Michael Martino Show

AI should not be deployed where it is most visible, it should be deployed where it removes the most friction from the system.    In most government organizations, that friction lives deep in the back office.    If you fix that, the front office will take care of itself. Ignore it and no amount of AI in the front will save you. 

Delivering large cloud platforms in iterations is not easy.    It requires:  discipline in architecture  maturity in governance  a fundamental shift in how organizations think about delivery    When done right you stop treating delivery as a single high-risk event and start treating it as a controlled, continuous system and that’s ultimately what large-scale transformation needs to become.     Not...

When transparency is done right, trust increases, not because everything is going well—but because everyone understands what’s actually happening.     Decision-making becomes faster and more grounded. Issues get resolved earlier and the program becomes resilient, because it can adapt in real time.   

A system integrator is not just a vendor and they’re not just a builder.    They are the execution engine of your transformation and that engine only works if it's:  properly integrated into your operating model   governed with clarity and discipline   it’s aligned to outcomes—not just deliverables     If you get the SI model right, you accelerate delivery, reduce risk, and actually achieve tr...

What are we comparing  At a high level, Waterfall and Agile are not just delivery methods—they represent fundamentally different beliefs about how work gets done.    Waterfall assumes:  Requirements can be known upfront  Change is a risk to be controlled  Delivery should be sequential and gated    Agile assumes:  Requirements evolve  Change is expected and valuable  Delivery should be iterative an...

Should AI shape your operating model?    Eventually, it has to.    You can start by adding AI tools to existing processes but the real opportunity comes when you step back and ask, "If AI could do a large portion of our operational work how would we design this organization from scratch?"    The organizations that ask that question early will be the ones that build the next generation of...

Today we’re going to talk about something that is quickly becoming one of the most important topics in public-sector leadership: how artificial intelligence can transform government service.    For decades, government modernization has mostly meant digitization.    Governments took paper forms and turned them into online forms. They took in-person transactions and moved them to websites. Portals,...

Delivering an out-of-the-box implementation is not about resisting change -- it is about choosing the right kind of change. Rather than bending the platform to match the past, the organization adapts its processes to match the future.    That takes discipline and leadership.    It also takes a clear understanding that the value of modern platforms comes not from how much you change them—but from h...

Today we’re going to talk about a decision that quietly determines whether your transformation succeeds or becomes a multi-year recovery effort.    When should you get out of the box and bend the system to your business and when should you let the platform redefine your business processes?    This is not a technology debate – it's an operating model decision.    If you’re in government, or run...

Start with outcomes   There’s a famous line from How Big Things Get Done by Bent Flyvbjerg:  Big projects don’t fail because they’re ambitious. They fail because they’re poorly governed and poorly scoped.    In government, transformation often starts with a solution:  “We need a new case management system.”  “We need AI.”  “We need to modernize.”    That’s activity language. Successful programs st...

Governments are very good at approving transformation but they are much less disciplined at benefiting from it.    If you’re leading a large-scale modernization — digital platform replacement, service transformation, AI implementation, operating model redesign — you need a benefit realization framework that is operational, measurable, and governed.    Start with business outcomes  Most benefit fra...

Start with the business outcome     Before you build anything, define the operational objective. Are you trying to:  Increase first-contact resolution?  Reduce case backlog?  Improve eligibility accuracy?  Shorten processing time?  Lower cost per transaction?    This is not about “using AI.” This is about improving a measurable public-sector performance indicator. If you can’t tie your AI agent to...

Let’s start with a common pattern. An organization says, “We need to improve CX.”     They launch:  a CX initiative  build journey maps  identify pain point   redesign touchpoints.    All this and nothing changes.    Why?    The underlying workflow — the intake process, the routing logic, the approval hierarchy, the eligibility rules, the system architecture — stays the same, and that’s where expe...

 First Contact Resolution isn’t a contact center metric -- it’s a journey outcome. If you try to “train your way” to FCR without fixing the journey, you’ll fail every single time.    Why first contact resolution is misunderstood  Let’s start with the misconception. Most organizations treat First Contact Resolution as a:  frontline performance issue  coaching issue  script compliance problem.    So...

One of the biggest misconceptions is this belief that AI maturity equals removing humans from decisions.    The narrative goes something like this:  “AI will eliminate manual work.”   “AI will replace decision-making.”   “AI will automate the frontline.”    AI reduces repetitive effort but mature AI strategies don’t remove humans. They reposition humans because AI doesn’t replace judgment — it cha...

AI is already operating inside your organization. Your staff are using generative AI tools to draft emails, summarize policy documents, analyze data, and prep briefing notes.    All of this is happening without a coherent, enterprise-level strategy.    Which means decisions about AI are being made:  individually  inconsistently  invisibly    That’s not innovation. That’s unmanaged risk.    An AI s...

Many organizations are building initiatives around activities, projects, and deliverables—instead of anchoring them in clear business outcomes.    If you’ve ever sat in a steering committee where someone says,  “We delivered everything we promised… but the metrics didn’t change,”  this episode is for you.    Activity is not impact  Most organizations don’t actually have a strategy execution proble...

Most government operating models are built around:  programs  policies  channels  functional silos.    Government is organized by what it does and  not by what the customer experiences. As a result, the customer journey is fragmented.    A citizen starts a service in one channel, gets handed off to another team, repeats their story, hits a policy boundary, and eventually gives up—or escalates.   ...

The Illusion of AI readiness  Many governments believe they are AI-ready because they’ve:   published an AI strategy  piloted a chatbot  created an ethics framework  stood up a data or innovation office    All of that is important -- none of it, on its own, equals readiness.    True AI readiness is not about technology adoption--it’s about organizational transformation. AI doesn’t simply automate...

Before we talk about plans, we need to ground the conversation.    An AI agent is not just a chatbot that answers FAQs.    An AI agent is a system that can:  interpret intent  take action across systems  follow defined rules and policies  escalate appropriately  learn within controlled boundaries.    In a government context, that could mean an agent that:  guides a citizen through eligibility, app...

Trust in government services isn’t a “nice to have.”    It’s not a branding exercise.    It’s not a communications problem.    Trust is an operational outcome.    Trust matters   Most people don’t want to interact with government. They interact because they need:  benefits  healthcare  licenses  permits.    Trust determines whether they:  comply willingly or reluctantly  believe the information th...

AI is not a single thing. It’s not one system. It's also not a magic button you bolt onto a broken process.    When most people say “AI,” they’re lumping together:  machine learning  predictive analytics  natural language processing  Generative AI  intelligent automation    Each of these has very different implications for government.    The mistake many agencies make is jumping straight to th...

Most government operating models were not designed for the environment we are operating in today.    They were designed for stability.     They were designed for predictability.    They were designed for policy-driven, siloed execution.    Today’s environment demands something very different:  citizens expect seamless digital services  policy changes are faster and more frequent  funding pressures...

What omnichannel really means   Let’s start by getting clear: Omnichannel isn’t just offering multiple ways to get help.    It’s about offering connected ways.    When someone starts a process on a website, then moves to a chatbot for clarification, gets a follow-up call from an agent, and ends up submitting documents through a mobile app.    The experience should feel like one single conversation...

What Do We Mean by a “Dynamic” Operating Model?  A dynamic operating model has three defining characteristics. It:  separates what must be stable from what must be adaptable  enables rapid reconfiguration of work—not people  is managed as a product, not a one-time design    This means:  Strategy can shift without rewriting job descriptions  New services can be launched without new org charts  Capa...