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, not four disconnected ones. 

Citizens shouldn’t have to repeat information. 

 
Agents shouldn’t have to guess what’s already happened. 

 
And nobody should feel like they’re starting from scratch every time they reach out. 

This isn’t a “nice-to-have” anymore. 

 
People interact with banks, airlines, and retailers every day — and these industries have raised expectations. Government has to catch up. 

 Why government needs omnichannel 

 Government services are often complex: regulations, eligibility, documentation, and identity verification.  

People don’t reach out because they want to. They reach out because they have to. 

The burden is on government to simplify. 

Think about these real-world scenarios: 

Someone starts a benefits application online, but gets stuck on a confusing question. 

A person calls a service center to clarify something, then receives a letter in the mail asking for the same information they already submitted. 

Or my favorite, the “print this PDF, sign it, scan it, and email it back” request. In 2026... 

An omnichannel experience ensures every interaction is informed by the previous one — regardless of channel. That makes service delivery more efficient, more accessible, and more human. 

 
The four pillars of government omnichannel 

 There are four building blocks I always look for when designing an omnichannel ecosystem for public services: 

Unified Identity 

One login. One profile. 
Whether you’re online, in person, or calling in — the system knows who you are. 
This eliminates duplication, errors, and delays. 

Shared Context 

Agents can see your history — what forms you’ve started, what documents you submitted, what chatbot interactions happened earlier that day. 
This reduces frustration and improves trust. 

Seamless Channel Transition 

Citizens should be able to pick up right where they left off: 

Start online → continue on live chat 

Visit in person → get digital follow-up 

Start by phone → complete on mobile 

No resets. No “please explain that again.” 

Consistent Policies and Messaging 

Every channel needs to deliver the same rules, the same answers, the same deadlines. 
  

Conflicting information destroys confidence faster than anything. 

One experience. Citizen-focused. 

 
Making it real 

 Omnichannel transformation doesn’t happen overnight. Here’s how government teams can build momentum: 

Start with ONE high-volume journey. 
Maybe it’s benefits, licensing, permitting, or payments. Don’t boil the ocean — focus where there’s impact. 

Map the end-to-end journey. 
Find the drop-offs, the confusion points, the hand-offs that break the experience. 

Integrate data first — channels second. 
The technology that shares context is more important than the interfaces people see. 

Design for accessibility and inclusion. 
Omnichannel is equal-opportunity service — not digital-only. 

Empower employees. 
  

Front-line staff are the glue. Give them tools that show the whole picture. 

Measure continually. 
  

What gets measured gets improved. 

What is success? 

 When omnichannel is working, you’ll see: 

• Shorter resolution times 

• Less call volume driven by confusion 

• Higher adoption of digital services 

• Better public trust and satisfaction 

• Lower operational cost 

What Do We Mean by a “Dynamic” Operating Model? 

A dynamic operating model has three defining characteristics. It: 

1. separates what must be stable from what must be adaptable 

2. enables rapid reconfiguration of work—not people 

3. 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 

• Capacity can move without months of approval cycles 

The key point for today’s episode is you do not design a dynamic operating model -- you plan for its continuous development. 

Step One: Anchor the Model in Outcomes, Not Structure  

The first mistake organizations make is starting with structure. 

Who reports where. 
  

Which division owns what. 
  

Where digital or CX should “sit.” 

A dynamic operating model starts somewhere else entirely: outcomes. 

Your plan must begin by answering three questions: 

1. What outcomes matter most to the people we serve? 

2. What outcomes matter most to the organization? 

3. Where are those outcomes currently constrained? 

In government, this might be: 

• Time to decision 

• Ease of compliance 

• Quality of service recovery 

In enterprise, it might be: 

• Speed to market 

• Cost to serve 

• Retention and loyalty 

Your operating model exists to reliably produce outcomes under changing conditions. 
  

If the outcomes are unclear, the model will always be sketchy. 

Step Two: Identify the “Fixed Spine” of the Organization 

Every dynamic operating model has a stable foundation. 

This includes: 

• Core governance 

• Financial controls 

• Risk management 

• Legislative or regulatory obligations 

• Enterprise platforms and data foundations 

Your plan must explicitly document: 

• What cannot change frequently 

• What should not change frequently 

This is not a limitation—it’s an enabler. When people know what is fixed, they are far more comfortable adapting everything else. Dynamic organizations are not chaotic.  They are clear about their non-negotiables. 

