1 00:00:00,480 --> 00:00:03,960 Speaker 1: Since you're a subscriber to this Bloomberg podcast, we thought 2 00:00:04,000 --> 00:00:07,480 Speaker 1: you'd be interested in a new four episode sponsored podcast 3 00:00:07,600 --> 00:00:12,640 Speaker 1: called The ROI Rules of AI, produced by IBM and 4 00:00:12,640 --> 00:00:16,759 Speaker 1: Bloomberg Media Studios. It explores how business leaders are thinking 5 00:00:16,800 --> 00:00:22,000 Speaker 1: about the return on investment of artificial intelligence projects. You 6 00:00:22,000 --> 00:00:26,439 Speaker 1: can subscribe wherever you listen to your favorite podcasts. Here's 7 00:00:26,560 --> 00:00:31,720 Speaker 1: a recent episode. Imagine you work in the procurement office 8 00:00:31,760 --> 00:00:34,839 Speaker 1: of a major company. You've been assigned to find a 9 00:00:34,920 --> 00:00:39,600 Speaker 1: supplier for a key component of your flagship product. You 10 00:00:39,680 --> 00:00:43,239 Speaker 1: need to limit your company's risk, so you begin by 11 00:00:43,320 --> 00:00:50,519 Speaker 1: asking is a potential supplier financially healthy? Are they being sued? 12 00:00:52,240 --> 00:00:56,360 Speaker 1: How do they score on environmental, social and governance metrics? 13 00:00:57,160 --> 00:00:59,840 Speaker 1: What are the odds that supplier could be temporarily shut 14 00:00:59,920 --> 00:01:06,600 Speaker 1: down by a war or a hurricane? And those are 15 00:01:06,640 --> 00:01:09,640 Speaker 1: just some of the questions you'd have to answer. It 16 00:01:09,680 --> 00:01:14,399 Speaker 1: could take you days to thoroughly investigate just one potential supplier. 17 00:01:15,160 --> 00:01:19,880 Speaker 2: The problem was efficiency. What we found is that every 18 00:01:19,959 --> 00:01:22,320 Speaker 2: one of these tasks are pretty time consuming. 19 00:01:23,920 --> 00:01:28,319 Speaker 1: That's Gary Kotovitz, chief data and analytics officer at Dunham Bradstreet. 20 00:01:29,000 --> 00:01:31,800 Speaker 1: His company is just out with a new product powered 21 00:01:31,840 --> 00:01:37,080 Speaker 1: by artificial intelligence that enables procurement professionals to research suppliers quickly. 22 00:01:38,520 --> 00:01:40,840 Speaker 1: This is the story of how they built it and 23 00:01:40,959 --> 00:01:48,040 Speaker 1: what they and their clients learned along the way. From 24 00:01:48,080 --> 00:01:51,960 Speaker 1: IBM and Bloomberg Media Studios. This is the ROI Rules 25 00:01:52,080 --> 00:01:57,160 Speaker 1: of AI and I'm your host, Edward Adams. On this podcast, 26 00:01:57,320 --> 00:02:01,120 Speaker 1: we're exploring how organizations of all sizes are using AI 27 00:02:01,320 --> 00:02:05,160 Speaker 1: to transform their operations, aiming to increase their return on 28 00:02:05,280 --> 00:02:15,800 Speaker 1: investment and that of their customers. There's no more storied 29 00:02:15,880 --> 00:02:18,480 Speaker 1: company in financial data than Done In Bradstreet. 30 00:02:19,000 --> 00:02:21,840 Speaker 2: Dun and Bradstreet is a data and analytics company that's 31 00:02:21,919 --> 00:02:25,040 Speaker 2: been around for almost two hundred years. We collect data 32 00:02:25,200 --> 00:02:28,760 Speaker 2: on over five hundred and ninety million private companies and 33 00:02:28,880 --> 00:02:34,640 Speaker 2: we provide our customers insights into supply chain management, credit decisioning, 34 00:02:34,880 --> 00:02:37,400 Speaker 2: lending decisioning, and sales and marketing. 