---
title: "What happens to the payback period if fewer customer queries are automated than expected?"
url: "https://pegamento.nl/en/contact-center/what-happens-to-the-payback-period-if-fewer-customer-queries-are-automated-than-expected/"
lang: "en"
type: "post"
description: "Payback period longer than expected? Discover how a disappointing automation rate affects your business case."
last_modified: "2026-10-21T09:38:53+00:00"
categories: [Contact Center]
translations:
  nl: "https://pegamento.nl/contactcenter/wat-gebeurt-er-met-de-terugverdientijd-als-minder-klantvragen-worden-geautomatiseerd-dan-verwacht/"
---

# What happens to the payback period if fewer customer queries are automated than expected?

If fewer customer queries are automated than expected, the payback period of your investment increases immediately. The extent of this extension depends on how far the actual automation rate deviates from the forecast and which cost savings are lost as a result. This article answers the most frequently asked questions about payback period, automation rate, and what to do if the business case does not turn out as planned.

## How do you calculate the payback period if the automation rate is disappointing?

The payback period for disappointing automation is calculated by multiplying the actual savings per automated interaction by the realized volume, and comparing that against the total investment. If you achieve 30% automation instead of the planned 50%, you also earn back proportionally less per month, making the payback period longer.

In concrete terms, the calculation works as follows: suppose you have 10,000 customer queries per month and the average handling costs per query are 8 euros. With 50% automation, you save 40,000 euros per month. With 30% automation, that saving drops to 24,000 euros per month. If your investment is 200,000 euros, the payback period changes from five months to over eight months.

It is important that you also take indirect savings into account in your calculation. Fewer repeat contacts, lower error costs, and reduced pressure on employees all count, even if the direct automation rate lags behind. A [thorough business analysis](https://pegamento.nl/en/business-analysis/) helps you map out this broader value.

## Which factors determine whether the automation rate meets the forecast?

The automation rate in customer contact is determined by the quality of the available data, the complexity of the queries, the integration with backend systems, and customer behavior. If one or more of these factors are disappointing, the desired percentage will not be achieved.

The most important factors at a glance:

- **Data quality and training data:** AI models perform better when they have more representative examples. Incomplete or inconsistent historical data reduces accuracy and thus the automation percentage.

- **Complexity of customer queries:** Simple, repetitive questions are well-suited for automation. Questions with a lot of context, emotion, or exceptions require human handling.

- **Integrations with source systems:** A chatbot or virtual assistant can only fully automate if it has access to customer data, contract information, and statuses. Missing integrations severely limit the automation rate.

- **Customer acceptance:** Customers who prefer human contact will bypass automated channels. This lowers the actual share of automated handling.

- **Process design:** A poorly designed conversation flow or unclear escalation routes cause automation to break off sooner than necessary.

When you know in advance which factors pose the greatest risk, you can take targeted measures before the automation goes live. This prevents a disappointing rate from being a surprise afterwards.

## When is a longer payback period still acceptable?

A longer payback period is acceptable if the investment provides strategic value in addition to direct cost savings, such as better accessibility, higher customer satisfaction, or addressing staff shortages. Payback period is then only part of the total consideration.

There are situations in which a longer payback period is consciously accepted:

- The organization is struggling with structural staff shortages and cannot handle the current volume in any other way.

- The reputational damage of long waiting times or poor accessibility outweighs the delayed cost savings.

- The investment creates a technological foundation on which future automation can be expanded more easily and cheaply.

- The automation rate grows as the system processes more data and is better trained, which improves the payback period over time.

A payback period of twelve to eighteen months is considered acceptable in most service organizations, especially if the qualitative benefits are measurable. If it becomes longer than two years, it is useful to reconsider the scope or approach.

## How do you adjust a business case if the forecast is not met?

If the automation rate lags behind the forecast, you adjust the business case by first identifying the cause, then recalculating the benefits based on actual figures, and finally determining which adjustments can still increase the rate. Adjusting is not a failure, but a sign of mature program management.

A practical step-by-step approach:

- **Analyze the deviation:** Which questions are not being automated and why? Is it a technical problem, a process bottleneck, or customer behavior?

- **Recalculate the benefits realistically:** Use actual volumes and handling percentages instead of assumptions. An honest business case is worth more than an optimistic one that is incorrect.

- **Identify quick wins:** Are there sub-processes or specific question categories where the automation rate is high? Increase the reach of those successes.

- **Adjust the scope:** Sometimes it is wiser to concentrate the investment on the questions that are best suited for automation, rather than continuing to manage a broad but poorly performing system.

- **Communicate internally with transparency:** Stakeholders who agreed based on the original forecast are entitled to an updated view. Transparency strengthens trust for the next phase.

## What is a realistic expected automation percentage for customer contact?

A realistic automation percentage for customer contact usually lies between 20% and 50%, depending on the type of organization, the complexity of the queries, and the maturity of the technical infrastructure. Higher percentages are possible but require a strong data foundation and well-designed processes.

