The AI Phenomenon: What It Means for Global Business Today and Tomorrow

Reflections from Two Globally Savvy Business-Tech Gurus

The growing global availability of AI technologies powered by increasing amounts of relevant data to gather knowledge, improve performance, and advance with agility is having a dramatic impact on the world of business. Research from the April 2018 World Economic Outlook indicates that the spread of knowledge and technology across borders has intensified because of globalization. The impact is near-term benefits and far-reaching possibilities for gains in process automation and productivity, product innovation, market expansion, compliance conformity, and supply chain efficiency. Evidence also points to fraudsters and other malefactors increasing their use of AI, suggesting that the cost of failing to engage in this area could be very significant.

We at Dun & Bradstreet well know how critical it is for organizations, operating in both local markets and globally, to have access to the business insights they need to make informed decisions. In today’s world, that means taking full advantage of the best technologies and fit-for-purpose data. That is why Dun & Bradstreet’s Worldwide Network (WWN) is an intrinsic part of our global presence. An unrivaled alliance of Dun & Bradstreet and leading business information providers from Europe, Asia, Africa, and the Americas, the WWN serves as the means for us to discover and curate data and offer solutions to our customers in the far-reaching places of the world.

A recent annual assembly, this year in Mexico, brought together our WWN CEOs and senior leaders to discuss both short- and longer-term opportunities and challenges for growth and increased value to our customers in their local and global markets. During the event, Anthony Scriffignano, Ph.D., Chief Data Scientist at Dun & Bradstreet, and Anastassia Lauterbach, Ph.D., a former Director of Dun & Bradstreet and Chairman of the Innovation & Technology Committee, delivered a keynote about the present and future opportunities in AI for the business community. With Anastassia discussing the business of AI and Anthony focused on the science of AI, I had the opportunity to ask both of them for their reflections on the event and this topic.


Melissa: Hello Anastassia and Anthony. Thank you for sharing your thoughts on how you’re seeing business adapt globally as a result of ever-increasing disruptive technology development and unprecedented data growth. We read about AI every day and some may feel it’s become an overused term. What is your perception of the degree of mindshare that AI occupies with our organization? How important is it really?

Anastassia: AI capabilities will decide on the competitive position of our company in the industry, and its resilience vis à vis new entrants. Though we use elements of machine learning to better leverage our data assets, we still need to update IT architecture and build an ecosystem of partners, freelancers, industrial experts, advisors, and scientists to be on the cutting edge of AI. This will not happen overnight. Maintaining focus and aligning the organization around what one day might become a comprehensive AI strategy are important considerations. Looking into how we do R&D, build prototypes, market them, and scale products enabled by AI is key.

Anthony: We have been talking about AI and related capabilities for some time now. This year, I see more evidence that we are putting a stronger innovation focus in this area globally. We have dramatically expanded corpora of increasingly complex data. Additionally, the focus on data privacy and data localization globally has put more pressure on businesses everywhere to make sure that they glean as much as possible from their data. Data is seen as a strategic asset, and the ability to derive increasingly powerful insights from it is a critical differentiator.

Melissa: You both have extensive global business experience. How is the evolution of AI different in different parts of the world?

Anthony: A big part of the evolution of AI stems from the volume, variety, and veracity of information available. Different parts of the world have different challenges, giving rise to different focus in the AI space. For example, in many developing countries, there are not large amounts of historical data available to feed traditional regressive machine learning algorithms. In these regions, methods which focus on extracting inference from subtle cues in unstructured data can be very powerful. In mature markets, where much more historical data exists, there may be challenges with “fake data” or other bias in the data, so anomaly detection and heuristics may be more of a focus. There is no shortage of variety in the methods of focus around the world. It’s a very exciting time to be working in the space.

Anastassia: North America has been historically on the cutting edge of machine learning innovation. In 2017, the continent partly lost its leadership to China. That country is committed to become the global leader in AI by 2030 and will invest $150 billion to implement this strategic goal. In the last year, we have seen amazing scientific publications on language, computer vision, and Generative Adversarial Networks from China. I hope traditional businesses in the US recognize they might lose competitive edge to their Chinese counterparts and rethink how they deploy internal resources, partner with academia, and handle their data.

Melissa: How do you think our global data perspective uniquely positions Dun & Bradstreet to take advantage of the opportunities afforded by AI in the modern context to deliver relevant value to the business community?

Anastassia: There is no one single player in the world possessing knowledge on businesses and their relationships at the level of Dun & Bradstreet. Still our competitors target us with much less sophisticated data. Data is just a start of a conversation with a potential customer. We can help solve difficult problems for industries, as we can see what no one else can, and identify a grain of truth in the ocean of data noise. Technologies like social listening, geospatial targeting, and understanding of semantics across different language families would help to differentiate us.

