This is an excerpt from the upcoming Designed Analytics report, Business 4.0: Building Technology-Driven Organizations, which will be published on 06/01/2024.

Most of this report has been focused on how to build your technology capability in the form of a platform, that allows you to operate seamlessly as a technology company. But the fact is that even if you have built a perfect technology platform that integrates seamlessly with your core competency, you need the other two key enablers of processes and people to make it happen. As you may have assumed, the platform vision has modified business processes at the core of its architecture.

Let us explore that further using an example from Chapter 3. The example is illustrated in Figure 4.

Recall that the core of a technology-driven company is that many actions and decisions that directly enhance core competency are turbocharged by technology. Customer experience is the starting point for improving, solidifying, and enhancing core competency.

Suppose you want your platform to exchange seamless information between customer experience and product design, which can be leveraged by product managers. In that case, you need to develop a process around that. I am unaware of any CX solution that culls and translates customer feedback into product design recommendations. The fact is, with current generative AI capabilities, we can develop this capability quickly.

However, behind the technology, one of the key elements is the process. That process is standardizing guidelines around culling feedback into the design/feature recommendations category. For a large organization, the number of interactions they have with their customers may run into millions. For interactions and feedback that get translated into any form of text, audio, or video, capabilities exist to understand which of these pertain to meaningful product enhancement opportunities. 

But the guidelines around what is meaningful and what is not (from a product enhancement perspective) need to be structured. That is the process aspect. If that is not properly defined, it will be garbage in, garbage out.

The training data is at the core of any AI solution’s efficiency. The process aspect helps define the guidelines, which can then be used to generate training data. But who develops these guidelines? Your people, your product managers.