 
Step Three: Design for Flow, Not Functions  

The third element of your plan is shifting how work is organized. 

Traditional operating models organize around: 

• functions 

• programs 

• channels 

Dynamic operating models organize around value flows. 

That means: 

• End-to-end journeys 

• Products and services 

• Capabilities that cut across silos 

Your plan should define: 

• The major value streams that deliver outcomes 

• The capabilities required to support those streams 

• How those capabilities are shared, not duplicated 

This is where agility actually comes from—not from agile ceremonies, but from reducing handoffs and ownership ambiguity. 

Step Four: Build an Evolution Roadmap, Not a Target State  

This is the most important shift. 

Static operating models aim for a “future state.” 

 
Dynamic operating models plan for perpetual evolution. 

Your plan should include: 

• A 12–18 month evolution roadmap 

• Clear hypotheses about what changes will unlock value 

• Lightweight governance for testing and adjusting 

Think in terms of: 

• “If we change this capability…” 

• “If we move ownership here…” 

• “If we standardize this platform…” 

Then measure, learn, and adapt. 

A dynamic operating model is never finished. 
  

To wrap 

Developing a dynamic operating model is not a design exercise—it’s a leadership commitment. 

It requires leaders to: 

• Let go of false certainty 

• Reward learning, not just compliance 

• Invest in capability, not just capacity 

This isn’t just about technology. It’s about how residents, businesses, service providers, and employees interact with government across every channel—phone, web, email, chat, mobile apps, in-person, kiosks, partner portals—everything. 

So, how do you align all those channels into a consistent, intentional experience? That’s where a Channel Experience Roadmap comes in. 

Why Channel Planning Matters in Government 

Government clients don’t wake up thinking, “Which channel am I going to use today?” 

• They think: 
  “I need to renew my license.” 
  “I want to file a claim.” 
  “I need help with my payment.” 

The channel should never be the focus—it’s simply the path. 

The reality is most governments did not design their channels. 
  

They inherited them. 

Phone lines existed. 
  

Websites were added. 
  

Portals popped up. 
  

Mobile apps came later. 

Suddenly, there was a fragmented system of disconnected experiences. 

No wonder residents repeat themselves, employees scramble to find information, and service providers feel frustrated. 

The Channel Experience Roadmap is your strategy to bring consistency, clarity, and intentional design into your multi-channel world. 

 
The Five Principles of a Good Channel Experience 

Before we build the roadmap, let’s agree on what a good channel experience looks like.  

It should be: 

- Consistent – No matter where I start, the information, branding, and process feel the same. 

• Seamless – citizens can move from web to phone without losing context. 

• Directed – channels have clear purposes--governments don’t rely on ‘trial and error.’ 

• Equitable – digital-first does not mean digital-only--accessibility remains core. 

• Measurable – governments can track performance and citizen satisfaction for each touchpoint. 

The Four Stages of Building a Channel Experience Roadmap 

Stage 1: Discovery — Understand Today’s Channel Landscape 

Start by mapping what you have today. Not just the technology—but what role it plays. 

Stage 2: Use data—call volumes, web analytics, customer feedback—but also run workshops with frontline employees, call agents, digital teams, policy staff. 

You’re not just mapping channels. 
  

You’re mapping behaviours, expectations, and frustrations. 

 
Define Channel Purpose — Give Each Channel a Job 

Think of your channels like a team: 

• The website might be for self-service—forms, FAQs, calculators, applications. 

• The contact center might provide guided support for more complex issues. 

• Live chat could be your triage—quick questions before escalating to a specialist. 

• In-person offices might exist for legal processes, accessibility, or trust-building. 

• AI chatbots handle routine transactions and information requests. 

By clarifying the role and boundaries of each channel, you help citizens choose the right one—without guessing. 

Stage 3: Design the Future State Channel Mix 

Now, create a blueprint.  

Ask: 

• Where should automation live? 

• Where should humans stay central? 

• How do channels support one another? 

• How do we integrate CRM, case management, and knowledge bases for continuity? 

 For example: 
A resident starts on the web. The system detects complexity, offers live chat. Chat agent escalates to phone. The agent already sees the full digital history. No repetition. No frustration. 

