Conversational AI: Employee and customer experience at the forefront

Business communication is broken at its current state. Instant gratification is overshadowing long-drawn interactions, irrespective of whether they are human and meaningful. Which is also why I need to thank you for landing here and giving this long-form content a piece of chance. We are a dying breed.

I’m here to talk about conversational AI. And how conversational AI is shaping up business communication. On that note, I want to share two events I experienced with different banks.

First one, a legacy bank: I’m still thinking about that time last week when I called my well-renowned bank's customer service line and spent 8 minutes (it’s a lot, okay?) navigating an archaic IVR system, only to be greeted by a representative who had no context of my issue with a DEMAT account. 

Second one, a small digital bank: This is a modern digital bank that I also use for convenience. They surprised me with a conversational voice AI when I tried to sort out a credit card application. All it required was clear replies from me in English to pass on information or direct me to the next course of action. 

No points for guessing where I’m doing the bulk of my transactions and investments. That’s why conversational AI is now a competitive necessity.

What is conversational AI?

Conversational AI refers to technologies that enable computers to process and respond to human language in a natural and meaningful way. 

Unlike traditional rule-based chatbots that follow set paths, conversational AI uses machine learning and natural language processing (NLP) to engage in dynamic interactions, along with seamless context switching — it’s like handling two or more requests in a single conversation.

Conversational AI differs from chatbots in the sense that it feels like interacting with a knowledgeable person — someone who remembers your preferences, understands nuance and gets better at responding with every interaction to help you better.

These systems power everything from customer service virtual assistants to voice-activated devices like Alexa and Siri, as well as sophisticated agentic AI solutions that can handle complex business processes.

The global conversational AI market was projected to reach $11.64 billion in 2024, and is expected to grow at a CAGR of 23.7% between 2025-2030. This projected growth is explosive and truly reflects the conversational AI’s clutch over the technology market and B2C needs.

Importance of conversational AI

Conversational AI is being adopted across industries with retail and ecommerce leading the wave. This shows how industries closest to the general consumers (B2C) have lapped up conversational AI with open arms.

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Beyond the numbers, there's also a qualitative shift underway. Conversational interfaces are fast becoming the preferred way for people to interact with technology. 

Gartner predicted that 50% of knowledge workers will use a virtual assistant every day in 2025, which is up from 25% in 2021. And conversational AI isn’t just churning out incremental improvements. It actually represents a new cornerstone in business performance.

The why and how of conversational AI

Why are businesses rushing to implement conversational AI? The answer lies in changing consumer expectations and technological maturity.

Modern customers expect:

• 24/7 availability
• Immediate responses
• Personalized services
• Seamless experiences across channels
• Minimal effort to get their issues resolved.

Modern employees expect:

• Less mundane, tedious tasks
• To automate basic user queries
• More relationship-building than manual work
• Focussing more on high-intent prospects
• Ways to keep leads engaged when they are away.

Traditional service models simply can't deliver on these expectations cost-effectively. Conversational AI can handle most of your routine queries, qualify leads 24x7, pick up context across business applications — thereby dramatically reducing the manual burden and user effort, while improving customer and employee experience.

How conversational AI implementation looks like at a process-level

1. Identifying high-value use cases where conversations can solve real business problems
2. Selecting the right technological approach (rule-based, AI-driven, or hybrid agents)
3. Training the system with relevant data and knowledge
4. Integrating with existing systems and channels
5. Continuously monitoring and improving based on user interactions.

Consider the above image, for instance. With Gallabox’s conversational AI, businesses can build a WhatsApp AI agent that takes context from different tools like CRM, payment interface, calendar, sheets, and other sources to hold an elaborate conversation and resolve the queries of prospects and customers.

Key components of conversational AI

Some basic housekeeping to warm you up before diving into the world of conversational AI. Understanding conversational AI requires knowledge of its key components, which are usually quite technical and developer-centric. But let’s go through them with as less jargon as possible:

Natural language understanding (NLU)

NLU is the system's ability to comprehend human language input. Today’s NLU models can detect intent (what the user wants to convey and accomplish), extract specific pieces of information like dates or requests, and understand sentiment (the emotional tone behind the message).

A study by Stanford University found that the latest NLU models can understand context at near-human levels in many scenarios, with error rates decreasing by over 40% between 2019 to 2023.

Natural language generation (NLG)

NLG is a counterpart to NLU which creates natural, coherent responses that sound human. Advanced GenAI systems can adjust tone, complexity, and style based on the context and user preferences.

For instance, here’s Gallabox’s shared team inbox which can assist agents in crafting better responses during sales, marketing, and support scenarios.

Dialog management

This orchestrates the entire flow of the conversation, maintaining context over multiple turns and deciding when to ask for clarification, provide information, or take action. 

The most sophisticated systems can handle when humans switch topics, interrupt the natural flow of the interaction, and show multiple intents in a single conversation. Basically, it’s about discerning the context behind a conversation like two humans having a one-on-one conversation, which usually has multiple threads.

