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Michael Martino Show
Michael
247 episodes
3 days ago
Hot takes, industry insights, and advice from experts - focusing on the continued pursuit of Digital and Business Transformation, Government Transformation, digital coaching and martial arts training. Episodes are short, to the point, and jammed packed with info. We will get you in and out with maximum content in short bursts.
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Business
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All content for Michael Martino Show is the property of Michael and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
Hot takes, industry insights, and advice from experts - focusing on the continued pursuit of Digital and Business Transformation, Government Transformation, digital coaching and martial arts training. Episodes are short, to the point, and jammed packed with info. We will get you in and out with maximum content in short bursts.
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Business
Episodes (20/247)
Michael Martino Show
Mastering Customer Experience Management

What Is Customer Experience Management?  

Customer experience management is the discipline of understanding, designing, and optimizing every interaction a customer has with your brand—across channels, over time. 

 

It’s not just about having a call center or a website—it’s about orchestrating a journey that’s seamless, personalized, and responsive to the customer’s needs. 

CXM includes three big components: 

  • Journey mapping – identifying key customer touchpoints 

  • Listening and measuring – collecting feedback, analytics, and sentiment 

  • Acting on insights – using data to improve and personalize service 

 

Great CXM is proactive, not reactive. It’s about anticipating needs before the customer has to ask. 

 
Why CXM Often Falls Short 

Most organizations still struggle with CXM because they confuse it with customer service. CXM is not just about solving problems—it’s about preventing them. 

 

Here are a few common pitfalls: 

  • Siloed departments that don’t share customer data 

  • Inconsistent experiences across channels 

  • Metrics that focus on internal efficiency, not customer satisfaction 

  • Not listening to the customer voice at scale 

 

The New Rules of Engagement  

Let’s talk about what good CXM looks like in 2025. The expectations have changed: 

  • Omnichannel is a must – Your customers expect to move from web to chat to phone without repeating themselves 

  • Speed + empathy = satisfaction – Fast service matters, but so does making the customer feel heard 

  • Personalization isn’t creepy—it’s expected – Use data to show you understand your customer’s history and needs 

  • AI is the co-pilot, not the replacement – Use automation to make agents faster and more effective, not to eliminate human touch 

  • Forward-thinking organizations are using customer journey analytics, real-time feedback loops, and AI-driven insights to constantly iterate and improve 

 
Building a CXM Strategy  

What can you do today to get better at customer experience management?  

  • Map your customer journeys—know where the friction is 

  • Break down data silos—connect your systems 

  • Implement real-time feedback channels—voice of the customer tools 

  • Use AI to surface insights—but always validate with humans 

  • Empower frontline staff—because great CX is delivered by people, not systems 

 

CXM isn’t a one-time project. It’s a mindset. A way of working. A commitment to being better every single day. 

Show more...
6 days ago
4 minutes 15 seconds

Michael Martino Show
How governments can use AI to deliver an omnichannel experience

People don’t care about channels. They care about outcomes. They just want quick, accurate help—whether it’s on the phone, online, chat, AI self-service, or in person. 

 

That’s where AI-powered omnichannel experiences come in. 

 
What Does Omnichannel Actually Mean? 

Omnichannel isn’t just about being available on lots of channels. It’s about seamlessly connecting those channels so the experience feels continuous and  

consistent. 

 

For governments, this might mean a citizen starts a chat on the website, gets an update later via SMS, and then finishes the process over the phone—with no need to repeat themselves or restart the interaction. 

 

This is where AI becomes a game-changer—because it can integrate, automate, and personalize across all those channels at scale. 

 

The Challenge Governments Face 

Governments aren’t like startups. They’ve got: 

  • Legacy systems 

  • Siloed departments 

  • Compliance and privacy rules 

  • Tight budgets 

  • High expectations from the public 

 

Delivering a smooth omnichannel experience across all that? It’s not easy. 

Governments can't rip everything out and start over. With the right AI tools and strategy, they can layer intelligence onto existing infrastructure to drive better service. 

 

How AI Enables Omnichannel for the Public Sector 

1. Unified Virtual Agents 
AI-powered virtual assistants can operate across web chat, SMS, email, and voice. With proper integration, they can access case data, answer questions, and complete simple transactions—24/7. When the AI hands off to a human, the full context is preserved. 

2. Natural Language Understanding (NLU) 
Citizens don’t always know the right “terms” to use. AI can interpret plain language across channels, whether someone says, “I lost my health card”, “I need a replacement,” or “I can't find my ID.” 

3. Predictive Routing and Sentiment Analysis 
AI can detect frustration in voice or text and escalate to live help. It can also route inquiries to the right department before a human even picks up. 

4. Personalized Outreach 
Through machine learning and analytics, AI can segment citizens and send reminders or nudges through their preferred channel—whether it’s email, app notifications, or even automated calls. For example: “It’s time to renew your vehicle registration.” 

5. Analytics and Feedback Loops 
Governments can use AI to analyze interactions across all channels, spot trends, and identify areas for improvement—fast. 

 
For Leaders 

1. Start with the citizen journey 
Map out common use cases and pain points. Look for where people drop off, get confused, or need to switch channels. 

2. Start small 
Begin with one service or department and expand. Omnichannel isn’t built overnight—it evolves iteratively. 

3. Break down silos 
Create cross-functional teams that include IT, service delivery, policy, and communications. AI only works if the data and processes behind the scenes are aligned. 

4. Choose tools that integrate 
Look for AI platforms that can connect across existing CRM, contact center, and web systems—not just one shiny point solution. 

5. Be transparent 
Let users know when they’re interacting with AI—and why. When done right, this builds trust. 

 
To Wrap 

The future of public service is not just digital—it’s connected. 

 

By using AI to create a seamless experience across web, phone, chat, and in-person interactions, governments can: 

  • Reduce frustration 

  • Improve access 

  • Free up staff time 

  • Deliver better outcomes for the people they serve 

 

AI won’t fix everything—but when used thoughtfully, it can help governments meet people where they are—and carry the conversation across every channel. 

Show more...
1 week ago
6 minutes 3 seconds

Michael Martino Show
Episode 7: The Future of AI in Contact Centers

We’ve come a long way from static IVRs and robotic scripts. Now we’re staring down a future where AI doesn’t just respond—it anticipates, emotes, adapts, and collaborates. 

 

What’s Next in AI: Proactive, Emotional, and Agentic 

Let’s start with what’s coming around the corner. 

 

We’re moving from reactive AI—responding to questions and routing calls—to proactive AI. Imagine a contact center that knows a customer is likely to churn before they even say a word. AI systems will use behavioral signals, sentiment trends, and historical data to predict issues and offer personalized solutions before customers reach out. 

 

Next is emotional intelligence. Generative AI is can understand tone, intent, and even empathy in real-time. Voice analysis, sentiment detection, and dynamic response generation will allow bots to sound more human—and more importantly—be more human in their interactions.  

 

That means recognizing frustration, expressing understanding, and escalating at just the right moment. 

 

Finally, we’re entering the era of agentic workflows. These are AI agents that don’t just answer a question—they act. They navigate systems, update records, draft follow-ups, trigger refunds, and even collaborate with other agents—human or machine—to solve complex problems. This is where generative AI meets automation in a powerful, orchestrated way. 

 
The Role of Generative AI and Multimodal Interactions 

Let’s talk about the engine driving this evolution—Generative AI. 

 

It’s not just about chatbots anymore. GenAI is enabling systems that can: 

  • draft personalized emails based on a conversation. 

  • summarize long service interactions instantly. 

  • translate customer intent into backend tasks. 

  • generate knowledge base articles or training scripts on demand. 

 

Even more transformative is the shift to multimodal AI. These are models that don’t just understand text—they process voice, images, documents, and even video. 

 

Imagine a customer sending a picture of a broken product. AI: 

  • analyzes it 

  • confirms warranty status, and initiates a replacement—without human intervention.  

 

Imagine a voice call where the AI: 

  • picks up on frustration 

  • adjusts its tone, and  

  • flags the interaction to a supervisor in real time. 

 

We’re heading toward seamless, channel-agnostic experiences powered by AI that understands in every dimension. 