Build the Roadmap — Prioritize, Phase, Align 

This is where channels meet strategy. 

Categorize your roadmap into: 

Now (0–12 months): 
Fix high-friction issues, improve digital content, introduce chatbot pilots, reduce call volume through better web design. 

Next (1–3 years): 
Integrate CRM systems, improve automation, align processes, redesign call center workflows, unify authentication. 

Later (3–5 years): 
Implement AI-driven personalization, omnichannel case tracking, proactive notifications, digital identity integration. 

Each milestone should tie back to customer outcomes, not just technology upgrades. 

The current landscape 

 If you’ve ever worked with a worker’s compensation system — whether you’re a case manager, an employer, or a healthcare provider — you know it can be complex. 

Paperwork. Phone calls. Forms that look like they haven’t been redesigned since the 1990s. And long waits for updates or approvals. 

Over the past decade, many worker’s compensation boards have made large strides to modernize. 

The reality is we’re still in the early stages of digital transformation. Much of the system is still reactive — waiting for an injury, waiting for paperwork, waiting for decisions. 

The next wave — powered by artificial intelligence — is going to flip that model. 

The AI opportunity 

Instead of an injured worker waiting days for updates, they receive instant status notifications through a digital assistant — one that speaks their language, understands their claim, and guides them step-by-step through recovery. 

AI could review medical notes and match them to prior claims to recommend next steps or identify potential barriers to return-to-work — not to replace case managers, but to augment their judgment. 

It’s the difference between a reactive system and a proactive one. 

AI can also help identify patterns across industries — spotting emerging workplace risks or predicting where safety interventions will have the most impact before injuries even happen. 

Think about that for a second: a worker’s compensation system that prevents injuries, not just responds to them. 

The digital worker’s journey 

 Let’s walk through what a future claim might look like in this AI-powered world. 

A worker is injured at work. Instead of filling out forms by hand, their employer logs the incident digitally — or even automatically through connected safety systems. 

Within seconds, the worker receives a message on their phone: “We’re here to help. Let’s get started.” 

An AI assistant collects basic details — location, injury type, symptoms — and starts a preliminary claim file. It schedules a medical assessment, connects the worker with a nurse advisor, and provides personalized information about expected recovery timelines. 

Behind the scenes, AI reviews the report for completeness, cross-checks eligibility, and identifies potential red flags that need a human case manager’s review. 

Within hours, not weeks, the claim is in motion. 

As the worker recovers, AI tools track progress, identify when return-to-work plans might need adjusting, and alert case managers when human support is most needed. 

It’s not just faster — it’s smarter, fairer, and more human. 

 
Data, ethics, and trust 

This journey hinges on something critical--trust. 

We’re talking about sensitive personal information — medical records, income details, workplace data. AI systems must handle this responsibly, securely, and ethically. 

Transparency is key. Workers need to understand how their information is being used. Case managers need to see how AI reached its recommendations. 

We can’t let “black box” decision-making creep into something as sensitive as worker recovery. 

The best systems will build AI with people, not for them — using co-design, testing, and strong privacy safeguards. 

The road ahead 

What does the next decade of worker’s compensation look like? 

There will be fewer forms and more conversations. 
  

Fewer delays and more real-time updates. 
  

Fewer silos — and more integrated data across healthcare, employers, and compensation boards. 

AI will quietly power the background — predicting, connecting, and simplifying — while humans focus on empathy, fairness, and recovery. 

In that future, worker’s compensation won’t just be a safety net. It’ll be a human-centered system — faster, more transparent, and ultimately more caring. 

For most of the last century, healthcare was built around hospitals, clinics, and specialists. You got sick, you went somewhere, and someone took care of you. 

But the future? It’s distributed. It’s digital. It’s everywhere. 

We’re seeing the rise of virtual care, where patients can consult with doctors from home — and not just for colds or prescriptions. Mental health support, chronic disease management, post-operative follow-ups — all happening online. 

In Ontario, for example, telehealth visits skyrocketed during the pandemic and have now stabilized at levels far higher than before. That’s not just a shift in technology — it’s a shift in expectations. 

People now expect the same kind of seamless, personalized experiences they get from their bank, their airline, or even their fitness tracker — in their healthcare too. 

Then there’s the data side. 

We’ve entered an era where digital health records, AI, and wearables are merging into something incredibly powerful — a real-time picture of a person’s health journey. 

