Series: Four perspectives you shouldn’t miss when selecting a Conversational AI platform – #3 Total Cost of Ownership
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 total cost of ownership when choosing the ideal Conversational AI platform.
Total Cost of Ownership
Identify the hidden costs of the deployment models
What is a deployment model? Well, most CAI platforms can be offered in different ways: SaaS, on-prem, hosted, opens source etc. Let’s assume you and your security team have done the homework and that all the deployment model options are on the table. What cost aspects an important to consider?
Does a pre-pay model cover a hidden cost? A prepay model is a commercial model often linked to the deployment model where the client needs to commit to a certain resource consumption or ambition. The dynamics of a pre-paid model is quite often “you pay for consumption X, if you consume more you need to pay more, if you consume less, you still pay X”. One aspect I recommend you to consider is that Conversational AI projects typically are a bit difficult to estimate, then can go slower or faster than expected. They can hit a larger or smaller audience than targeted. A pre-paid model can easily actually in hindsight turn out to be quite expensive.
Another hidden cost is the IT management cost of running and maintaining the installation yourself. Even if you pay for a license + support for an on-prem version you still need to pay for hardware, daily management, tools for monitoring or on-call support in the middle of the night. Plus think about what your team will spend time with. If you scope a team for building, but they spend most of their time on infra issues, that’s quite expensive.
Always try to think about the hidden cost of each model, to be able to try to estimate the total cost of ownership. Think about how much it will cost after first deployment for maintenance and testing, etc., across the lifecycle. Do not overlook the fact that a Conversational AI platform needs to support all the phases after going live as well. This is where version control, published flags, re-factoring, regression testing, analytics, performance dashboards, improvement dashboards, etc., come into play. If you choose a tool that only helps you with your first build, you will end up paying a lot more from the efficiencies incurred later. Always think about the entire lifecycle of the program and what is needed in different phases. Make sure you select a tool that is up to the job.
Will your resources spend the time on the right thing?
Are your resources efficient across the program lifecycle?
Everyone wants to be productive. That is the same across all disciplines and industries and for sure is true for conversational AI developers. All conversational AI developers I know of, both love to build and to deploy. They want to be productive in their domain.
So, try to see past the fancy words and promises of quick wins in Conversational AI and ask yourself – will this tool make my team productive?
And not only productive in their first 1-2 month in the project but also two years from now when things have scaled up and become more encompassing but also more complex. Great teams build great bots – your job when selecting a Conversational AI tool is to help the team be as productive as possible across the entire program lifecycle.