 
How Leaders Can Prepare 

Here’s the part that’s easy to overlook: how do we prepare? 

 

It starts with governance. Leaders need to define guardrails around data privacy, model usage, escalation paths, and ethical considerations. Without structure, it’s chaos. 

 

Next, skills and training. This future isn’t about replacing agents—it’s about augmenting them. Invest in training your workforce to work with AI: interpreting insights, validating responses, and using AI as a co-pilot, not a crutch. 

 

Then there’s infrastructure. Many contact centers are still running on legacy systems that can’t integrate with modern AI tools. Think modular, API-driven, cloud-first architecture. It’s not sexy, but it’s essential. 

 

Finally—partnerships and pilots. Don’t wait for perfection. Start small, iterate fast, and learn in the real world. That’s where true transformation happens. 

 
Strategy Over Shiny Tools 

AI is not the strategy 

It’s a tool. Don’t chase the trend—define the outcome you want, then use AI to help get you there. 

 

Don’t over-automate 

AI should elevate the human experience, not eliminate it. Use it to remove friction, not empathy. 

 

Balance innovation with intention 

The best contact centers of the future will be those that marry cutting-edge tech with rock-solid fundamentals—governance, empathy, and a relentless focus on the customer. 

 

The future isn’t five years away—it’s here 

Your competitors are piloting proactive AI, deploying GenAI assistants, and rethinking workflows. If you're still evaluating, you’re already behind. 

 

Show more...
3 weeks ago
5 minutes 30 seconds

Michael Martino Show
Episode 6: Avoiding AI Pitfalls

Today, we’re breaking down: 

  • common mistakes organizations make when implementing AI 

  • why so many AI pilots fail to scale 

  • how to build a strong AI foundation from the start 

  • what we can learn from those who’ve already stumbled. 

 
Implementation mistakes 

1. Bad Data: 
AI is only as good as the data you feed it. If your data is outdated, incomplete, or inconsistent across systems, your AI is going to reflect that. I’ve seen bots go live with training data that didn’t reflect the actual questions customers ask. Result? Frustrated users, high abandonment, and no ROI. 

 

2. No Change Management: 
You can’t just plug in AI and expect magic. If your teams aren’t trained, if there’s fear, confusion, or resistance — adoption will stall. Frontline agents need to know how the AI helps them, not replaces them. Leaders need to communicate clearly. Change management isn’t optional — it’s essential. 

 

3. Overhyping AI: 
Some vendors and internal champions oversell AI as the silver bullet. But AI isn’t going to fix a broken process — it’s going to expose it. You need to set realistic expectations. Start small, prove value, then scale. 

 
Why AI pilots fail to scale 
You launch a chatbot in one department. It goes okay. Then when you try to scale everything breaks.  

 

Why? 

 

Lack of strategic alignment 
The pilot solved a local problem — but didn’t fit into a broader enterprise strategy. It was a siloed win with no path to broader adoption. 

 

No operational readiness 
Many organizations forget to build the support systems around the AI. Who updates the bot content? Who retrains the model? Who measures success? AI at scale needs ownership, process, and infrastructure. 

 

Culture and leadership 
If leaders don’t champion the value, if users don’t trust it, the pilot stays stuck in in pilot mode. To scale AI, people need to believe in it — and see the benefit. 

 
Building the right foundation 
So how do you avoid these pitfalls? It starts with building the right foundation. 

 

1. Governance 
You need clear roles and responsibilities: 

  1. Who owns the AI strategy?  

  2. Who signs off on changes?  

  3. How do you ensure ethical use, compliance, and privacy? 

 

Governance isn’t bureaucracy — it’s how you scale responsibly. 

 

2. Training 
Train your teams. Not just the tech teams, but your agents, managers, and executives. Everyone needs a base level of AI literacy. If your employees don’t understand the AI, they won’t use it. Worse — they’ll work around it. 

 

3. Iteration 
AI is not a “set it and forget it” solution. You need a feedback loop. Look at performance. Talk to users. Iterate often. The most successful AI deployments I’ve seen have one thing in common: a culture of continuous improvement. 

 
 

Learn from other companies 

You don’t have to learn everything the hard way. There are case studies, post-mortems, and war stories out there. Learn from them. 

 

A major telco launched a voice bot without involving the contact center. It worked in the lab, but in production? Callers hated it. Agents weren’t trained to take over from the bot. NPS dropped like a rock. Lesson? Bring frontline teams in early and often. 

 

A government agency tried to automate benefit eligibility using a model trained on old data. What happened? The AI reinforced biases. Applications from vulnerable groups were flagged more often for review. Public trust eroded. Lesson? AI needs oversight, diverse data, and ethical review. 

 

These aren’t tech failures — they’re leadership failures and they’re preventable. 

 
To wrap 

AI has enormous potential, but it’s not plug-and-play. Avoiding the pitfalls starts with: 

  • treating data like an asset 

  • investing in change management 

  • being realistic about what AI can — and can’t — do 

  • building a foundation rooted in governance, training, and iteration. 

  • remembering, you’re not alone. Learn from others. Share your wins and your stumbles.  


Show more...
3 weeks ago
6 minutes 12 seconds

Michael Martino Show
Episode 5: AI Ethics, Trust, and Transparency

AI is reshaping how organizations serve their customers — from handling routine inquiries with chatbots to supporting agents with real-time prompts. But just because we can automate something doesn't mean we should — at least, not without asking tough questions first. 

 

What’s ethical AI? It’s AI that respects the rights of customers, minimizes harm, and operates with accountability. In customer service, this means no hidden bots, no manipulative nudges, and no shortcuts around customer consent. 

 

It also means that when AI makes decisions — like prioritizing tickets, flagging fraud, or recommending products — we have to ask: Is it fair? Is it unbiased? Would we stand by that decision if it affected us? 

 

Bias in models 

AI models are trained on data. And data — especially historical data — often reflects human bias. If past hiring decisions were discriminatory, an AI trained on that data will likely perpetuate that pattern. If customer service feedback skews negatively toward certain accents or demographics, guess what the model learns? 

 

Bias isn’t always obvious. It can be subtle, statistical — even unintentional. This is why organizations must evaluate their models for fairness and audit them regularly. Not just when something goes wrong. But proactively — as a part of responsible AI governance. 

 

Explainability and data privacy 

Explainability means you can understand why AI made a decision. It’s not about cracking open the code — it’s about being able to say, in plain language, “The model recommended this refund because X, Y, and Z.” 

 

This is especially important when AI is part of decision-making — like whether a customer qualifies for a loyalty offer, or if a complaint gets escalated. 

Customers don’t want a black box. They want clarity. Transparency builds  

confidence. 

 

Data isn’t just fuel for AI — it’s a matter of consent, ownership, and trust. 

 
Letting customers know they're talking to AI 

Here’s a simple question: Should customers be told when they’re speaking with an AI instead of a human? 

 

The answer is yes — absolutely. 

 

Hiding AI behind a human persona erodes trust. It sets expectations the system can’t meet. But when customers know they’re interacting with a virtual agent — and it performs well — they’re often impressed. 

 

People are okay with AI, as long as it's clear, helpful, and honest. In fact, many prefer it for quick tasks — no hold music, no repetition, just answers. 

 

So don’t be afraid to introduce your AI assistant. Give it a name, define its purpose, and make the boundaries clear. Let it handle what it’s good at, and seamlessly hand off to a human when needed. 

 

This kind of transparency isn’t just ethical — it’s practical. 

 

Regulation and compliance 

Governments around the world are catching up to AI. The EU’s AI Act, the U.S. Executive Order on AI, Canada’s Artificial Intelligence and Data Act (AIDA) — these aren’t just red tape.  

 

They’re guardrails for safety, fairness, and accountability. 

 

For businesses, regulation isn’t a threat — it’s an opportunity. Following the rules forces better design, more robust governance, and ultimately, better outcomes for customers. 

 

In a few years, compliance with AI ethics and transparency standards won’t be optional — it’ll be a baseline expectation. The smart companies are getting ahead of it now. 

 

To wrap 

AI in customer service has massive potential — to deliver faster, more personalized, and more scalable support. But that potential only becomes value when it’s used responsibly. 