Imagine a future where your smartwatch doesn’t just count steps, but predicts stress patterns--where your electronic health record automatically alerts your care team to subtle warning signs before a crisis.  

Think about where AI can personalize your treatment plan based on your genetics, lifestyle, and environment. 

That’s the promise of predictive health — care that doesn’t wait for you to get sick. 

That promise also brings new challenges such as privacy, interoperability, and trust. 

We can’t build digital health on technology alone. We have to build it on relationships — between patients, clinicians, and systems that speak the same language. 

 Let’s talk about the patient experience — because this is where design really matters. 

Often, healthcare feels like a maze. You’re passed from one system to another, one login to another, one referral to another. Digital health gives us a chance to fix that — to make care feel more coordinated, more human. 

Imagine logging into one secure platform and seeing your appointments, prescriptions, referrals, and lab results — all in one place. Add in real-time messaging with your care team, personalized health education, and proactive reminders. 

That’s not science fiction — it’s happening in places like Estonia, Denmark, and even some Canadian provinces that are piloting integrated digital health portals. 

When done right, these systems don’t just save time — they save lives. They give patients control. 

 
Of course, technology alone doesn’t guarantee better care. 

We’ve all seen examples of flashy digital tools that fail to gain traction because they weren’t designed with clinicians in mind, or they didn’t fit into existing workflows. 

That’s why the future of digital health depends on co-design — bringing doctors, nurses, patients, and technologists to the same table. 

There’s another big shift underway — from treatment to prevention. 

Digital health enables us to spot trends early — changes in sleep, diet, movement, even tone of voice — that may indicate something deeper. 

There are real barriers: 

• Outdated systems that don’t talk to each other. 

• Privacy laws that weren’t built for the age of AI. 

• Clinician burnout and digital fatigue. 

• Technology moving faster than policy. 

Governments will have to lead with vision — setting standards, protecting citizens’ data, and ensuring equitable access. Because a digital health revolution that leaves behind rural communities, seniors, or low-income populations isn’t progress — it’s a new kind of divide. 

The future of digital health must be inclusive, ethical, and human-centered. 

 So where does that leave us? 

We’re at a crossroads — between the healthcare system we’ve always known, and the one we can now imagine. 

Digital health has the power to make care more connected, more compassionate, and more effective than ever before. 

Today we are going to talk about how governments deliver services, and what the future holds for those delivery channels. 

We hear plenty about “digital government,” “online portals,” or “e-services,” but that only tells part of the story. Behind that is a complex ecosystem of channels—web, mobile, call centers, kiosks, in-person offices, even physical mail—and the big question is--how will that ecosystem evolve over the next 5 to 10 years to meet citizen expectations, build trust, and operate efficiently? 

The current state  

Before we look forward, it’s helpful to understand where many governments are today—and what’s holding progress back. 

Channel fragmentation and legacy systems 

Many government agencies developed their channels—web portal, phone center, physical offices, mail, etc.—in silos, often tied to legacy IT systems or department boundaries. That leads to fragmentation. Citizens may start an application on a website, get stuck, and have to go to a physical office or call a hotline. That handoff is often awkward and disconnected. 

Channel shift and self-service pressures 

Governments often aim to “shift” users from assisted or in-person channels to digital self-service channels. That is sometimes measured via a “channel shift KPI”—the share of interactions handled online (or “self-service”) versus via in-person or call channels.  

The appeal is clear: digital channels scale better, cost less per interaction, and can be available 24/7. But there’s always a base of users who need—or prefer—high-touch support--because of complexity, accessibility, language, or trust issues. 

Rising citizen expectations, trust, and adoption gap 

Citizens expect experiences analogous to private-sector digital services, but adoption is uneven.  

In addition, satisfaction with online government services often lags the private sector by more than 20%.  

Internal process, culture, and change constraints 

Even when the vision is there, the execution hits resistance: legacy processes, staff unfamiliar with new channels, budget siloes, legal/regulatory constraints, risk aversion, and procurement issues. Transforming channels isn’t just technology—it’s changing workflows, roles, incentives, and culture. 

Emerging trends and the future of channels  

Let's look ahead--what are the forces and innovations shaping how governments will deliver services in the coming decade? 

