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TTEC Digital Executive Interview

The Future of CX, AI, and Data Modernization with John Seeds, Global CRM Portfolio Leader, TTEC Digital

Customer experience is undergoing one of the most significant transformations in decades. AI is accelerating innovation, data is becoming both an asset and a liability, and organizations are struggling to modernize while keeping pace with rapid change. To explore these shifts, Sheri Greenhaus, Managing Partner of CrmXchange, sat down with John Seeds, Global CRM Portfolio Leader at TTEC Digital, for a candid conversation about the convergence of CRM, CCaaS, data, and AI and what companies must do to thrive in the next era of customer experience.

Seeds shares how TTEC Digital evolved from its BPO roots into a modern CX technology powerhouse, why AI is both misunderstood and under‑utilized, and how organizations can avoid the pitfalls that derail AI initiatives. He also offers a clear view of where the industry is heading, what companies should prioritize, and why security will become the next major disruptor in CX.

Sheri Greenhaus: Tell me a bit about TTEC.

John Seeds: TTEC has two major sides: our BPO arm and our Digital arm, which represents the systems integration and technology side of the business. The company started as a BPO 40 years ago, founded by Ken Tuchman who is still our CEO and Chairman. Around 2012, he realized the software agents were using “sucked,” and that we could deliver far better outcomes if we improved the technology behind the agent experience. That kicked off a series of acquisitions across major platforms.

I joined through the 2021 acquisition of Avtex, which brought CX consulting, data analytics, and IP to complement our technical implementation work. That combination became the foundation for launching TTEC Digital as a formal brand on March 2023.

Internally, the value proposition has been building for years. In 2022, we defined a vision around the convergence of CRM, CCaaS, contact center, data, and AI. We call it “The Badge,” a playbook that puts customer experience at the center of operations. Today, that convergence is happening everywhere, especially as hyperscalers deepen their role in AI.

Sheri Greenhaus: You mentioned Cisco and Genesys, but you haven’t acquired them. Do you partner with them?

John Seeds: Yes. We provide professional services. My CRM portfolio team includes experts in Salesforce, Zendesk, ServiceNow, and other platforms. We don’t resell licenses. We configure, integrate, tune, and optimize these systems so clients actually achieve the outcomes they expect. Many organizations struggle with poor integrations or unclear process ownership, and every partner brings its own flavor of AI. Activating AI in one system while pulling it through another can create tokenization and performance issues.

And this is where seamless context transfer becomes essential. Customers should never have to repeat their information to multiple agents — we’ve all experienced that frustration. That’s why we work closely with clients to understand how their systems connect, determine where data should flow, identify where AI should be activated, and build the steps needed to deliver a smoother, more consistent customer experience. AI finally gives us a real chance to achieve the visions we’ve been talking about for 15 years because MCPs now allow richer context transfer between systems.

Sheri Greenhaus: So, you sit down with an organization, map workflows, follow the data, identify pain points, and recommend where to start?

John Seeds: That’s our ideal scenario, but most clients aren’t there yet. Nearly half the world still runs on on‑premise contact centers. They want AI, but the first step is simple: stop trapping data in legacy systems that prevent it from flowing. You can’t move at the speed of AI if your technology is anchored in the past.

From there, our approach is grounded in practical, hands‑on consulting, not abstract strategy decks. We help clients modernize one channel at a time, improve IVR, integrate systems, and eliminate the need for customers to repeat themselves. And at the center of all of this is data: CRM exposes it, CCaaS collects it, but the data itself is what enables transformation.

Many organizations believe they must clean their data before they can use AI. We say the opposite: AI is one of the best tools to clean your data. It can surface duplications and inconsistencies faster and more accurately than any human or traditional application, especially within unstructured data and knowledge bases. If you ran a promotion six months ago, AI can instantly determine whether the system is surfacing today’s price or the outdated one and flag the discrepancy.

Companies are beginning to understand that continuous cleansing, surfacing, and analysis of their data is essential, particularly for unstructured information. And the good news is that this can be done within the frameworks they already have. There’s no need for a massive new “star destroyer” platform. With the right guidance and a few strategic tweaks, organizations can build data practices that scale and finally unlock the CX improvements they’ve been chasing for years.

A major part of our work is helping clients clean, organize, and structure their data so it actually supports the outcomes they’re trying to achieve. That means examining how new data is ingested, where it lives, and what happens when a process runs inside the contact center — what gets overwritten, what persists, and how those day‑to‑day decisions affect long‑term data integrity. Fixing data once isn’t enough; it’s outdated tomorrow. We focus on building systems that scale with the client’s operations and create an ecosystem they can trust.

And this leads directly to one of the biggest concerns we hear: reporting. In almost every recent client conversation, someone says, “We can’t trust our reporting.” One system shows one number, another system shows something different, and for years companies have relied on whatever reporting tool happened to be built into a platform. Those tools are no longer adequate because they don’t account for what’s happening across the rest of the environment.

