Use-cases for generative AI

Omnichannel customer communication

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As explored in the first post in this series, generative artificial intelligence (AI) is a ground-breaking technology that uses large language models (LLM) to create powerful tools for working with text, images, and video. Publicly available generative AI applications, like ChatGPT, can create text, images and computer code that is indistinguishable from human efforts. What’s more, these programs can complete their output in fractions of a second, providing a seamless, real-time experience for those that interact with it. This has the potential to revolutionize how we work, from digital chat assistants to automated data extraction and analysis.

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Generative AI is already showing its potential to transform the workplace. According to the Harvard Business Review, workers using generative AI tools:

  • finished 12.2% more tasks on average
  • completed tasks 25.1% more quickly
  • and produced 40% higher quality results.

Generative AI is still in its infancy, and while its use-cases will likely expand exponentially in the coming years, we will focus on one area where this technology is already having an impact – omnichannel customer communication. This refers to the practice of providing consistent and seamless interactions with customers across multiple channels and platforms, such as phone, email, chat, social media, web, or mobile.

In the modern, digital economy, omnichannel communication is the key to providing a high-quality customer experience (CX). Over 85% of online shoppers start a purchase on one device and finish on another.1 For example, they may research a product on a laptop, then buy it on their mobile. Customers don’t want to receive different information each time they switch channels or be forced to re-start the buying process from scratch. When it comes to customer service, companies that provide a consistent, joined-up experience across different channels retain 89% of their customers.2

However, omnichannel customer communication also poses challenges, including managing large volumes of customer queries, the pressure to provide fast responses across multiple channels, and the need to ensure quality and accuracy. In addition, a one-size-fits-all approach is no longer good enough. Customers don’t want stock responses, they want a personalized service, that reflects their unique preferences and behaviors, which can limit the scope of automation.



There are a number of ways that generative AI can be used to transform omnichannel customer communication, helping companies to improve customer satisfaction, boost loyalty and retention and ultimately drive revenue growth.


Generative AI can extract and summarize key information from large amounts of text

This means content can be quickly re-formatted and re-used for different social media channels. For example, a blog post could be condensed and turned into a tweet, a LinkedIn post, or even a script for a video or podcast. The AI tool could also scour a company’s output and ensure it is accurate and consistent across all channels, creating a ‘single voice’ for the consumer

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Company communications are frequently accompanied by images and infographics

Generative AI can create these images from scratch, or edit, format or add captions to existing images. Companies can also use generative AI to create voiceovers and captions for videos, or foreign language subtitles. This can greatly reduce the time and cost it requires to create compelling and engaging audio-visual content.


Generative AI can also support the strategic and planning functions

It can analyze huge volumes of data, and create suggestions for editorial content, or create calendars based on the optimal time to post information for maximum impact. It can undertake keyword research for search engine optimization (SEO) and input the keywords directly into the text. Because of its ability to learn, generative AI can also provide feedback and tips on how to improve the content quality and delivery.


Generative AI can also create realistic written content in response to user prompts

From the customer service perspective, generative AI can also create realistic written content in response to user prompts, including emails, chat, or social media inquiries. AI can evaluate what the intent of the query is, analyze past inquiries from the customer across different channels, identify the information needed, and then draft a response that can be reviewed by a human to ensure quality and compliance.

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Generative AI also means that responses are tailored to each individual

This allows companies to massively increase their production capacity and handle a far greater volume of communication. The creative and intuitive nature of generative AI also means that responses are tailored to each individual, based on their specific queries or needs. AI models can be trained to emulate certain personas so that the experience feels like human dialogue.


Customer service agents can also use generative AI to answer complex queries

Customer service agents can also use generative AI to answer complex queries in areas where they may not have the necessary level of expertise. Novice or lower-skilled workers, in particular, can see a significant uplift in productivity when using generative AI,3 requiring less managerial intervention.


Generative AI models like GPT-4 (which is the basis for Chat GPT) are trained by analyzing information scraped from the internet. These are known as foundational models due to their broad nature. However, domain-specific applications based on custom data sets, can produce more accurate and relevant results for specific business use cases. SPS is already working on a number of these applications. For example, we have used tools from Microsoft Open AI – the organization behind Chat GPT – to analyse customer requests sent to a telecommunications company. The AI solution creates a short summary of the request, extracting the most relevant information. The request is then classified by intent, such as whether the customer is looking to cancel their contract, claim a refund, or is requesting a new number. This increases the productivity of the service agents, who are able to quickly triage all requests and process the responses.

The requests can also be automatically translated, so a single, centralized team can handle customer inputs across different markets. In the future, the solution may be expanded so that it can automatically create responses to each customer, based on their query and their history with the company. These will be automatically formatted to the customer’s preferred method of communication, whether it is email, chat or printed letter. Each response would be reviewed by a person to provide feedback and improve the systems over time.

When it comes to using generative AI in the workplace we are still in the early stages, but the future already looks extremely exciting. Look out for the next article in this series where we discuss how generative AI can enhance employee productivity across the organization.

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