Understanding Automation Rate: AI Agents and Customer Service Automation and What They Mean for Your Brand
27 Jan 2025
6.5 mins
Introduction to Automation Rate
Automation Rate refers to the percentage of tasks or processes within a system that are automated using technology or software solutions. In the context of business processes, Automation Rate measures the extent to which repetitive tasks are handled automatically, freeing up human resources for more strategic and creative work. Imagine a customer service team where routine inquiries, such as order status checks or FAQs, are managed by AI. This not only speeds up response times but also allows human agents to focus on more complex issues that require a personal touch. A higher Automation Rate indicates increased efficiency and reduced manual workload, leading to faster response times and improved overall productivity.
The AI Arms Race and the Lure of High Automation Rates
When exploring AI Agents for customer service, you’ll find various technology platforms boasting impressive automation rates. As a buyer, it’s easy to be drawn to the highest numbers. However, it’s crucial to scrutinize these figures carefully.
Let’s imagine that you’re looking into AI Agents for customer service. You keep seeing different technology platforms offering 50%, 60%, 70% automation rates for their customers from the get go. As a buyer, it’s tempting to be attracted to the platforms offering the biggest number.
But within that arms race to be the platform with the biggest number, as a discerning buyer of software, you should be putting as much scrutiny on these figures as possible during the buying process.
With that in mind, let’s explain what some of these numbers might mean, and share some tips and suggestions to shape your buying criteria.
Do you want to automate as much as possible?
To some of you reading this, the answer will be “Yes, obviously I want to automate everything I possibly can.” While to others you will be thinking “WOAH woah woah, I want to preserve as much of the human touch as possible”.
If that human touch is an important part of your brand promise then it’s only natural that you’ll want to take the automation promise more slowly. So the idea of automating 70%+ of your customer queries feels kind of crazy.
While automating a high percentage of customer queries may seem daunting, it can play a crucial role in enhancing productivity by allowing human agents to focus on more complex tasks.
The truth is that you could probably automate 100% of your customer service queries next week, but you’d cause as many problems as you’d solve. Doing it wrong can cost you; leading to you giving out free products, refunding at the wrong points, and even giving customers wrong answers that open you up to lawsuits.
Plus it’s true that, however good AI is at automating customer service queries, there are always moments when a human will win out. Maybe it’s because the person can be more creative in their response, can think of a workaround that an AI doesn’t know about, or has expertise that you cannot train.
So for most brands there is a sweet spot, somewhere between 0 and 100% automation. For a lot of brands we speak to it ends up being somewhere between 30-60%. Even at the lower end, when done well, that could represent the work of 6 people in a 20 person customer service team. That’s of course assuming that automation rate implies complete resolutions.
Decoding "Automation Rate" in Customer Service AI
Automation rate is a term, a bit like deflection rate, that can have different meanings depending on who is using it. Every platform will have its own definition of what that means.
At the very basic level, automation rate could mean that the AI has provided some sort of automated response to a customer. By this definition, an automated confirmation could be considered an “automation”. Of course it would mean that you get to 100% automation rate pretty quickly. But that’s not providing much value to anyone.
So therefore it could be the first “meaningful” automation. But what does meaningful really mean? This will obviously vary from platform to platform, but here is an example of a partial automation that is meaningful:
Imagine that you are complaining about an item that arrived damaged. An agent would typically ask for pictures or some sort of proof of damage before assessing whether a refund or replacement is in order. The issue is that over email or any other asynchronous chat, this process can delay the conversation and slow down getting to the bottom of things for the customer.
By automating routine tasks, businesses can significantly reduce manual errors, leading to faster and more accurate customer service.
An AI Agent could easily do everything up to the point of assessing (it can even do the assessing, but that is less easy). It can detect why the customer is reaching out, and then instantly ask for pictures or proof. This means that the agent has everything they need to make the assessment without the delay of going back and forth.
Of course it’s possible that for some providers, automation rate and resolution rate are interchangeable. So when a ticket is fully resolved by AI, and only AI, then the provider would count that towards automation rate.
It’s also possible that the headline automation rate relates to a subsection of tickets where certain things have been excluded. So the automation rate may mean that “Of all the tickets that were filtered through our platform, we automated this many”. Naturally there are some tickets that an AI agent would not be asked to address – for example sensitive topics – so these could be taken out of the count.
