Series: Four perspectives you shouldn’t miss when selecting a Conversational AI platform – #4 Cross Vertical Readiness

As part of a 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. In this third post, he will focus on the importance of cross vertical readiness when choosing the ideal Conversational AI platform.

Cross Vertical Readiness

Most companies explore multiple use cases, make sure your tools are not locked in a vertical

How many conversational bots do you have in your company? If not many, ask around in your network and you might be surprised that there are often more than one deployed across a business. Most companies explore Conversational AI in both internal (employee facing) and external (customer and supplier facing) use cases. Therefore, it is important to think a head when select a platform for conversational AI. How many areas do we want to explore? Are we sure we only try to build one type of solution?
I recommend considering if the toolset you are selecting is horizontal or verticalized – meaning is it optimized for a use case or industry (vertical) or optimized for agility across industries and use cases (horizontal).

Because internal and external use cases are actually very different, even in the same industry. This is where it is important for you to think about which models benefits outweighs its weaknesses. I would like to point out some of the strengths of a horizontal solution, and the first one is that you can use the same development platform across your different use cases. This would also enable you to get support from a larger community than if you go for industry specific solutions that all your competitors are using. Remember, the conversational AI-space is all about building. Find the tool that gives you the most edge in your development process.

One can think about this from a different lens. What are the strengths of pre-trained solutions for a specific use case? You typically save time sourcing training data; you might have pre-built knowledge that you can reuse. This can be beneficial, but also has limitations. if you want to make changes, or you need to add specific training data that is unique for you can you do that? And does the use-case specific package help you in another use case?

There are probably exceptions, but the simple rule is likely that a Conversational AI platform either is pre-built and then a bit limited in terms of customizations OR more flexible and then you have to build more yourself. We think this is a world of builders and would recommend you to really think about the long-term benefits of choosing a horizontal solution.

It is really about building, so packages that are reusable across verticals are typically more valuable than use-case specific ones.

It might sound like the advice I’m giving now is contradicting my previous statement but here it goes – pre-built knowledge can really save you time! There is a reason why communities end up building great things together, everyone benefits in efficiency from the contributions of others. So, no doubt, pre-built packages are great. What might be surprising though, is that in Conversational AI, the pre-built packaged need to be very small to be useful. And with small, I mean for example a collection of intents instead of a complete “pre-built bot”. An integration package for salesforce can easily be re-used if it only includes the basic building blocks of the API connection to salesforce. Because then it can easily be incorporated in many different use cases – internal sales support, internal reporting, external customer support, etc. But a pre-trained “external customer support” package might even take more time to adapt to a different use case. Because you need to review and adapt every single component of the package and if you end up needing to restructure you might as well start with a white piece of paper instead. So really discuss with your vendor about pre-built packages, but make sure they are modular and fairly small. And remember to share modules you have built yourself with the community. That is typically a great way to share work and find improvements.

Your company and clients probably need a very specific tonality, make sure you can control it yourself

My last advice is probably a bit difficult to map in the top 4 I’ve mentioned – but I chose to file it under “Cross vertical readiness”. This is my take: Try to build your bots to reflect your company value and chose the tonalities that help build your brand. This is likely not easy, and I am not sure you really can get much help on that from external parties. You need to have your culture and brand promoters to be involved. Why is this relevant when selecting a conversational AI platform? Because in the end we recommend that you make sure that can collaborate with different competencies across your business and that you are in control so that you can design exactly the experience your users should have. Your customers will spend time interacting with your bot, it is one of the few bidirectional interactions your will have with your customer base – don’t waste it! Be in control, build your brand!