How to Transform Your Approach To Customer Support Using AI

Churchill Leonard
May 11, 2023
min read
February 22, 2024
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Transform your customer support with AI: Harness the power of artificial intelligence to enhance customer experiences and drive growth
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Since OpenAI released ChatGPT barely five months ago, general-purpose AI has gone from being an idealistic moonshot that’s always 10 - 15 years away & has begun shaking up billion-dollar industries.

Over the past five months, we’ve seen the following:

  • Microsoft Bing released an AI-powered interface for their search engine,
  • Google launched Bard, a contender in the field of AI-powered search
  • The rise of prompt engineering, the art of crafting cues for ChatGPT to return finer answers
  • Endless conversations about how AI will transform or even eliminate industries like writing, design, and even software development

But, it’s easy to get caught up in the hype; meanwhile, it’s far more important to wonder if there’s a way you can harness the power of artificial intelligence & use it to engage your customers, resolve their queries, and enhance the customer experience, especially given the attention that ChatGPT’s chat interface has created.

Specifically, customer support is a critical aspect of any business, and providing seamless customer experiences is essential for building brand loyalty and driving growth. However, traditional customer support methods can be time-consuming, inefficient, and often fail to meet the expectations of modern customers.

The truth is the generative AI industry is nascent and changing rapidly. Still, it’s developed enough to the point where you can harness its power to transform your company’s approach to customer service—this article will help you understand how.

So, what are the challenges of traditional customer support?

Traditional customer support depends on a team of highly-trained product experts whose knowledge of your product continues to evolve — but that model creates friction around the costs of training new agents, ramping them up quickly, and their capacity to retain and recall helpful product resources on the fly.

Training costs & high turnover

ProcedureFlow’s June 2021 report on The State of Contact Center Training shows that it takes 6 - 12 weeks to onboard and train new agents, during which your expenses can go as high as $5k per trainee— and that’s before you factor in training fees, HR, or IT costs.

For all that outlay, customer support has some of the most dismal attrition rates & it’s not unusual to see turnover rates as high as 45 percent in a single year.

Given that it costs 6 - 9 months’ worth of an employee’s salary to hire their replacement, it’d cost you up to $32.8k to replace a customer support agent earning $43,745 annually (Glassdoor, April 2023).

So, on the one hand, it takes a while to coach support agents to the point where they can navigate customer queries with little hand-holding, and on the other, there’s no guarantee that they’ll stick around.

Knowledge retention & ramp time

As we mentioned earlier, training your support agents aims to turn them into product experts who can confidently handle customer issues, navigate internal workflows and processes, and use your support infrastructure to its full potential.

But suppose you’re losing a good chunk of your workforce to churn—even if it's significantly below the industry benchmark of 45 percent —you’ll always be playing catch-up, training agents from scratch, and not extracting all you’ve invested into coaching your trainees.


According to SuperOffice, nearly half (46 percent) of customers expect companies to respond to their queries within four hours, while 12 percent expect to hear back within 15 minutes or less. Even when your customer service agents are experienced with your product, common FAQs, and issues users face they won't constantly hit those benchmarks by referencing their memory or trying to dig through product docs & help desk articles.

The benefits of AI in customer support

AI is changing the game regarding customer support, offering a range of benefits that traditional methods can't match. Here are just a few of the advantages of using AI in customer support:

Localize product documentation & provide multilingual support

Traditional localization platforms like Lokalise & Weglot require that brands that serve international audiences hire translators to translate their websites and resources into different languages, word-for-word.

Language models like GPT-4 continue to improve their proficiency in dozens of languages & GPT-4 precisely has surpassed existing AI benchmarks for over 26 languages, both famous lingua franca like French, Mandarin, and Hindi and low-resources languages like Swahili and Welsh that have limited training data.

You can leverage that capability to:

  • Localize your website in dozens of languages.
  • Automatically recognize and reply to customer queries in their language.
  • Offer to either render product resources in your default language or translate to your users’ lingua franca based on their current location.

Answer technical queries by syncing your chatbots with your product’s backend

Until now, chatbots have been limited to serving customers pre-written answers and product docs using an if-then sequence. But, they fail at solving creative challenges and have always needed a human in the loop to take over conversations if a user needs help with a technical issue or a unique challenge.

By training a language model on your internal data and product backend, you can build intelligent chatbots that can reference live technical resources to see why a user can’t log in or complete some desired action.

Use NLP to classify support tickets appropriately

As your company (& product’s complexity) grows, you’ll start to find patterns in your support queries. You might even begin to group them into separate buckets: pricing, technical issues, account management, i.e., sign-ups and upgrades, competitor comparisons, onboarding, etc.

For instance, queries from users who’re looking to upgrade to your Enterprise tier will often mention your main competitors, trying to understand how your product/service measures up to theirs; support tickets about your pricing will specifically mention subscription tiers such as your Basic, Pro, and, Enterprise plans; account management queries will mention keywords that indicate users are trying to log in, signup, or invite their team members.

Now, if you use a basic automation sequence that groups support tickets and assigns them to agents based on the keywords they contain, you will have a high margin of error simply because of the context your users will be mentioning those keywords.

Natural language processing can interpret queries, understand their context, classify them accurately, sort leads, and route support tickets to the appropriate agent.