AI, automation and conversational agents 

Artificial intelligence and automation are a central lever. Routine, high-volume inquiries or tasks—“What’s the status of my permit?” or “How do I renew?”—can increasingly be handled by chatbots or voice agents. Deloitte calls this an “AI-amplified future of work,” freeing human staff for more complex or discretionary cases. 

Behind the scenes, workflow automation can route, validate, and even auto-resolve cases. This reduces human error and accelerates response times.  

Predictive analytics can also anticipate bottlenecks—if filings surge in a region, the system could proactively allocate more resources or roll out an “express lane” channel. 

Channel orchestration  

Rather than independent silos, future channel delivery will be orchestrated--the citizen can start in one channel and continue in another with full continuity (e.g. start on mobile, pick up with an agent, finish in a physical office). 

Designing for channel continuity requires shared session context, identity/authentication, stateful case tracking, and standardized APIs across systems. 

To wrap 
The future of government channel delivery is not about choosing digital over in-person, but about orchestrating a rich, secure, inclusive, and seamless ecosystem of channels—powered by AI, embedded in everyday life, and designed around citizen needs. 

What we mean by “Digital Government 2.0” 

“Digital government 2.0” isn’t just tech procurement. It’s a shift in how government works: 

Citizen-centric design — services built around life events: birth, starting school, moving, starting a business, retirement 

Shared platforms — identity, payments, notifications, document exchange that any department can plug into 

Data as infrastructure — high-quality, governed data with clear ownership and audit trails 

Secure-by-default — zero-trust architectures, privacy engineering, and verifiable logs 

Delivery culture — multidisciplinary teams shipping small, learning fast, scaling what works 

Digital is no longer a channel--it’s the method. 

 
Five predictions for 2030 

 Let’s time-travel to the near future.  

Prediction 1: Government AI copilots become mundane—and that’s good 
Clerks and analysts will use AI copilots for drafting letters, summarizing case files, and routing inquiries—always with human accountability. The win isn’t sci-fi; it’s cycle-time: decisions in days, not months. 

Prediction 2: Digital identity goes mainstream, with privacy controls citizens can see 
Think secure sign-in that travels across services, plus consent dashboards showing who accessed your data and why. Expect strong authentication (passkeys), granular consent, and “data receipts.” 

Prediction 3: Services become “event-triggered” 
Instead of applying for everything, citizens will get proactive offers when the system knows they’re eligible—like childcare benefits after a birth is registered—opt-in, transparent, revocable. 

Prediction 4: Interoperability beats modernization 
We won’t replace every legacy system; we’ll wrap and route. Lightweight APIs, data catalogs, and canonical schemas will let old and new systems talk without million-dollar rewrites. 

Prediction 5: Trust becomes the KPI. 
Yes, we’ll still measure cost and speed. But the north star will be trust—privacy incidents down, resolution times down, satisfaction up. Publish the metrics. Earn the confidence. 

Myth vs. Reality 

 Myth: “AI will replace frontline staff.” 
Reality: It will augment them—freeing time for judgment calls and complex cases. The value is quality + equity, not headcount reduction. 

Myth: “We need a big bang system replacement.” 
Reality: Modernization via thin slices wins: wrap legacy, expose APIs, migrate workloads incrementally. 

Myth: “Privacy and innovation are a trade-off.” 
Reality: Privacy engineering—differential privacy, role-based access, encryption at rest and in transit—enables innovation by making it safe to connect data. 

 
The risks 

As always I will give you both sides of the story. 

 
The risks are real: 

Algorithmic bias — require bias assessments, publish model cards, enable human overrides. 

Vendor lock-in — insist on open standards, data export, and exit plans. 

Security debt — patch cadence, red-team exercises, and tabletop incident drills. 

Digital divide — blend online, phone, mail, and in-person options; fund community intermediaries. 

 The future of digital government isn’t a shiny app. It’s an operating system for the public interest—compassion baked into code, accountability baked into data, and services that work the first time, every time. That’s the bar. 

Why cross-functional teams matter 

Let’s start with the “why.” 

AI projects in government aren’t like rolling out a new app in Silicon Valley. A model that predicts traffic congestion, or flags fraudulent tax claims, or helps prioritize emergency services—these are high-stakes solutions. 

If you only put data scientists in a room, you’ll get technically sound models, sure—but they may not align with policy, may not respect privacy laws, or may simply confuse end-users. 

