Speaker 1: Since you're a subscriber to this Bloomberg podcast, we thought
Speaker 1: you'd be interested in a new four episode sponsored podcast
Speaker 1: called The ROI Rules of AI, produced by IBM and
Speaker 1: Bloomberg Media Studios. It explores how business leaders are thinking
Speaker 1: about the return on investment of artificial intelligence projects. You
Speaker 1: can subscribe wherever you listen to your favorite podcasts. Here's
Speaker 1: a recent episode. Imagine you work in the procurement office
Speaker 1: of a major company. You've been assigned to find a
Speaker 1: supplier for a key component of your flagship product. You
Speaker 1: need to limit your company's risk, so you begin by
Speaker 1: asking is a potential supplier financially healthy? Are they being sued?
Speaker 1: How do they score on environmental, social and governance metrics?
Speaker 1: What are the odds that supplier could be temporarily shut
Speaker 1: down by a war or a hurricane? And those are
Speaker 1: just some of the questions you'd have to answer. It
Speaker 1: could take you days to thoroughly investigate just one potential supplier.
Speaker 2: The problem was efficiency. What we found is that every
Speaker 2: one of these tasks are pretty time consuming.
Speaker 1: That's Gary Kotovitz, chief data and analytics officer at Dunham Bradstreet.
Speaker 1: His company is just out with a new product powered
Speaker 1: by artificial intelligence that enables procurement professionals to research suppliers quickly.
Speaker 1: This is the story of how they built it and
Speaker 1: what they and their clients learned along the way. From
Speaker 1: IBM and Bloomberg Media Studios. This is the ROI Rules
Speaker 1: of AI and I'm your host, Edward Adams. On this podcast,
Speaker 1: we're exploring how organizations of all sizes are using AI
Speaker 1: to transform their operations, aiming to increase their return on
Speaker 1: investment and that of their customers. There's no more storied
Speaker 1: company in financial data than Done In Bradstreet.
Speaker 2: Dun and Bradstreet is a data and analytics company that's
Speaker 2: been around for almost two hundred years. We collect data
Speaker 2: on over five hundred and ninety million private companies and
Speaker 2: we provide our customers insights into supply chain management, credit decisioning,
Speaker 2: lending decisioning, and sales and marketing.
Speaker 1: Whether you're buying or selling, you need the kind of
Speaker 1: information Done In Bradstreet collects. Sales staff use it to
Speaker 1: prospect for potential customers, Banks use it to assess the
Speaker 1: credit worthiness of a company applying for a loan, and
Speaker 1: procurement professional to use it to de risk their supply chains,
Speaker 1: and if the pandemic taught company is anything, it's that
Speaker 1: supply chains have a host of risks, both foreseen and unforeseen.
Speaker 1: It's the job of the procurement staff to anticipate what
Speaker 1: could go wrong and mitigate those risks. Dunham brad Street
Speaker 1: has long provided access to its data cloud through its
Speaker 1: own digital interface and through third party procurement applications. A
Speaker 1: procurement staffer researching a potential supplier, might I want to look.
Speaker 2: At their EHG score, I want to look at their
Speaker 2: credit score, I want to look at their supply chain profile,
Speaker 2: or I want to look at where they're physically located.
Speaker 2: So all those lookups that you would typically do take time.
Speaker 1: To save procurement staff time. Dun and brad Street worked
Speaker 1: with IBM and it's Watson x AI and data platform
Speaker 1: to create a new natural language interface called Ask Procurement,
Speaker 1: where procurement officers can ask questions as simple as.
Speaker 2: Give me everything I need to know about company ABC.
Speaker 1: Or staff can search for all their specific procurement criteria
Speaker 1: at once, such as asking for widget manufacturers which have
Speaker 1: strong credit, low debt to equity ratios, and are minority
Speaker 1: owned from an initial list of suppliers generated by ask,
Speaker 1: procurement staff can further narrow the prospects by asking additional questions.
Speaker 1: The product took about six months to build and began
Speaker 1: being offered to customers in early November. It's already paid
Speaker 1: dividends for dun and Bradstreet. According to Code Ofmits.
Speaker 2: Their return investment is two things. Accuracy as it relates
Speaker 2: to their decision making. Do I have all the information
Speaker 2: readily available to me in order to make the right decision?