Machine learning

What truly differentiates modern conversational AI is its ability to learn and improve with every case or interaction. By analyzing patterns from millions of conversations, these systems can continuously refine their understanding to come up with better responses. And don’t forget the business outcome you secure with your AI’s continuous learning. You get to deliver more resolution at less number of replies or exchanges per conversation.

Integration capabilities

Effective conversational AI cannot function well in isolation. It needs to talk to all your systems and consolidate data and processes from them. 

Look out for a conversational AI or agentic AI solution that connects with CRM and adjacent systems, knowledge bases, inventory management, marketing automation, messaging apps, social media, and other business-critical channels to provide accurate, actionable responses.

Use cases of conversational AI

The applications of conversational AI span across every industry and business function.

Sales and marketing

Conversational AI is transforming the sales process through intelligent product recommendations, guided shopping experiences, and proactive engagement. In fact, there’s research to quantify that conversational marketing increases conversion rates by 42% on average.

Customer service 

Customer service remains the most common use case for chatbot implementation, handling everything from basic FAQs to complex troubleshooting. AI-powered support can resolve up to 70% of inquiries without human intervention, reducing wait times by 68%.

Healthcare

From appointment scheduling to symptom checking and medication reminders, conversational AI is improving patient experiences and outcomes. In fact, according to a National Library of Medicine study, a huge 91% of the respondents were in favor of AI-based symptom checkers for self-care, especially if the AI also directed them to a physician based on the context.

Financial services

Banks, insurance, and other financial services companies use conversational AI for everything from account inquiries to fraud detection and providing financial advice. According to Juniper Research, banks are estimated to have saved up to $7.3 billion as of 2023, by slashing operational costs with chatbot implementation.

Travel and hospitality

Airlines, hotels, and travel agencies are leveraging conversational AI to transform the travel experience. From booking confirmations to itinerary changes and real-time updates, conversational AI is making travel smoother and more personalized than ever.

Gallabox’s customers from the travel industry stand testament to the power of messaging in scaling revenue growth. Look at Pickyourtrail, for instance.

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Education

Educational institutions and online learning platforms are using conversational AI to enhance student engagement and provide personalized learning support. From admissions inquiries to course help, these applications are making student empowerment accessible just through messaging.

When the exam is done, it triggers a webhook in Gallabox. The certificate is generated in the background and sent automatically via Gallabox to the student on WhatsApp.

Omri Gonen

Founder, Educenter

Real estate

Property searches, viewings, and even mortgage processes are being transformed through conversational AI on messaging channels like WhatsApp. Agents and brokerages use AI assistants to qualify leads, schedule viewings, and answer detailed questions about properties.

Ecommerce

WhatsApp has become a powerful sales channel for e-commerce businesses, with AI agents guiding customers through product discovery, comparison, and purchasing. Global spending for conversational commerce will reach $290 billion by 2025, up from $41 billion in 2021. This reinforces the role of messaging apps like WhatsApp in every market out there. 

Examples of conversational AI

Let's look at some real-world implementations that showcase what's possible when quick-thinking brands and state-of-the-art conversational AI come together.

Nubank

The Brazilian fintech powerhouse Nubank uses OpenAI’s GPT‑4o and GPT‑4o mini language models, to provide personalized banking services to its 114-million-strong customer base from Brazil, Mexico, and Colombia. 

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Their conversational AI system handles everything from balance inquiries and fraud detection to bill payments and credit limit increases, resolving 50% of tier-1 customer queries without human intervention.

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Zara's shopping concierge

Fashion retailer Zara allows customers to browse collections, check stock, view designs in AR mode, and complete purchases through their AI shopping assistant. Zara’s system also remembers style preferences and size information, making personalized recommendations based on previous purchases and browsing behavior on WhatsApp and other user-preferred messaging channels.

Amazon’s Rufus

Amazon’s recently rolled-out conversational assistant, Rufus, enables customers to find products and provides personalized recommendations based on prompts and preferences. It can also do comparisons and help users decide what to buy.

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Benefits of conversational AI

Let’s get to the cost and operational benefits of conversational AI. Allow me to explain the benefits of conversational AI through the lens of Gallabox’s WhatsApp AI agents, (which is essentially a conversational AI set up on WhatsApp).

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But the benefits of implementing conversational AI extend beyond cost savings. Here are some of the biggest benefits:

1. Enhanced customer experience

By providing immediate, personalized responses around the clock, conversational AI dramatically improves customer satisfaction. A PwC study found that 80% of US consumers value speed, convenience, and knowledgeable assistance as the most important elements of a positive customer experience — all of which are the core functionalities of a well-implemented conversational AI system.

2. Operational efficiency

Apart from handling higher volumes, conversational AI ensures consistency in responses and compliance with policies. This also means empowering employees with a consolidated system that can be a co-pilot while they write their responses to customers.

3. Valuable customer insights

Every conversation generates data that can inform product development, marketing strategies, and service improvements. So conversational AI can create a treasure trove out of every customer or prospect interaction and convert them into structured, actionable insights for the brand. 

