NICE has released an online tool that calculates how much money and time contact centers could save with AI.
It takes minutes to run a rough calculation through the "AI Value Calculator", with users inputting just a few key data points.
These include their location, team size, average call/chat time, call volumes, chat volumes, average wrap time, agent hourly pay, and industry.
From there, the tool allows users to set a target for the percentage of interactions they aspire to automate.
Then comes the result, which NICE notes is based on "industry averages".
Given this, actual savings may vary significantly. Nevertheless, Stephen Davies, VP of International Marketing at NICE, believes it’s an excellent way to show contact centers what’s possible.
Announcing the AI Value Calculator launch on LinkedIn, Davies wrote:
So many CCaaS players hide behind slideware and shiny AI messaging. Time to show the market what value really looks like!
Alongside the headline price saving, the tool estimates potential time savings in hours per month.
Additionally, it offers insight into the possible impact on various customer- and employee-focused metrics. These include first contact resolution (FCR), customer satisfaction (CSAT), agent attrition, average handling time (AHT), agent satisfaction, and agent onboarding time.
Critically, NICE details exactly how it has made its forecast across each metric. However, it also stresses that contact centers can reach out to book an "in-depth" review for a more detailed and accurate projection.
NICE’s Mission: To Showcase What’s Possible Beyond Generic AI & Point Solutions
The AI Value Calculator is an excellent tool to spark conversation and engage contact center leaders.
However, it also supports NICE’s key message of showcasing what’s possible beyond the "generic AI" and point solutions.
Such solutions have risen to the fore since the AI hype cycle kicked into overdrive, post-ChatGPT-3.
Yet, according to Barry Cooper, President of NICE CX, these tools prevent contact centers from realizing the full benefits of AI.
During a recent interview with CX Today, Cooper posited that the models must be trained specifically for CX use cases.