Anthony: Clearly, having one common approach to the curation of a single global view of business entities, especially large multinational organizations, is a huge advantage. Additionally, the ability to see the connectedness of the integrated value chain (customers, vendors, common beneficial owners) gives rise to a rich set of research questions to be tackled with advanced AI methods. Consider the convergence of phenomena such as autonomous devices, which will increasingly engage in commerce without human intervention; fintech, which brings new technologies into the decision-making space that was traditionally occupied by well-understood credit decisioning processes; and AI. Understanding a complex dynamic like this playing out around the world is a wonderful fit to Dun & Bradstreet’s unique global data perspective. Others may be very smart and have advanced tools, but they simply can’t see what we can see in our data.

Melissa: Anastassia, how is the role of directors and officers of corporations changing as a result of disruptive emerging technologies like AI?

Anastassia: Corporate boards are a mirror of what is happening within businesses. They often struggle to catch up with the high speed of technology disruption. The Dun & Bradstreet board is very diverse in terms of international business experience, gender, and industry knowledge. Keeping an open mind and learning on a daily basis is important for us. AI, cybersecurity, and blockchain are new topics for most boards, so our board should invest time to understand patterns, recognize risks, and align new challenges within our governance model.

Melissa: Anthony, how is data science changing as a result of AI’s evolution?

Anthony: AI has been around in data science for quite some time. Recently, however, there is heavy management focus on this area of innovation, as well as pressure coming from events in the media that are related to AI capabilities used for both good and bad. The challenge in data science is to avoid the rush to “try out” every new method simply because of the attraction of something new. Now, more than ever, it is important to instill an understanding of preconditions, problem formulation, bias, and other analytical principles that are critical to any inference from data. In some respects, we need to speed up by learning faster and across a broader set of competencies. In other respects, there are areas where we need to slow down a bit and challenge what we would have to believe in order to use a certain method or what new types of bias using such a method may introduce into a business decision-making process.

Melissa: Thanks again to both of you for your thoughtful perspectives on what many feel is a complicated yet important topic. What do you think we can we look forward to as the AI dialogue continues?

Anastassia: I prefer to think about AI as an opportunity for Dun & Bradstreet. Imagine new offerings like cybersecurity profiles of suppliers, social data insights on major competitors and their products, forecasting of data lost or not reported due to natural disasters and other catastrophic events, reports on consequences of mergers for supply chains and the economics of an industry that might be enhanced using AI … In addition, we might want to consider how partnerships are structured in our business, how we leverage our Worldwide Network to its full potential, and how we are perceived as thought leaders on B2B data and risks. Once again, this will not happen overnight. Nevertheless, slow is smooth, and smooth leads to fast.

Anthony: I have been recommending paying careful attention to a number of areas, including changes in regulation (e.g., privacy, explainable AI), AI used for mal-intent (e.g., cybercrime and new types of cyber-intrusion), issues related to autonomous devices (e.g., goal modification without human intervention), and convergence of AI and other disruptive evolutions (such as IoT). Overall, I would not recommend having a separate “AI strategy” but rather to consider that AI in many forms will increasingly permeate various aspects of business enterprise. The challenges for leaders include knowledge retention, upgrading existing skills, and talent acquisition/retention. Perhaps now more than ever, it is important for leaders to have a long view on evolutions like AI innovation. Quick wins are possible for sure, but some of these capabilities will take some time to mature in the organization and the opportunity cost of failing to engage is simply too overwhelming.


The advancement of and access to data-powered technologies continues to accelerate globally. Richard Baldwin, professor of international economics at the Graduate Institute of International and Development Studies, Geneva, calls it the “holy cow” moment. Expounded in The Great Convergence: Information Technology and the New Globalization, Baldwin explains, “There’s a point at which the exponential path of technological growth crosses the straight line of human expectation, and it’s the point at which the real power of this technology that we’ve alternately over- and under-estimated fully dawns on us. I call it the “holy cow” moment. We haven’t quite reached it yet with information and communication technology (ICT) and its meaning for globalization. When we do, it will not be the result of a single, sudden event.”

As the AI phenomenon continues, the business world will continue to watch, implement, and adapt. The results will surely vary by industry, geography, technical prowess, and appetite for new and possibly risky ventures. But suffice it to stay, AI’s impact is being felt today and will only continue to expand in the future.

Perhaps novelist William Gibson said it best, “The future is already here – it’s just not very evenly distributed.”

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