35 00:02:38,520 --> 00:02:41,560 Speaker 1: Whether you're buying or selling, you need the kind of 36 00:02:41,639 --> 00:02:45,799 Speaker 1: information Done In Bradstreet collects. Sales staff use it to 37 00:02:45,919 --> 00:02:50,160 Speaker 1: prospect for potential customers, Banks use it to assess the 38 00:02:50,280 --> 00:02:53,560 Speaker 1: credit worthiness of a company applying for a loan, and 39 00:02:53,720 --> 00:02:57,960 Speaker 1: procurement professional to use it to de risk their supply chains, 40 00:02:58,560 --> 00:03:01,440 Speaker 1: and if the pandemic taught company is anything, it's that 41 00:03:01,560 --> 00:03:06,200 Speaker 1: supply chains have a host of risks, both foreseen and unforeseen. 42 00:03:07,080 --> 00:03:10,480 Speaker 1: It's the job of the procurement staff to anticipate what 43 00:03:10,720 --> 00:03:15,040 Speaker 1: could go wrong and mitigate those risks. Dunham brad Street 44 00:03:15,080 --> 00:03:18,440 Speaker 1: has long provided access to its data cloud through its 45 00:03:18,480 --> 00:03:23,400 Speaker 1: own digital interface and through third party procurement applications. A 46 00:03:23,480 --> 00:03:27,640 Speaker 1: procurement staffer researching a potential supplier, might I want to look. 47 00:03:27,560 --> 00:03:29,840 Speaker 2: At their EHG score, I want to look at their 48 00:03:30,280 --> 00:03:34,239 Speaker 2: credit score, I want to look at their supply chain profile, 49 00:03:34,480 --> 00:03:36,600 Speaker 2: or I want to look at where they're physically located. 50 00:03:36,720 --> 00:03:41,600 Speaker 2: So all those lookups that you would typically do take time. 51 00:03:48,360 --> 00:03:51,880 Speaker 1: To save procurement staff time. Dun and brad Street worked 52 00:03:51,920 --> 00:03:55,720 Speaker 1: with IBM and it's Watson x AI and data platform 53 00:03:56,080 --> 00:04:00,840 Speaker 1: to create a new natural language interface called Ask Procurement, 54 00:04:01,240 --> 00:04:04,800 Speaker 1: where procurement officers can ask questions as simple as. 55 00:04:05,080 --> 00:04:08,760 Speaker 2: Give me everything I need to know about company ABC. 56 00:04:09,560 --> 00:04:13,120 Speaker 1: Or staff can search for all their specific procurement criteria 57 00:04:13,280 --> 00:04:17,440 Speaker 1: at once, such as asking for widget manufacturers which have 58 00:04:17,839 --> 00:04:22,160 Speaker 1: strong credit, low debt to equity ratios, and are minority 59 00:04:22,279 --> 00:04:26,240 Speaker 1: owned from an initial list of suppliers generated by ask, 60 00:04:26,360 --> 00:04:31,200 Speaker 1: procurement staff can further narrow the prospects by asking additional questions. 61 00:04:32,240 --> 00:04:35,320 Speaker 1: The product took about six months to build and began 62 00:04:35,480 --> 00:04:38,960 Speaker 1: being offered to customers in early November. It's already paid 63 00:04:39,000 --> 00:04:41,880 Speaker 1: dividends for dun and Bradstreet. According to Code Ofmits. 64 00:04:42,600 --> 00:04:46,440 Speaker 2: Their return investment is two things. Accuracy as it relates 65 00:04:46,480 --> 00:04:49,320 Speaker 2: to their decision making. Do I have all the information 66 00:04:49,880 --> 00:04:52,560 Speaker 2: readily available to me in order to make the right decision? 67 00:04:52,640 --> 00:04:55,839 Speaker 2: The second is efficiency and productivity. 