That’s why modern data estates and data lakes have become essential. In environments like Azure, you build a centralized, modern data foundation where all business functions can run, and where data flows in and out of the application‑layer systems your users interact with. It’s the only way to get consistent, reliable reporting.

Sheri Greenhaus: If reporting isn’t correct, especially for public companies, doesn’t that create liability?

John Seeds: Absolutely. CX improvements often drive loyalty and reduce churn, but those benefits aren’t always immediately measurable. Liability, however, is measurable. If inaccurate data exposes a company to litigation risk, that becomes a major driver for funding modernization. In many cases, liability concerns accelerate investment more than anything else.

Sheri Greenhaus: We are starting to see frontline staff excited about AI because it saves time for applications such as automated wrap‑ups, pulling data from multiple systems. Do you see those inside organizations?

John Seeds: One hundred percent. That’s where platform‑level AI shines. Genesys and Salesforce are doing great work with wrap‑up automation and contact center functionality.

But AI initially came out with a narrative of massive cost savings — the idea that companies could replace people with AI. That hurt the industry. Companies let people go and are now hiring them back. AI augments; it doesn’t replace.

To help clients adopt AI safely, we often encourage them to start with internal help desks. The contact center is a rich environment for AI because agents juggle 9–12 systems and constant context switching.

Sheri Greenhaus: Some vendors say they’re building industry‑specific LLMs to reduce hallucinations. Will each industry end up with its own subset?

John Seeds: Yes. We’re already seeing micro LLMs or tiny LLMs. Vendors use foundational models — Gemini, Vertex, OpenAI, Claude — and tune them with industry‑specific context.

We’ve built these for clients. For example, a major music event venue company wanted to streamline RFP responses. We built an LLM trained on their RFPs, compartmentalized for their needs.

This will expand across industries such as banks, healthcare, retail — each with its own LLMs updated with laws, regulations, fraud patterns, and domain‑specific knowledge.

Sheri Greenhaus: Many AI pilots aren’t rolling out; they’re failing. Is it because things move so fast that without constant monitoring, everything falls apart?

John Seeds: Yes. That’s why we created AI observability.

We’ve built dashboards that monitor ethics, hallucinations, accuracy, drift, and performance. They answer the question: “Who’s watching the AI?”

Vendors are still catching up. Microsoft only recently released Agent 365 for Copilot monitoring.

Observability must also include cost transparency. Companies blow through AI budgets because no one understands token costs. Every platform prices tokens differently, and consumption models make budgeting unpredictable.

We’re heading toward a world with multiple AIs, not one. Observability across all of them is essential.

Sheri Greenhaus: Companies rushed into AI without a plan. Now they’re slowing down, identifying real issues, and planning. Smaller companies seem to be doing better. Customer service isn’t improving. In fact, it seems to be getting worse.

John Seeds: Smaller companies can move faster because they’re not weighed down by massive legacy systems. But we’re also seeing companies re‑enter buying cycles. They paused for fear of buying something AI would make obsolete. Now they understand AI better and are investing again.

Software companies that infuse AI and build MCP protocols will survive. Those that don’t will fade away. We’re seeing a more traditional technology advancement cycle take shape.

Sheri Greenhaus: Where do you think we’ll be a year from now?

John Seeds: Looking ahead, I think the next year will be defined less by individual AI breakthroughs and more by how deeply AI companies such as OpenAI, WhisperFlow, Claude, and others embed themselves into the CX technology stack. The “headless” trend won’t be a passing phase; it will permeate the industry. We’re already seeing movement toward agent desktops built on interfaces like ChatGPT, Gemini, Slack, or even Teams. Google is pushing Gemini as the operating environment itself. Salesforce wants Slack to be the front end for everything. Microsoft is doing the same with Teams. That shift will fundamentally disrupt the CCaaS market.

Sheri Greenhaus: If you had to give companies three “must do” actions to be successful, what would they be?

John Seeds:   

Must Do #1: Fix Your Data. Build a modern data estate. Create systems that scale with your data usage. This is essential for thriving in the AI era.

Must Do #2: Think Bigger. Too many organizations are still stuck in yesterday’s model of customer service. They haven’t fully embraced digital channels, and they haven’t leaned into modern capabilities like intelligent virtual agents (IVAs) or AI‑driven voice experiences.

Must Do #3: Lean into Partnerships. The talent doesn’t exist internally. These are new technologies and new ways of thinking. Partners bring depth across ecosystems and collaborate constantly.

Sheri Greenhaus: Anything important we haven’t discussed?

John Seeds: Security.

As AI interacts with your data and customers, security must enter the conversation in a new way. It will create new markets and new leaders. Cisco is resurging because of its Splunk acquisition and focus on secure CX.

Over the next twelve months, watch how security becomes central to CX, especially AI‑led CX. It will reshape the landscape.

Sheri Greenhaus: If companies can get their data right, think bigger about the experience, and choose the right partners, they’ll be in a better position to meet the expectations of their customers.