Cross-functional teams bring all the key perspectives together. It’s about ensuring technology serves the mission and the citizen, not the other way around. 

 
Who should be on the team 

Think of it like building a bridge—engineers alone can’t do it. You need city planners, safety inspectors, and yes, the people who will walk across that bridge every day. 

For government AI, here are the core roles: 

• Product Manager – They create the product vision and make all the product decisions 

• Policy Experts and Legal Advisors: They make sure the solution complies with laws, ethical standards, and public mandates. 

• Data Scientists and Engineers: They design and train the models. 

• IT and Cybersecurity Staff: They ensure infrastructure is secure, resilient, and scalable. 

• Frontline Workers or Service Staff: These are the people who actually interact with citizens—whether it’s call-center staff, social workers, or inspectors. They help ground the solution in real-world workflows. 

• Change Management Specialists: Because let’s face it—AI adoption is as much about people as it is about code. 

• Citizen Voice: Whether through advisory panels, user testing, or surveys, the public perspective must be heard. 

That combination of expertise is what turns an AI project into a real public service. 

 
Overcoming roadblocks 

Government projects often stall because of silos, risk aversion, and unclear accountability. 

Some ways to overcome this are: 

• Shared Goals and Metrics. Instead of each department measuring success differently, define one mission metric. For example, “reduce wait times for benefits by 30%” rather than just “deploy an AI chatbot.” 

• Agile, Not Just Waterfall. Cross-functional teams thrive when they can test, learn, and adjust. Pilot projects with limited scope are less risky and build confidence. 

• Transparent Communication. Regular stand-ups and open documentation keep everyone aligned. It’s amazing how many issues disappear when legal, IT, and data teams actually talk every week. 

To wrap 

• Start small. Don’t aim to “AI-ify” an entire department. Begin with one process, one workflow, one citizen experience. 

• Form a core team. Pick one policy lead, one technologist, and one frontline worker. Expand as you go. 

• Invest in trust. Create spaces where people can challenge assumptions without fear. Government culture can be hierarchical, but innovation requires openness. 

• Celebrate wins. When a small pilot reduces paperwork time by 15%, shout it from the rooftops. Momentum matters. 

AI isn’t about replacing public servants—it’s about empowering them. When governments build cross-functional teams, they don’t just deliver technology.  

They deliver trust, transparency, and better outcomes for citizens. 

In this episode, we’re going to explore: 

• Why citizen experience is now the north star for government transformation, 

• How AI is being used to improve accessibility, responsiveness, and trust, and 

• What agencies can do right now to start their AI journey responsibly. 

The new expectation  

Citizens today expect their government to be as responsive as their favorite app.  

They want fast, accurate answers — anytime, anywhere. 

The problem?  

Government systems weren’t built for that. Many are decades old, scattered across silos, and full of friction. 

Here is where AI steps in. AI can help government deliver what I like to call “service at the speed of life.” That means anticipating citizen needs, responding instantly, and simplifying complex processes. 

For example, when a citizen applies for a permit, pays a tax, or asks a question — they don’t care about which department owns what. They just want a clear answer and an easy experience. 

AI gives governments the tools to provide that — by connecting data, automating routine tasks, and delivering personalized, human-like interactions 24/7. 

 
How AI is transforming the citizen experience 

Intelligent Virtual Agents. 
These are more than just chatbots. They use natural language understanding to handle thousands of different questions — from “What’s my application status?” to “How do I renew my license?” 

In Canada, for example, several departments are using AI agents to reduce wait times and free up human staff for more complex cases. In some cases, citizens get answers in seconds instead of waiting hours on hold. 

Predictive and proactive services 
Imagine a world where the government not only responds to citizens, but anticipates their needs. AI can analyze patterns to identify when someone might need help — like reminding a senior about a benefit renewal, or notifying a driver that their license is about to expire. 

This is where AI can make government feel truly human — not by replacing empathy, but by enabling it at scale. 

Accessibility 
AI-powered translation, voice recognition, and text simplification tools are making government services more inclusive than ever before. Whether someone speaks a different language, has low vision, or struggles with complex forms — AI can bridge that gap. 

When governments use AI this way, they’re not just improving efficiency — they’re building equity into the system. 

