Why building chatbots that enable is easier than you think

With LLMs and low-code tools revolutionising chatbot development, the biggest hurdle in building AI-chatbots for customer experience might just be mindset.

We may be well within the era of transformative AI, but for many businesses artificial intelligence continues to carry a certain air of mystery. This perception is notable when it comes to creating in-house AI-powered chatbots, tools that come with buzz as well as reluctance.

Building a customised chatbot to serve consumers often sounds like a monumental task—time-intensive, expensive, and requiring technical wizardry that a company cannot muster on its own. And we’ve all been haunted by experiences of clumsy, awkward interactions with chatbots that ended with the user’s frenetic typing: “let me speak to a human”.

But what if the reality was far simpler? What if creating an AI-powered chatbot that truly enables your customers wasn’t a multi-month project, but something you could prototype in as little time as two days?

Thanks to advances in large language models (LLMs) like OpenAI’s GPT and accessible tools such as Twilio’s CustomerAI, this is no longer a hypothetical scenario. Businesses of all sizes can rapidly build, test, and deploy chatbots that are functional, embody their unique brand identity and can solve customer needs to a surprising degree. What is more, a prototype is all a company needs to get the journey off the ground, as AI chatbots will evolve dynamically with usage, learning from insights and developing surprising sophistication.

The market is already responding to the potential. As many as 39% of e-commerce CEOs believe CX chatbots are among the most promising applications of AI, according to a March 2024 report from First Insight. The appetite is there on the consumer side as well, with the annual IBM Consumer Study survey showing eight in 10 consumers want AI to help them in their customer journey.

In this article, we’ll try to further demystify the process of building AI-powered chatbots. We will explain why LLMs make building in-house chatbots more accessible than ever before, and explore why the real barrier isn’t technical but a shift in mindset. And hopefully, by the end, you’ll see why the chatbot you’ve been dreaming of -or its prototype anyway- could actually be just 48 hours away.

🗣️The power of LLMs and the potential of “no-code” solutions

The reluctance of many companies to experiment with in-house chatbots is not surprising. In the not-so-distant past, creating a chatbot required building intricate rule-based systems and training algorithms from scratch. Developers had to teach these bots every possible response to customer queries and guide them through interactions that were often difficult to predict. The process was slow, expensive, and prone to errors.

The shift begins with understanding how LLMs have reshaped the AI landscape. These pre-trained models already understand natural language, context, and nuances, and have changed the game by eliminating the need for ground-up development, making a chatbot faster to implement. By leveraging these models, businesses can focus on tailoring the chatbot to their specific needs and brand voice, rather than reinventing the wheel of automated communication.

Elizabeth Toby, Head of Marketing, Digital & AI at NICE, recently summed it up quite aptly, when discussing how conversational AI models revolutionise the customer experience landscape in a podcast by MIT Technology Review.

"With generative AI, we're able to say something like, "Can I get a direct flight from X to Y at this time with these parameters?", and the self-service in question can respond back in a human-readable, fully formed answer that's targeting only what I've asked and nothing else. Without having me to click into lots of different links, sort for myself and really make me feel like the interface that I've been using isn't actually meeting my needs”.

What is more, LLM models have also given rise to various tools that are accessible but intuitive, making them perfect for rapid prototyping. Take Twilio’s CustomerAI, for example, a tool that enables businesses to describe their campaign goals, define audiences, and select communication channels. The AI takes care of the rest, building customer journeys automatically. Or EvaluAgent’s Auto-QA system which enhances contact centre operations by identifying key conversation moments, summarising interactions, and suggesting improvements—all powered by LLMs.

These tools can do the heavy lifting, allowing you to focus on the customer touchpoints, customer experience, and helping set the parameters of your customer journey. They have actually given birth to what is called the “no-code”, or “low-code” approach, referring to solutions to building AI chatbot prototypes that require little beyond accurate prompting. As noted in a recent Lifehacker article on no-code AI development, “Modern AI platforms… allow users to build fully functional chatbots using simple prompts and intuitive workflows, no coding required.”

Of course, prompting the right parameters or handling sensitive customer data is by no means an easy endeavour. But it is definitely easier than building an AI-powered bot from scratch.

🌳Roots and branches: the power of advanced decision trees

Another misconception about chatbots is that they need to be entirely free-form and conversational to be effective. In reality, most successful chatbots use a hybrid approach: leveraging LLMs for conversational context while integrating decision trees to guide specific outcomes. This is where the magic of chatbots that enable customer interactions truly happens, as businesses can leverage their built experience in customer support and customer experience with the powers of LLM models and tools.