Speaker 2: The second is efficiency and productivity.
Speaker 1: In the process of working with customers to build the product,
Speaker 1: Done and Bradstreet learned a lot about customer workflows.
Speaker 2: You start to understand do you typically look for an
Speaker 2: HG score and a credit profile or do you typically
Speaker 2: look for an EHG score and let's say corporate ownership
Speaker 2: and those two questions the most important to majority of
Speaker 2: our customers or is it something else so that starts
Speaker 2: to overtime give you a lot of sort of intelligence
Speaker 2: around how your customers interact with your data and the
Speaker 2: kind of workflows you need to design.
Speaker 1: And the customers also got an education about what generative
Speaker 1: AI can and can't do.
Speaker 2: Jennai itself, as we know, is a brand new concept
Speaker 2: for many customers, and I think one of our biggest
Speaker 2: challenges as we were building it is getting people to
Speaker 2: understand the kind of value it can provide them. Now
Speaker 2: that you know what it can do, customers have this
Speaker 2: sort of aha moment and then from there they start
Speaker 2: to kind of say, Okay, well I understand it, so
Speaker 2: this is everything I want out of it.
Speaker 1: Early users of the product have found that they are
Speaker 1: reducing the time it took them to vet potential vendors
Speaker 1: by an average of ten to twenty percent. Code of
Speaker 1: It says in sizable companies where the procurement team can
Speaker 1: number in thousands, that's the significant savings which can be
Speaker 1: used to address more strategic procurement issues. Dun Bradstreet chose
Speaker 1: IBM because it could play multiple roles in the process
Speaker 1: of creating the product.
Speaker 2: So IBM an amazing partner, and they partner with their
Speaker 2: customers I think from multiple different dimensions. One is they
Speaker 2: are a obviously technology provider. IBM is also a customer.
Speaker 2: They are a consumer of this procurement product. There's certain
Speaker 2: expertise that they brought. So as we started to use
Speaker 2: Watson X platform and the tech that related to it.
Speaker 2: They have a build team that helped us gather the
Speaker 2: requirements as well as actually develop.
Speaker 1: Dunn and Bradstreet's experience building ass procurement holds lessons for
Speaker 1: other companies starting their AI journeys. According to Dave McDonald,
Speaker 1: general manager of the US Industry Market for IBM.
Speaker 3: First, I would say most transformational projects, like starting with
Speaker 3: generative AI, are all about people, process and technology.
Speaker 2: So let's start with people in process.
Speaker 3: AI shouldn't be an it only led initiative because it
Speaker 3: kind of becomes a science project and rarely gets to
Speaker 3: that business benefit and the return on investment that people
Speaker 3: are looking.
Speaker 2: For that drive the value.
Speaker 3: So our suggestion is you always need to have a
Speaker 3: line of business sponsor who is going to directly benefit
Speaker 3: from the outcome of the AI project. And it can't
Speaker 3: just be kind of a simplistic ask a question to
Speaker 3: get an answer. It's got to impact and change a
Speaker 3: business process. So people in process are number one. Number
Speaker 3: two is a large language model. If you're using one
Speaker 3: that everybody has access to, is giving everybody the same answers.
Speaker 3: It doesn't really give you competitive advantage. So being able
Speaker 3: to combine private data that others don't have access to
Speaker 3: with the traditional large language model capabilities of natural language
Speaker 3: processing and speech that is what's going to drive it done.
Speaker 1: Bradstreet is now turning its attention to creating Phase two
Speaker 1: of ass Procurement, which will enable customers to integrate their
Speaker 1: own data about suppliers into the data that done in
Speaker 1: Bradstreet provides. Code. Itz believes that increasingly procurement department will
Speaker 1: allow staff from other departments to interact with the product,
Speaker 1: saving them yet more time.
Speaker 2: The stakeholders are able to ask and get the questions
Speaker 2: answered themselves. That alleviates a lot of the unnecessary tasks
Speaker 2: that a procumber professional is engaged with today, which is
Speaker 2: answering questions about where's my order.
Speaker 1: This has been the ROI Rules of AI, a podcast
Speaker 1: from IBM and Bloomberg Media Studios. If you like what
Speaker 1: you hear, subscribe and leave us a review. I'm Edward Adams.
Speaker 1: Thanks for listening.