AI QuickCheck in Customer Care: Potential, ROI, and Roadmap in 5 Business Days

Artificial intelligence is transforming customer service not only technologically but also operationally: the reasons for contact are shifting, average handling times (AHT) are decreasing, costs per contact are changing, and teams must establish new processes, roles, and quality mechanisms.

In practice, this often creates a gap between “We want to use AI” and “We can confidently decide where AI is truly worthwhile.” Without a robust data foundation (volume, AHT, cost per contact, reason-for-contact logic, empathy vs. rule-based approaches), AI is either implemented too slowly (competitive disadvantage) or too quickly (misplaced priorities, acceptance issues, quality risks).

This is exactly where an AI potential analysis with ROI logic comes in: It identifies automation potential per contact reason, translates it into a robust cost-benefit analysis, and delivers an actionable roadmap (quick wins vs. medium-term initiatives)—as a door opener in 5 business days.

From projects at various levels of service maturity, he is familiar with the typical starting point: Many organizations neither have an accurate understanding of their AHT nor complete transparency regarding contact reasons, volumes, service levels, or FCR. That is precisely why his approach begins with structured data collection—and ends with a business case suitable for management.

The typical starting point that Karsten Kleiner addresses is characterized by mounting pressure to adopt AI (“We have to do something with AI”) without any clarity on where the greatest impact lies.

Transparency is often lacking because contact volume, channel mix, AHT, and cost per contact are only roughly estimated or inconsistent. At the same time, priorities remain unclear: voicebot, chatbot, or agent assist—which is best suited for which contact reasons?

Added to this is a quality and fit risk, because certain inquiries are highly personalized or empathy-driven—such as consultations or complex complaints—and should deliberately remain “human.”

Finally, friction arises in procurement and compliance because AI budgets are difficult to approve without a robust ROI case. Companies in this situation rarely look for “just another AI tool.” They’re looking for a basis for decision-making that is fast, quantifiable, and actionable.

The process is designed to work within a short assessment window and provide an executive summary at the end.

First , project information and basic KPIsare collected, specifically the total volume of contacts per month, the number of agents (FTE), the agents’ gross hourly rate, and—if available—CSAT, service level, and FCR. The goal is to establish a consistent baseline that supports the ROI analysis.

Next, the channel mix, including AHT per channel, is structured. Channel-specific AHT values are used for the ROI calculation—for example, for phone, email, and webchat; from these, the tool automatically derives a weighted total AHT as well as cost per contact per channel, calculated from AHT and the hourly rate.

In the third step, a full-cost model is defined to ensure that the ROI is not “embellished.” This model accounts for one-time consulting costs (analysis phase, implementation, hypercare), AI licensing and technology costs for voicebots, chatbots, and agent assist (either flat-rate or per-contact pricing), infrastructure and project overhead costs such as integration, change management/training, testing, and quality assurance, and ongoing operating costs for support and maintenance/updates, including annual cost increases.

In the fourth step, contact reasons are evaluated based on task logic, and recommendations are derived from this analysis. For each contact reason, the category, complexity, need for empathy, rule-based nature, and data availability are systematically recorded. Based on this, the tool recommends the appropriate solution: for example, a voicebot for highly rule-based inquiries, a chatbot for standard text-based inquiries, an AI Agent Assist to support agents in mixed cases, or, in highly individualized, less rule-based advisory cases, a human agent.

In the sample calculation within the e-commerce context of the case demo, key management metrics emerged that directly support decision-making: The automation potential was 70 percent—specifically, 11,150 out of 15,950 contacts per month. The net annual savings after costs amounted to 59k euros. At the same time, the analysis showed gross annual savings on agent costs of 260k euros (before license costs) as well as annual license and operating costs of 96k euros. The net ROI in Year 1 was 39 percent, with a payback period of 31 months. The effect was particularly evident in the cost per contact: it dropped from 5.14 euros today to 2.60 euros with AI, corresponding to a savings of 2.54 euros per contact.

Sensitivity Analysis: Three Scenarios for Decision Robustness

Instead of presenting just a best-case scenario, the tool provides a sensitivity analysis that objectifies discussions and makes risks transparent. In the conservative scenario, there was a net savings of 35k euros per year with a payback period of 52 months, based on 60 percent of the expected automation rate. In the realistic scenario, the net savings were 59k euros per year with a payback period of 31 months, based on the baseline assumption (100 percent implementation) and an assumed growth rate of 5 percent per year. In the optimistic scenario, net savings of 76k euros per year were reported with a payback period of 24 months, driven by faster adoption (130 percent) and 7.5 percent annual growth.

AI Potential Matrix: Clear Classification of What AI Takes Over—and What Remains Deliberately Human

The results are presented as a potential matrix that maps specific contact reasons to the appropriate solution types. One example of a voicebot use case is the shipment status inquiry, which typically involves high volume and is heavily rule-based. Typical chatbot scenarios include invoice explanations, password resets, checking product availability, or address changes. In the demo, the AI Agent Assist is shown to be particularly well-suited for order cancellations. In contrast, reasons for contact such as filing complaints about delivery delays, contract cancellations, technical error reports, or general product advice are best handled by humans. The key point, therefore, is not to “automate everything,” but to prioritize correctly.

Roadmap & Implementation: Quick Wins Instead of a Mammoth Project

Based on the results, a roadmap can be developed that is operationally realistic and produces rapid results. In the first 0 to 3 months, the focus is on quick wins—that is, high-volume, low-complexity issues with clear data, such as shipment status or password resets. In 3 to 6 months, use cases with medium complexity or higher integration requirements follow. Strategic topics with a timeline of 6+ months are methodically planned but were not prominent in the demo. Typical QuickCheck deliverables include an ROI case study with a cost model, a prioritized list of use cases, a rough implementation plan covering the pilot phase, shortlisting, proposal, and rollout, as well as export files (CSV/PDF) for further use in presentations and decision-making documents.

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