7 Steps to Implement Generative AI in Customer Service

Since OpenAI launched ChatGPT in November 2022, businesses have begun looking at customer engagement from a new lens. Leading the pack with GPT-4, it's changed how businesses deliver customer service, offering responses that are surprisingly human-like.

Interestingly enough, just last week, in a Financial Times interview, Sam Altman revealed that GPT-5 might be on the horizon, potentially packing “superintelligence.” This revelation could be the future where AI could dramatically reshape customer service with striking depth and intuition.

While this promises exciting possibilities, businesses like yours must remain vigilant. You need to balance generative AI in customer service with careful oversight, ensuring responses are precise, unbiased, and follow contact center compliance. Let’s explore the impact of GenAI on customer service.

What is generative AI?

Generative AI is a subset of artificial intelligence that can generate new content, including text, images, codes, product designs and music, by decoding patterns within its input data. Central to this technology are Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer).

LLMs are trained on vast datasets, enabling them to understand and produce completely unique, human-like content. They can summarize notes, distill information, answer questions, compare and contrast entities, perform competitive analysis and even create code, making them versatile tools for a variety of applications.

In customer service, this capability translates into chatbots and virtual agents that can understand and respond to customer inquiries with remarkable accuracy and relevance. Generative AI in customer service can also improve service personalization, complex problem-solving and feedback analysis.

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6 benefits of GenAI in customer service

While generative AI isn't a replacement for human agents, it enhances customer service in several distinct ways: 

1. After-hours support
   Generative AI doesn't sleep. While expanding globally, businesses face the challenge of responding to off-hour inquiries. Generative AI in customer service bridges this gap, offering immediate assistance when live agents are off-duty — ensuring customers get timely responses, regardless of time zones or holidays. 
   
2. Reduced costs
   Expanding a human customer service team is costly, not just in salaries but also in call center agent training and infrastructure. Generative AI sidesteps these expenses, providing a cost-effective way to handle a large volume of interactions, particularly for startups and small businesses looking to maximize their resources.
   
3. Adaptable growth
   As your business expands, generative AI effortlessly handles the surge in customer queries. Automating your customer service with generative AI is quite easy. It’s faster to deploy and often renders low computing costs. Such scalability prevents the overload of human agents and avoids the steep costs of hiring more agents.
   
4. Customized Interactions
   AI can utilize customer history and preferences for personalized service, a task demanding extensive time and effort from human agents. With a deep pool of customer data, generative AI can cleverly recommend things like perfect add-ons to recent buys, special loyalty rewards or even customized upgrades, making each customer feel uniquely understood.
   

   70% of Contact Center Leaders are Confident of Generative AI’s Personalization Prowess

   We recently did a market study with CCW Digital to uncover the state of generative AI in contact centers and found that: 

   • 70% of contact centers are confident of GenAI’s personalization power 
   • Befitting the optimism, 86% are planning to invest heavily in Gen AI 
   • Leaders find maximum benefit in self-service where Generative AI can add speed, agility and personalization 

   Are you behind the curve? Get ahead by staying abreast of GenAI trends, risks and considerations. Grab your copy of the CCW study below.

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5. Unwavering quality
   With generative AI, the quality of customer service remains consistent. Unlike human agents who might have varying contact center skills or energy, AI maintains a steady, reliable standard of service, ensuring every customer feels equally valued. Moreover, generative AI also automatically assesses and ensures strong adherence to your brand guidelines across interactions.
   
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6. Multilingual support
   Offering support in multiple languages typically involves a diverse, often expensive, multilingual team. Generative AI simplifies this by providing multilingual support effortlessly, enabling you to cater to a wider, global audience without the logistical challenges of a multilingual workforce.

Read More: How To Use AI Effectively in Customer Service

How to implement generative AI in customer service

Generative AI shows great potential — and it's a promise that has CEOs taking notice. In fact, 75% of them are betting big on it, believing the adopters of the most advanced generative AI will seize a high competitive advantage.

To navigate this journey, you can follow the “DAPI” Approach — a roadmap developed to guide you through each critical phase of successful AI implementation.

• Determine the strategic vision and metrics
• Analyze data and select the tool
• Program AI
• Iterate and improve

These phases can further be broken down into seven distinct steps.

Step #1: Define what you need to achieve

Start by laying out a blueprint of your business goals. This clarity guides your AI strategy and helps measure its impact. Are you aiming to speed up responses, or maybe you're focusing on enhancing customer self-service?

1. Select appropriate KPIs like customer response time, customer satisfaction ratings or self-service completion rates to track progress.
2. Establish a testing timeline for implementing and evaluating the AI system, including milestones and review points.
3. Research and benchmark your goals against industry standards to ensure they are competitive and achievable.
   
   Learn More: What Is Competitive Benchmarking?

Step #2: Gather and analyze customer service data

Your current customer service data is a sea of insights. It reveals not just the what, but the how of customer interactions, forming a baseline to improve upon.

1. Dive into your service logs to understand common queries and response times.
2. Conduct sentiment analysis on customer feedback to identify pain points.
3. Interview your support team for firsthand insights into frequent customer challenges.
   
