AI-Supported Business Email Automation with Summarize & Respond Features Powered by RAG

Many times when you open your email first thing in the day, you see an overwhelming number of unread messages piling up from clients, partners, or colleagues. As soon as you see all that, you realise just how long it is going to take to sort through it all, figure out what the heck is going on, and respond to each message. In some cases, depending on the amount of email received, the amount of time it would take to process the emails could take several hours (if you manually work through them). That is why traditional methods of processing emails by scrolling through emails with the intent of copying and pasting an email template for your business processes have become obsolete. Today, many businesses use technology to automate their email process using Artificial Intelligence (AI) — This includes Utilising Summarisation and Response (RAG), AI reads the emails for you, captures the most important elements, responds to all inquiries accurately, gives you the ability to integrate this process into your current workflows, and eliminates the need for any coding and programming.

"Using RAG reduces the amount of manual email processing by as much as 70% and returns the responses to the business in a matter of minutes rather than hours."

What is Email Automation using AI?

Core Benefits and Capabilities of Email Automation Using AI

As an organisation, AI-based email automation is a system that allows the program to read and process all incoming emails without requiring a human being to read or analyse them. This provides a business a "fast" and "automated" way to communicate with customers while relieving staff from a significant workload. Email automation using AI enables companies to respond quickly to inquiries and limit workloads on support staff while providing customers with accurate responses in a timely manner. Email automation using AI automatically sorts incoming email inquiries by topic (e.g., sales, support, HR) and allows for a better organised collection of information. Long emails are condensed into brief summaries that allow people to be able to ascertain the important details of the email content in a timely manner. Also, any response to these emails will be in the same style and appropriate format. Any customer relationship management (CRM) system or other system can automatically be updated with the most recent client data through AI. More specifically, AI saves on average 15+ hours a week in email processing time, increases routing times to the client's original question by 60–80%, and improves communication quality resulting in increased customer loyalty.

For example, one e-commerce client uses to take 4 hours a day to respond to customer questions regarding order status. After implementing AI into his company processes, he has automatically processed 85% of simple customer questions about order status within 10 seconds, lowering average response times from 3 hours down to 5 minutes.

How Email Marketing and Communications are Revolutionized by AI

Newsletters were typically dull and template based. Now AI considers the entire interaction history to develop a personalized offer for each individual client based on how he/she interacts with the company and what his/her interests are. Therefore AI provides a personalized offer for each individual client with an email tone that fits the recipient's personality.

According to a 2024 McKinsey Report, AI is projected to increase email conversion rates by 25% to 40% through creating more personalized experiences for clients and creating more timely and relevant client responses. The B2B email sector benefits greatly from the AI process of responding to the original email inquiry within 10 minutes, which increases the likelihood of closing a deal by 3x compared to responding a few hours later.

AI will Scan and Summarise Emails

AI will Read and Highlight Important Parts of an Email (who, what, when, how much, and why?)

For example, a client sends an email that has been 400 words long, on Delivery Issues, and AI has condensed the message down to the following "Client Ivanov has not [received] [order #12345] ([Due] [10-15]) [Refund or Reship] [Phone Number Provided]". This [will] save a Manager [1 Minute] on every single email that comes through.

In Crypto Exchanges, AI Analytics had previously taken anywhere between [20-30 Minutes] to analyze queries from Traders using [News], [On-Chain Metrics], and [Social] Media including [Telegram]. Now, the process has been reduced to about [10 Seconds] allowing for [hundreds of times] faster [Response Times].

Automated Response — Creating [Speed] and [Relevancy]. {AI Selects Responses by itself using Contextual Data, Corporate Knowledge, and Historical Correspondence Data}. At the Same Time The Style and Tone of The Letter Will Be Adapted to The Client, as it will not use [Cookie-Cutter Templates]. The Average Time to Respond Has Gone From [Hours, to Minutes]. The Accuracy of The Responses (Responses That Did Not Need to Be Edited) Has Ranged From [80% - 90%]. For Complex or Technical Questions, AI Will Prepare a Draft For Review For The Manager To Complete in [20-30 Seconds], Rather Than [Writing From Scratch for Several Minutes].

A marketing agency that handles an extensive amount of commercial proposal requests saw AI-generated emails go from no time to almost no time to respond to customer requests after being trained for just one month. During that initial month, this agency processed 2400 customer requests, with only 79% of the emails needing any edits. Additionally, while the average response time dropped from three hours before implementation of AI-generated emails to a mere two minutes after implementation.