Simple, well-structured questions, such as opening hours, status updates, or frequently asked questions, lend themselves to automation percentages of 60% to 80% within that specific question type. More complex questions with a lot of context or emotional weight often remain below 20%. The total percentage is therefore a weighted average across all question categories.

Organizations that are just starting to automate customer queries often start with a rate of 20% to 30% and gradually build it up as the system processes more data and processes are refined. This is a healthy and manageable approach, even if it means the payback period in the first phase is longer than eventually necessary.

Do not expect 80% automation in the first year if your organization is still working with fragmented systems and incomplete customer data. The automation rate grows along with the quality of the underlying infrastructure.

## How Pegamento helps with a realistic business case for customer contact automation

We help organizations to map out the ROI of contact center automation in an honest and well-founded way, including the risks if the automation rate is disappointing. Our approach combines advice, technology, and execution under one roof, so you don’t have to deal with multiple suppliers pointing at each other if results lag behind.

What we specifically offer:

- **Business analysis and feasibility test:** We map out which customer queries are truly suitable for automation and what percentage is realistic for your situation.

- **Honest business case with scenarios:** We don’t just calculate the optimistic scenario, but also what happens if the automation rate turns out 10 or 20 percentage points lower.

- **Phased implementation with proven modules:** No costly custom work, but a smart combination of proven building blocks that are deployed step-by-step based on what provides immediate value.

- **AI and Agentic AI for customer contact:** Our AI solutions go beyond simple chatbots. Agentic AI, the evolution from executive automation to self-thinking assistants that take independent initiative, increases the automation rate even for more complex question patterns.

- **Continuous optimization:** After going live, we actively monitor the automation rate and adjust where necessary, so that the business case proves itself in practice.

Do you want to know what a realistic return is for your organization and how to keep the payback period manageable? [Contact us](https://pegamento.nl/en/contact-us/) for a non-binding conversation.

## Frequently Asked Questions

###
How do I know if my forecast for the automation rate was realistic?

Compare your initial assumptions with the actual distribution of customer queries: are the question categories you estimated as automatable actually simple and repetitive? If your forecast was based on general benchmarks instead of an analysis of your own customer contact data, there is a high chance that the assumptions were too optimistic. A good check is to measure the accuracy per question category separately, so you can see where the system is and isn’t performing.

###
What are the most common mistakes when creating a business case for customer contact automation?

The most common mistake is using a single, optimistic scenario without taking into account disappointing automation rates or delayed integrations. In addition, indirect costs such as project management, training, and ongoing maintenance are often underestimated, while they can significantly extend the payback period. A third common mistake is not including customer acceptance as a variable: if customers structurally bypass automated channels, this has a direct effect on the realized savings.

###
How can I actively increase the automation rate after the system has gone live?

Start with a thorough analysis of the questions the system is currently still forwarding to an employee: often these are questions that could still be automated with a better conversation flow or an additional integration. In addition, ensure a continuous training process where new question patterns and customer formulations are added to the AI model. Small adjustments in the process design, such as clearer selection menus or proactive information provision, can already increase the automation rate by a few percentage points without major additional investments.

###
What do I do if internal stakeholders lose confidence because the automation rate is disappointing?

Be proactive and transparent: present an updated business case with actual figures and a concrete plan to improve the rate, instead of waiting for stakeholders to ask questions themselves. Also mention the results that are being achieved, such as higher customer satisfaction or reduced pressure on employees, so that the broader picture remains visible. You restore trust fastest by showing measurable improvements in a short period of time, so consciously choose quick wins that deliver quickly demonstrable results.

###
Does it make sense to invest in Agentic AI if my basic automation is already disappointing?

That depends heavily on the cause of the disappointing basic automation. If the problem lies in incomplete integrations or poor data quality, Agentic AI will not solve that and it is wiser to first strengthen those foundations. If the automation rate is lagging because questions are too complex for a simple chatbot, Agentic AI can indeed make the difference: self-thinking assistants can handle multi-step processes and independently retrieve information from multiple systems. Get advice on which layer of automation provides the most return for your specific question mix.

###
How do I measure the indirect savings of customer contact automation if the direct automation rate is disappointing?

Look at measurable indicators such as the decrease in repeat contact (customers calling multiple times about the same subject), the average handling time for employees who now have to answer fewer routine questions, and the error rate in manual processing. Even with a lower automation rate, the system can support employees with relevant customer information, reducing the average call duration. Document these effects quantitatively so you can include them in the adjusted business case as a supplement to the direct cost savings.

###
When is it wise to limit the scope of an automation project instead of continuing to aim for a high total percentage?

Scope limitation is wise if it turns out that a large part of the question volume consists of complex, context-dependent questions that structurally show high escalation rates. By focusing on the question categories where the automation rate is high, you achieve a positive business case faster and build internal trust for a subsequent expansion phase. A narrower but well-performing system delivers more than a broad system with an average low accuracy, both in terms of costs and customer experience.