   Read More: How To Use AI Effectively in Customer Service 

Step #3: Choose the right AI tool

Selecting the appropriate AI tool is akin to finding a key member for your team. Just like an eCommerce company thrives with an AI that's a whiz at analyzing purchase histories and campaign interactions to personalize shopping, a tech company needs an AI adept at combing through knowledge base articles for retrieving technical information. Your generative AI must make sense of your business's unique pulse.

1. Assess whether pre-built AI solutions from major platforms align with your needs, or if a custom-built API is more suitable.
2. Evaluate integration capabilities with your current customer service software.
3. Choose an AI solution that can scale with your company, handling increased data and complexity over time.
   
   Help is on its way: Here’s a Guide to Find the Best AI-Powered Customer Service Software

Step #4: Upload and categorize datasets

Training your AI with relevant data is akin to providing it with a rich learning experience. The more it knows, the better it assists.

1. To provide a solid training base, collect data from diverse customer engagements, including chat logs, email exchanges and call transcripts.
2. Categorize data for context in buckets like complaint resolution, product inquiries, or billing questions.
3. Scrub the data of personally identifiable information to maintain customer privacy and comply with data protection regulations.

Step #5: Program and train AI models

This is where your generative AI begins to learn the ropes of your specific customer service scenarios.

1. Choose a suitable neural network model, such as a transformer or recurrent neural network, for the foundational training of your AI.
2. Include a range of interaction styles in the training data to prepare the AI for different customer temperaments and inquiry types.
3. Engage in multiple training rounds, progressively incorporating more complex and varied data sets.
4. Continuously test the AI model with new, unseen data to assess its understanding and response accuracy.

Step #6: Test and refine

Consider this phase as the dress rehearsal before the big show. Before full deployment, testing the AI in a controlled setting helps identify and rectify any issues.

1. Deploy generative AI in customer service to a control group of internal support agents or a limited customer segment.
2. Keep a close eye on the performance metrics. Are the response times quick? Is the information accurate? Monitor these aspects and your contact center metrics rigorously.

Step #7: Implement and seek feedback

Deploying generative AI in customer service is not the end but the beginning of an ongoing journey. Be rigorous in your efforts to get cold, hard performance feedback from your direct users.

1. Actively seek feedback via customer surveys. What are they loving? Where are they facing issues? Use these insights to tweak and polish your AI.
2. Use conversation analytics to bring out unreported but trending sentiments around your AI’s performance in customer speech.
   

Use cases: Generative AI in customer service for businesses

GenAI can prove to be an unequaled tool to, you know, really understand how and where your customers interact with you. As you’d expect, almost half of the leaders in a McKinsey survey believe generative AI will greatly help to map customer journeys. But how does that translate?

It means you’d nail every single service interaction, every time, at every critical customer touchpoint. Here’s how generative AI is changing customer service, one use case at a time.

Use case #1: Dynamic AI chatbots for resolutions

Moving beyond the black-and-white logic of traditional bots, generative AI chatbots bring a fluid, human-like understanding to customer interactions. They can handle dynamic queries by tapping into live databases and unified customer profiles in a powerful CX tool.

For instance, a customer asking about their bank account balance after a phishing scare gets real-time, accurate information.

Even complex tasks like rescheduling a multi-city flight itinerary, which involves changes in flight dates, seat preferences and meal options, become straightforward, with the AI guiding or even completing the process for the customer.

Stay a nay, many steps ahead: Your smart chatbots can even predict CSAT  before the customer even gets riled up to give a sub-optimal rating. AND notify you in real time so you can fix it before anything breaks. It’s all AI magic.

Use case #2: Advanced sentiment analysis

Feedback is gold, but mining it is a challenge. Generative AI dives into the ocean of customer feedback, extracting valuable insights regardless of volume. From parsing through millions of reviews to picking up subtle sentiment cues, it helps businesses understand customer pain points and joys at scale. This deep dive into customer emotions is invaluable for refining products and services.

It can also read between the lines during conversations to aptly adjust its tone and manner to suit the user.

For example, a customer responding to a light-hearted ad campaign would receive responses with the same zeal, whereas a customer reporting fraud would trigger the AI to keep its demeanor serious and succinct.

Use case #3: Enhancing agent productivity

Every customer service agent dreams of having a co-pilot that ensures they always say the right thing. Generative AI is that sidekick. It brings relevance and boosts accuracy — ensuring high contact center agent productivity and first-call resolution rates.

GenAI works like a cheat sheet that pulls the perfect answer from the knowledge base or automatically summarizes and categorizes cases so your team has all the information within earshot.

Use case #4: AI-driven agent training and quality assurance

Consistency in customer service quality is a tall order, but generative AI makes it achievable.

It acts as a coach, setting customized scoring parameters for agents, offering them insights into their interactions and guiding them towards better decision-making. This AI-driven customer service coaching ensures that every interaction is not just a transaction but a step towards building stronger relationships.

Meet Sprinklr AI+: Your GenAI wingman for customer service

Generative AI is flipping the script on customer service and it's happening at warp speed. We're moving from a world where AI simply reacts, to one where it anticipates and acts, almost like it's reading customers' minds. More than just a step up, it's a leap into a future where customer service becomes the heart of the business, purely in sync with data-driven insights.

Sprinklr AI+ can be your friend in this journey if you let it. Easy to deploy, with a foundational infrastructure built to fit your business needs like Tetris. Oh, and our Responsible AI is unsleeping on compliance and brand governance.

Get to experience it first-hand!