 

That means: 

  • checking for bias 

  • designing explainable systems 

  • protecting data 

  • being transparent about AI’s role 

  • building with ethics at the core. 

 

If you do that — not only do we avoid harm — we actually build trust.  

 

That's it for today, next time we will talk about Avoiding AI Pitfalls 

 

 

Show more...
4 weeks ago
5 minutes 29 seconds

Michael Martino Show
Episode 4: Personalization at Scale

Personalization used to mean inserting someone’s name into an email subject line. Maybe a birthday discount. That was cute—for 2010. But today’s customers expect more.  

 

At its core, AI turns data into insight. Every click, call, and conversation creates a trail of behavioral data. AI ingests that information—purchase history, browsing patterns, service inquiries, even tone of voice—and builds what we call a dynamic customer profile. 

 

This goes far beyond demographics. We’re talking about preferences, intentions, friction points. When done right, AI enables service that feels intuitive. You’re not asking the customer to repeat themselves. You’re surfacing the right offer at the right moment. You’re proactively solving problems—sometimes before the customer even notices them. 


The best part? This level of service builds trust. It drives engagement, conversion, and loyalty. But it hinges on understanding customer intent—and for that, we need to talk about Natural Language Understanding. 

 
Using NLU to Tailor Responses 

Natural Language Understanding—or NLU—is what allows AI systems to understand human language in all its messy, nuanced glory. 

 

When a customer says, “Hey, I’m locked out of my account,” they’re not just stating a fact. There’s urgency. There’s frustration. NLU doesn’t just decode the words—it deciphers the meaning, sentiment, and context. 

 

In a contact center, NLU helps bots respond with empathy—escalating sensitive issues to a human agent or offering tailored self-service options. In digital channels, it powers intelligent search, smart FAQs, and contextual product recommendations. 

 

The effectiveness of NLU is only as good as the data it learns from. That brings us to one of the thorniest challenges in personalization: data integration. 

 
Data integration challenges 

Personalization at scale requires unified data. But most organizations are drowning in disconnected systems—CRM, billing, marketing, service, web analytics. Each one holds a piece of the puzzle. Few are designed to talk to each other. 

 

Overcoming it means making some strategic choices: 

Invest in a robust customer data platform (CDP) 
This acts as the central brain, aggregating data from across systems into a unified customer profile. 

 

Adopt real-time data architecture 
Stale data leads to missed opportunities. Personalization depends on real-time triggers—not yesterday’s spreadsheet. 

 

Break down silos with APIs and middleware. 
Modern integration layers allow legacy systems to participate in real-time orchestration without full rip-and-replace projects. 

 

Establish strong data governance. 
You’re dealing with sensitive customer data—so transparency, consent management, and security must be built-in from the start. 

 


Measuring ROI in loyalty and retention 

Personalization at scale isn’t just a shiny object—it delivers measurable business outcomes. 

 

How do you track ROI? 

Start with these metrics: 

  • Customer Retention Rate—if personalization is working, your customers stay longer. They’re more likely to renew, re-subscribe, or come back. 

  • Net Promoter Score (NPS)--personalized experiences tend to drive higher satisfaction. Customers feel seen, heard, and valued. 

  • Customer Lifetime Value (CLV)--when you tailor experiences, you increase the likelihood of repeat purchases and upsell opportunities. 

  • Engagement Rates—are your customers opening emails, clicking offers, interacting with self-service tools? These micro-metrics build a picture of relevance and trust. 

  • Cost-to-Serve Reduction—Ironically, personalization often makes service more efficient. Proactive outreach prevents repeat calls. Intelligent routing reduces handle time. 

 

Companies that have embraced AI-powered personalization are seeing double-digit lifts in loyalty metrics and significant reductions in churn. That’s not soft ROI—that’s bottom-line impact. 

 

Show more...
4 weeks ago
7 minutes 8 seconds

Michael Martino Show
Episode 3: Rethinking the Agent Role

One of the biggest misconceptions is AI is going to replace agents.  

 

AI doesn’t replace agents—it supports them. 

 

AI handles repetitive, low-value tasks like looking up data, summarizing past interactions, or suggesting knowledge articles. That frees up the human agent to do what they do best—use empathy, judgment, and creativity to solve real customer problems. 

 

When AI handles the routine, human agents can focus on the relationship.  

 

Traditionally, coaching was reactive. You would listen to a few random calls, pull together a feedback session, and maybe do a training refresh every quarter. 

 

With AI, coaching becomes real-time and personalized. 

 

Imagine an agent is on a call, and an AI-powered assistant is listening in—not to judge, but to help. It detects rising frustration in the customer’s voice and suggests a calming phrase. It flags a missed upsell opportunity and prompts a reminder. It highlights a policy update that the agent might not be aware of. All in the moment. 

 

It’s like having a coach in your ear—but instead of whispering criticism, it’s offering support. 

 

And the best part? This kind of coaching is available to every agent, not just the top performers or the ones under review. It democratizes growth. 

 

We’re seeing this in real-world deployments. AI provides insights not only into what was said, but how it was said—tone, pace, empathy. This opens the door to a new kind of development plan that’s grounded in data and focused on continuous improvement. 

 

Performance analytics  

For years, agents have been measured on handle time, after-call work, and first-contact resolution. Useful metrics, but incomplete. They rarely tell the full story. 

 

AI changes that. 

 

Now, we can measure customer sentiment, intent resolution, empathy, and even trust. AI can surface patterns across thousands of interactions—what’s working, what’s not, and where support is needed. 

 

Instead of burying agents in dashboards, modern systems can surface just-in-time nudges or personalized scorecards that actually mean something. 

 

Imagine AI helping your agents with prompts such as “Your customer satisfaction score is improving when you pause to confirm understanding. Keep it up.” That’s the kind of feedback that builds confidence and pride—not anxiety. 

 

These analytics don’t just help managers manage better—they help agents grow faster. And that’s a win for everyone. 

 

Redefining roles and upskilling staff 

Now here’s where the real transformation happens—redefining the agent role. 

 

When agents are no longer stuck on repeat mode—doing password resets or updating addresses—they’re freed to tackle more complex, meaningful work. 

 

They become problem solvers, relationship builders, experience designers. 

 

This shift doesn’t happen on its own. It requires upskilling. 

 

We need to invest in teaching agents new skills—like digital fluency, emotional intelligence, adaptive thinking. We need to move away from script-reading and move toward conversation design. And we need to empower agents to work with AI, not around it. 

 

Some organizations are already doing this. They’re creating hybrid roles—part customer support, part insight analyst. Others are developing internal career pathways that turn frontline agents into automation designers, bot trainers, or quality coaches. 

 

When agents see a future, they invest in the present. That’s how we improve job satisfaction. 

 

No one likes doing robotic work. But everyone wants to feel like they matter. 

 
 

 Designing for human-AI collaboration 

As we rethink the agent role, we need to ask better questions. 

 

Not “How do we cut headcount with AI?” 

 

But “How do we elevate people by giving them the tools to succeed?” 

 

This isn’t about removing humans from the equation. It’s about redesigning the system so humans and AI complement each other. 

 

That means building new training programs, rethinking performance incentives, and putting trust at the center of everything. 

 

Show more...
4 weeks ago
6 minutes 14 seconds

Michael Martino Show
Episode 2: From IVRs to Intelligent Agents

If you've ever slammed “zero” on your phone just to get past a robot or been stuck in a loop with a chatbot that keeps saying, “I didn’t get that,” this one’s for you. We’re going to take a look at how customer service automation has evolved—from those frustrating IVRs to today’s dynamic, intelligent virtual agents. 

 

The Big Shift: From decision trees to dynamic AI 

Old systems followed a script. 
  

Modern systems understand intent. 

 

Instead of forcing a customer through a pre-defined path, today’s AI can dynamically interpret what someone is asking—even if they say it in different ways, with typos, slang, or emotion. 

 

The leap came from three major technological advances: 

  • Natural Language Processing (NLP) – AI learned to understand human speech and text with much greater nuance. 

  • Contextual Memory – Systems began to remember what you just said and use that context to shape the next response. 

