Balancing AI Innovation and Control in Service Ops
AI empowers service teams to respond faster to customer needs and streamline daily operations. But with rapid adoption comes new challenges.
Teams are struggling to unlock value and increase efficiency while simultaneously maintaining control of data and privacy. As AI reshapes customer experience and internal processes, how do you balance the benefits with the risks?
AI adoption in the workplace has reached a tipping point, with over three-quarters of professionals now using it daily, according to Splunk’s State of Observability 2025 report. Nowhere is this shift more noticeable than in service operations, where teams are turning to AI to improve speed and efficiency. In fact, a McKinsey study found that over half of organisations have boosted revenue and decreased costs since bringing AI into their service ops.
However, many leaders also say AI makes it harder to keep tabs on what’s happening behind the scenes. As AI plays a larger role in both customer experience and daily operations, it’s clear that making the most of these tools will require more than a “set and forget” mindset. In this article, we will look at some of the common considerations teams encounter when using AI to augment service operations, outlining key initiatives to manage risk and quality.
AI elevates the customer experience and redefines value
Ensuring that customers remain happy is one of the best ways to maximise revenue for your service and create lifetime value. Providing personalised, assistive experiences for customers is a sweet spot for AI, but the path to achieving that outcome isn’t always linear.
Do you approach AI implementation with a “big bang-style" delivery or take it step-by-step? Those who succeed often have a visionary north star, while also setting expectations for incremental delivery. For example, you can start by having AI support customer decisions about which products or service composition to choose based on their personal preferences and situations. In retail, this could be a decision support system embedded into customer-facing apps that makes suggestions such as "a three-course dinner with friends, one of whom is vegetarian," with a direct link to add the associated ingredients to your grocery basket.
But many organisations miss the full potential of AI by focusing solely on cost reduction and efficiency. When you measure AI’s impact only by what you save, you overlook its ability to drive customer retention, build out new business, and create richer, more valuable journeys that would not have existed otherwise. This narrow scope can leave significant value on the table.
Success with AI in service operations means capturing these higher order benefits. It’s not just about how much you save, but how much new value you create: are customers engaging, returning, and spending more because of a smoother, more personalised journey? True success is defined by continuously measuring these outcomes in real-time.
Most customer journeys today follow a limited set of predictable paths. In an online shop, for example, customers typically move from viewing, to adding items to a cart, and then to purchasing an item, following a pre-determined and highly optimized journey. As elements of this path are refined or replaced by an AI assistant or agent, organisations should not only rethink how the customer experience will evolve over time, but also how these changes will affect operational delivery. AI-driven interactions can raise expectations early in the journey such as speed, personalisation, and accuracy that the downstream organisation needs to meet consistently and cost-efficiently. Failure to align the end-to-end process with these elevated expectations risks eroding trust and undermining the very experience the AI was intended to improve.
Harnessing data insights as AI scales
As you introduce more AI assistants and agents into your customer experiences, you’ll see a sharp rise in machine data generated from agent-to-agent interactions. This data spike will require greater real-time visibility and the ability to correlate information across your systems to ensure operations run as planned. With the right strategy, these insights can reveal the ideal customer journey, highlight whether services are available, when and where they’re needed, and help quickly detect security or fraud risks.
In light of this imminent data influx, ensure you are prepared to take advantage of the related insights with a strategy that builds and expands your service visibility.
Empowering operators and managing quality and risk
Identifying and responding to negative customer experiences is critical, whether customers are using AI-enabled services or not. AI can automate detection, investigation, and response to customer issues, such as automatically baselining page load times of a customer facing application, raising alerts to operations teams when the experience is unusually slow. Agents can enrich these alerts, gathering up additional context about the upstream and downstream services to make it simpler for an analyst to triage, while also assisting them with investigations. To deliver on these types of experiences operations teams must address several key considerations:
- How much of your operations will be automated? Will they be fully automated or analyst assisted?
- Are you using AI to create better, more actionable signals for your operations team to act on?
- Will you redefine response playbooks with agentic workflows?
- Will you consolidate all your operations data into a single place or are you going to rely on agents to provide a holistic view across multi-vendor ecosystems?
- If you handle sensitive data, how will you ensure that agents have the appropriate permissions, so the right information is surfaced to the right analyst at the right time?
- Who will be responsible for operating and maintaining your AI assistants and agents?
Mapping your current and future state for adopting AI helps your team start from a place of preparedness and empowerment.
Successful AI adoption also depends on foundational elements: securing and observing your AI use to manage new risks like model jailbreaking, documenting supply chains and information flows (especially with external providers), and mapping agentic identities for investigation. For service quality, ensure your observability strategy includes AI solution availability and performance, implement synthetic testing for accuracy, tackle AI usage costs, and continuously optimise using machine data. Measure AI ROI to track if your strategy is delivering value for your customers and your business overall.
Success depends on thoughtful and methodical steps: redefining ROI, managing risk, and creating contingency plans. By laying the groundwork now, you’ll help boost your processes, bottom line, and customer satisfaction for the long term.
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