4. Scalability

Unlike human support teams, conversational AI can handle sudden spikes in volume without degradation in service. Imagine the run of Black Friday, and a few many agents scurrying around to respond to the huge uptick in queries. A conversational AI bot here would empower these agents to concentrate only on the more serious queries while the AI takes care of all the simpler interactions. 

Best practices for implementing conversational AI strategy

We’re here at the much overlooked part. Everyone wants a piece of conversational AI. But end up burning a lot of resources without a clearcut action plan before deployment. Successful conversational AI implementation requires more than just deploying technology:

1. Start with clear business objectives

Define what success looks like in measurable terms. Is it reducing support costs? Increasing conversion rates? Improving CSAT scores? Your objectives should guide every aspect of implementation.

2. Focus on specific use cases

Rather than trying to boil the ocean, identify high-value, well-defined scenarios where conversational AI can excel. Get the buy-in of sales, marketing, and support functions to ensure clarity.

3. Design for conversation, not transactions

The most effective implementations think beyond simple Q&A or FAQs to create natural, flowing conversations. This means accounting for clarifications, context switching, and social elements of conversation. Transactions will be a by-product of a good conversation flow.

4. Plan for the handoff

Even the most advanced AI will sometimes need to transfer to a human. Make this transition seamless by providing full conversation context to the agent. And more importantly, set up cues for the conversational AI system to know when to transfer to a human agent.

5. Continuously tweak and improve

Use conversation analytics to identify gaps in understanding new user intents, conversions, sentiment, resolutions, and opportunities for better feedback loops. The goal shouldn’t be to make your AI stand out from human involvement, but to make it helpful and efficient while still reflecting your brand personality. 

Challenges for implementing conversational AI strategy

Don’t be spooked by this. Understanding the challenges behind conversational AI implementation will only help you set realistic expectations and be wary of the usual traps.

1. Language and cultural nuances

Language is incredibly complex with cultural references, change in sentence structures, language gaps, and regional variations. Building systems that handle this diversity requires sophisticated NLP capabilities and extensive training data.

2. Integration complexity

Connecting conversational interfaces with relatively old tech stacks can be technically challenging. Especially in legacy businesses, with most customer data platforms and repositories built in-house, the systems remain siloed and disconnected — unlike more modern businesses using SaaS tools that are better equipped with integration needs.

3. User adoption

Getting customers comfortable with AI interactions requires thoughtful change management. People might be skeptical about talking to a bot and be concerned about how the data is being processed. 

4. Ethical considerations

Issues like privacy, bias, and transparency must be addressed proactively. With less control over their initial build, we can’t assure that AI systems wouldn’t inadvertently perpetuate biases present in their training data.

5. Measuring success

Defining and tracking the right metrics can be challenging. Beyond customer effort score, bot-resolved cases, containment rates and CSAT, organizations need to consider business outcomes like increased revenue and customer lifetime value.

Future trends and opportunities

Based on where conversational AI and agentic interfaces right now, we could take an educated guess on how they’re going to shape up the future: 

1. Multimodal interactions

Future systems will seamlessly blend text, voice, images, and video in a single conversation. WhatsApp Business API platforms like Gallabox are already onto this now.

2. Emotion recognition

Detecting and responding appropriately to user emotions will make interactions more empathetic and effective. 

3. Proactive engagement

Rather than waiting for users to initiate contact, AI will increasingly reach out with timely, relevant information based on certain user behavior triggers or signals. 

4. Specialized domain experts

Instead of general-purpose assistants, we'll see the rise of deeply knowledgeable AI specialists in fields like healthcare, finance, and legal services. These systems will combine conversational abilities with professional-level domain expertise.

FAQ questions for conversational AI

1.How does conversational AI differ from traditional chatbots?

Traditional chatbots follow predefined rules and decision trees, limiting them to scenarios their creators anticipated. They typically recognize specific keywords or phrases and deliver corresponding pre-written responses.

Conversational AI, in contrast, uses machine learning to understand intent rather than just keywords. It can handle unexpected inputs, learn from interactions, maintain context across a conversation, and generate original responses rather than selecting from a script.

2.How does conversational AI handle follow-up or clarification questions?

Modern conversational AI systems maintain context throughout an interaction, allowing them to understand follow-up questions that would be ambiguous in isolation.

For example, if a customer asks, "What's your return policy?" followed by "What about damaged items?", the system understands that the second question refers to returning damaged merchandise.

The future of the sales-marketing-support nexus is conversational!

Conversational AI has matured beyond simple chatbots to become a sophisticated engagement channel that can understand, learn, and adapt.

The most successful implementations will be creating systems that are not just smart, but helpful, ethical, and aligned with both business objectives and customer needs.

At Gallabox, we're helping businesses navigate this conversational future, providing the tools and expertise needed to create meaningful AI interactions that drive real business results on WhatsApp. 

Whether you're just beginning your conversational AI journey or looking to enhance existing capabilities, Gallabox can help your business scale its sales, marketing, and support with WhatsApp AI agents.

Try Gallabox for free today and explore how we can help you implement effective, revenue-generating conversational AI flows tailored to your specific needs on WhatsApp — a platform with more than 3 billion monthly active users.