The Big Bot Wave

 The Big Bot Wave

Although artificial intelligence (AI) has been around for decades, of late a new wave of “.ai” is sweeping the technology world. This time around, it has manifested in the form of bots.

Tech giants of Silicon Valley have ventured into this space, either by building bots themselves — such as Cortana, Siri, or Alexa — and/or by coming up with bot-building platforms like Wit.ai or API.ai. Other smaller companies and startups were also quick to jump on this bandwagon, resulting in an overflowing “bot landscape” in the otherwise human-dominated world.

But that’s just the tip of the iceberg! On Facebook Messenger alone there have been approximately 35,000 bots built in last six months — news bots, weather bots, and scheduling bots, to name just a few.

The need to appeal to millennials who grew up with texting as the primary mode of communication and the popularity of messaging channels, such as Facebook Messenger, SMS, WhatsApp, and Slack, have paved way for the emergence of chatbots. Chatbots allow users to simply text their needs and be responded to. Having a personal connection with their customers in the channel of their choice has created a huge business need for chatbots in the enterprise sector. And the DIY-no-coding-needed bot platforms and several custom bot development startups are trying to fulfil the prophecy of “be where your customers are.”

The Business Need

Some say  bots are the apps of the future, while others disagree. However, the important question to ask at this point is “Are these bots solving any core business pain points in the best possible way?”

Sadly, the answer is more of a no than a yesCritics say that the current bots are just a bot-ified version of existing apps or fun toys at best. Only bots that address this question effectively will succeed, just like apps or pretty much any other software solution

The best use cases for bots are the ones where there is a real need for communicating with a human and therefore a preference for getting things done in natural language or at least in a natural “flow.” It is essential to first carve out a tightly defined use case that fits this criteria, rather than trying to force-fit an existing app into a bot. Of course, bot design comes later which includes building conversational flowcharts (or equivalent representations) of typical business flows, followed by an understanding of what things are best done via rich UI elements vs. plain text.

Are Banks Ready for Bots?

My decade-long career in the banking sector naturally makes me curious about how bots are transforming this industry. A super-intelligent, highly-personalized bot that manages your finances, credit card expenses, mortgage payments, tuition fees, medical bills, and savings while simultaneously optimizing your investment portfolio based on market trends may seem to be a fantasy.

Yet many fintech startups and some future-looking banks have started scratching the surface of the botiverse. One such startup is Kasisto, whose consumer banking bot MyKAI provides a natural language interface to all your bank and credit card accounts. You can ask the bot about your expenditures in various categories (food, entertainment, etc.) or even ask it to send money to your friends via Venmo. There are also several other personal finance bots available now like TrimAbe, and Penny.

As recent articles suggest, banks are also looking for new ways to communicate and serve their customers better. For example, the Amex bot enables you to connect your Amex account to Facebook Messenger to receive notifications related to purchases, card benefits, promotions, etc.

Although these are nice use cases, they do not solve one of the most critical pain points that banks have – customer service. For example, if you want to interact with your bank to make a credit card or loan payment, you would still need to go via one of the traditional routes – app, website, interactive voice response (IVR), or a call to the customer support number. IVR systems are clunky and impersonal, and users often get lost in their complex labyrinth of button-navigated options. Phone calls for such mundane tasks are irritating for customers due to long wait times and are often quite expensive for the businesses to support.

Although apps and websites provide an asynchronous channel for customers, they are often expensive to build, maintain and customize across various platforms (iOS vs. android vs. windows), browsers, and form factors. Also, with ever-increasing number of apps, the precious real estate of your phone starts getting cluttered and diminished.

A bot simplifies a lot of this. Bots seamlessly integrate within existing popular channels of communications using simple APIs, thus providing the “build-once-deploy-everywhere” advantage. The UI is relatively simple with a few basic components like choice menus, carousels, and URLs., making them easily adaptable for various organizations in an industry. The main challenge in bot building is conversation design and understanding and responding in natural language, which, in general is a hard problem in the areas of natural language processing (NLP), AI, and machine learning.

So how do you get around this challenge? The key is to first identify common, yet simple, use cases that can be solved by a bot in a step-by-step fashion. For example, the most commonly performed operations during a loan lifecycle are generating monthly statements, instalment/lump-sum payments, loan payoffs, or requests for extensions. Each of these tasks can be clearly explained using a simple step-by-step process without much customization (imagine the steps involved in paying your credit card bill).

So, the first version of the bot can be purely menu driven for addressing these frequent use cases. More complicated processes like loan origination, refinancing, or renewal, that often need human involvement, can be tackled later. Also, the bot can be made smarter progressively, by adding simple layers of NLP, for example, understanding dates/times or simple intents entered via free text.

How Can SAP Support the Bot Wave?

With enterprise offerings in many domains, SAP has robust backend systems with feature sets that address essential business process flows. There are existing, standard, secure enterprise services (APIs) for most processes, which can be used to build bots very quickly. For example, a lending bot that lets consumers perform most common operations via a messaging channel of their choice could be extremely lucrative to small financial organizations that are ready to invest in a core banking backend system but do not have a mobile app to integrate it with, or are looking for ways to improve customer interaction.

In essence, the rich heritage of enterprise backend applications that SAP offers coupled with the right business use case will drive the success for enterprise bots.

All in all, 2016 can be described as the year of bot emergence, and recent trends show that moving forward, there will be considerably more involvement of bots in our daily lives.  Seventy percent of American consumers say they prefer using chatbots to interact with companies for common interactions over other channels. So, bots and companies that cater to such needs will gain and flourish.