For Cresta’s Head of EMEA, Winning with Enterprise AI Means Scaling Employee Expertise

The traditional playbook for enterprise AI was to buy the most advanced model available. But with tight budgets and rising expectations for speed and scale, that approach can be too costly for many. Now, some experts say the "technology-first" approach is all wrong, anyway. Instead of a new tool, the winning differentiator is the tacit knowledge of a company's most experienced employees.

The leader behind this philosophy is Mark Meghezzi, Cresta’s Head of Europe, the Middle East, and Africa. With over a decade of experience in senior executive roles across the AI and telecommunications industries, Meghezzi has led large, global teams for private equity-backed AI firms and publicly traded giants alike. Today, he firmly believes the future of customer experience centers on scaling existing human expertise instead of replacing people.

"From my experience, the best answers tend to come from within the building. There's no one better at speaking to an AT&T customer than the person who's spoken to 10,000 customers, and knows how to navigate every conversation, fix the problem, and renew the contract," Meghezzi says. For contact centers, he sees the institutional knowledge held by their top performers as the untapped fuel for AI transformation.

Given the shifting landscape in Europe, a focus on internal expertise and its amplification by AI is particularly timely, Meghezzi continues. Driven by hard-won lessons from past innovation cycles, the standard tech adoption lag between the US and EMEA is shrinking.

• The cost of falling behind: Calling it a "bias to speed," he describes a market in which European leaders, influenced by American investors, have adopted a more optimistic approach, even as they navigate regional hurdles like works councils, unions, and rights with a nuanced, on-the-ground understanding. "What the last number of years have taught us in Europe is that time waits for no one. If you're not at the cutting edge of deploying, testing, learning, and trialing the best technology on the market, then your competitors almost certainly are," Meghezzi explains.

Meanwhile, AI adoption rates vary, with regulated sectors like healthcare and finance still proceeding cautiously. But the business case is too compelling to ignore for high-volume contact centers, Meghezzi says.

• A natural application: The productivity gains are so clear that even risk-averse industries are taking notice, Meghezzi says. "In customer contact settings that are high volume and high complexity, there is a natural application that will be hard to ignore, even if it is differently paced across different verticals." However, this inevitability also confronts executives with two fundamental challenges: the instability of any underlying technology and the classic build-versus-buy dilemma.

• First things first: Meghezzi's advice for navigating the complexity? Anchor every decision in what your business and customers actually need. "My guidance for decision-making is to first think about what's important to your business and to your customers. Work back from what you're trying to achieve rather than placing all of your bets on one specific vendor or technology stack," he says.

• A strategic calculation: On the "build vs. buy" question, Meghezzi's answer is straightforward: "As somebody who sits on the vendor side, the natural inclination is always to recommend buying. But the truth is that it won't always be the case." For him, the primary advantage of buying is speed. Here, the time to value is significantly faster than building a solution from scratch. "By and large, the reason companies are buying is because of the economies of scale, the ability for application-level providers to synthesize very big, very complex models into really useful use cases," he adds.

At its core, the framework is about scaling internal expertise, Meghezzi concludes. But the most essential ingredient for success is the quality of institutional knowledge within the organization itself. For him, the real promise of AI in customer experience lies in its ability to transform timeless management headaches—like coaching, training, and retaining top talent—into a scalable, company-wide advantage. Or, as Meghezzi puts it: "The art of what we can now do with AI is make every customer conversation that bit better, that bit nearer to the best agent or the best adviser. That was previously a challenging coaching, training, onboarding, attrition, and management job."