Six Reasons Why Chatbots are Taking Over Customer Service

This post originally appeared as Six Reasons Why Chatbots are Taking Over Customer Service on COO, Carlos Meléndez’, Under Development blog at InfoWorld and is reprinted with permission from IDG.


Did you happen to call a retail site with questions about your order this past holiday season? Chances are you interacted with a robot. Or, maybe you changed your healthcare plan during open enrollment and had questions about your coverage. The person on the other end, may well have been a bot.

Chatbots are drastically changing the way customer service is provided in a variety of industries and opening up untethered possibilities for continued growth.

According to a report by Grand View Research, the global chatbot market is expected to reach $1.23 billion by 2025, a compounded annual growth rate (CAGR) of 24.3 percent; and within the global chatbot market, approximately 45 percent of end users prefer chatbots as the primary mode of communication for customer service inquires. Why is that?

The rise of smart devices, voice recognition advances and AI-powered apps means more customers today expect businesses to be reachable any time, know their likes and needs in advance, and respond instantly.

Given these requirements, chatbots are becoming a necessity for communicating with customers in real time.

Here’s what’s driving the growth of chatbots:

1. Advances in AI.

Smarter AI capabilities are now allowing companies to predict what their customers will need or buy based on their order history and from what they browse most frequently so the cart will already be prefilled with products the bot will pick. Such AI advances are allowing companies to know what you want before you even know you want it and enabling powerful new bots that seem to know you better than your closest relative.

The focus on the user experience. Today, customers not only expect 24/7 support, but they expect that support to be customized to their specific needs. The dreaded phone loop hell of the past decade, is giving rise to emboldened customers who refuse to wait in a phone queue to have their issues resolved or questions answered.

2. People spending less time on social media.

Today, people spend more time on messaging apps, such as Messenger and Viber than on social networking apps. This trend means people prefer speaking and being spoken to over social media. Chatbots are filling a unique need for voice recognition and taking over the role of humans.

3. Reduced development costs.

Since chatbots are basically server-side applications with a very basic user interface, they have been considerably simpler and faster to develop, release and maintain than mobile apps. Major software companies, like Microsoft, IBM, and Google have made available free development tools and frameworks that let you develop sophisticated robots at low costs using the latest advances in Artificial Intelligence, Natural Language Processing, Speech Recognition and other technologies.

4. Growth of conversation APIs.

Development of conversation APIs, which can interact machine-to-human and support queries in natural language instead of programming languages, will increase. For customer-facing applications, conversational APIs will be increasingly used in banking, insurance and healthcare.

5. The humanizing-technology trend.

Advances in technology have brought about a digital world that has made many things possible. One things that has been missing however is the human touch. Companies are looking for a way to efficiently connect with customers on a more human level and chatbots are becoming carefully programmed to understand the common pain points, slight inflections in a customer’s voice and present answers in an empathetic way, with a voice consistent with the brand.

Keeping Humans in the Loop

While chatbots are clearly reshaping the role of customer service, the industry now recognizes that it can’t do so without human collaboration. For example, a chatbot can handle questions about the cost of a mortgage, what it covers, average monthly payments or questions about receipt of payment. But sometimes customers have more subjective questions. Through artificial intelligence, a smart chatbot can be prompted to recognize such situations and pass the call on to a human.

Chatbots became mainstream last year and continue to gain steam in the new year as the more personal touch they can provide in an increasingly digital world continues to be refined. The key to their success, however, is continued learning that enables them to leverage relevant data to truly connect with specific customers and augment the role of humans.

Puerto Rico Tech Jumps Operational Hurdles and Client Challenges amid Hurricane Fallout

Four months out from Hurricane Maria, and the tech sector in Puerto Rico is still recovering from its aftermath. One thing for sure is the resilience and ingenuity that nearshore companies here demonstrated in not missing a beat when it comes to customer service, and developing and delivering on customer projects. You can read about it in the Matt Kendall’s Nearshore Americas story:

At Wovenware, we have continued to move full steam ahead, recently announcing a new practice for the development of AI software, e.g., chatbots, machine learning and deep learning algorithms. Our practice includes creating a private crowd for training data, expanding our team experienced data scientists and implementing a new GPU-based workstation.