  • Agentic AI – These are tools that don’t just answer—they act. They understand, decide, and even initiate follow-ups. 

 

This shift—from rules-based automation to dynamic, learning systems—is what makes today’s tools feel less like clunky machines and more like actual assistants. 

 
 

Chatbots, Voice Bots, and IVAs—What’s the Difference? 

Let’s break down the terminology. People use these terms interchangeably, but there are important differences: 

 

1. Chatbots 

These are text-based tools embedded on websites or apps. They’re often the first point of contact. Early chatbots were basic—they could only respond to pre-set keywords. Today, with AI-powered chatbots, they can actually understand intent, respond naturally, and even escalate issues to live agents. 

Used well, they reduce wait times, deflect calls, and provide 24/7 service. 

 

2. Voice Bots 

Think of these as chatbots that can talk. They’re used in call centers, replacing or enhancing traditional IVRs. Instead of pressing numbers, you just speak. 

A voice bot today can authenticate you, understand your issue, and complete transactions—without ever needing a human. 

 

3. Intelligent Virtual Agents (IVAs) 

This is where things get really interesting. IVAs go beyond scripted responses. These are agentic systems that can reason, decide, and even perform tasks on your behalf. 

 

They’re connected to back-end systems. They know your customer history, they can schedule appointments, issue refunds, check inventory, and more. 

 

They’re not just reactive—they’re proactive. They might reach out to you before you even realize there’s an issue. 

 

This is the future of customer service—personalized, autonomous, and intelligent. 

 
Why is this evolution critical today? 

  • Rising Customer Expectations – People want instant, personalized service. 

  • Cost Pressures – Contact centers are expensive to run. Automation helps scale without scaling headcount. 

  • Talent Shortages – Agent turnover is high. Intelligent tools augment the workforce and improve the agent experience. 

  • 24/7 Demands – AI doesn’t sleep. It’s always-on, always-learning. With GenAI and agentic models, we’re no longer just automating tasks—we’re co-creating solutions in real-time. These tools are becoming teammates. 


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1 month ago
6 minutes 12 seconds

Michael Martino Show
Episode 1: The AI Awakening – Why Now in Contact Centers?

In this series we will unpack the value, challenges, and future of AI in modern customer experience operations.  

 

Today, we're kicking off the series with a critical episode in our Power of AI in the Contact Center series. 

 

We’re talking about how artificial intelligence — once a flashy novelty — has evolved into a business necessity. We’ll look at what’s happening in contact centers right now:  

  • the cost pressures 

  • rising customer expectations 

  • talent shortages  
     

AI — From Gimmick to Game-Changer 

Not that long ago, AI in the contact center was seen as something interesting.  

 

Something cool to demo at a tech conference. Virtual agents that could handle a few FAQs. Sentiment analysis tools that looked impressive on a dashboard but didn’t change much about how agents worked. 

 

AI is now foundational. It's about making the people in your organization smarter, faster, and more effective. It’s about streamlining the messy, repetitive work and giving your customers 24/7 answers — not just service windows. 

 

This shift didn’t happen overnight. It’s been pushed along by three undeniable realities that contact centers are grappling with today. 

 
The Current State of Contact Centers 

Contact centers are under real pressure — from all sides. 

 

Cost 

Budgets are tight. Leaders are being asked to deliver better service, but often with fewer resources. Outsourcing isn’t the silver bullet it used to be. Everyone’s looking to do more with less. 

 

Customer expectations  

People want answers right away. They expect personalization. They don’t want to repeat their issue four times. We live in an on-demand world — if Netflix can get it right, customers expect the same from their insurance company or government agency. 

 

Talent shortages.  

It’s tough to recruit and retain great contact center talent. It’s a demanding job. Attrition is high. Training takes time. Turnover happens, it hits hard — right in your quality and service levels. 

 

What do you do when you have to reduce costs, serve customers better, and keep fewer agents engaged and effective? 

 

This is exactly where AI steps in. 

 
 

Why AI matters now 

AI isn’t the future. It’s the present. In the contact center, it’s showing up in four core ways. 

 

1. Agent assist 
AI tools are helping agents in real time — surfacing the right knowledge article, suggesting the next best action, or even summarizing the call so the agent can focus on the customer, not the admin. It’s like having a digital co-pilot who never sleeps and doesn’t forget. 

 

2. Automation 
We’re not just talking about basic chatbots anymore. We're talking about sophisticated virtual agents that can handle end-to-end processes — resetting passwords, checking claim statuses, even making changes to a customer’s account — all without human intervention. That’s saving time, money, and letting human agents focus on higher-value interactions. 

 

3. Analytics 
AI can analyze massive volumes of customer interactions — voice, chat, email — and pull out patterns. What are people calling about? Where are they getting stuck? What’s driving negative sentiment? This turns the contact center into a strategic listening post for the whole organization. 

 

4. Always-on service 
AI makes it possible to deliver 24/7 support. Not just outside business hours, but across time zones, channels, and languages. Customers don’t want to wait until Monday morning — and now, they don’t have to. 

 
It’s not about replacing people — it’s about empowering them 

AI takes care of the tedious, repetitive tasks so your human agents can do what they do best — solve complex problems, show empathy, and build relationships. 

 

You’re not automating away the people — you’re augmenting their capability.  

 

That’s how you build a modern contact center that can scale, deliver exceptional experiences, and actually retain great talent. 

 

 

Coming up next, we’ll be discussing going from IVRs to intelligent agents  

Show more...
1 month ago
6 minutes 6 seconds

Michael Martino Show
AI Agents in the Contact Center – A New Era of Customer Experience

What are AI Agents 

 When people hear “AI agent,” they often think of those clunky, rules-based bots from a decade ago. You know — the ones that make you type “representative” five times before giving up and transferring you. 

 

That’s not what we’re talking about. 

 

Today’s AI agents are autonomous, adaptive, and context-aware. They can learn, make decisions, and take actions — just like a human agent would — but at machine speed and scale. They don’t just follow scripts. They assess, they understand, and they execute. 

 

They can: 

  • Handle complex customer queries end to end 

  • Escalate when needed 

  • Pull data from multiple systems 

  • Even collaborate with human agents in real time 

  • Think of them as your new digital co-workers — not just tools, but teammates. 

 
Why contact centers are ground zero 

Contact centers are high-volume, high-cost, and high-friction. They are ripe for transformation. 

 

You’ve got: 

  • Skyrocketing customer expectations 

  • High turnover 

  • Constant training costs 

  • Increasing demand for 24/7 support 

 

AI agents can take the load. They work around the clock, don’t burn out, and can be trained in minutes instead of weeks. 

 
Real-world scenario 

A customer contacts your support center about a suspicious charge on their account. 

 

An AI agent: 

  • Authenticates the customer using natural language and voice biometrics 

  • Retrieves their recent transactions 

  • Flags the suspicious charge using anomaly detection 

  • Offers to freeze the card, reissue a new one, and start a dispute claim 

  • And wraps it up with a summary email — all in under two minutes 

 

No hold time. No transfers. No scripts. 

 

Now imagine scaling that across thousands of similar calls per day. 

 

Meanwhile, your human agents are focused on edge cases — fraud investigations, vulnerable customers, legal inquiries. Work that requires empathy, judgement, and creativity. 

 

That’s the partnership — AI does the heavy lifting, humans deliver the heart. 

 
What to watch out for 

But let’s not drink our own Kool-Aid. 

 

There are pitfalls, and they’re real. 

 

First — data quality. AI agents are only as smart as the data they’re fed. Garbage in, garbage out. 

 

Second — governance. You need to know how these agents are making decisions. You need explainability, audit trails, and the ability to intervene when things go sideways. 

 

Third — customer trust. Just because you can automate a conversation doesn’t mean you should. There’s a time for AI and a time for a human voice. 

 

Fourth— employee engagement. AI agents shouldn’t be replacing your staff — they should be empowering them. Give your people tools that make their work more meaningful, not more robotic. 

 
Getting started 

If you’re leading a contact center and wondering where to begin, here’s my playbook: 

  • Start small — pick one high-volume use case like password resets or billing inquiries. 

  • Use hybrid models — AI handles the front end, human agents take over if needed. 

