Q: Has the role of artificial intelligence advanced when it comes to analytical decision-making and what are the concerns that come with that?
A: Yes. AI has created a much broader channel to insights that are driving decision making. And we're seeing real benefits within organizations. As one example, an organization that leveraged AI, specifically looking at cross-selling and upselling in their B2B channel, resulted in over an 8% increase in their B2B revenue. The advantages and ability to capitalize on insights are there, but we also have an issue with trust in AI. This is something that we're seeing emerging globally on the regulatory landscape as well as within organizations.
We have to ask questions. Are we properly managing the data that is feeding into AI models? Are we ensuring that we are managing bias to the best of our abilities to be able to trust AI models? And furthermore, we need to have governance over the models, the data – the full life cycle of the production of AI. This is something that on a region-by-region basis has the attention of regulators. We're seeing new regulations emerge in Europe and now in the US.
Q: How can data play a role in understanding how an organization's operations have an impact on the environment?
A: As we look to measure our carbon footprint and hit targets related to how we will reduce our overall carbon footprints, one of the foundational areas that enable this is the data – data coming off sensor devices that are measuring carbon, methane and other things. Going back to that trust in data and trust in AI, we have to be able to trust those data sources. Putting methods in place that enable us to not only capture that information, but then use it to measure and inform future decisions as it relates to how we can impact our carbon footprint is absolutely imperative.