Bringing Relief to Puerto Rico During the Season of Giving

A big shout out to Radiant Solutions, a leading provider of geospatial data and analytics services, which decided to raise much-needed funds for Puerto Rico during this season of giving. The company donated more than $7,000 to the Island Relief program of Foundation for Puerto Rico which is being used to provide food, clothing and basic necessities to the many people still recovering from Hurricane Maria.

As a cause near and dear to our hearts, Wovenware was proud to contribute $2,000 toward the final Radiant Solutions total. During this holiday season, the effects of Hurricane Maria continue to be felt across the island, with many still without power, water or food, and working to rebuild destroyed homes. I’m sure that the kindness of Radiant Solutions brought some joy to Puerto Rico during the holidays, as well as renewed hope for the future.

Many thanks also go out to Foundation for Puerto Rico, ConPRmetidos and many other organizations, which have been working tirelessly to bring relief to those affected by the storm, and to bring a strong future to the island.

On the Path to Pragmatic AI – It’s More Realistic Than You Think

While a lot of the buzz in artificial intelligence (AI) is centered around pure or open AI – human-like machines that look, speak and react like people – the real benefit today lies in Pragmatic AI. In fact, this type of solution, designed to solve specific real-world problems, is really the only viable AI option around today. And, while it may not get the same hype or fanfare as its pure AI cousin, the business benefits that Pragmatic AI can deliver cannot be denied.

With Pragmatic AI apps, organizations can sort through huge amounts of data quickly, and benefit from increased productivity, faster decision making, improved customer service and more.

Working Hand in Hand with Humans

Rather than trying to supplant humans, Pragmatic AI augments human capabilities through machine learning apps that handle discrete tasks.

For example, developing an app to help commuters more accurately predict when their buses will arrive requires a combination of human and machine collaboration. First, humans need to sort through a massive amount of satellite images and identify which ones are buses.

A data scientist uses large quantities of this data, along with custom-designed algorithms, to “teach” deep learning apps to “see” buses. Once this trained app is connected with live satellite feeds, it can then predict the bus schedule in real time.

Sorting through vast amounts of data and images, labeling and cleaning them are critical to the development of accurate algorithms. Some firms employ a dedicated private crowd, or group of data specialists, whose sole job is to do this sorting to expedite the process of preparing the large amount of clean, extremely precise identification of specific objects and data needed to ensure the highest quality algorithms.

Pragmatic AI is being used in numerous industries — to help call centers provide a better customer experience, predict defects in medical products before they go to market, identify the latest cancer treatments to meet a patient’s needs, improve the care of diabetes patients and much more.

The Challenges

With the significant business benefits that Pragmatic AI delivers, what’s holding organizations back from embracing it right now? There are several challenges that need to be addressed:

  • Knowing where it’s needed. Before organizations can implement a Pragmatic AI solution they need to figure out the specific problems that can benefit from Pragmatic AI. What are the key performance indicators (KPIs) that they want to measure? Where would AI fit in the business workflow?
  • Making sure they have the right data. Without accurate data, companies are not going to get good outputs. Consider a company that wants to reduce some of the calls to a call center using a virtual agent or chatbot. To create an effective solution, the company needs to accurately identify the top problems callers have so they can program the chatbot to proactively address them. If they don’t capture this information accurately, it will not do much to help alleviate the volume of calls.
  • Finding the right people for the task. It’s no secret that there’s a shortage of data scientists and data engineers. Most companies don’t have these resources in-house so they need to look externally to find them.
    Despite these challenges, there are clear steps that companies can take to implement Pragmatic AI in their organizations.

Here are some initial first steps they should take:

Find the right partner. Pragmatic AI requires advanced skills, experience and the right resources. The partner should have a team of data engineers and data scientists with the advanced skills to handle the complexities of deep-learning algorithms. Some firms are offering specialized “Insight-as-a-Service” practices, leveraging their expertise in a particular area and best practices gathered from successful AI-based projects to address a company’s AI needs more quickly and accurately.

