While developments have been meteoric over the last five years, there are significant stumbling blocks that many enterprises may not consider before racing ahead on their AI journey.
The blind spot for enterprises? Dirty data.
Unpicking dirty data can aid the implementation of AI and help encourage broader adoption across enterprises. Conversely, bad data management can leave decision makers wondering why things aren’t going well.
Take the Same Approach to Data as You Would to Everyday Tasks - The AI Cheat Sheet
Brion Johnson, Director of Presales at TechSee, explained this in further detail by using the analogy of motorcycle journeys.
On the surface, it seems like a simple A to B journey, but scratch the surface, and you’ll find considerations such as road conditions, health conditions, time, cost, and safety.
The same can be said for their respective AI journeys in enterprise terms. Instead, companies should “try to think, what do we need to do to plan effectively and avoid dangerous situations? Many things are always involved and require multiple data sets.”
If you don’t have this information, then AI rollout could be hampered - obtaining this information is integral. To do this, organizations must find that source of truth when managing their data. For TechSee, visual customer engagement is the key to embarking on an AI journey.
Johnson explained, “Who or what is the trusted source is step one of this process. Industries and companies that are looking to rely on any information need to vet it, whether text, audio, or visual.”
Data needs to be trusted, it needs to be confirmed by someone or something that authoritatively says it is the correct information.
Visual Customer Engagement Can be the Cornerstone to Better Customer Support
While TechSee focuses on being a visual customer engagement platform that leverages AI and computer vision to enhance customer support and service automation, Johnson explained that synthesizing data, whatever the approach, into a “simple question and then providing discrete directions or suggestions to resolve that situation is key.”
TechSee’s use of visual data allows for the extraction of this synthesized information and helps improve customer support.
“At TechSee, we can provide that patchwork where we know who you are and what device you have a challenge with. We know the problem. We know how to solve it on the computer vision side, because we have that trusted, verified data.”
But What About Enterprises Already on Their AI Journey?
While TechSee offers expertise in implementing clean data across enterprises, a growing dilemma exists in this field: early AI adopters are hitting dirty data roadblocks due to a lack of preparation.
Johnson highlighted how this can be a significant barrier to the wider adoption of AI and has the opposite impact on customer support as intended.
