The ROI Rules of AI: Procuring Success (Sponsored Content)
09:50 Share

Since you're a subscriber to this Bloomberg podcast, we thought you'd be interested in a new four-episode sponsored podcast called The ROI Rules of AI. Produced by IBM and Bloomberg Media Studios, it explores how business leaders are thinking about the return on investment of artificial intelligence projects. You can subscribe wherever you listen to your favorite podcasts. Here's a recent episode.

Imagine you work in the procurement office of a major company. You've been assigned to find a supplier for a key component of your flagship product. You need to limit your company's risk. So you begin by asking, is a potential supplier financially healthy? Are they being sued? How do they score on environmental, social, and governance metrics? What are the odds the supplier could be temporarily shut down by a war

or a hurricane? And those are just some of the questions you'd have to answer. It could take you days to thoroughly investigate just one potential supplier. The problem was efficiency. What we found is that every one of these tasks are pretty time-consuming. That's Gary Kotovitz, Chief Data and Analytics Officer at Dun & Bradstreet.

His company is just out with a new product powered by artificial intelligence that enables procurement professionals to research suppliers quickly. This is the story of how they built it and what they and their clients learned along the way. From IBM and Bloomberg Media Studios, this is the ROI Rules of AI, and I'm your host, Edward Adams. ♪

On this podcast, we're exploring how organizations of all sizes are using AI to transform their operations, aiming to increase their return on investment and that of their customers.

There's no more storied company in financial data than Dun & Bradstreet. Dun & Bradstreet is a data and analytics company that's been around for almost 200 years. We collect data on over 590 million private companies, and we provide our customers insights into supply chain management, credit decisioning, lending decisioning, and sales and marketing. Whether you're buying or selling, you need the kind of information Dun & Bradstreet collects.

Sales staff use it to prospect for potential customers. Banks use it to assess the creditworthiness of a company applying for a loan. And procurement professionals use it to de-risk their supply chains. And if the pandemic taught companies anything, it's that supply chains have a host of risks, both foreseen and unforeseen. It's the job of the procurement staff to anticipate what could go wrong and mitigate those risks.

Dun & Bradstreet has long provided access to its data cloud through its own digital interface and through third-party procurement applications. A procurement staffer researching a potential supplier might: I want to look at their ESG score, I want to look at their credit score, I want to look at their supply chain profile, or I want to look at where they're physically located. So all those lookups that you would typically do take time.

To save procurement staff time, Dun & Bradstreet worked with IBM and its Watson X AI and Data platform to create a new natural language interface called Ask Procurement, where procurement officers can ask questions as simple as... Give me everything I need to know about company ABC.

Or staff can search for all their specific procurement criteria at once, such as asking for widget manufacturers which have strong credit, low debt-to-equity ratios, and are minority-owned. From an initial list of suppliers generated by Ask Procurement, staff can further narrow the prospects by asking additional questions. The product took about six months to build and began being offered to customers in early November.

It's already paid dividends for Dun & Bradstreet, according to Kotovitz. The return investment is two things. Accuracy as it relates to their decision-making. Do I have all the information readily available to me in order to make the right decision? The second is efficiency and productivity.

In the process of working with customers to build the product, Dun & Bradstreet learned a lot about customer workflows. You start to understand, do you typically look for an ESG score and a credit profile, or do you typically look for an ESG score and, let's say, corporate ownership? And are those two questions the most important to the majority of our customers, or is it something else?

So that starts to, over time, give you a lot of intelligence around how your customers interact with your data and the kind of workflows you need to design. And the customers also got an education about what generative AI can and can't do. Gen AI itself, as we know, is a brand new concept for many customers. And I think one of our biggest challenges as we were building it

is getting people to understand the kind of value it can provide them. Now that you know what it can do, customers have this sort of aha moment. And then from there, they start to kind of say, OK, well, I understand it now. So this is everything I want out of it. Early users of the product have found that they are reducing the time it took them to vet potential vendors by an average of 10 to 20 percent, Codavet says.

In sizable companies, where the procurement team can number in the thousands, that's a significant savings, which can be used to address more strategic procurement issues. Dun & Bradstreet chose IBM because it could play multiple roles in the process of creating the product. So IBM is an amazing partner, and they...

Partner with their customers, I think, from multiple different dimensions. One is they are a, obviously, technology provider. IBM is also a customer. They are a consumer of this procurement product. There's certain expertise that they brought. So as we started to use Watson X platform and the tech stack related to it, they have a build team that helped us

gathered the requirements as well as actually develop. Dun & Bradstreet's experience building asset procurement holds lessons for other companies starting their AI journeys, according to Dave McDonald, General Manager of the U.S. Industry Market for IBM. First, I would say most transformational projects, like starting with generative AI, are all about people, process, and technology. So let's start with people and process.

AI shouldn't be an IT-only led initiative.

Because it kind of becomes a science project and rarely gets to that business benefit and the return on investment that people are looking for that drive the value. So our suggestion is you always need to have a line of business sponsor who is going to directly benefit from the outcome of the AI project. And it can't just be kind of a simplistic, ask a question, get an answer. It's got to impact and change a business process. So people in process are number one. Number two is...

A large language model, if you're using one that everybody has access to, is giving everybody the same answers. It doesn't really give you competitive advantage. So being able to combine private data that others don't have access to with the traditional large language model capabilities of natural language processing and speech, that is what's going to drive it.

Dun & Bradstreet is now turning its attention to creating Phase 2 of Ask Procurement, which will enable customers to integrate their own data about suppliers into the data that Dun & Bradstreet provides. Kotevitz believes that, increasingly, procurement departments will allow staff from other departments to interact with the product, saving them yet more time.

The stakeholders are able to ask and get the questions answered themselves. That alleviates a lot of the unnecessary tasks that a procurement professional is engaged with today, which is answering questions about where is my order? This has been the ROI Rules of AI, a podcast from IBM and Bloomberg Media Studios. If you like what you hear, subscribe and leave us a review.

I'm Edward Adams. Thanks for listening.