Trust is Key
The buzz of AI is rightfully an exciting breakthrough in the tech space, but has yet to be fully adopted or trusted by accompanying workforces. This is case, we can see that clients and customers are hesitant to trust this new product. The second reviewer provides the perspective that AI rapidly improves over time and can be very efficient for the company, but does not address how people’s trust of AI does not grow at the same rate. Oftentimes, we can observe incredible accuracy rates that AI continues to improve, but customers will nonetheless opt for a trusted human source. When thinking about the decision making process, the intended outcomes of this model are only effective in the case that they have customer retention. Since the main focus of the initiative is to save money on their margins, I have to re-emphasize the perspective from the first reviewer, where the focus should be on the margins and even pivoting the AI to help internally as opposed to externally. This is why I would not recommend launching the product to all the customers the way they had originally intended.
Tradeoffs: A Slow Adoption
Though a full launch may not be the best decision for the company, the team should still be considering modified versions of the AI release in order to reach Parity with competitors. If we take a more realistic stance to the untrained AI model, we can see that by prioritizing AI, they run the risk of sacrificing quality. Even if this is only an issue at the beginning before the AI bot is fully trained, this quick launch runs the risk of losing their customers that value quality, for example their highest paying customer immediately rejecting the idea. This could demote their brand image and they could move into a space with lower margins for lower quality. There exists a strong stigma in the current B2B industry in which AI is not trusted for its privacy or reliability, which could be detrimental to the company even if these issues are resolved, as customers may not be able to conceptualize this effective mitigation. Therefore, I propose an internal launch of AI to help internal operations and slowly launching the solution to early adopter customers who are open to exploring the increased efficiency.
