organization is actually ready to outsource network management. It's a candid look at where managed services are headed, and what CIOs need to get right before they hand over the keys. What You'll Learn: Why the biggest threat to network reliability isn't technology, it's a retiring generation of engineers with no replacement pipelineHow AI first level one support is changing the shape of IT departments, and where human judgment still has to step inWhat "least cost routing" for AI tokens looks like, and why orchestration is becoming its own disciplineWhy agentic AI acting on your network carries real security risk, and what guardrails actually helpThe case for bringing LLMs in-house versus relying on public models, and how that calculus is shiftingHow machine learning has quietly been part of network management platforms for over a decadeThe signs your organization is actually ready to hand network management to an outsourced providerWhat compliance frameworks like HIPAA and PCI mean for choosing a managed services partner

    Practical Lessons on AI Orchestration and Managed Network Services from XTIUM's Frank Scanga

  2. Aug 12

    Amplix VP Stanton Smith on Why AI Readiness Starts With Clean Data, Not New Tools

    Enterprises are racing to adopt AI in the contact center, but Stanton Smith, VP of CX Consulting and Solution Engineering at Amplix, has a warning: layer AI onto bad data or broken processes, and you'll only automate the dysfunction faster. In this Inside Amplix episode, Shawn Cordner sits down with Stanton to unpack what transformation consulting really looks like: aligning people, process, and technology before any tech decision gets made. They cover how Amplix uses call and interaction data to surface disconnects between leadership, middle management, and frontline teams, why shorter handle times don't always mean better customer experience, and what true AI readiness requires (hint: it starts with clean data, not a new platform). Stanton also breaks down the risk and knowledge gaps slowing agentic AI adoption, and shares what separates a transformation engagement that sticks from one that doesn't. What You'll Learn: Why layering AI onto bad data or broken processes only makes the problem worse, fasterWhat transformation consulting actually is, and why it starts with people and process, not technologyHow Amplix uses call and interaction data (not just opinion) to surface disconnects between leadership, management, and frontline teamsWhy shorter handle times don't always mean a better customer experienceWhat "AI readiness" really requires, and why clean, organized data matters more than the AI tool itselfThe difference between AI agents automating back-office workflows and AI agents engaging customers directlyWhy risk and lack of knowledge, not lack of interest, are what's slowing agentic AI adoption in the contact centerHow to prioritize transformation opportunities by impact versus lift, so you see results early

    Amplix VP Stanton Smith on Why AI Readiness Starts With Clean Data, Not New Tools

  3. Aug 5

    Quiq's Mike Zinne on the Nine Mistakes Sinking AI Contact Center Deployments

    Most enterprises approach AI in the contact center backwards, and the mistakes are costly enough to derail an entire deployment. In this episode, Shawn sits down with Mike Zinne, Chief Revenue and Experience Officer at Quiq, an AI agent company built for customer journeys, to walk through nine of the most common (and expensive) missteps companies make. They cover why starting too small actually creates worse customer experiences, how leading with technology instead of the customer journey sets projects up to fail, and why chasing the wrong metrics like containment rate can mask whether AI is actually solving anything. Mike also breaks down siloed channel deployments, data readiness paralysis, stakeholder alignment across legal, IT, and brand teams, and why traditional UAT doesn't work for conversational AI. If your contact center is on the roadmap for AI this year, this episode maps out exactly where the landmines are. What You'll Learn: Why starting too small with AI agents creates more customer frustration than starting with real capabilityHow to map customer journeys first and let the technology conversation come secondWhy containment rate is the wrong metric, and what true resolution actually looks likeHow customer effort score, CSAT, and NPS each tell a different (and incomplete) part of the storyWhy siloed, channel-by-channel AI deployments create a disjointed experience and more work for your teamHow to avoid data readiness paralysis by borrowing the same test a human agent already passesWhy traditional UAT doesn't work for conversational AI, and what testing should look like insteadHow to manage the stakeholder web (legal, IT, brand, board) that makes AI projects more complex than typical tech rollouts

    Quiq's Mike Zinne on the Nine Mistakes Sinking AI Contact Center Deployments

  4. Jul 29

    Amplix EVP Mike Dolloff on Vendor Lock-In, Token Costs, and the Future of AI in CX

