In 2020, it’s no surprise that modern, tech-forward finance companies are looking to adopt conversational AI platforms to maximize business efficiency and customer experience (CX). One of the best use cases for automated texting platforms is to automate top of the funnel sales tasks (like qualification, customer re-engagement and cold lead follow up).
When it comes to conversational AI software, it can be challenging to choose between the flexibility of building a custom solution, and the speed and reliability of a tried and tested third-party solution. It’s not just a matter of cost, it’s important to consider the efficiency, fit for your business’ needs, capacity and goals.
Before making the decision to build or buy, consider the following questions:
What are the business issues you’re trying to solve with conversational AI, and how unique are they?
For example, if you’re looking for straightforward, top of the funnel functionality such as quoting, qualification, re-engagement or follow up, there are third-party solutions to choose from that can be customized to your needs.
If you’re looking for complex, multi-faceted functionality, deep API support, or you’re solving a problem that is specific to your business, you may have trouble finding a workable solution on the market.
Do you have the funds you need to see this project through to completion?
Building a custom conversational AI platform costs more upfront than purchasing something that’s pre-built. Depending on the size and profitability of your business, this can be a deal-breaker.
What are the time constraints for implementing your solution?
Launching a fully-loaded custom solution takes time, which your business may not have. Additionally, with new AI-based technology such as conversational AI, it will require additional resources and time that traditional software does not.
Examples include: building, training and maintaining your own NLP model and designing and maintaining your own conversation scripts.
The answers to the above questions will help guide your decision, but it’s important to consider the pros and cons of each approach before pulling the trigger. To help you decide, we’ve laid out some of the pros and cons of building and buying.
Customization and scale: One of the biggest pros of building your own conversational AI solution is that it will include all of the functionality you need to operate in exactly the ways you need it to. The end experience will be tailored to your unique business model and challenges. Total control over the development also allows you to add any new features as needed, so your business can continue to run smoothly. You can start with a prototype and grow the software as your revenues grow.
Greater control: As the sole owner of the solution, you will have full control over user options, security measures and updates.
Deeper integration: Building your own solution means you can ensure seamless integration with any existing internal software, tools and processes already in use.
Significant upfront cost: Like anything that is deeply customized, a custom solution will cost a lot more than a third-party implementation.
Time to build: It takes time to identify your organization’s workflow processes and develop the software to optimize them. You have to be prepared to spend significant time gathering and learning this new information. Additionally, you should be prepared to dedicated significant time to training and improving your AI models with real production training data.
Internal resources & AI domain knowledge: Conversational AI is an entirely new skill set. In our experience, teams that chose to build will need to hire conversation designers, AI and natural language developers. Companies likely do not already have these resources internally.
Lower upfront cost: If cost is a concern and you have limited resources to launch (funds and/or team), buying your conversational AI solution may be the best option, since it is typically more cost effective to buy and implement.
Rapid deployment: Ready-made AI solutions only require days or weeks of configuration and onboarding. If an existing conversational AI solution meets most of your needs, there may be little value in investing time and money in developing a version of something that is already out there.
Updates, new features and ongoing maintenance: Third-party providers want to stay competitive. As well as keeping up with maintenance, a third-party solution will often be updated regularly with new features and functionality.
Pre-trained AI models and domain knowledge: Third-party AI providers invest large amounts of money and time training and maintaining their AI Models. This means that once you’re onboarded, you’re guaranteed to have one of the best experiences out of the box. Additionally, they have the expertise and team in place to execute conversation design and develop specifically for AI and natural language.
Less customization: While many third-party AI solutions enable customizations, the solution usually won’t be custom built for you.
Less control: The provider controls the solution’s updates, volume capacity, and functionalities. All of the key decisions on the software features and future are out of your hands.
There are a lot of factors to consider when making the decision to buy conversational AI software or develop a custom solution from scratch to meet all your unique business needs. If you’re on the fence and seeking more information on buying a solution for your Fintech or Finance business, feel free to book a discovery call and we can help you decide which solution is right for you.
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