Building trust and transparency 

Governments can’t simply roll out AI and hope for the best. Citizen trust is everything. And if AI is seen as secretive or unfair, that trust can erode fast. 

That’s why transparency is key. Citizens should know when they’re interacting with AI and understand how their data is used. 

Explainable AI — systems that can describe how they make decisions — will be crucial for maintaining accountability. 

Data privacy must be non-negotiable. Governments need strong governance frameworks that clearly define what data is collected, how it’s protected, and how it’s used to improve services — not to profile or exclude anyone. 

AI should never be about replacing human judgment, but about enhancing it — giving public servants better tools to serve people with speed, empathy, and fairness. 

Getting Started — A Playbook for Governments 

Step 1 — Start with the citizen journey. 
Don’t start with the tech. Start with the problem. Identify the biggest pain points for citizens — maybe it’s long call wait times, confusing forms, or inconsistent information. Then ask: how can AI help solve this problem? 

Step 2 — Build small, scale fast. 
Pilot AI in one service area. For example, an AI assistant for unemployment benefits or driver’s license renewals. Measure the results, get feedback, and scale from there. 

Step 3 — Train and empower employees. 
AI works best when public servants understand how to use it. Investing in digital literacy and AI training helps staff become partners in innovation, not victims of it. 

The Changing Role of Contact Centre Agents 

If we rewind 10 or 15 years, the job of a contact centre agent was often about following a script. You answered the call, read from the knowledge base, and handled routine requests. 

Today, automation and AI have taken over those routine, repetitive interactions. Customers reset their passwords online. They get status updates through a bot. They pay bills with an app. 

That means the calls that do reach a live agent? They’re harder. They’re higher stakes. And they almost always require judgment, empathy, and critical thinking. 

So the role of the agent has shifted from “transaction processor” to “problem solver, advocate, and brand ambassador.” 

Skill #1: Empathy and Emotional Intelligence 

The number one skill that matters right now is empathy. When a customer reaches a live agent, chances are they’re already frustrated. They may have tried self-service, the website, or a chatbot, and now they’re here—looking for help from a real human being. 

Agents who can listen actively, acknowledge emotions, and validate concerns make the biggest impact. Customers don’t just want the problem solved—they want to feel heard. 

That’s where emotional intelligence comes in: reading tone, sensing frustration or confusion, and adjusting your communication style to match the customer’s state of mind. 

In a world full of automation, empathy has become the ultimate differentiator. 

Skill #2: Critical Thinking and Problem Solving 

The second critical skill is problem solving. Because the easy questions—the “what’s my balance” type of queries—never make it to an agent anymore. What’s left are the complex issues that require judgment, creativity, and decision-making. 

That means contact centre professionals need to be comfortable navigating ambiguity. They need to know how to look beyond the script, connect the dots, and sometimes even challenge the process to do what’s right for the customer. 

It’s not just about answering questions—it’s about owning the customer’s problem until it’s solved. 

Skill #3: Digital Fluency 

The third skill set is digital fluency. Customers are omnichannel. They may start on chat, move to email, then pick up the phone. Agents need to be comfortable switching between platforms, handling multiple systems, and even working alongside AI assistants. 

Digital fluency doesn’t just mean using tools—it also means understanding how customers use digital. Agents who can guide a customer through a process online, explain how to use self-service features, or troubleshoot an app issue provide enormous value. 

The contact centre of today isn’t just about phones—it’s about navigating a digital ecosystem. 

Skill #4: Adaptability and Continuous Learning 

The fourth essential skill is adaptability. Let’s be honest—technology in contact centres is changing fast. New AI tools, new CRM platforms, new workflows. The half-life of skills is shrinking. 

The best agents today are those who can learn, unlearn, and relearn quickly. They’re curious. They don’t just resist change—they lean into it. 

And adaptability isn’t just about technology. It’s also about adapting to new customer expectations, new policies, even unexpected situations like service outages or crises. 

Leaders should be building a culture where learning is continuous and adaptability is celebrated. 

Skill #5: Communication Mastery 

Whether it’s voice, chat, or email, clear communication is the foundation of great customer experience. 

It’s about choosing words that build trust, explaining complex things simply, and avoiding jargon. And in digital channels like chat, it’s about striking the right balance between speed, accuracy, and tone. 

Agents who can communicate with clarity and warmth stand out—and customers notice.