Question: What lessons or tips do you have from your experiences about how to make customer focused AI projects a success?
The tips that we’ve seen are, one, we need to get started now. It’s important to remember that getting started is not just experimenting with the technology, but is also going through the process of reimagining what our business can be, and thinking about how we orchestrate that experience with the technology fitting in. We see many companies have started by leading with the tech first, and at different points in time that made sense, but now the technology has gotten to a level of maturity where we really need to reimagine the experience. The second thing I would say is, just as we orchestrate that end-to-end experience, just to make sure that we continue to manage the expectations of our business stakeholders. You know, we don’t want AI to be a one-shot effort. It’s really important that this is something that is an ongoing continuous improvement that evolves over time.
I think one of the most important things is reskilling and retooling your own staff to create this next generation of employees that really understand data and can leverage data. It takes a long time to get AI technologies right, and over time, while you’re leveraging things like MLOps and orchestration and data pipelining, you can accelerate some aspects. You have to say, how can I redesign my entire corporation and the way we process and relate to customers using algorithms and data in a way that could enhance the customer journey?
Question: Where do you see the AI space going in the future? And how do you think CIOs should be positioning their businesses to take advantage?
Customer service is a space where we see a lot of movement towards. There’s been a lot of effort in manufacturing to think about the concept of a digital twin and being able to do predictive maintenance on that digital twin to be able to get ahead of the failure and resolve any issues that happen before the machine goes down. And we’re working with a technology provider that is actually thinking about the way they do customer service the same way. We should think about each of the users of their technology platform and create a digital twin for each of those. And then, through the behaviors of how they use our product to be able to get ahead of any issues that we have by providing a proactive outbound experience.
There’s a trust issue in many companies now with AI, and what organizations need to do more of is to be able to educate their business leaders on how to think about AI, and also then to be able to put the right controls in place so they can get the insights that they need and understand how the AI got to the answers that it did.
Question: How can CIOs manage the danger of biased algorithms and put trust in these AIs?
The topic of algorithmic bias has been hot, not just across industries, but also nations. And governing bodies that are looking at how algorithmic bias can impact AI, and it may slow down the adoption. The idea here is that you have to have transparency around the algorithm, so people know how the decisions are made in the systems.
When we look at it in the context of how we can use algorithms, we have to also realize that the power of AI is in its adaptability, it’s the ability to change these algorithms as we learn more. And whether it’s the machine that’s learning, or it’s aided by human augmented knowledge, that power to really create an adaptive enterprise is where I think we’re going to see the benefits.