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.
For those finding themselves in this position, he suggests implementing the 80-20 rule. “Tackle the biggest problems first. Think about one of the support issues that drives up challenges.”
After considering this and identifying the top issues causing significant problems, enterprises can consider where automation can fit into the solution rather than simply implementing AI.
TechSee takes each example as its own unique challenge, but the ones seen in the industry are typically “customer-facing issues that seem complex at first, but perhaps with some automation, some asynchronous processes, we can then get the trusted, reliable, verified information and then do an automation task,” according to Johnson.
Internal and External Objectives are Synced – But Are Often Different Paths
While guiding both the end user and the company to a resolution is the same objective, they can often be very different paths.
On this, Johnson told CX Today, “We need to think not only about the external facing things, but the internal facing things that make it easy for us to do. This is where AI and, in Techsee’s case, visuals, can make the biggest impacts.”
AI’s promise in customer experience is undeniable, but without clean, trusted data, even the most advanced AI-driven initiatives can falter.
Enterprises rushing ahead in their AI journeys must recognize that data quality is not just an operational concern – it’s a strategic necessity.
TechSee’s approach reinforces this by leveraging visual customer engagement to ensure data integrity and reliability.
Their expertise in synthesizing information allows businesses to verify, refine, and deploy AI to maximize efficiency and enhance customer interactions.