Generate probable answers for a human agent to review & edit as required

In their current iterations, language models like Google’s Bard, GPT-4, or Facebook’s LLaMa are extremely powerful pattern recognition engines—they can ingest large volumes of data & find threads that run through them.

Secondly, as language models, they can process natural language & “understand” the context of a user’s prompt or query, whether they’re trying to reset their password, upgrade to a pricier tier, or solve a technical challenge.

But, as we covered already, while they can be highly accurate, without access to the correct training data, these models tend to be confidently wrong, especially if it’s a one-of-a-kind problem or if a customer phrases their queries awkwardly enough to elude the model’s grasp.

You don’t need to wait for GPT-8 or some super-advanced AI model that can process requests more accurately. Instead, you can put a human in the loop as a quality control agent.

This way, you can use an AI model as a first-opinion machine that generates plausible replies and submit them to a human agent to parse & determine whether the answer is the best reply possible for your user’s needs.

This can even become a positive feedback loop where your local deployment of a language model evolves quickly, learns more about your customer support tactics, and keeps getting trained by your customer support agents editing its pre-generated replies.

Answer basic Q&A queries quickly & accurately

ChatGPT has a worrying tendency to confidently generate incorrect, vague, or short-sighted answers without clarifying your intent, conducting some background research, or referencing a knowledge base. It’s unsurprising since the model doesn’t fundamentally understand your business model, pricing structure, your product’s engineering, or the real-world challenges a customer experiences.

At best, it tries to match patterns based on the parameters it’s been trained on—that’s what makes it a large-language model—it can’t think, but, like the nerdy kid in your Physics class who’s memorized the textbook cover-to-cover, it can answer correctly when it can match its interpretation of a query with patterns in its corpus.

That’s a way of saying that right now, GPT-4 (i.e., OpenAI’s most recent LLM) can answer basic questions quickly & accurately as long as it's been trained on the correct data—it’s not an expert at processing complex logical sequences, but, combined with a chat-based interface, LLMs can ingest your product documentation, case studies, guides, and help library and accurately reply straightforward user queries.

So, try to limit your usage to:

  • Basic queries about pricing, discounts, team plans, etc. — I downgraded to Scribe’s Pro plan; will I get a refund for the unused three months of the annual Enterprise tier?
  • Known issues — Why is my IP address blocked? I was asked to reach out to support directly to get the limit removed.
  • Simple DIY technical issues — My account dashboard refuses to update after my latest transaction.

In each of the scenarios mentioned above, the problems the customers in question are trying to solve are known queries that have a standard fix; a human agent wouldn’t need to do any poking around to find the issue since it’s a common issue & a language model that’s been trained on your product & help documentation would reduce your average handle time significantly by generating those replies extremely fast & courteously.

Benefits of using Scribe to scale up your customer service

Scribe is the fastest way of creating customer success resources and using them to coach customers at scale — just click record, and our extension & desktop app will capture everything you interact with on-screen, break them up into snips, highlighting which buttons you press, and the actions you carry out to complete specific actions.

‎And that’s it! Scribe handles everything else, including annotating your slides and highlighting details, all in <1 minute.

Embed how-to guides on your website, helpdesk pages, etc.

Scribe is designed to fit into the rest of your customer service infrastructure, support your product experts, and help you answer common customer queries and FAQs.

You can embed Scribe docs on your website, helpdesk pages, your live chat window, and your mobile application — essentially everywhere your customers can access your content.


Reduce your support team’s backlog with DIY resources

No matter how much AI advances, humans will always need to be in the loop to clarify AI-generated responses or answer user queries that may not have been cataloged previously.

But, for most of your support tickets that are simply FAQs and common bugs rehashed differently, Scribe serves as a repository your support agents can point users to after confirming that they understand what they need help with.

Speed up the support process

One of AI’s fastest advantages over the human factor is speed since artificial intelligence models can refer to a broader database than a human can & answer queries even faster. Scribe helps you add AI to your team to help you improve metrics like your first reply time (FRT) and reduce your average handle time (AHT) by closing tickets faster with minimal human input.

Or, like Seth List, Talon.One’s former Head of Global Sales Development puts it,

“Scribe allows me to build my SDR team’s processes in 1/10th of the time it took before.”

Third-party integrations with the rest of your support stack

Scribe integrates with the rest of your customer support infrastructure and your learning management system. You can embed Scribe docs in Zendesk, Help Scout, HubSpot, ServiceNow, Stonly, Trainual, and hundreds of other SaaS platforms where your support systems and processes live.

Whether you choose to use Scribe as a customer-facing guidance solution or embed it inside the internal tools you have inside your company, Scribe will help your stakeholders complete tasks faster without reading lengthy docs.


Scribe’s Basic plan is free forever, and our Pro tier starts at $12 per user, per month if your entire team makes the switch. For less than $150 per user/year, we’ve helped companies of all sizes create guide docs that make it easy to:


Scribe transforms customer support with AI-powered process documentation

Scribe helps growing companies scale their support by turning their processes into step-by-step guides in <1 minute.

Whether you’re trying to help customers complete tasks like—

  • Complete your onboarding process.
  • Reset their password.
  • Report a bug or an error.
  • Contact support & upgrade to a paid tier.

Scribe offers an AI-powered documentation engine that helps you capture snapshots of your processes, SOPs, and workflows, annotate screenshots, and generate step-by-step training manuals and guide docs by simply completing the task with our extension installed.

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