In addition to having a strong team, the right partner also must have the right resources – such as specialized GPU-based servers. The massive computing power these supercomputers offer enable users to rapidly train extremely large deep learning datasets in as little as two hours, instead of the hundreds of hours it would take on a CPU-based system.

Get the data in order. A good pragmatic AI solution is only as good as the data, so companies must ensure that they know where all their data is, clean it and centrally store it.

Also, it’s important to consider that developing an app is only the beginning. The data and subsequently, the algorithms, need to be continually refined and re-trained, requiring ongoing work.

Pragmatic AI is very do-able, and organizations can begin seeing results very quickly. It can be implemented in small, incremental steps by identifying specific problems in key areas and addressing them. By augmenting human activities with machine learning, all types of companies can work better, faster and smarter – and position themselves for significant competitive advantage.

Wovenware Launches Artificial Intelligence Practice to Support Increased Demand for Deep Learning Solutions

Wovenware, a nearshore provider of smart software solutions, today announced that it has launched a practice specifically focused on the development of Artificial Intelligence (AI) solutions, including chatbots, predictive analytics, machine learning and deep learning applications. By leveraging its expanded team of data scientists, advanced AI server, private crowd and best practices, Wovenware is enabling organizations to gain greater business value by automating critical tasks and using data-driven insights to make better business decisions.

“While most companies recognize the unprecedented benefits that AI-based solutions provide, the complexity of deep-learning algorithms, the shortage of skilled data scientists and the need for specialized GPU-based servers and workstations make it difficult for them to transition to AI-based computing on their own,” said Christian Gonzalez, CEO and co-founder, Wovenware. “Our focused AI practice is designed to help companies accelerate their AI transformation initiatives and begin reaping the competitive advantages immediately.”

Expanded Team of Data Scientists for the Full Lifecyle of Services

Wovenware’s new AI practice provides the full lifecycle of machine learning services — from preparing training data to deploying deep learning algorithms. Led by Leslie de Jesus, data scientist and Innovation Director, Wovenware has appointed a team of data scientists to work alongside its more than 50 software engineers to implement key AI-based initiatives for clients in industries from government and manufacturing to healthcare, transportation and more.

In addition to its highly focused expertise, the team will draw upon best practices gathered from its successful AI-based projects. Some of the projects include helping a medical device manufacturer predict the likelihood of failed components; developing deep learning algorithms that enable a government agency to identify specific objects on a map; or creating a chatbot so that a university can better respond to inbound student questions.

Private Crowd

Sorting through vast amounts of data and images, labeling it and cleaning it are critical to the development of accurate algorithms. Wovenware is one of the first companies to employ a dedicated private crowd, or group of data specialists, to accomplish this task and expedite the process of preparing the large amount of clean and accurate data that AI apps require.

The team of more than 50 Wovenware data specialists who comprise the private crowd understand the nuances of how algorithms are taught and how smart apps can learn, to provide extremely accurate and precise identification of objects, images and data sets to ensure the highest quality algorithms.

GPU System for Deep Learning

Wovenware partnered with systems integrator, Microway, to design an advanced, purpose-built supercomputer that provides the GPU capacity required for today’s deep-learning applications.

“We worked closely with Wovenware to design a GPU-based system that that will allow it to rapidly train extremely large deep learning datasets– a process that could take hundreds of hours on a CPU-based system. The Wovenware team tells us it is achieving these results in as little as two hours on the new GPU-accelerated deployment” said Stephen Fried, Founder and President of Microway. “We’re thrilled that Microway’s factory integration of deep learning software is helping Wovenware achieve these results quickly and efficiently.”

Staying on Course Despite the Ravages of Hurricane Maria

How did we survive the impact of Hurricane Maria? One approach was to treat our business like an emergency room and triage the most important client needs first. By evaluating priorities and deadlines, we were able to re-assign workloads to address the most pressing needs first and make sure all client needs are being met.

Check out the Entrepreneur Magazine article where COO Carlos Meléndez shares his experience and lessons learned: