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Email automation: Beyond the hype

Email automation
Email has long been viewed as too difficult to analyse and automate, given its unstructured format and dependence on human understanding. However, due to the latest advances in natural language processing, the automation and mass understanding of repetitive, low-value email is within reach.  

Email is a huge cost centre for business. Just below phone calls, it is the most expensive channel to manage and process - with an average cost of £4.55 per email. If that doesn’t seem significant, consider that the average employee spends three and half hours of the working day just in Outlook. Over the course of a year, the cost of manual email processing can be as high as £10,000 per every single employee. 

Harder to measure, but more severe, is the indirect impact email has on innovation, new business and value creation. It constrains productivity and growth as skilled employees are forced to spend their valuable time simply reading and responding to these messages. Nearly a third (30%) of workers say it is their biggest distraction from real work.

There are many strategies that employees can use to more efficiently manage their inboxes. Yet none address the underlying problem. Manual email processing - especially of basic, low-value requests - is rarely a good use of time. Making the process more efficient just enables more emails to be sent, read and replied to. Business’s unhealthy relationship with email continues regardless.

Ultimately, there’s only so much that employees and email providers can do to ease the burden of email. To free employees from the inbox, businesses have to step in with automation.

The barriers to email automation

The question is, can email actually be analysed and automated? Emails are processed today in much the same way they have been since the 1970s - manually, with a human on both ends of the workflow. It’s only very recently that a new generation of AI-based solutions has started to emerge which claim to automate the process of sending and receiving emails from end-to-end. 

Just how realistic are these promises, and is a new solution even necessary? Many change and transformation leaders have recognised the hidden cost of email. The most common approach has been to migrate these emails to a case management or workflow system. However, while this has given the process more structure and accountability, it’s failed to reduce the amount of work required. In fact, such systems can actually increase manual effort where employees are required to manually log case types and other forms of management information. 

On the other end of the spectrum, some change leaders have attempted to innovate, developing their own rules-based automation systems. Yet such projects more than often turn into costly failures before they can even be deployed. As a result, change leaders have become only more skeptical of email automation. Meanwhile, the business decides that throwing more people at the problem is the only solution to exploding email volumes.

The challenge faced by these and similar solutions is that successful email automation requires both understanding and execution. We have solutions that cover the latter process. An RPA bot, for example, could easily forward a message or respond with a predetermined canned response. The problem is there’s no guarantee that these actions will be the best ones for that message without some sort of underlying intelligence. Yet solutions for the crucial understanding phase have been much harder to find.

Natural language is the missing link. The vast majority of emails are communicated in freeform, unstructured and largely conversational natural language. This is fine for human employees, but machines have traditionally struggled to comprehend it. Even when exposed to pre-trained language models, most solutions fail to appreciate the unique context and demands of the specific use case, leading to incorrect decisions and bad outcomes.

NLP: Unlocking email automation

However, despite these challenges it’s important not to dismiss email automation. In the last few years, advances in natural language processing (NLP) have made the technology a game-changer in helping machines understand natural language. With today’s powerful NLP models, the end-to-end automation of low-value repetitive requests has become viable for the first time.

With the advent of Transformer-based architectures, we have seen the emergence of giant language models able to train on truly vast amounts of data. Alongside the expansion of computing power, this has led to the creation of models that are sophisticated enough to reliably understand email communications. In fact, the latest general language understanding evaluations show that NLP models now routinely and consistently outperform humans in reading comprehension tasks.

The practice of Active Learning also produces models that are more reliable and valuable within their business context. This describes the training of models when human subject matter experts (SMEs) are present to monitor and correct predictions before the model is deployed. By absorbing the knowledge and experience of SMEs, NLP models gain an appreciation of the unique business context. This enables them to recognise the intent behind specialist language and jargon, helping them respond appropriately to messages and take the next best action every time.

Closing the loop: NLP meets automation

Of course, process understanding is only the first part of successful email automation. Once you have detailed visibility into a comms-based process, you can see what parts of the process are redundant and what is repetitive and transactional. You should then find ways to eliminate the unnecessary procedures, removing them from the workflow, and to automate those repetitive parts of the process. 

However, to execute the appropriate response to a message, an additional automation component has to be present to carry out the action. That’s why the latest NLP-based solutions allow for easy integration with automation solutions like RPA. When working in concert, these technologies allow businesses to understand and then correctly action requests and emails, at speed and at scale.

Yet, expectations need to be tempered. Not every message can be handed off to a machine. Most emails are routine and repetitive, but some of the most important ones will need special expertise, more detailed work and further engagement with colleagues and customers. Typically, between 10 and 50% of communications processes can either be automated or eliminated - leaving the remainder of the work to human employees. 

There will always be messages that humans need to read and respond to, but the automation of low-level transactional emails is crucial to freeing them up for their most valuable work. 

Learn more about how the enterprise is using NLP and Conversational Data Intelligence to eliminate inefficient requests and automate transactional conversational work.

How NLP completes the intelligent automation stack

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