68 00:04:56,800 --> 00:04:59,640 Speaker 1: In the process of working with customers to build the product, 69 00:05:00,040 --> 00:05:03,479 Speaker 1: Done and Bradstreet learned a lot about customer workflows. 70 00:05:04,000 --> 00:05:06,640 Speaker 2: You start to understand do you typically look for an 71 00:05:06,839 --> 00:05:09,240 Speaker 2: HG score and a credit profile or do you typically 72 00:05:09,320 --> 00:05:12,960 Speaker 2: look for an EHG score and let's say corporate ownership 73 00:05:13,320 --> 00:05:17,000 Speaker 2: and those two questions the most important to majority of 74 00:05:17,080 --> 00:05:20,680 Speaker 2: our customers or is it something else so that starts 75 00:05:20,720 --> 00:05:23,800 Speaker 2: to overtime give you a lot of sort of intelligence 76 00:05:23,839 --> 00:05:27,720 Speaker 2: around how your customers interact with your data and the 77 00:05:27,839 --> 00:05:29,880 Speaker 2: kind of workflows you need to design. 78 00:05:30,880 --> 00:05:34,560 Speaker 1: And the customers also got an education about what generative 79 00:05:34,600 --> 00:05:36,480 Speaker 1: AI can and can't do. 80 00:05:37,160 --> 00:05:40,600 Speaker 2: Jennai itself, as we know, is a brand new concept 81 00:05:40,960 --> 00:05:44,880 Speaker 2: for many customers, and I think one of our biggest 82 00:05:44,960 --> 00:05:49,040 Speaker 2: challenges as we were building it is getting people to 83 00:05:49,160 --> 00:05:51,480 Speaker 2: understand the kind of value it can provide them. Now 84 00:05:51,520 --> 00:05:54,520 Speaker 2: that you know what it can do, customers have this 85 00:05:54,839 --> 00:05:57,480 Speaker 2: sort of aha moment and then from there they start 86 00:05:57,520 --> 00:06:00,440 Speaker 2: to kind of say, Okay, well I understand it, so 87 00:06:00,720 --> 00:06:02,240 Speaker 2: this is everything I want out of it. 88 00:06:03,200 --> 00:06:05,680 Speaker 1: Early users of the product have found that they are 89 00:06:05,760 --> 00:06:09,000 Speaker 1: reducing the time it took them to vet potential vendors 90 00:06:09,400 --> 00:06:12,120 Speaker 1: by an average of ten to twenty percent. Code of 91 00:06:12,160 --> 00:06:16,640 Speaker 1: It says in sizable companies where the procurement team can 92 00:06:16,720 --> 00:06:20,800 Speaker 1: number in thousands, that's the significant savings which can be 93 00:06:20,960 --> 00:06:31,919 Speaker 1: used to address more strategic procurement issues. Dun Bradstreet chose 94 00:06:32,000 --> 00:06:35,800 Speaker 1: IBM because it could play multiple roles in the process 95 00:06:35,880 --> 00:06:37,080 Speaker 1: of creating the product. 96 00:06:38,080 --> 00:06:44,800 Speaker 2: So IBM an amazing partner, and they partner with their 97 00:06:44,880 --> 00:06:50,120 Speaker 2: customers I think from multiple different dimensions. One is they 98 00:06:50,160 --> 00:06:55,560 Speaker 2: are a obviously technology provider. IBM is also a customer. 99 00:06:56,160 --> 00:06:59,960 Speaker 2: They are a consumer of this procurement product. There's certain 100 00:07:00,120 --> 00:07:05,440 Speaker 2: expertise that they brought. So as we started to use 101 00:07:05,800 --> 00:07:09,280 Speaker 2: Watson X platform and the tech that related to it. 102 00:07:09,960 --> 00:07:13,640 Speaker 2: They have a build team that helped us gather the 103 00:07:13,720 --> 00:07:16,720 Speaker 2: requirements as well as actually develop. 