    Contact center automation is closer to mainstream than the hype suggests, but roughly half of enterprises still haven't put AI to work on the floor. In this Inside Amplix episode, Shawn sits down with Mike Dolloff, EVP of Sales and Account Management at Amplix, to unpack where adoption really stands, why back-end use cases like agent assist and QA are often outpacing customer-facing bots, and why bolting AI onto a broken process just makes it a more expensive broken process. They also cover the buy vs. build calculus, the growing risk of vendor lock-in as AI pricing shifts, and how the CRM market is quietly reshaping the CX tech stack. If you're deciding where to spend your next automation dollar, this is the practical read you need. What You'll Learn: Why roughly half of contact centers still haven't deployed AI, and why that's not as far behind as it soundsThe real split between front-end AI (chat, voice agents) and back-end AI (agent assist, QA, summarization), and why adoption is happening faster on the back endWhy good AI layered on a broken process just creates a faster, more expensive broken processHow to sequence an AI rollout: fix the process, prove a small win, then scaleWhy the buy vs. build calculus in the contact center still favors buying, and where that logic starts to break down with agentic AIThe vendor lock-in risk nobody priced in: what happens to your AI business case when token costs jump 30%How the CRM market (Salesforce, ServiceNow, Zendesk) is pushing further into CX, and what that means for your tech stackMike's practical advice for picking your first AI project and building momentum from a fast, visible win

    Amplix EVP Mike Dolloff on Vendor Lock-In, Token Costs, and the Future of AI in CX

  5. Jul 22

    Practical AI Governance Lessons from a 40-Year Healthcare Technology Leader and CIO

    Forty years into a career spanning healthcare and technology, Bill Fandrich has landed on one lesson that outlasts every hype cycle: it's not about the technology. In this episode, the former CIO of Blue Cross Blue Shield of Michigan, now Chief Healthcare Officer at Pellera and founder of MN One Nexus, joins Shawn to unpack why so many AI investments fail to deliver value, and what real value analysis (as opposed to a simple ROI calculation) actually looks like. They cover cloud's early lessons on governance and vendor lock-in, Bill's three-tier framework for AI (productivity tools, business initiatives, and big bets), and the responsible AI framework he built to give 1,500 employees safe, sandboxed room to experiment. Along the way, Bill uses the GLP-1 drug boom as a live example of how technology, policy, and economics collide in healthcare. For any IT leader trying to separate real transformation from hype, this one's a masterclass. What You'll Learn: Why "it's not about the technology" has held true across four decades of IT transformation, from mainframes to cloud to AIHow to run a value analysis instead of a standard ROI calculation, and why the difference matters for your AI business caseA three-tier framework for AI investment: productivity tools, business-led initiatives, and big betsHow to build a responsible AI framework that gives your workforce room to experiment without exposing sensitive dataWhy vendor lock-in is the wrong thing to optimize against right now, and what cloud's early years taught us about itHow discipline, not unchecked experimentation, is actually what enables speed and innovationWhy healthcare margins are being squeezed from every direction, and where technology can move the needleWhat the GLP-1 drug boom reveals about the collision of technology, policy, and healthcare economics

    Practical AI Governance Lessons from a 40-Year Healthcare Technology Leader and CIO

  6. Jul 16

    Amplix Cybersecurity Leader Sanjay Deo on Claude Mythos and the New Speed of Cyberattacks

    AI is now finding and exploiting vulnerabilities in seconds, not weeks, and most enterprises still have unpatched machines and end-of-life systems sitting exposed on their networks. In this Inside Amplix episode, Shawn sits down with Sanjay Deo, who leads Amplix's cybersecurity practice nationally, to unpack Claude Mythos, the purpose-built security model at the center of Project Glasswing, and what its limited release to vetted cybersecurity vendors means for the industry. Sanjay shares his read on why layered defense matters more than ever, how the conversation with boards and C-suites has shifted from budget defense to urgent risk management, and why patching discipline is now the baseline every enterprise needs to get right. It's a candid look at how fast the threat landscape is moving, and what technology leaders can actually control. What You'll Learn: What Claude Mythos is and why Sanjay sees it as fundamentally different from earlier jailbroken or generic AI models used for exploitationHow Project Glasswing works, from limited release to vetted cybersecurity vendors to how those vendors are building defensive capability from what they learnWhy unpatched and end-of-life systems, not the model itself, are the real exposure most enterprises are sitting onHow the timeline from vulnerability discovery to exploitation has compressed from months to seconds, and what that means for your patching cadenceWhy layered defense (perimeter, EDR, MDR, and beyond) matters more than any single point of protectionHow board and C-suite cybersecurity conversations have shifted from "how much do you need" to "is that enough"What technology leaders can actually control right now: patching discipline, exfiltration prevention, and assuming a breach will happenWhere Sanjay sees the cybersecurity industry heading over the next six months to two years as defensive tools race to keep pace

