CityBiz Q&A

Originally published in citybiz
Dan O’Malley
CEO, Engageware

Engageware started as a pioneer in online appointment scheduling, but now the company positions itself as a leader in AI-powered customer engagement. What’s been the evolution and guiding philosophy behind that transformation? 

Engageware began by solving a hard operational problem: getting the right customer to the right expert at the right time. Scheduling was our initial product but we learned that great customer experience, especially in regulated industries, depends on more than a single appointment. Our customers asked us for help over the full journey: clear answers, the right handoffs, and confidence at every step. 

As enterprises started using real-world interaction and outcome data to rethink engagement, it became clear that AI could be a tremendous tool.  But AI couldn’t just be fast, it had to be accurate, context-aware, governed, and measurable in production. That’s the philosophy behind our evolution: enterprise-grade AI that improves outcomes while protecting the trust regulated organizations run on. 

Scheduling remains a core pillar, but today it’s part of a larger platform that connects AI customer agents, governed knowledge, and human expertise, so customers get the right answers, reach the right people, and get to resolution faster without sacrificing confidence. 

Engageware is often described as a leading provider of AI-powered customer experience for regulated industries. What does that leadership look like in practice?

In practice, leadership means three things: scale, governance, and trust. 

We operate at enterprise scale. Engageware has orchestrated 1B+ AI interactions and is trusted by 600+ organizations, including 25% of the top banks and 20% of the top telecoms in the Americas. And we’re not just automating FAQs. Our AI agents answer detailed questions, such as explaining a billing statement in detail, and take actions on a customer’s behalf, like transferring money. And when needed, we schedule time for customers with the right human experts. 

Scale matters, but trust matters more. Banks and other regulated institutions need customer engagement that’s secure, compliant, and consistent, even when it’s personalized. Our goal isn’t to replace human connection, it’s to make it more effective. The result: more issues resolved on the first try, fewer unnecessary contacts, and a better experience for customers. 

Engageware emphasizes a “human + AI” model in its agent/digital stack. How do you decide which tasks should be automated versus handed-off to a human? And how do you prevent automation from undermining trust in regulated environments? 

We start with a simple rule: automate what’s repeatable and safe, and escalate everything else to a human. 

AI handles high-volume, predictable work well — common questions, guided workflows, scheduling, and routing. When a case becomes ambiguous, high-stakes, or policy-sensitive, it goes to a human specialist, with the full context intact so they can resolve it quickly and confidently. 

Preventing trust erosion comes down to guardrails, clear handoff rules, and governance. In regulated industries like banking, insurance, and telecom, a single bad interaction can damage years of brand equity, so AI efficiency and human oversight have to work together. 

Data and intelligence are core to engagement. What role does unstructured data (documents, customer conversation transcripts, external signals) play in your roadmap? 

Unstructured data — chat logs, call transcripts, policy documents, web content — is where most customer intent actually lives. The opportunity is turning that raw data into insight that teams can act on with confidence. 

A key part of our approach is flexibility in how customers use it. Some organizations want answers drawn from whatever relevant content is available. Others, especially in regulated environments, prefer to answer first from approved, curated content, and only fall back to broader unstructured sources when needed. Our platform supports both models, so institutions can match their AI strategy to their risk tolerance and governance requirements. 

The goal is a system that doesn’t just match keywords. When a customer mentions “refinance,” it understands the context, connects it to that customer’s situation, and drives the right outcome: the right answer, the next best step, or a handoff to the right specialist. 

When you talk about serving “regulated” industries, what does that mean to Engageware? How is it different working with customers in those industries? 

In regulated industries, the bar is different. Trust, compliance, and accountability aren’t optional, they’re foundational. Banks, insurers, telecoms, and other regulated organizations operate in environments where every interaction, data exchange, and integration must meet high standards for security, reliability, and auditability. 

And it’s not just regulators, your technology choices have to withstand scrutiny from auditors and boards. That’s why we design for enterprise governance: policy alignment, traceability, and integration into the systems institutions already run, with complete visibility and control.  

In the crowded CX & engagement technology landscape, what differentiators does Engageware lean on, especially in regulated verticals like banking, insurance, and telecom?  

A few things set us apart. First, scale and production experience — we handle billions of interactions for large regulated organizations, and that real-world experience makes the platform smarter over time. Second, governance is built in from the ground up, not added as an afterthought. Compliance, auditability, and control are core to the architecture, which matters enormously in banking, insurance, and telecom. Third, we connect AI agents, appointment scheduling, and knowledge management across existing systems and ecosystems, without requiring costly rip-and-replace projects. The result is transformation that’s practical and measurable, tied directly to outcomes like lower cost-to-serve, higher conversion, and more consistent customer experiences. 

Many people are surprised to hear that adoption of AI-driven service agents in Latin America is outpacing the U.S. by several years. How did that happen, and what can U.S. financial and telecom companies learn from it? 

It’s true. Adoption of AI customer service agents in Latin America is three to five years ahead of the U.S. It may sound surprising, given U.S. leadership in technology, but profit per account in Latin America can be substantially lower than the U.S.  There is a greater need to drive down costs, and LATAM regulated industries have done a good job of adopting AI. 

That early adoption also meant earlier learning. We’ve seen the full spectrum of adoption patterns: what works, what doesn’t, and how AI can coexist with human service models in high-volume, regulated environments. As U.S. institutions accelerate adoption, the lesson is to anchor AI in operating reality: start with the use cases that matter, design governance upfront, measure outcomes relentlessly, and scale what works. Done right, you get efficiency and stronger compliance, trust, and ROI from day one. 

What’s the most challenging internal tension you manage when it comes to growth and innovation vs. risk control and legacy constraints? How do you navigate it? 

The tension between speed and certainty is constant. Our clients expect innovation, be it generative AI or proactive engagement, and they expect us to move fast. At the same time, they operate under strict regulatory, security, and operational constraints. 

We navigate this with principles: every initiative must deliver clear customer value and pass a risk and compliance lens before it scales. We run dual tracks: sandbox environments for experimentation, and hardened production modules for real-world deployment. We also design modularly so higher-risk components are isolated until they’re mature and safe. It’s creative agility under disciplined guardrails. 

Looking ahead 3 to 5 years from now, what change in customer expectations or technology do you think will force Engageware to reinvent itself? And how are you preparing?  

The next shift is toward anticipatory experiences that feel proactive and personalized. They’ll be driven by real-time interaction and outcome data while staying governed. That means predicting needs across key lifecycle moments and acting with precision. 

To do that well, you need more signals: real-time behavior data, third-party inputs, and adaptive feedback loops, alongside real-time orchestration. Enterprises will expect AI to be more flexible and more impactful, but also more controllable. 

We’re refining our architecture so it can absorb model advances without compromising governance. We’re continuing to build a modular core engine, investing in data infrastructure, and partnering where it accelerates learning, so customers can move fast and stay safe. 

To effectively serve customers in regulated industries, how does Engageware approach the onboarding process and the overall lifecycle of the relationship?

We treat onboarding as value realization, not implementation. The goal is to get to outcomes early and then expand with discipline. Our mindset is simple: real value isn’t achieved when you’re comfortable. It comes from being willing to say, “this isn’t good enough yet,” and then fixing it. 

That means starting with the highest-impact use cases, establishing governance from day one, and iterating using real-world interaction and outcome data. When that feedback loop is tight, adoption follows, and improvements compound across customer experience and operational performance.