Deutsche Telekom Services Europe Romania: It’s not simply AI that will transform the Business Services industry

Authors: Martina Welslau, Finance & HR Director; Mădălin Popescu, Managing Director 

There is no shortage of enthusiasm for AI. Everywhere we look, across industries, organizations are deploying AI agents and building portfolios of use cases. Investment is accelerating, expectations are high and (almost) all executive leadership is asking how quickly AI can deliver productivity and growth.

However, the current AI hype cycle suggests that our expectations are moving faster than our ability to create sustainable business value. Gartner’s latest analysis on hype cycles places many agentic AI technologies around the early stages and peak of inflated expectations. From here on, adoption is going to accelerate, yet Gartner also observes that today’s focus remains largely on incremental automation rather than radical transformation. More to the point, fully autonomous agents are not ready for most enterprise use cases and the hype around „agentizing” is making it increasingly difficult to distinguish actual new capabilities from relabeled legacy automation technologies.

How do we interpret this as business leaders? Does it mean we need to slow down? We think it is an argument for becoming more deliberate of where and how we accelerate. This requires us to look beyond AI.

GBS was always changing

Our industry has spent decades learning how to make work more efficient, this is our DNA, our rasion d’être. Our model of consolidating, standardizing, automating, and creating economies of scale, has delivered considerable value and it will continue to do so. But, at the same time, expectations are broadening. Our customers ask for next-generation capabilities, above seamless customer experience, digital transformation, and constant innovation. These demands create an interesting tension, as GBS cannot abandon the discipline that made it successful to begin with. But maybe it can change the way we perceive it: costs, productivity and standardization are becoming the foundation and not as much the destination.

The value is in the workflow

Companies seem to approach AI in the same way we approached the earlier ways of automation: identify a task, apply the right technology and calculate the efficiency gain. If

we apply the same strategy to AI, we can create value, but it is unlikely to produce sustainable transformation. This is a fact. Recent Deloitte research illustrates the gap with almost half of respondents introducing AI without redesigning the workflows or role around it and only 12 percent redesigning at scale with a new operating model. Agentic AI adds even more pressure because the technology is moving beyond assistance to being capable of end-to-end workflows. We believe our collective GBS mindset needs to shift from automation to orchestration. The value of an individual agent is limited, while the greater opportunity comes with specialized agents working across enterprise systems, interacting with established automation, involving people where judgement and accountability is requires and coordinating with other agents. GBS has the expertise, the experience, and the foundation, it can become the environment where agentic enterprise is actually built.

For leaders this also changes the conversation around investment. The business case goes beyond licenses, agents, or efficiency. Rather, we need a deeper holistic understanding of whether AI improves cycle times, quality, customer outcomes, risk, scalability, and ultimately the economics of service.

People remain the operating system

Another important aspect to consider is what we do with the released capacity. If an AI agent performs activities that previously occupied a large part of someone’s working day, the leadership shouldn’t only ask how much capacity was saved, but also what to do with it. This is another reason why we need to move the conversation beyond efficiency, to value. For GBS this creates a pivotal opportunity. The expertise and knowledge our teams have accumulated can be redirected toward higher-value work: solving complex problems, improving processes, identifying new opportunities, working more closely with customers, and helping the business grow. This transition is, of course, not without risks. Releasing capacity without a plan can result in either unrealized productivity or simply another cost-reduction exercise. We need to actively reskill people for the work we want them to take on next, to develop their capabilities in areas such as process design, data and AI literacy, critical thinking, customer orientation and the ability to work with and supervise intelligent systems. The goal is not to replace the humans but to place them where they can create the most value. Getting there, however, is a shared responsibility. Leadership needs to provide clear direction, manage the change deliberately and create opportunities for people to build the needed capabilities. At the same time, employees need to be open to the change and have a proactive role in shaping their own professional development.

Beyond the peak of expectations

Every transformative technology, starting with the steam engine, goes through a period in which expectations run ahead of reality, and AI will be no exception. For GBS, the most important question is what remains after the hype recedes. Our belief is that it will be a fundamental change in how work is organized and our next step should be to evolve from shared services to orchestrated enterprise capabilities. This requires end-to-end process redesign, investment discipline, new capabilities, governance, conscious decisions about data sovereignty and, above all, leadership.

The next chapter of GBS will not be written by AI alone. How we deliberately turn its potential into value is where we will leave our mark.