Q: Where have companies made progress with data and analytics during the pandemic, and what work still needs to be done going forward?
A: The use of analytics has made great strides throughout the pandemic, and one of the trends we're seeing is that companies continue to invest aggressively. In an EY study, 93% of companies said they plan to continue increasing their investments in data and analytics. But throughout the pandemic, organizations have invested in different ways. Many organizations realized they needed to catch up. Investing in just the basics is closely tied to how it creates value.
There were many organizations that took advantage of the pandemic in ways that accelerated other kinds of advances in AI. One example is conversational agents. As organizations grapple with supply and demand issues in their customer service channels, they are turning to conversational AI as a way to take advantage of new advances. But we're not quite there yet in terms of perfection or anything close to it. Many mistakes are still made. That's why organizations continue to position analytics and AI as an IT issue, when they should be looking at AI as a business issue and actually improving digital literacy across the organization accordingly.
Q: What should today's CIOs focus on and dedicate resources to as they look to the future?
A: Well, the most important thing is the result. When we think about how data and analytics are used within organizations, we need to relentlessly strive and focus on extracting value from analytics. The other is to advocate for cultural change. As I mentioned earlier, AI is a business problem, not just an IT problem, and driving change across the organization is also an area that CIOs should focus on.
Q: Has the role of artificial intelligence advanced when it comes to analytical decision-making? What are the concerns associated with it?
A: Yes. AI has created broader channels to insights that drive decision-making. And we are seeing real benefits within our organizations. As an example, organizations that leveraged AI and specifically focused on cross-selling and up-selling in their B2B channels increased their B2B revenue by more than 8%. Despite the benefits and ability to leverage insights, there is also the issue of trust in AI. This is something that is emerging globally, not only within organizations but also in regulation.
I have to ask. Is the data that goes into the AI model well controlled? Are we ensuring that we have maximum control over bias so that we can trust the AI model? Additionally, the data that goes into the model and the data, i.e. the AI production must be governed throughout its life cycle. This is attracting the attention of regional regulators. New regulations are being introduced in Europe and now in the United States.
Q: What role can data play in understanding how an organization's operations impact the environment?
A: When we're trying to measure our carbon footprint and achieve our goals around how to reduce our overall carbon footprint, one of the fundamental areas that makes this possible is data. Data obtained from sensor devices that measure carbon, methane, etc. . Going back to trust in data and trust in AI, we have to be able to trust those data sources. Putting in place methods that can not only capture that information, but also measure information related to how it impacts carbon emissions and use it to inform future decisions. Absolutely essential.
