A survey of 150 customer-service and IT leaders shows a striking gap between the breadth of AI deployment and financial results. No respondent reported a significant cost reduction from AI, and two thirds reported higher costs. The cause lies not only in the technology: companies use too many platforms, have inconsistent governance and add new tools to operating models designed before AI.
The spread of AI in customer service does not by itself mean cheaper operations. A new TTEC Digital survey of 150 leaders in customer service, contact centers and IT shows almost the opposite of the expectations with which many companies introduced the technology.
None of the respondents said their organization had achieved a real reduction in costs thanks to AI. Two thirds instead reported higher operating costs. The study identifies the combination of an outdated operating model, fragmented data and weak technology governance as the main problem.
Sixty percent of the organizations surveyed use seven or more different platforms. Technical integration does not mean operational visibility, however. Only 43 percent of leaders were highly confident that they could clearly identify everywhere AI was being used across the customer journey. This creates room for duplicate tools and automation that nobody governs centrally.
Capabilities are another weakness. Ninety percent of managers are confident in deploying AI itself, but not a single respondent said the company had no gaps in internal capabilities. According to the study, more mature organizations keep AI strategy and governance in-house and use external partners mainly to accelerate technical implementation.
Governance is a separate problem. Only 29 percent of organizations use a formal cross-functional governance mechanism consistently. In 64 percent, rules are applied differently across the company. This is especially dangerous in customer service because one customer can move through marketing, sales, self-service and the contact center during a single journey.
The practical question, therefore, is not how many AI tools you operate but whether you have visibility across the entire customer journey. At every automated point, it should be clear who owns it, which data it uses, which business outcome it is meant to improve and how escalation to a human is handled.
Respondents listed modernization of data foundations, better coordination across functions and clearer linkage between customer experience and the company’s financial results among the main planned investment priorities for 2027.
KEY TERMS
- Customer-service operating model: The way processes, responsibilities, data and technologies are allocated when serving customers.
- Fragmented technology foundation: A collection of multiple systems that may be connected but do not provide one governed view of the process.
- AI governance: Rules for ownership, use, oversight and accountability for AI systems.
- Measurable return: A financial or operational result that can be directly linked to a specific technology deployment.