104 00:07:17,760 --> 00:07:21,920 Speaker 1: Dunn and Bradstreet's experience building ass procurement holds lessons for 105 00:07:22,040 --> 00:07:26,480 Speaker 1: other companies starting their AI journeys. According to Dave McDonald, 106 00:07:26,960 --> 00:07:30,680 Speaker 1: general manager of the US Industry Market for IBM. 107 00:07:31,120 --> 00:07:36,160 Speaker 3: First, I would say most transformational projects, like starting with 108 00:07:36,240 --> 00:07:39,960 Speaker 3: generative AI, are all about people, process and technology. 109 00:07:40,360 --> 00:07:42,000 Speaker 2: So let's start with people in process. 110 00:07:43,400 --> 00:07:48,720 Speaker 3: AI shouldn't be an it only led initiative because it 111 00:07:48,800 --> 00:07:51,600 Speaker 3: kind of becomes a science project and rarely gets to 112 00:07:51,720 --> 00:07:54,360 Speaker 3: that business benefit and the return on investment that people 113 00:07:54,400 --> 00:07:55,560 Speaker 3: are looking. 114 00:07:55,360 --> 00:07:56,520 Speaker 2: For that drive the value. 115 00:07:57,080 --> 00:08:00,040 Speaker 3: So our suggestion is you always need to have a 116 00:08:00,200 --> 00:08:03,400 Speaker 3: line of business sponsor who is going to directly benefit 117 00:08:03,480 --> 00:08:07,040 Speaker 3: from the outcome of the AI project. And it can't 118 00:08:07,160 --> 00:08:10,960 Speaker 3: just be kind of a simplistic ask a question to 119 00:08:10,960 --> 00:08:13,000 Speaker 3: get an answer. It's got to impact and change a 120 00:08:13,080 --> 00:08:16,400 Speaker 3: business process. So people in process are number one. Number 121 00:08:16,480 --> 00:08:21,160 Speaker 3: two is a large language model. If you're using one 122 00:08:21,200 --> 00:08:24,600 Speaker 3: that everybody has access to, is giving everybody the same answers. 123 00:08:24,960 --> 00:08:28,360 Speaker 3: It doesn't really give you competitive advantage. So being able 124 00:08:28,440 --> 00:08:33,640 Speaker 3: to combine private data that others don't have access to 125 00:08:33,960 --> 00:08:37,880 Speaker 3: with the traditional large language model capabilities of natural language 126 00:08:37,960 --> 00:08:42,080 Speaker 3: processing and speech that is what's going to drive it done. 127 00:08:42,160 --> 00:08:45,360 Speaker 1: Bradstreet is now turning its attention to creating Phase two 128 00:08:45,800 --> 00:08:49,720 Speaker 1: of ass Procurement, which will enable customers to integrate their 129 00:08:49,800 --> 00:08:53,120 Speaker 1: own data about suppliers into the data that done in 130 00:08:53,200 --> 00:09:00,319 Speaker 1: Bradstreet provides. Code. Itz believes that increasingly procurement department will 131 00:09:00,360 --> 00:09:04,120 Speaker 1: allow staff from other departments to interact with the product, 132 00:09:04,679 --> 00:09:06,559 Speaker 1: saving them yet more time. 133 00:09:07,640 --> 00:09:10,960 Speaker 2: The stakeholders are able to ask and get the questions 134 00:09:11,480 --> 00:09:16,440 Speaker 2: answered themselves. That alleviates a lot of the unnecessary tasks 135 00:09:16,480 --> 00:09:19,120 Speaker 2: that a procumber professional is engaged with today, which is 136 00:09:19,200 --> 00:09:21,560 Speaker 2: answering questions about where's my order. 137 00:09:27,520 --> 00:09:31,120 Speaker 1: This has been the ROI Rules of AI, a podcast 138 00:09:31,200 --> 00:09:34,880 Speaker 1: from IBM and Bloomberg Media Studios. If you like what 139 00:09:35,000 --> 00:09:39,160 Speaker 1: you hear, subscribe and leave us a review. I'm Edward Adams. 140 00:09:39,679 --> 00:09:40,840 Speaker 1: Thanks for listening.