  • Train continuously — AI agents get smarter with feedback. Build in learning loops. 

  • Design with empathy — your customers will thank you for making the experience feel human, not robotic. 

  • Measure impact — look at handle time, resolution rates, CSAT, and agent satisfaction. 

 

To wrap 

AI agents are not about replacing humans — they’re about reimagining work. 

 

In the contact center, they free up human agents to be more strategic, more empathetic, and more valuable. They turn your support operation from a cost center into a competitive advantage. 

 

We’re entering a new era — one where intelligent collaboration between humans and AI isn’t a novelty… it’s the standard. 

 

Ask yourself — what could your team achieve if they had digital agents handling the grunt work? 

 

That’s the future of service. And it’s here now. 

Show more...
1 month ago
5 minutes 11 seconds

Michael Martino Show
The Rise of AI Agents – Beyond the Bot

Today, I want to talk about something that’s moving fast—the rise of AI agents. 

 

Not just chatbots. Not just machine learning models that spit out answers. But agentic AI—autonomous systems that can take initiative, make decisions, and complete tasks on your behalf. These aren’t just tools. They’re becoming co-workers, collaborators, and in some cases—replacements. 

 

From tools to agents 

For the last decade, we’ve gotten used to AI as an enhancer. Recommendation engines. Predictive analytics. Natural language processing. All of that made our tools smarter—but they still needed us to tell them what to do. 

 

What’s changing now is autonomy. 

 

AI agents are systems that can understand context, interpret goals, plan multiple steps, and take action. Not just reactive, but proactive. They can book meetings, draft reports, analyze data, schedule follow-ups, and escalate decisions when needed. 

 

Think of it like this: we used to program software to perform tasks. Now we’re starting to describe outcomes—and the agent figures out how to get us there. 

 
Why now? 

Why is this shift happening now? 

 

It comes down to three things: 

  • Foundation Models: We’ve seen massive improvements in large language models—like GPT-4, Claude, and Gemini. These models aren’t just good at language—they’re starting to reason, plan, and simulate dialogue. That’s the engine. 

  • Multi-Modal Capabilities: AI agents can now process text, images, audio, and even video. That makes them more versatile. An agent can read an invoice, listen to a voicemail, and generate a response—all in the same workflow. 

  • Agent Frameworks and Infrastructure: We’re seeing the emergence of agent ecosystems like OpenAI’s Auto-GPT, LangChain, Microsoft’s Copilot framework, and others. These give developers the building blocks to create agents with memory, goals, and even personalities. 

 

Now, it’s not so far-fetched to have an AI agent act like a junior analyst, a research assistant, or even a frontline customer service rep. 

 
The other side of the story 

 With all this autonomy—what are the risks? 

 

Accountability is a big one. Who’s responsible when an agent makes the wrong call? We’re entering a new grey zone where the output might seem intelligent, but the underlying reasoning could be flawed. 

 

Bias and hallucination still happen. Just because an agent sounds confident doesn’t mean it’s right. 

 

Displacement. These agents are beginning to do tasks that used to be entry points for junior talent. We have to be honest about that. 

 

Opportunity 

Here’s where the opportunity lies—instead of replacing people, the best implementations of agentic AI are augmenting them. Helping them move faster, make better decisions, and focus on human strengths—empathy, judgment, creativity, leadership. 

 
What you should you think about 

If you’re a leader in an organization, here’s what you need to be asking—what processes in your team are repetitive, rules-based, or data-heavy? Those are prime candidates for AI agents. 

 

Do your teams have the digital fluency to interact with these agents effectively? 

How are you thinking about trust? Will your people trust the output? Will your customers? 

 

Are you ready to reimagine roles and responsibilities as agents take on more of the “doing,” so your people can spend more time “thinking”? 

 

This isn’t science fiction. The rise of AI agents is happening now. The question isn’t if you’ll use them—it’s how you’ll integrate them in a way that’s responsible, strategic, and human-centered. 

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1 month ago
6 minutes 1 second

Michael Martino Show
The Integrator Tangle: Accountability in Multi-Vendor Transformations

Welcome back to The Michael Martino Show 

 

Today, I want to talk about something that’s become a hallmark of modern transformation program – working with multiple service integrators. 

 

On paper, it makes sense. You bring in specialized vendors to focus on different layers of the tech stack. One’s great at infrastructure, another leads the cloud migration, a third owns user experience, and maybe another is handling legacy retirement. 

 

While specialization is powerful, it introduce a kind of complexity that’s hard to unwind once it sets in. 

 

Why does working with multiple integrators add so much complexity? Also, how do you hold them accountable—to keep costs down, avoid scope creep, and still deliver a high-quality product? 

 

Once you're in-flight, the problems emerge fast: 

  • Conflicting delivery methodologies -- One integrator is Agile, another is waterfall, and a third thinks Agile means running a daily stand-up with no backlog. Chaos 

  • Finger-pointing: Something breaks and the infrastructure partner blames the app team, who blames the API vendor, who says “we followed the spec.” Meanwhile, your product manager is stuck in the middle 

  • Lack of a common architecture—If you don’t impose a centralized architecture strategy early, everyone builds in a vacuum—and you end up with a Frankenstein platform stitched together by PowerPoint and good intentions. 

  • Culture –Some integrators embrace collaboration. Others operate like they’re the smartest people in the room, and they’d rather win than align. 

 
The illusion of accountability 

Now here’s the real kicker—when things start to go off the rails, the question becomes –Who is actually accountable? 

 

If no one’s responsible for the whole, then no one’s truly responsible. 


This is where governance has to step up—or everything falls apart. 

 

What can you do? 

If you're managing a transformation with multiple service integrators, here’s how you build accountability into the structure: 

1. Appoint on leader 

You need one party responsible for integration across the board. Sometimes it’s an internal team with strong architecture and delivery governance. Other times, it’s a lead integrator who coordinates the others under a master services agreement. But it has to be clear who owns the end-to-end performance. 

2. Design for collaboration from the start 

Make interdependencies explicit in contracts. Define shared deliverables. Hold joint planning sessions. Create integration boards. And most importantly—make sure each partner understands how their work fits into the larger ecosystem. 

3. Use incentives wisely 

Tie payments to outcomes, not just tasks. If you’re launching a new claims intake system, don’t just pay for code delivered—pay for working integrations, successful tests, and user adoption. Make everyone’s success interdependent. 

4. Install strong product ownership 

The organization needs internal leaders who can speak business and tech. Product owners who understand that decisions about functionality, sequencing, and risk can’t be left to vendors alone. You can’t outsource judgment. 

5. Centralize architecture and QA 

This is non-negotiable. You need a single team defining the data model, the integration standards, the security posture. Same with testing. If every integrator is testing in their own sandbox, you’re not testing the system—you’re testing wishful thinking. 

 
To wrap 

Working with multiple service integrators doesn’t have to be a disaster. But it will be if you don’t lead decisively. 

 

You need clarity. Governance. Technical leadership. An obsession with the whole, not just the parts. 

 

Transformation isn’t about managing vendors—it’s about orchestrating outcomes. If you’re not careful, you’ll end up with a beautifully executed failure. 

 

The question is –Who’s accountable for the whole? And are your partners aligned to deliver it? 

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1 month ago
4 minutes 21 seconds

Michael Martino Show
Digital Literacy: The New Core Competency

Today we’re going to talk about something that’s become foundational—not just for digital transformation, not just for modern workplaces, but for nearly every job on the market today. 

 

Digital literacy. 

 

This isn’t just about knowing how to use Excel or send an email anymore. It’s about being adaptable, confident with new tools, and understanding how technology changes workflows, relationships, and expectations.  

 

If your organization isn't actively growing this muscle, you're falling behind. Fast. 

 

Why digital literacy matters now more than ever 

Ten years ago, you might have gotten by with some basic digital skills. 

 

Today?  

 

Every job—whether you're in HR, finance, customer service, operations, or policy—has a digital element. AI tools, collaboration platforms, CRMs, ticketing systems, self-service portals—they’re everywhere. 

 

The technology is changing faster than ever. Employers are no longer just hiring for what you know—they’re hiring for how fast you can learn.  

