What data are you using and where is it coming from? Depending on its source, time, and environment, data changes in value as it reckons with the moment in time it’s used. Email, social media, news, voice, podcasts, and direct interactions provide a wealth of insight, but must be considered from varied perspectives and intents of origin. With such varied vernacular, each medium and situation will have unique challenges that arise when testing the accuracy of the data they produce. Tapping from multiple sources requires its own set of analyses to realize the information’s full potential and to connect the insight generated from data as it relates to the source.
Without strategic analytics, multiple data sources can create their own set of challenges on top of the ones they are meant to solve. When correctly interpreted through AI analytics, the puzzle pieces of knowledge, needs, employee expectations, and optimum workflows form a complete picture that can improve accuracy, satisfaction, efficiency, and the overall bottom line.
It is important to train AI to understand what it can in a channel before considering how to action the AI’s output. We need the tools to know how well it’s interpreting the data sets and how they compare from channel to channel. We need to know if the AI is performing well, whether it is cost-effective, and how well it reduces the time it takes an executive or customer to manage a challenge. These factors should create higher employee retention, better conversion rates, and reduced cost structures.
With proper interpretation, it can detect mood, fatigue, and the result of challenges. It can measure sentiment alongside the context of the conversation. Add the analytics in and you can customize all the nuances of a user’s journey to move them in more effective ways. Well-planned AI automating these conversations will adjust on the fly to increase and track performance and remove unnecessary steps in the process.
Data is a vast, multifaceted landscape where strategy matters. AI provides proven maps to navigate the journey. When done effectively, AI proves its value of change over time and exposes issues that went previously undetected to build a solid foundation for managing budgets. The resonating upshot is that the cost of deploying AI isn’t a cost at all. It is an investment with a return that saves on budget and fills in brilliantly as a key adjunct for the success of your organization.
Vince Lynch is the CEO of IV.AI and speaks to the topic of AI and data ethics at the application layer. Both Vince and IV.AI are long-time contributors in the CCNG community.
