During the first chatbot wave of 2016, the tool came with more frustrations than benefits. To this day, many people wince when they reach out to customer service and get an artificial response.

However, we're in the midst of a quieter, yet more significant chatbot boom. The all-knowing chatbots we thought to be the future have been replaced by specialized bots, and the results are outstanding.

As chatbots' duties grow more sophisticated, so does their very definition.

A chatbot was used to conduct basic text conversations to imitate human interaction. Today, it is now a multipurpose piece of AI-powered software that enables a machine to not just react, but to comprehend.

Thanks to utilizing NLP (Natural Language Processing) — the automatic manipulation of natural language — most modern chatbots can map user input and intent, classifying the message and preparing a fitting, human response. That opens up tons of possibilities.

The global chatbot market is expected to hit $10.5 billion by 2026. The NLP industry? $26.4 billion by 2024. And success stories across industries are no longer predictions; they're a reality.

Emirates Vacations' chatbot has boosted engagement rates by 87%. The JPMorgan chatbot saves the company 360,000 hours annually. LEGO's chatbot has reached 2.96 million users. The list goes on.

Why did the first chatbot revolution fail?

In 2016, Microsoft referred to chatbots as an indispensable piece of technology, Facebook was hyping its Messenger platform and thousands of businesses began commissioning their own chatbots. But the results were mediocre.

Chatbots were meant to simplify things and save time — but often ended up doing the opposite. They couldn't understand enough human language or process enough data to do what companies had promised. Boundless user complaints often led companies to employ human handlers, to ensure that the bots were effective.

For instance, Facebook's bot was shut down after it was revealed that 70% of its responses came from people. On its own, it couldn't handle complex requests.

There were some success stories in cases where companies didn't promise too much too soon. However, the overzealous goal of replacing human agents ended in disappointment.

We used to think that chatbots could utilize infinite knowledge to help with anything. Now, we know that's not actually the case. The opportunity lies in specialized bots solving business- and case-specific problems.

How AI/ML improved chatbot tech

There are currently two types of chatbots that the majority of industries use: rule-based chatbots and chatbots that utilize AI/ML.

The first type remains straightforward: The bot is programmed to respond to explicit commands. The second is using the aforementioned NLP and ML algorithms.

AI, ML and NLP technology overcomes the limits of rule-based programming. The main goal is to solve a particular problem or to lead a conversation without the skills we view as crucial to the process — a.k.a. the human aspect.

In other words, today's technology helps the bot to not only learn from users, but actually understand them. The result is customers speaking to chatbots as they would to a human.

But to be clear, it's not all wins.

In early 2021, Scatter Lab's AI-driven chatbot Lee Luda logged 70 million chats on Facebook. She was a bot capable of playful small talk, with language patterns based on about 100 billion KakaoTalk messages. It was a great achievement — until the bot veered into hate speech about minorities.

As developers explained that Lee Luda required more time to learn, this case reminded us just how much a chatbot's reliability depends on its NLP capabilities and the data on which it is trained.

Two ways to develop a chatbot

Companies can either use an existing platform or they can build a bot from scratch.

Using a platform — like Slack or Facebook Messenger — is easier but more limiting because you cannot use the bot on any other platform. That's why many businesses have turned to the second option: using chatbot development tools to create their own bots, which they can use anywhere.

Building a stand-alone bot can be done using tools and frameworks, like IBM's Watson, wit.ai, and others. This isn't easy. So if you decide to go for it, make sure you're solving the right problem with the appropriate approach.

From years in the field, the STRV Data Science department has learned the importance of avoiding misplaced motivations. Before deciding on which chatbot works for your business, there are multiple steps that every business needs to take; otherwise, you risk losing time, resources and customers.

Define the exact problem you're trying to solve to establish the chatbot's target specialty. Just because an ML-driven bot is possible doesn't mean it's a necessity. Then, outline precise requirements and expectations down to the last details. Finally, don't underestimate the importance of interactive improvements and implement in a way that makes sense for your users' needs.

Most industries are on board

Used as a targeted tool, chatbots can increase engagement up to 90% and sales by 67%. In 2020, 57% of businesses said conversational bots deliver substantial ROI for minimal effort. Such incomparable numbers are why bots have immense benefits for industries such as fintech, healthcare, retail/ecommerce, education and travel.

Below are some examples of successful chatbots and their impact within these industries.

In a decade, struggles will be obsolete

The past two years of remote interactions have accelerated the adoption of chatbots. 81% of industry leaders say the pandemic changed their technological needs, and the majority of consumers now prefer chatbots over other customer service channels.

In the near future, 75% to 90% of queries will be handled by bots. Businesses will begin allowing users to pay directly over live chats. The usage of websites will decrease because chatbots will take care of the exhaustive browsing process. And interactions with AI will be indistinguishable from a conversation between two people.

How quickly we move towards that future is in the hands of those leading today's chatbot revolution. The question businesses should now be asking isn't if they'll jump on the bandwagon, but when.

Jan Maly is the head of data science at STRV, a software design and engineering company based in Los Angeles. Maly has worked as a data scientist and software engineer since 2014.