Retrieval-Augmented Generation (RAG) Technology Impact on Email Automation

RAG is the process in which the AI looks for the information it needs in source documents, such as knowledge bases, CRMs, etc., and generates an accurate response based on that information. Without using RAG, the AI only has access to basic knowledge and frequently provides incorrect or outdated information to its users.

RAG consists of two primary processes: the first step is Retrieval (gathering information), and the second step is Generation (creating an email response). The technology extracts relevant portions (or "fragments") of the information that will be used to generate an email response from documents, FAQs, Internal Regulations, etc., and uses that extracted data to construct an appropriate email response.

RAG is especially beneficial in B2B as the specifics of the email response (tariffs, terms, etc.) could be key elements in making a deal happen or not happen. If RAG was not available, the standard language model would give a generic response that could not effectively represent the B2B customer and thus may lead to a loss of trust in the AI-generated email response system. Stanford HAI (2024) reports that RAG reduces the error rate of AI-generated emails from 20-30% down to 3-5%, thereby significantly increasing the degree of trust a B2B customer feels in the AI-generated email response system.

Use Cases for RAG in Business

Logistics: Instead of spending 5-7 minutes responding to deliverables, a company now takes only 15 seconds to respond because of Rapid Data Retrieval through CRM and Tracking Service integration.

Integration between CRM or email systems with Artificial Intelligence (AI) allows for a more effective use of AI by incorporating an API that connects the two systems. This integration makes it possible to automatically update client details with AI, saving you time on data entry and decreasing the amount of errors you may encounter during this process. One example would be the following; if a client requests an order change, the AI will process that request, update the task within the CRM, and also change the deal status all in 20 seconds, while at the same time alerting the client. Gmail or Microsoft Outlook can establish API connections through OAuth (Open Authentication) protocols; therefore, everything is reliable and secure. Keys used to access these services are encrypted and securely stored.

Case Studies of Expanded AI-Powered Email Automation Applications with RAG

Growth in Sales and Customer Loyalty

AI enabled an industrial equipment selling firm to reduce its commercial proposal preparation time from 15 minutes to 2 minutes. The increase in conversion rates from 12 percent to 18 percent, and the increase in the average order value by 7 percent due to timely cross-selling recommendations.

A Fintech Company used RAG Automation to Automate Processing of Verification Requests

Prior to using RAG Automation for processing over 200 identical verification requests, an average of 4 hours was spent processing these verification requests, currently RAG Artificial Intelligence (AI) can process 5800 similar inquiries per month at a 92 percent level of accuracy without any operator intervention. The NPS of the Fintech Company increased from 45 to 62.

Optimization of Operational Processes

An AI agent resolved employee-related HR requests within 5 minutes with no operator intervention, whereas previously it took over 3 days due to having to rely on manual intervention between accounting and HR systems. In this way, HR professionals have been able to free up 12 hours of time per week to concentrate on more value-added initiatives. For an e-commerce company, during peak hours, an AI system handles approximately 70 percent of the typical requests related to orders. The average response time is reduced from 6 hours to 10 minutes, resulting in a 40 percent reduction in negative sentiment from customers.

FAQ: Common Questions Related to AI-Powered Email Automation

Accuracy of AI Agent's Responses

With the use of RAG, the accuracy level of responses to typical inquiries ranges from 85 to 95 percent, provided that the knowledge base is updated on a routine basis. For complicated inquiries, AI makes a draft response and provides that draft to an operator for refinement within approximately 30 seconds.

Do I need to be a programmer to set up this email automation?

No, ASCN.AI can be set up and used via the use of a 'no code' or visual interface application, so all you need is a token to connect an API. This makes it incredibly easy and fast to set up.

Security of Data

Data security is ensured by the use of encrypted tokens, data being sent via HTTPS, and secure data access through the use of OAuth 2.0. For highly regulated or sensitive industries, additional encryption or logging security features can be used.

What happens if AI does not know the answer to a request?

If an AI's confidence level is less than 70 percent regarding the answer, the AI will automatically assign the inquiry to a live operator, leaving the client with a notification on the approximate wait time.

Implementation Timeframe

Implementation timeframes for standard case scenarios are 3 to 7 days. Timeframes for more complex integrations may take up to 4 weeks depending upon customer requirements and specifications.