 

Can you adapt? Can you figure it out? That’s what matters. 

 

Digital literacy is no longer a “nice to have.” It’s core to your ability to contribute. It’s the baseline for innovation. 

 
 

What employers are looking for 

Let’s flip perspectives for a moment. You’re hiring someone new. What’s more valuable—a candidate who’s an expert in one platform, or someone who shows they can learn any new tool thrown their way? 

 

It’s the second one, every time. 

 

Can you learn in the flow of work? Are you curious about tools? Do you understand how to make data-driven decisions? Can you navigate digital conversations—chats, collaborative docs, workflows? 

 

This is the new baseline.  
 

Building digital fluency 

  • Make learning accessible and continuous 

  •  Forget long one-off courses. Use bite-sized videos, on-demand modules, and guided pathways that meet people where they are. 

  • Measure digital confidence, not just skills—but how people feel about using tech.  

  • Embedded learning into real work. Training within the context of actual job tasks. Learn new systems by using them—with safety nets 

  • Celebrate early adopters 

  • Make learning part of performance 

 
If digital literacy matters, it needs to show up in goals, development plans, and performance conversations.  

 

This isn’t about turning every employee into a tech expert. It’s about ensuring that everyone is digitally confident—so they can thrive, adapt, and grow in a modern workplace. 

 
To wrap 

Tools are only going to keep evolving. AI, automation, virtual collaboration—they’re not going away –they’re accelerating. 

 

If you want to prepare your people for what’s next, we need to move past digital literacy as a checkbox. 

 

You need to embed it into your culture. Into how we lead, how we learn, and how we work. 

Show more...
1 month ago
4 minutes 9 seconds

Michael Martino Show
The Buy vs. Build Shift: Letting Go of ‘We’re Unique’

For anyone involved in transformation you are have probably heard the pharse “We’re different. We’re unique. Off-the-shelf solutions won’t work for us.” 

 

 Let’s talk about why so many organizations believe this, and how that belief might be costing them more than they realize—not just in dollars, but in agility, scalability, and long-term customer value. 

 

The shift from build to buy 

In the past, building software from scratch gave organizations full control. You could tailor it to every policy, every exception, every preference.  

 

That control came with baggage:  

  • long timelines 

  • huge budgets 

  • large technical debt that nobody wanted to own. 

 

Today, modern platforms—especially in the cloud—are robust, scalable, and updated regularly. 

 

They: 

  • are configurable  

  • come with built-in security, analytics, even compliance tools.  

 

You’re not starting from zero –That’s why we’re seeing this shift toward buying. It’s faster, safer, and in most cases, cheaper in the long run. The value is there. 

 
The we're different story 

Here’s where many modernization efforts get stuck. Despite the clear advantages of buying, teams push back with the idea that they are somehow different—too complex, too regulated, too unique. 

 

Every organization has its quirks—but are you truly unique? Or are we just deeply attached to the way we’ve always done things? 

 

“We’re different” is often code for “we don’t want to change.” 

 

When you use that mindset to push for heavy customization of out-of-the-box software, we’re not only recreating our legacy pain—we’re actively rejecting the best practices that come built into these new platforms. 

 
3. Buying AND evolving 

How do you move forward? 

 

Reframe the conversation -- buying software doesn’t mean you lose your identity.  

It means you’re choosing a foundation that’s built to evolve—and you evolve with it. 

 

Instead of asking, “How can this product fit our process?” Ask, “How can our process adapt to this product’s best practices?” 

 

This is where leadership plays a critical role.  You need transformation champions who are willing to: 

  • challenge legacy thinking 

  • question the true value of customizations 

  • Elevate the idea that adapting to a product can actually be a strategic advantage 

 

Protecting and growing your investment 

Customizations cost more than just money.  

 

Customizations: 

  • block upgrades 

  • create dependencies 

  • increase maintenance overhead  

  • isolate you from the innovation roadmaps that modern platforms offer. 

 

If you adopt a vendor solution and keep it vanilla—or close to it—you benefit from regular improvements, broader community testing, and shared innovation.  

 

You’re not out there alone. 

 

That’s how you protect your investment. 
  

That’s how you ensure it grows with you—not against you. 

 

The more aligned you are to the product’s roadmap, the more confident you can be in scaling, integrating, and evolving. 

 
5. Influencing the culture to embrace change 

How do we influence businesses to move past the “we’re different” mindset? 

 

Storytelling 

 

Show examples from other industries or jurisdictions. Show what’s possible when an organization leans into a product’s strengths instead of bending it to legacy processes. 

 

It also means investing in change leadership. 
  

Get your business teams into the demos. Let them see what modern platforms can do out of the box. Let them see how the best practices work. 
  

Resistance softens when staff realize they’re not giving something up—they’re gaining something better. 

 

Finally, measure and celebrate the wins. When you launch faster, when you reduce maintenance costs, when upgrades take days instead of months—share that story.  

 

That’s how you shift the culture. 

 
 

To wrap 

Letting go of what made your organization “special” can feel risky.  

 

The real risk today is falling behind because of legacy thinking. 

 

 

Show more...
2 months ago
5 minutes 19 seconds

Michael Martino Show
Designing Culture for Transformation Success

If you're in the middle of a business transformation—or about to start one—this episode is for you. Because here’s the truth: you can have the best strategy, the best roadmap, and the best technology... but if your culture isn’t aligned with where you’re going, none of it will stick. 

 
Culture doesn't just happen 

We think of culture as something abstract, like the personality of an organization that simply emerges over time. But the reality is that culture can—and must—be designed, especially during transformation. 

 

Think of culture as the operating system of your organization. It sets the rules for how people behave, how decisions are made, and how teams collaborate. If you’re moving toward a future that demands agility, digital thinking, or customer-centricity, your current culture might not be built to support that. 

 

That’s where intentional design comes in. 

 

So what does that look like? 
  

It means asking questions like: 

  • What values will drive the behaviours we need to succeed? 

  • What rituals, norms, and language do we want to reinforce? 

  • How do we reward and recognize the behaviours that support our transformation goals? 

 

Designing culture isn't about slogans on a wall. It’s about creating the conditions—leadership, systems, and incentives—that make the desired culture real. 

 
 

Preparing the organization  

Here’s the thing about transformation: it’s uncomfortable. It creates ambiguity. It challenges the status quo. 

 

So how do you prepare the organization? 

 

Communication 
Not just a one-time launch, but ongoing, transparent, two-way conversations. People need to know why the transformation is happening, what success looks like, and—just as importantly—how it affects them. Don’t sugarcoat the hard parts. People can handle the truth if they feel respected. 

 

Learning 
Give your teams permission to try, fail, and learn. If you’re shifting from a command-and-control environment to a more experimental model, you need to make people feel safe taking those first steps. 

 

Leadership 
Culture change starts with leadership. If leaders continue to act in old ways—hoarding decisions, avoiding risk, ignoring frontline input—then no amount of posters or town halls will matter. Your people are watching what leaders do, not what they say. 

 

Change 
This is where HR, performance management, and incentives come into play. If you’re asking teams to work in new ways but still evaluating them on old criteria, the culture won’t shift. Align your systems with your strategy. 

 

How do you know if you are on the right track? 

Culture change is slow. It’s not something you flip a switch on. 

 

You will start to see signals: 

  • You’ll hear different questions in meetings—more about outcomes than outputs. 

  • You’ll see cross-functional collaboration happening more naturally. 

  • You’ll notice teams owning their problems and solutions. 

  • Most important—you’ll hear more people saying, “I feel like I can contribute,” or, “I understand where we’re going.” 

 

That’s when you know your culture is shifting to support your transformation goals. 

 
To wrap 

Transformation isn’t just about new tools, or new policies, or new org charts. It’s about people. People thrive in cultures that are clear, inclusive, and empowering. 

 

Don’t leave culture to chance. Design it. Invest in it. And let it be the engine that powers your transformation forward. 

Show more...
2 months ago
4 minutes 15 seconds

Michael Martino Show
The Governance Gap: Why Good Governance Often Fails

If you've ever worked on a steering committee, managed a digital program, or tried to modernize a government process, you’ve probably felt the sting of a governance model that looked great on paper—but completely fell apart in practice. 

