Are you aware of hidden costs from Artificial Intelligence Vendors?

For Financial Institutions, choosing the right vendor for Intelligent Virtual Assistant(IVA) technology involves various factors.

In this blog, we will deep dive into Pricing.

Pricing plays a critical role in the decision-making of financial institutions. Financial institutions prefer vendors that offer predictable and transparent pricing.

There are multiple pricing in the industry and many of them involve hidden costs that mislead financial institutions.

Let us take a look at how interface.ai’s pricing compares with other IVA players in the industry

Pay for performance

With other vendors:

The majority of the vendors operate with a fixed fee pricing where FIs pay for the technology irrespective of whether they use it or not.

There might be a lot of instances where the AI does not work – the FIs, however, will continue to pay for the technology.

Also, on average ~10% of the calls are usually blank calls and FIs will end up paying for these calls as well with this pricing.

With interface.ai:

FIs pay only when interface.ai automates their member inquiries.

interface.ai is the only vendor, where the Financial Institution only pays for the results and not just for using it.

Post Launch AI Fine-tuning at no cost (Managed Services)

IVAs would need ongoing maintenance and support post-launch, which would necessitate a dedicated team with AI expertise.

With Other Vendors:

FI’s need to hire additional employees with AI expertise, to manage the IVA after the launch.

With interface.ai

interface.ai manages the end-to-end AI implementation and provides ongoing support at no cost to FIs. With this FIs need not worry about hiring additional employees with AI expertise.

A dedicated team from interface.ai will handle the implementation and AI-fine-tuning including training the AI with new questions.

Integrations to existing and new systems are also managed by interface.ai at no additional cost.

Furthermore, this team consistently maintains and monitors all the workflows as per the approvals from FIs.

This ongoing AI fine-tuning improves customer experiences & maximizes automation at no cost;

With interface.ai, implementing IVA technology no longer requires Financial Institutions to be AI specialists or hire additional resources.

To know more about interface.ai’s world-class Managed Services, click here.

Hidden Costs

With Other Vendors:

There are various hidden costs that will increase the cost of ownership and implementation by over 100%.

As mentioned above, all the vendors might not convey the need of hiring additional staff for the post-launch activities and maintenance of the IVA.

FIs might also need to pay for just using their platform/product irrespective of the performance or the results it is providing.

With interface.ai:

There are no upfront costs or hidden costs. interface.ai’s pricing model is transparent with FI’s only paying for the impact generated by their IVA.

interface.ai is the only vendor which provides these impact guarantees in writing.

Here is why interface.ai is the most preferred vendor for Intelligent Virtual Assistant.

Conclusion:

Financial Institutions are leveraging IVA to enhance Customer Experience & Operational Efficiencies. By partnering with interface.ai, Financial Institutions can have predictable costs and ROI.

Most of the vendors do not provide white-glove services in the post-launch phase of the IVA and the FIs are left to fend for themselves. FIs find it difficult to manage & improve the technology themselves and would require hiring resources with AI expertise which costs a lot of money and supervision.

In addition to providing transparency with performance-based pricing, interface.ai manages the end-to-end process including ongoing post-launch support at no cost with its Managed Services offering.

Choosing between interface.ai and another vendor when it comes to an AI setup is purely a choice of win-win or lose-lose.

You can read more about the impact we have enabled and customer testimonials here – https://interface.ai/case-studies/