Decision trees allow businesses to map out customer journeys in detail, and can also help inject a brand’s tone of voice and personality into the chatbot’s responses. What makes them particularly valuable is that they can be best developed in-house using resources most businesses already possess. Existing FAQs, support logs, and customer feedback provide a wealth of data to define these conversational paths. By focusing on keywords that frequently appear in customer queries, companies can further fine-tune the process.

Amit Jhawar, CEO of the SMS and email marketing platform Attentive which uses AI for SMS and email chatbots, recently explained in an article that in cases where someone has a simple question that a decision tree could easily predict, a bot can do a better job than a human.

“If I want to reset my password or have a quick question about shipping, a chatbot is a great first line of defence. It never gets tired. It knows all the answers and the use cases and can get through the process with low variability, high consistency and immediate responses”.

The true power of a good decision tree also lies in knowing when its branches should end. Complex or idiosyncratic queries may require human intervention, as customers facing complex issues are known to value the empathy and understanding of human agents. The best approach is one where chatbots handle a bulk of routine issues, looping in human representatives and agents to focus on intricate problems that demand deeper understanding.

Take the example of the skincare brand Delavie, which also uses chatbots and whose founder Kyle Landry also shares his insights in the aforementioned article. At Delavie, a tool developed in-house scans keywords in customer emails, routing routine questions to a chatbot about simple queries like tracking numbers or reward points. But more complex inquiries— like inquiries about how Delavie products mix with other skin-care products— are directed to human representatives. “You can’t assume AI will cover everything. Otherwise, you end up frustrating consumers with a vicious loop”, the founder argues.

Jhawar from Attentive explains that many AI models categorise inquiries into predefined “buckets”—product, shipping, account, or sizing—with corresponding responses. “A good model handles 60-80% of questions, but if it lacks confidence in an answer, it escalates to a human” he adds.

🌀Deploy the MVP and start iterating

After grasping the potential of LLMs tools, and using your insights to map the right decision trees, it’s time to get a prototype chatbot up and running. Creating a prototype doesn’t require months of work—in fact, with the right approach, you can go from ideation to a working chatbot in just 48 hours. The key is starting with a minimum viable product (MVP): define the chatbot’s purpose, map out basic decision trees, and build it using an LLM-based platform. Once live, the iterative process begins, allowing real-world interactions to shape and refine the chatbot.

As Nektarios Sylligkardakis, AI Solutions Advisor at helvia.ai, puts it: “A chatbot MVP is more than an automation tool—it’s your gateway to understanding users better. By quickly deploying, iterating, and analysing its performance, you can refine your chatbot, address user needs, and uncover valuable insights to improve both your chatbot and the overall customer experience.”

This constant iteration cycle is where AI chatbots truly evolve. Modern LLM platforms allow businesses to test customer experience flows in real-time, making refinements easy and immediate. By monitoring unanswered questions, conversation drop-offs, and sentiment trends, businesses can pinpoint where users struggle and fine-tune the chatbot accordingly. This process not only improves automation but also reveals deeper insights about customer needs—insights that can influence product development, service improvements, and even business strategy.

Launching an MVP chatbot also provides an opportunity for rapid experimentation. Features such as A/B testing, versioning, and funnel analysis allow teams to tweak conversation flows, assess different response styles, and optimise engagement. The best chatbots aren’t static—they continuously learn and improve based on real interactions. Unlike traditional software that requires lengthy updates, AI chatbots thrive on adaptation, becoming more sophisticated with every exchange.

🤯The real challenge is mindset

There is no doubt that technical barriers to building AI-powered chatbots have largely disappeared. The process is faster, easier, and more intuitive than it has ever been. LLMs and chatbot tools are the building blocks that have democratised chatbot building. Defying the right parameters and decision trees is the key that turns these tools into customised solutions where your acquired customer insights can truly sign. And launching a prototype with a constant iterative approach will soon give you a constantly evolving chatbot that becomes increasingly effective.

For many businesses today, the biggest obstacle isn’t technology; it’s cold feet. Companies often hesitate to take the plunge, bogged down by outdated assumptions about AI. But success lies in adopting a nimble, experimental approach—one that prioritises rapid prototyping and learning over perfection.

So it turns out that, in today’s landscape, what you need the most in order to build powerful AI tools customised and tailored to your customer needs is the curiosity to experiment and the willingness to start. Building an AI chatbot is no longer a distant dream, but an accessible reality, available to anyone daring to take the first step. The tools are ready, the process is simpler than ever before. The only thing left might just be to get started.