 

Why does governance fail? What challenges do we face when trying to establish it? And most importantly, what do we need to do to make it actually work? 

 
 

The illusion of governance 

Just because a governance model exists doesn’t mean it’s effective. You can have all the right documents, charters, roles, and committees, but if nobody's being held accountable, if the decision-making process is murky, or if everyone’s avoiding tough calls—your governance structure is just window dressing. 

 

Governance fails when: 

  • There's no clear owner for outcomes 

  • Decision rights are scattered or unclear 

  • Transparency is limited to a handful of insiders 

  • The people in the room don’t feel empowered—or worse, don’t feel responsible. 

 

The result? Delays, duplication, confusion, and programs that lose momentum. 

 
Common governance pitfalls 

1. Over-engineered structures 
We sometimes build governance like we’re designing a spaceship—layer after layer, approvals upon approvals. It slows down delivery and actually removes clarity instead of creating it. 

 

2. The myth of consensus 
Trying to get everyone to agree before acting sounds great, but in practice it often paralyzes progress. Not every decision needs to be unanimous—some just need to be made and owned. 

 

3. Lack of transparency 
When decisions happen behind closed doors—or when information is selectively shared—it erodes trust. People disengage. They don’t feel part of the process, and that breaks the whole system down. 

 

4. Accountability without authority 
We put someone “in charge” of something but don’t give them the authority to make the calls they need to. That’s not fair—and it guarantees failure. 

 
The core ingredients of effective governance 

What does good governance actually look like: 

1. Clear accountability 
Every initiative, every process, every product—someone needs to own it. That doesn’t mean micromanaging; it means being responsible for outcomes, decisions, and trade-offs. Accountability has to be visible, not implied. 

2. Decision-making clarity 
Who decides what? When? How? Based on what information?  

 

If you can’t answer those questions quickly, your governance is already in trouble.  

Set clear decision rights, and don’t be afraid to document and socialize them. 

 

3. Transparency as a principle, not an afterthought 
Governance only works if people trust the process. That means sharing how and why decisions are made, opening up meeting notes, and welcoming scrutiny—not hiding from it. 

 

4. Empowered roles, not ornamental ones 
If you’re asking people to govern, make sure they have the authority, tools, and support to actually do it. Too many governance bodies are filled with people who attend but don’t influence anything. 

 

5. Regular reflection and adaptation 
Governance isn’t a “set it and forget it” thing. You need to revisit how it's working, what’s broken, and what needs to evolve. Build feedback loops into your governance model—quarterly retrospectives, even anonymous surveys. 

 
 

Making governance a leadership priority 

Governance is not a checkbox. It’s not about compliance—it’s about confidence. 
  

The confidence that people know what to do. The confidence that decisions are timely. The confidence that accountability means something. 

 

You have to model it. Be transparent about your decisions. Own your trade-offs.  

 

Give people below you the room—and the responsibility—to do the same. 

 

Governance is leadership in action. And it either builds momentum—or breaks it. 

Show more...
2 months ago
5 minutes 8 seconds

Michael Martino Show
Busting the Myths: Misconceptions About Business Modernization

Modernization programs are often misunderstood—by leadership, by teams, even by the public. And when expectations don’t line up with reality, we end up with frustration, delays, and resistance. 

 

Misconception #1 - Modernization is just about technology 

I can’t count how many times I’ve heard someone say, “We’re modernizing—we’re buying a new platform.” That’s like saying you’re renovating your house and thinking new appliances are going to fix a broken foundation. 

 

Modernization includes technology, but it's not just technology. 
 

It’s about process redesign, governance changes, culture shifts, and customer expectations. 

 

Strategy to address: 

  • Start by reframing the language. Don’t talk about modernization in terms of software; talk about outcomes. 

    •  “Here’s how we’re going to improve turnaround time for customers.” 

    • “Here’s how staff will spend less time on manual work.” 

  • If your narrative focuses on experience and results, technology becomes a tool—not the story. 

 
Misconception #2 - We’ll see the results right away 

The “flip-the-switch” myth. A lot of people think once you implement a new system or launch a new process, things will improve instantly. 

 

But the truth is, modernization is a journey. It requires adaptation, rework, and, yes—sometimes a little pain before the gain. 

 

Strategy to address: 

  • Set clear expectations upfront 

  • Use a roadmap with short-term wins and long-term goals 

  • Communicate early and often that progress is measured over time, and explain that a few bumps are part of the ride. 

 

 
Misconception #3 - Modernization means job losses 

This one is tricky and emotional. When employees hear “automation,” “efficiency,” or “AI,” they often translate that to “redundancy.” 

 

In some industries, that fear isn’t unfounded. But in many public and service-oriented organizations, modernization is more about reallocating talent to higher-value work—not cutting headcount. 

 

Strategy to address: 

  • Leaders need to be transparent. 
    Say it plainly: “This is about making your job easier, not eliminating it.” And show the benefits 

  • Create roles for super users, retrain staff, and elevate work that focuses on human judgment, creativity, and service 

  • People aren’t afraid of change—they’re afraid of loss. Focus on what they’ll gain. 

 
Misconception #4 - It’s IT’s problem 

I’ve been in rooms where entire departments check out of modernization discussions because they think, “This is a technology issue. Let IT handle it.” 

 

That mindset guarantees failure. Modernization touches everyone. If customer service, finance, operations, and frontline teams aren’t involved, you’re going to build the wrong thing in the wrong way. 

 

Strategy to address: 

  •  Create cross-functional modernization teams 

  •  Make it clear that business units own the outcomes. IT may be the enabler, but the business side defines the success 

  • Bring teams in from Day 1. Listen to how they work. Build with them—not for them. 

 
Misconception #5 -The vendor will solve everything 

The magic vendor who’s going to swoop in, fix our problems, and leave us with a bow-tied solution. No matter how great your vendor is, you still have to do the thinking. Vendors can provide tools and support, but they can’t define your business processes or manage internal change for you. 

 

Strategy to address: 

  • Treat vendors as partners, not saviors 

  • Do your internal work first: document your processes, clarify your goals, and define what success looks like. That way, when the vendor comes in, they’re building on your foundation—not guessing. 

 

If you bust the myths early, you will build trust—and with trust, change is not only possible, it’s sustainable. 

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2 months ago
6 minutes 9 seconds

Michael Martino Show
Accessibility by Design: Building Online Services That Work for Everyone

The word “accessibility” can sometimes feel like a compliance checkbox, a paragraph at the bottom of a project plan, or worse, something we bolt on after the fact.  

 

I want to challenge that. Real accessibility is about dignity and respect. 

 

It’s about building government services that include everyone 
 

Accessibility is not a feature – it’s the foundation 

 

Accessibility is not a “nice to have.” It’s not a line item.  

 

It’s a values decision. 

 

When your building an online service — say, a form to report a workplace injury or a portal to apply for benefits — you have to remember, you are not just building for the ideal user with a fast connection, full vision, and perfect dexterity.  


You are building for everyone — people with visual impairments, people using screen readers, people with cognitive differences, older adults who might not be as comfortable online, and yes — people in rural communities still struggling with slow internet. 

 

If the service doesn’t work for everyone, then it doesn’t work. 

 

Start early 

Accessibility isn’t something you audit at the end. It’s something you design into the process. 

 

When you are modernizing your services, you need to include people with lived experience. Not just to “test” the product at the end, but to co-design it from the start.  

 

Tools, teams, and trade-offs 

If you’re a product manager, service designer, or developer in government, here’s the truth: you need a baseline understanding of accessibility. Not everyone has to be a WCAG wizard, but everyone should understand the basics. 

 

Things like: 

  • Using semantic HTML — so screen readers can make sense of your content. 

  • Ensuring proper color contrast — so people with low vision can read your text. 

  • Making sure everything works with a keyboard — not just a mouse. 

  • Avoiding auto-play videos or flashing content that can trigger seizures or overwhelm users. 

 

To be clear -- these things don’t slow you down. They often make your service better for everyone. Ever tried filling out a complex form on your phone with one hand while holding a coffee? Accessibility design helps with that too. 