    Amplix Cybersecurity Leader Sanjay Deo on Claude Mythos and the New Speed of Cyberattacks

  7. Jul 8

    Omilia's John Diatto on Conversational AI, Orchestration, and the End of the IVR

    The line between deterministic and probabilistic AI is one of the most consequential, and least understood, decisions in enterprise contact center design. In this episode, Shawn sits down with John Diatto, who heads channel sales for Omilia in North America, to unpack why some interactions demand certainty (moving money, verifying identity) while others call for the flexibility of a probabilistic model. They cover the false binary between legacy IVR and conversational AI, why unified orchestration beats stitched-together vendor stacks, how explainability functions as legal protection rather than a nice-to-have feature, and how one enterprise deployment produced $24 million in annual savings by getting this balance right. What You'll Learn: Why the "IVR vs. conversational AI" framing is a false binary, and what the real decision actually isWhen to use a deterministic model versus a probabilistic one (and why moving money is never a "probably")What happened with Air Canada's chatbot, and why the court ruling should worry every enterprise deploying AIHow small language models trained on enterprise-specific data outperform giant, genericized LLMs for customer serviceWhy owning the full orchestration stack, not just the model, is what actually controls cost and latencyHow one Omilia deployment broke through a task-completion ceiling for $24 million in savingsWhy explainability isn't a feature, it's legal protection, and what "show me why it said that" should mean to your vendorHow the human-in-the-loop role is shifting from doer to supervisor as AI systems self-tune

    Omilia's John Diatto on Conversational AI, Orchestration, and the End of the IVR

  8. Jul 2

    Amplix President Adam Rennert on the State of CX, AI Hype, and Real Contact Center ROI

    Everyone wants AI in their contact center, but chasing the newest shiny object is often the wrong move. In this Inside Amplix episode, Shawn sits down with Amplix President Adam Rennert to break down the real state of CX heading into 2026: where AI is actually driving ROI (hint: it's not just the front end), why cloud holdouts are finally making the leap, and how license rationalization is quietly becoming one of the biggest cost-saving levers in the contact center. Adam also unpacks the platform-versus-best-of-breed debate, why vendor lock-in deserves an exit plan before you sign anything, and what separates buyers who are diligent about AI from the ones who get burned. What You'll Learn: The three trends driving CX decisions right now: AI pressure, cloud optimization, and the last on-premise holdouts finally movingWhy on-premise holdouts are finally making the jump to cloud, and why it's the transition period that scares people, not the change itselfHow to navigate platform vs. best-of-breed without ending up with 39 apps solving four problemsWhy license rationalization has become one of the most overlooked cost-saving levers in the contact centerWhy AI's real ROI isn't on the front end, and where the front, middle, and back end actually deliver valueWhat an exit plan should look like before you sign with any AI vendor, and why you need one now more than everWhy "AI-enabled" is becoming meaningless marketing language, and how to get underneath the term before you buyWhere to benchmark your organization on the AI adoption curve without feeling behind or moving recklessly fast

    Amplix President Adam Rennert on the State of CX, AI Hype, and Real Contact Center ROI
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About

Welcome to The Amplitude of Tech podcast, produced by Amplix, a leading technology advisory firm, where we bring the voices of technology thought leaders, subject matter experts, and enterprise IT decision makers to you to talk about today’s transformative technology and how it can create opportunities for increased success.

Information

  • Creator
    Amplix
  • Years Active
    2025 - 2026
  • Episodes
    31
  • Rating
    Clean
  • Copyright
    © 2026 The Amplitude of Tech
  • Show Website
    The Amplitude of Tech