Kim: Yeah. This is the really amazing thing about the cloud because once the data’s all there, amazing things can be done with it and innovation is happening like crazy. And we are seeing this now with everything happening with OpenAI and ChatGPT and all this. And in Power BI, we’ve shipped a bunch of AI capabilities in the platform. And an important aspect of the AI capabilities that have been really, really useful are the ones that business users can use. So things like natural language query where you can ask a question and get an answer as a chart, or a key influencer analysis where you can ask the system, “Hey, what’s influencing my cancellations? Which measures are influencing that?” And even with our latest AI feature, we actually use GPT-3 to generate code for business users to write measures in their dataset. So they can easily generate code to calculate year-over-year calculations or even more complex calculations just through natural language.

This really allows business users to dig into the data like they never have before and just to work with data and build that literacy that they never had before. And some of our biggest customers, there’s a retail company we work with where 40% of their users are using these features on a regular basis. So you have people who just used to open a report, get a number and move on. Now they can just do so much more with it and they can ask those questions themselves. Both it makes the business more efficient of course, because they don’t need data scientists doing this work. A business user can do it on their own, but man, it makes the business users, and the whole line of business, it opens up a whole set of possibilities that they never had before.

Laurel: And that’s a really great point. Anil, you don’t necessarily have to have data scientists to help with this kind of insights that you gained from the data. So you mentioned a number of back office operations like taxes and ERP or enterprise resource planning. So how else do you see people being empowered to make decisions and actually not just spend less time maybe in the depths of spreadsheets, but also then innovate and change the way that they offer goods and services?

Anil: Absolutely. That’s a great question. And Kim’s comment about OpenAI and ChatGPT bringing in a lot of differentiated thinking and capabilities, changing the roles itself of business users versus data scientists as part of it. How we look at some of the functional teams adopting these technologies is a multifold approach, correct? One, we see a close collaboration with the cloud service providers like Microsoft where that innovation and capabilities of AI, machine learning, for example, text mining. And simple things like text mining used to be a data science experiment before, we used to come out with a hypothesis, especially in health services. If somebody wants to take a stream of text and find out, “Hey, what’s a disease? What is a prescription, and what is a diagnosis?” All of that used to be a machine learning model that used to do it.

But Microsoft has open or applied AI capabilities, you can just send that stream of text and it’ll automatically give you output in terms of, “Hey, what’s a disease?” the categorization of disease versus symptom versus medication versus the doctor, out-of-the-box class classifies it for you. That’s a simple innovation, I’m not even talking about OpenAI or anything like that. If you got to use some of these capabilities, you’ve got to keep close touch with hyperscaler providers like Microsoft Azure who are pouring in a lot of investments into innovation and bringing these capabilities. And there are a lot of these tech forums. It can be a CDO [chief data officer] forum, it’s a tech innovation forum, it’s focus groups discussions that bring about innovative capabilities that can run on any hyperscaler. That’s another venue that we need to keep contact with. And one more thing I would say is tactically, when we are recommending architecture designed to customers, we recommend doing a very modular architecture so that the switch of capability becomes easier. For example, switching of OCR engines or language translations engines or a few examples where things are continuously maturing.

If you build your architecture in such a way that’s very modular, then that switch would be very easy as well. And ultimately it all boils down to a very diverse team that’s delivering these capabilities. Encouraging training, advanced training, and having that diverse skill mix of technology business like you talked about and mixing that up, obviously it brings new thinking to the team itself and thereby we’ll be able to adopt some of this innovation and capabilities that come out from the market itself. So that’s how I look at this impacting some of the large ERP or back-office transformations like operations or even tax. We can definitely use some of these capabilities there. For example, tax. For tax, there’s a whole big data stream that comes from unstructured data, it’s PDF documents, unformatted pieces of documents that we get, how do you make sense of it? There’s a whole big of AI capabilities that you can plug in that can bring the data into a structured format that regulators will believe as well. So quite a bit of impact from that.

Laurel: This gives a good example of what’s possible in the back office with so many operations now that the cloud platform hyperscalers like Microsoft Azure offer a number of these capabilities. How do companies then create interoperability opportunities between the cloud platform and the latest emerging technologies as well as staying really focused on data governance, especially for those highly regulated industries like finance and healthcare?

Anil: See, most enterprises have a good data governance set up where definitions are agreed on, and it is in the realm of regulations that that industry supports already. For example, if you look at the mortgage industry, somebody comes and asks you for a loan, there are certain elements of that customer, you can disclose to other parts of the organization, there are certain elements you cannot disclose. So that governance is well set up, from a data perspective. When it comes to applied AI services, Microsoft Azure and other platforms already take into consideration some of the ethical aspects of AI. What can we do with analytics from a prediction perspective? What can we not? So we’re covered from that standpoint.