 

Bring accessibility specialists in early. Test with real users. Use automated tools — sure — but don’t stop there. Combine tech with empathy. 

 

This is cultural, not just technical 

Accessibility isn’t just a line in your digital strategy. It’s a culture shift. It’s saying, “We care enough to build things right — not just fast.” That kind of mindset ripples through an organization. It shapes hiring. It shapes priorities. It shapes how you measure success. 

 

When you celebrate accessibility wins in your teams — when you highlight inclusive design in demos, in retros, in show-and-tells — you’re telling everyone, this matters. 

 

To wrap 

When you build accessible digital services, you do more than meet standards. You build trust. 

 

You tell people — no matter your abilities, your background, your bandwidth — this service is for you. You matter. 

 

Don't just digitize old systems -- reimagine how inclusive government can be. 

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2 months ago
3 minutes 48 seconds

Michael Martino Show
Agentic AI vs. Traditional AI – What's the Big Deal?

The Old AI: Reactive, Rigid, and Rule-Based 

Let’s start with what most of us think of when we hear "AI"—especially in the context of contact centers, customer support, or government digital services. 

 

You know the drill: a chatbot that’s only good for checking your account balance.  

 

A voice system that can maybe, maybe tell you your estimated wait time. Or some clunky automation that breaks the moment a customer steps out of the script. 

 

That’s traditional AI. Think of it as reactive and rule-based. 

 

It’s been around for decades—basically glorified “if-then” statements powered by machine learning. It responds to what you tell it, but it can’t really think ahead. It can’t take initiative or understand broader context. 

 

It had its place. It helped reduce some friction. Saved some time. But it was never transformative. 

 

Which brings us to now. 

 
The Shift: What Is Agentic AI? 

Now imagine an AI that doesn’t just wait for you to tell it what to do—but one that thinks alongside you. One that can plan, reason, adapt, and even ask clarifying questions. 

 

That’s Agentic AI. 

 

It’s not just reactive—it’s proactive. It can take a goal and figure out the steps to get there. It’s more like a junior analyst or a digital assistant with initiative than a glorified search box. 

 

Core Capabilities: Planning, Reasoning, Autonomy 

So what makes this possible? Three things: planning, reasoning, and autonomy. 

 

  • Planning: Agentic AI can break down goals into steps. You don’t need to hand-hold it. 

  • Reasoning: It can weigh options. Compare paths. Even simulate outcomes. 

  • Autonomy: This is the big one—it can operate across systems, remember previous actions, and come back with results without waiting on constant prompts. 

 

It’s like the difference between hiring someone who only does exactly what you tell them, versus someone who sees a problem and comes back with a solution. 

Wouldn’t you want the second one on your team? 

 

Real-World Use Case: A Government Agency Scenario 

Imagine you work in a government agency. You’ve got hundreds of services.  

 

Thousands of edge cases. And a customer base that spans every possible background, language, and need. 

 

You deploy traditional AI—it helps a bit, cuts some wait times. But people still get stuck. Agents still have to jump in. And your chatbot? It’s got a 30% success rate and lives in a silo. 

 

Now imagine deploying agentic AI. 

 

You give it a goal: “Help users determine their eligibility for housing assistance, and guide them through the process.” 

 

The AI doesn’t just answer questions—it walks people through eligibility, compares their info across different databases, pre-fills forms, flags missing documents, and even schedules follow-ups. 

 

All in natural language. 

 

All personalized. 

 

And all at scale. 

 

That’s not automation. That’s transformation. 

 

Human in the Loop: Still Critical 

Now let me be clear: Agentic AI doesn’t mean we get rid of humans. 

 

It makes humans more essential—but in better roles. 

 

Instead of answering the same question 500 times a day, your people are reviewing exceptions. Handling edge cases. Providing empathy and judgment where AI shouldn’t tread. 

 

Agentic AI handles the repeatable. Humans handle the meaningful. 

 

To wrap 

So why does this matter right now? 

 

The gap between agencies—or companies—that understand this shift and those that don’t? It’s getting wider every month. 

 

Traditional AI is table stakes. 

 

Agentic AI is the differentiator. 

 

If you’re leading service delivery and you’re not exploring what agentic systems can do—you’re going miss the leap.  

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2 months ago
5 minutes 15 seconds

Michael Martino Show
Escaping the Trap: Overcoming Technical Debt

 

What is technical debt? 

Think of it like financial debt. You take out a loan so you can get something now—speed, convenience, an MVP in time for launch. But just like financial debt, technical debt comes with interest. Over time, that interest looks like slower development cycles, harder-to-maintain code, brittle infrastructure, and frustrated teams. 

 

The real cost of technical debt 

What does technical debt costs a business. 

 

The thing about technical debt is it's invisible—until it isn’t. 

 

You're adding new features, but they take longer and longer to build. Your QA team is catching regressions in parts of the codebase that haven't been touched in months. Your best developers are spending more time debugging than innovating. 

 

It’s death by a thousand cuts. 

 

Developers spend 33% of their time dealing with technical debt. That’s one-third of your engineering capacity going toward... maintenance. 

 

That’s lost velocity. Lost morale. And ultimately, lost revenue. 

 

How to identify technical debt 

First, you have to identify it. You can't fix what you can't see. 

 

Start with code reviews and architecture audits. Look for red flags: 

  • Long methods with nested logic 

  • Duplicate code scattered across modules 

  • A single function that breaks three different features if you touch it 

  • Outdated libraries or frameworks 

  • Lack of automated tests 

 

Making the business case 

Here’s where a lot of teams struggle. They know the tech debt is a problem, but leadership isn’t buying it. 

 

How do you make the case?  

 

You translate it into business risk and opportunity cost. 

 

Don’t say, “Our code is messy.” Say, “We spend twice as long building features because we have to fight with the old code every time.” 
  

Don’t say, “We need to refactor.” Say, “This part of the system breaks every release and delays deployments by 3–5 days.” 

 

Attach metrics when you can: 

  • Deployment frequency 

  • Mean time to recovery 

  • Bug counts 

  • Team velocity 

  • Show that by investing time in fixing the foundation, you unlock speed, stability, and scalability. 

 

That’s something leadership can get behind. 

 

Strategies for paying down technical debt 

You made the case and got buy-in. Now what? 

 

1. Leave code better than you found it 
Every time someone touches a file, they clean up a little. Rename a variable, extract a function, delete dead code. Small, incremental wins. 

 

2. Debt sprints: 
Set aside one sprint a quarter—or even a couple of days—to tackle debt head-on. Treat it like feature work. Track it. Celebrate it. 

 

3. Embed refactoring in roadmaps: 
Pair refactoring with features. For example if you tweak a login flow, also update that outdated authentication module. 

 

4. Create a tech debt register: 
Track known debt areas in your backlog or project management tool. Prioritize it alongside user stories and bugs. 

 

5. Monitor and measure: 
Track progress. Use tools to measure code complexity, test coverage, or dependency freshness. If you’re not measuring it, it’s easy for it to slip again. 

 

Culture is the cure 

You can’t solve technical debt just with tools or tactics. 

 

It’s a culture problem. 

 

You need leadership that values sustainability over speed-at-any-cost. You need teams that feel safe to say, “This isn’t good enough, and we need to fix it.” You need a mindset that quality is everyone’s job—not just the architects or the senior devs. 

 

Investing in documentation. Writing tests. Reviewing code thoughtfully. These aren’t “nice to haves”—they’re what protect you from drowning in debt tomorrow. 

 

When that culture takes hold? Magic happens. Teams move faster. Systems become more resilient. And the codebase? It becomes an asset, not an anchor. 

 

The bottom line 

Technical debt isn’t failure—it’s a reality. Every project has it. The key is managing it intentionally. Choosing where to take on debt. Paying it down strategically. And never letting it get so big that it breaks you. 


Show more...
2 months ago
6 minutes 29 seconds

Michael Martino Show
Hot takes, industry insights, and advice from experts - focusing on the continued pursuit of Digital and Business Transformation, Government Transformation, digital coaching and martial arts training. Episodes are short, to the point, and jammed packed with info. We will get you in and out with maximum content in short bursts.