Chatbots – A Practical Use of AI and NLP That Can Benefit Your Organization Today
Date: October 24, 2017
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Situation Analysis

Since the late 2015, messaging apps have been dominating the market, when it comes to communicating over the web (see chart). This trend doesn’t seem to be subsiding any time soon and several companies have recognized it as the status quo of the infosphere. Although most of the communication that takes place there is text-based and fairly shallow, people still prefer it due to the convenience it offers, its speed, and its ease of use. As mobile devices are becoming more and more widespread and NLP / AI technologies more robust, these messaging apps are becoming more sophisticated. This manifests as a larger amount of them, but most importantly, on the a business case for chatbots, intelligent systems that handle natural language in a useful way, be it to help the user carry out tasks, retrieve valuable information, or just make contact with an organization.

Identified Problems

The main problem with this situation is that it disrupts the market, particularly when it comes to services or customer support, creating a certain level of instability. This results to a growing gap between the companies that can implement chatbot technology, and the companies that cannot. Another problem, is that the companies that can do it, don’t always have the world’s best interests in mind, while they are also the ones that will shape the development of this trend and to some extent, the chatbot technology. There are other problems that may stem from all these, but they are more generic and beyond the scope of this article.

Implications of these Problems

These problems all converge in a few implications that reach pretty much every company out there that has something to benefit from chatbots. The main implication is that an organization that doesn’t invest in chatbots is bound to be left behind. Even a basic chatbot is better than nothing at all. This is particularly important if that organization has customers in different time zones. Another implication is that chatbots are bound to become much better than humans in tackling customer-related problems, so even if an organization can afford to have human reps all over the world to handle customer inquires and basic requests, they are bound to be less adept than their AI-based counterparts. Still valuable, but more like a second option for most people.

Potential Solutions

The most effective solution to all this would be to democritize chatbots along with the technologies that enable them to be more useful to a larger audience. Although rule-based chatbots would be fairly easy to implement and would undoubtedly add some value to an organization, AI-based ones are bound to be more valuable and future-proof. Also, linking chatbots with databases so that they store conversations would enable their learning so that they can be both more effective and also personalized. Whatever the case, a more widespread usage of chatbots, across various industry sectors is bound to offset the potential issues that this new technology can bring, and also make them into something that can benefit your organization too.

What You Can Do Now

As AI-based chatbots are still an emerging technology, mature enough to be useful, yet novel enough to offer an edge to whoever utilizes it, you can look into ways to implement an AI powered chatbot system in your organization. Data Science Partnership can help you identify the business case of such an investment, while also provide you with the technical expertise, contract-based or in-house, that can undertake the implementation of such a system. Feel free to contact us at to learn more about how DSP can help your organization take advantage of this promising AI technology.

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Zacharias Voulgaris

Zach is the Chief Technical Officer at Data Science Partnership. He studied Production Engineering and Management at the Technical University of Crete, shifted to Computer Science through a Masters in Information Systems & Technology (City University of London), and then to Data Science through a PhD on Machine Learning (University of London). He has worked at Georgia Tech as a Research Fellow, at an e-marketing startup in Cyprus as an SEO manager, and as a Data Scientist in both Elavon (GA) and G2 (WA). He also was a Program Manager at Microsoft, on a data analytics pipeline for Bing.


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