Series: Four perspectives you shouldn’t miss when selecting a Conversational AI platform – #1 Tool Selection.

Businesses are quickly acknowledging the importance of Conversational AI (CAI) to increase their customer engagement and revenues. The question is no longer whether to deploy CAI, but rather which platform to use and how to leverage its capabilities.

In this series, Daniel Eriksson, Chief Innovation and Customer Success Officer at Artificial Solutions, gives insight on important aspects of a conversational AI platform that buyers often overlook.

For example- What does languages support really mean? What is localization? How do different deployment models impact the TCO? And maybe most importantly – How can the CAI platform not only help me during the first development sprints – but across the entire bot lifecycle?

Lessons learned to make bot developers more productive.

During the last six months, I’ve had a lot of conversations with companies (clients) and system integrators (partners) who have been building conversational bots. I’ve spoken with conversational bot developers, data linguistics reps, integration engineers, conversational designers, project managers, senior stakeholders, product owners and many more.

At the same time, I’ve talked to existing, new, prospective and former clients. These talks included people who had ambitious plans and succeeded, and others who have had plans where they have struggled to generate impact.

One of my goals for these discussions has been to find the answer to the questions: “How can we as CAI tech provider help you be more productive?”. This is a question we from Artificial Solutions will continue to ask, because the feedback from the bot developer community on our products is key for us in our mission to create products that make bot developers more productive.

However, in these conversations, another topic of discussion arouse that is the focus of this post: How do companies view their choice of CAI platform today – and what aspects would they recommend other companies to consider when selecting a CAI platform for bot development?

You could argue that this is probably a question best answered by someone who is considered an independent third party – and I fully agree with that. However, this is an issue for which it is worth having an open conversation, so I thought I’d share some of the learnings I took away from those discussions and added some of the learnings we at Artificial Solutions have from our 20+ year in the industry.

If this compilation is useful to you, or think I’ve missed something – don’t hesitate to leave your comments in our forum.

I’ve grouped the insights into four main themes which I think could be useful for companies to think about when reviewing CAI tooling for bot development and will share them in this daily series.  These are the “4 perspectives you shouldn’t miss when selecting a Conversational AI platform”:

Let’s run through them one by one. Curious to see if you agree.

1.    Select a tool your development team can grow with

See past the buzz-words like “awareness”, “understanding”, “self-learning”

Conversational AI is a fascinating space and still holds a lot of potential yet to be explored. Yet most companies who have experience of CAI tooling will tell you it’s all about engineering, and actually has a lot of resemblance to regular software or process flow development instead of being something ground-breaking new.

Sure, there are some terminologies both useful and specific for the space, like “intent recognition”, “entities” and “context”. These words are related to the Natural Language Understanding (NLU) part of a conversational bot.

Don’t get me wrong – those are complex concepts, but the conversational bot developers normally USE these functionalities they don’t develop them themselves. To the conversational bot developer, those types of functionalities are assumed to work and, in all fairness, most tooling support strong intent recognition, it’s more of a commodity functionality today. You can assume a toolkit has a good enough intent engine and move on.

However – be aware if the tooling you are selecting describes features with excessive AI-like buzzwords like “awareness”, “understanding” or “self-learning”. These aren’t wording a developer would use to describe the conversational bot they have developed (unless they are desperate for funding). So, I suggest you try to see past these buzzwords and select a tooling that uses concepts and terminology that better resembles how a developer would look at the world.

Can I be more specific? Sure, I prefer if well-established concepts like local or global variables (variable scope) are referred to as local or global variables rather than “The AI understands and remembers”. Review a tooling from the aspect – is the terminology used familiar to the developers who will use it to build bots.

Find the balance between pure coding & drag-and-drop

Have you ever heard about low-code, or no-code? In short, those concepts describe a user interface where a developer can configure or graphically design a process instead of having to write programming code. It is a great way to visualize how a program is executed and can be a quick way to build some things rapidly. Here comes the tricky part – for an effective Conversational AI solution with some ambition – you will still need to code. Your team will need to write code in some scripting language. If not, you will not be able to do the things you expect a bot to do. Do not shy away from this fact, the scripting and coding are super important to make a bot great. So, when you look at a toolset, evaluate it from the standpoint “how will the coding part work?”.

On the other hand, if you ONLY write code and never use any graphical representation you will run into another set of problems. How do you collaborate with the business? How do you involve team members with important insights that are needed for a successful bot implementation, but those team members do not read code? This one of the many aspects of why a graphical representation can be very useful.

Therefore, think about what balance between coding and graphical configuration you need. Because most companies need both for their CAI projects.

Consider what limitations you might encounter down the road.

There are a lot of CAI tooling in the market today available to developers. Your job is to make sure that you don’t select a tooling that is quick to build only the first MVP, but also is useful for every new generation of your bot. When your ambitions grow, and your insights on how you can deliver a better bot user experience starts to develop – you might realize that the tool you chose is holding you back.

For each generation of your conversational bot, there are multiple factors which you need to consider. If you build mode processes (and with that more intents) you run into one set of problems. If you want to make your flows more advanced, you run into another need for capabilities in the toolset. That’s when it might be too late to realize the tool is not up for the job.  

Try instead to evaluate a toolset from a scenario where you already have a quite extensive bot deployed. With this, try to think about the different tasks you might want to perform now. How do you perform refactoring and release small improvements in parallel? How do you organize all the processes/intents and perform a version control? How do you ensure reusability? What technical features might you want to explore?

Allow for creativity

Strive for greatness. We think great teams build great bots. Make sure you think about and choose a tool with which your engineers can be creative, and iterative with. The same way a good website is continuously updated, with new features tested, explored, expanded, or removed your conversational bot also needs continuous improvements. The freedom to explore, test, release and sometimes sunset ideas are key to unlocking your team’s creativity and ambition. A conversational bot program is no different. Think about what your expert implementors want to do and ensure that they have the capability in the tool to achieve their objective. Great teams build great bots unless the tool holds them back.

Don’t miss out on the following posts in this series to gain key perspectives to consider when selecting a conversational platform.

Daniel Eriksson
Author:
Daniel Eriksson joined Artificial Solutions in December 2020 as Chief Innovation and Customer Success Officer. He has 15 years of Business Development and Technology Leadership experience, the last 7 of which has been spent in the automation and Conversational AI space. Daniel has a Master of Science in Engineering Physics at the Royal Institute of Technology as well as Masters in Business and Economics at the Stockholm School of Economics. He lives in Stockholm, Sweden.