With so much uncertainty and a lack of standardization regarding these terms, it’s important to be crystal clear with any provider about their definitions.
Role of Machine Learning in Automation
Machine Learning (ML) plays a crucial role in automation by enabling systems to learn from data and improve their performance over time. ML algorithms can analyze vast amounts of data, identify patterns, and make predictions, allowing for more accurate and efficient automation. For instance, in customer service, ML can be used to predict common issues based on historical data and automatically provide solutions, reducing the need for human intervention. In business processes, ML can automate tasks such as data entry, customer service, and predictive maintenance, leading to improved accuracy, cost savings, and enhanced productivity. By continuously learning and adapting, ML-driven automation ensures that systems become more efficient and effective over time.
Quality vs. Quantity with Customer Service AI
Another differentiation is the quality of automation. Take these two ways of automating the same question: Where is my package?
Platform 1 may take the order number, look at a tracking number and send a tracking link to the customer to check themselves.
Platform 2 may check the warehouse management system or ERP, then check carriers, apply some business rules related to how long a package has been in transit, relay all the relevant information to the customer, and then offer a next step – a refund or replacement order.
In some situations, when a customer has merely lost their tracking number, platform 1 has done the job. In a lot of situations though, when a customer has their tracking number and is just wondering why things aren't happening, platform 1 is inadequate and platform 2 does the job, providing a more helpful and detailed answer.
It's possible that platform 1 will claim what they did was automation, even if the customer ends up escalating to a human. Therefore it's considered a success that pumps up the automation rate, depending on their definition.
Digital Transformation and Automation
Digital Transformation is the integration of digital technology into all areas of a business, fundamentally changing how it operates and delivers value to customers. Automation is a key component of Digital Transformation, enabling businesses to streamline processes, enhance efficiency, and improve customer experience. By automating repetitive tasks and manual effort, businesses can focus on high-value tasks, such as innovation, strategy, and customer engagement. For example, automating the initial stages of customer support can free up agents to handle more complex queries, leading to a more satisfying customer experience. This shift not only enhances productivity but also provides a competitive edge in the market, as businesses can respond more quickly and effectively to customer needs.
Automation Potential
Automation Potential refers to the potential for automation to improve business processes and increase efficiency. It involves identifying areas where automation can be applied, assessing the feasibility of automation, and evaluating the potential benefits. By analyzing Automation Potential, businesses can prioritize automation initiatives, allocate resources effectively, and measure the impact of automation on their operations. For instance, a company might start by automating data entry tasks, which are time-consuming and prone to human error, before moving on to more complex processes. By strategically implementing automation, businesses can achieve significant improvements in overall efficiency and productivity, ultimately driving better business outcomes.
The Cost of AI Automation: Understanding Pricing Models
Given most platforms charge per automation, this is important to get to the bottom of. Some platforms charge per message, others per ticket, others per resolution. Obviously each one is more valuable than the last, so naturally the price would be expected to go up.
But if you have a platform charging $0.10 a message, and another charging $0.50 per resolution, you could think you are getting less value for money from the latter. So unless you are clear on what exactly you are paying for with each provider you can get lost when making a conclusion.
Of course this also assumes that the different platforms are as good as one another at resolving issues, so that is something you will need to explore with demos.
How can you get to the bottom of things?
The simplest thing is to ask the provider to explain what these terms mean within their business, and compare that to any other providers you are speaking to.
You need to be clear about what each brand means otherwise you can be comparing apples to oranges.
When it comes to automation rate figures, it’s important to note:
Higher doesn’t always mean better
Quantity of automation doesn’t always mean quality
Automation rate might not be the same as resolution rate
Certain tickets which would never be automated may have been excluded
Charging per automation can lead to wildly different cost estimates
So take any number you see with a pinch of salt and dig a little deeper, and certainly don’t take any provider’s numbers as gospel without being clear about what they mean.
To learn more about how DigitalGenius can fully resolve customer service queries for you, take a look at our demo video here.
Future of Automation
The future of automation is exciting and rapidly evolving. Emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) are transforming the automation landscape. As these technologies continue to advance, businesses can expect to see increased efficiency, improved accuracy, and enhanced productivity. Moreover, the integration of automation with other technologies such as Internet of Things (IoT) and blockchain will enable businesses to create more complex and sophisticated automation workflows. As automation continues to evolve, businesses must stay ahead of the curve by investing in the latest technologies and developing the skills needed to succeed in an automated world.
