Kim: Yes. That’s the most amazing thing about the cloud because once all the data is there, amazing things can be done with it and innovation is happening like crazy. And we’re seeing that now with everything that’s going on with OpenAI and ChatGPT and all that. And in Power BI, we’ve shipped a ton of AI capabilities to the platform. And an important aspect of the AI capabilities that have been really useful are those that can be used by business users. So things like natural language query where you can ask a question and get an answer in the form of a graph, or key influencer analysis where you can ask the system, “Hey, what’s influencing my cancellations? What metrics are they influencing it?” And even with our latest AI feature, we actually use GPT-3 to generate code for business users to write measurements to their dataset. So they can easily generate code to calculate year-to-year calculations or even more complex calculations using only natural language.
This really allows business users to dig deep into data like never before and just work with data and build this literacy that they’ve never had before. And some of our biggest customers, there’s a retail company that we work with where 40% of their users use these features on a regular basis. So you have people who usually open a report, get a number and move on. Now they can just do so much more with it and they can ask these questions themselves. Both make the business more efficient, of course, because they don’t need data scientists to do that work. A business user can do it on their own, but man, it opens up a whole set of possibilities for business users, and the whole line of business, that they never had before.
Laurel: And that’s a really great point. Anil, you don’t need data scientists to help you with this kind of insight you got from the data. So you mentioned a number of back office operations like tax and ERP or enterprise resource planning. So how else do you see people being empowered to make decisions and not only spend less time perhaps in the depths of spreadsheets, but also innovate and change the way they deliver goods and services?
Anil: Absolutely. This is a great question. And Kim’s comment about OpenAI and ChatGPT bringing a lot of differentiated thinking and capabilities, changing the roles of business users versus data scientists as part of that. How we see some of the functional teams adopting these technologies is a multi-pronged approach, right? First, we see close collaboration with cloud service providers like Microsoft where this innovation and AI capabilities, machine learning, for example, text mining. And simple things like text mining used to be a data science experiment, we used to make a hypothesis, especially in health services. If someone wants to take a stream of text and figure out, “Hey, what’s a disease? What’s a prescription and what’s a diagnosis?” This all used to be a machine learning model that used to do this.
But Microsoft has open or applied AI capabilities, you can just send this stream of text and it will automatically give you results in terms of “Hey, what’s a disease?” the categorization of the disease versus the symptom versus the medication versus the doctor, sort it out for you. This is a simple innovation, I’m not even talking about OpenAI or anything like that. If you need to use some of these capabilities, you need to maintain close contact with hyperscale providers such as Microsoft Azure, who are investing heavily in innovation and providing these capabilities. And there are many such tech forums. It can be a CDO [chief data officer] forum, is a technology innovation forum, are focus group discussions that bring innovative capabilities that can run on any hyperscaler. This is another place we need to keep in touch with. And one more thing I would say is tactically, when we recommend a designed architecture to customers, we recommend doing a very modular architecture so that changing capabilities is easier. For example, changing OCR engines or language translation engines or some examples where things are continuously maturing.
If you build your architecture to be very modular, this change would also be very easy. And ultimately, it all comes down to a very diverse team that delivers those capabilities. Fostering training, advanced training and having that diverse mix of skills from the technology business as you talked about and mixing it up obviously brings new thinking to the team itself and so we’ll be able to adopt some of those innovations and capabilities let them come outside the market itself. This is how I see this affecting some of the big ERP or back-office transformations like operations or even tax. We can definitely use some of those capabilities there. For example, tax. On the tax side, there’s a whole big stream of data coming from unstructured data, it’s PDF documents, unformatted pieces of documents that we get, how do you find that? There are a large number of AI capabilities you can plug in that can bring data into a structured format that regulators will also believe. So some impact from that.
Laurel: This gives a good example of what is possible in the back office with so many operations now that cloud platform hyperscalers like Microsoft Azure offer a number of these capabilities. How do companies create opportunities for interoperability between the cloud platform and the latest emerging technologies, while remaining truly focused on data governance, especially for highly regulated industries like finance and healthcare?
Anil: Look, most companies have good data governance set up where definitions are agreed upon, and it’s in the realm of regulations that this industry already supports. For example, if you look at the mortgage industry, someone 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 that you cannot disclose. So the governance is well set up, from a data perspective. When it comes to applied AI services, Microsoft Azure and other platforms already take into account some of the ethical aspects of AI. What can we do with analytics from a predictive perspective? What can’t we do? So we are covered from that point of view.
