Fashion Tech Consortium Hosts Retail Executive Roundtable June Breakfast Event
Fashion Tech Consortium, an accelerator that links together brands, retailers, manufacturers and technology companies in order to provide highly assessed, enterprise-ready tech solutions in the fashion retail industry, hosted the Retail Executive Roundtable June Breakfast, at the Amali in New York City on June 27. Michael Reidbord, Founder and Principal at Fashion Tech Consortium, organized and moderated the intimate gathering for a three-speaker series and open discussion over breakfast. Nearly 20 participants, including founders, presidents, managers and advisors from a range of retail-focused industries—from Facebook, to Brooks Brothers, to artificial intelligence (AI)-focused analytics companies—attended the invite-only breakfast event.
- With the increasing implementation of AI in the retail and tech worlds, as well as the shift toward new forms of corporate structures such as self-management, human skills, including emotional intelligence and individual expertise, are becoming more necessary.
- AI, machine learning and deep learning are part of a chain of interconnected technologies. However, more data and test cases are needed for the technology to realistically integrate into many large-scale industries.
- Stagnant risk- and change-averse leadership, particularly in legacy retail industries, can lead to massive revenue losses, due to slow or ineffective adoption of data-driven technologies.
- The event concluded with a roundtable discussion of the future of the retail industry, with a specific focus on what future practices retailers should adopt in order to keep up with digitally-native companies such as Amazon.
Retail Executive Roundtable June Breakfast Event
On June 27, the Coresight Research team attended Fashion Tech Consortium’s Retail Executive Roundtable June Breakfast Event at the Amali in New York City, an event for a select group of high-level executives representing a diverse collection of the broader retail space. Michael Reidbord, Founder and Principal at Fashion Tech Consortium, who organized and moderated the breakfast event, said he hoped it would serve as a gathering place for person-to-person interactions across the greater retail industry, allowing for discussion of best practices and common understandings to grow organically from an intimate environment.
Nearly 20 professionals attended the event, and the speakers focused on data-driven technologies and the relationship between types of AI and the implementation—or lack thereof—of these technologies, as well as touching on the leadership mentalities and practices within retail companies. The conversations benefited from the diverse range of participants, from professionals in the software and technical side, to business executives, to analysts, to a professor with both personal and case experience on the topics.
Below, we share some highlights from the three talks and general discussions that followed.
Highlights from the Retail Executive Roundtable
The three guest lectures were followed by an open forum in which everyone at the table participated, with a focus on what future practices retailers should adopt in order to keep up with digitally-native companies such as Amazon.
Milton Pedraza, CEO, Luxury Institute—AI and the Importance of Human Skills
Milton Pedraza spoke on how emotional intelligence, expertise and person-to-person skills have come to the fore, especially with the expansion of AI in the retail and technology spaces and the growth of self-management practices. He emphasized the human aspects of the retail industry, raising three main points:
- Seemingly unintuitive business models that focus on the individual: Pedraza referenced companies such as Fusion Academy & Learning Center, a private and for-profit school that provides a one-to-one connection between students and teachers for grades 6–12, literally facilitating classrooms with one teacher for one student at a time. This example demonstrates that there are scalable businesses that focus on the individual and on specific, human needs.
- How AI has made industries more complex: Built off an observation by journalist and author Malcolm Gladwell, Pedraza spoke on how AI has not simplified the industries in which it is implemented, but rather has made them more complex. In an anecdotal example, he referenced how a tech guru had recently told him “expertise is dead!” Contrary to this belief, he says, AI has made expertise more important than ever. Consumers and providers need experts or need to become experts who know how to properly utilize AI and interpret large data sets.
- The progressive restructuring of major corporations and the value of self-management: Pedraza cited a white paper he wrote with Doug Kirkpatrick, author of Beyond Empowerment: The Age of the Self-Managed Organization. He highlighted the importance of emotional intelligence as a skill that needs to be cultivated, arguing that major companies are going to experience radical shifts in managerial hierarchies in which individuals will gain more power and positive groups dynamics will become more and more valuable.
Niaz Jalal, Head of Human Understanding and Virtual Humans, Facebook—the Progress of AI
Niaz Jalal continued the thread of AI from Pedraza’s talk, speaking on how AI, machine learning and deep learning are a continuous process, but that a lack of data and pilot programs hinders the growth of these technologies in larger companies. He outlined the similarities and differences of AI types and discussed some of the pitfalls with AI.
He began by discussing the origins of AI dating back to the 1950s, and the revolution that has taken place more recently, as cloud computing and data have become more accessible. He also differentiated between the two major types of AI: general and narrow.
- General AI: This is the type of AI that most movie-goers would initially associate with the term. It is unreachable for now and would imply that computers can perform tasks on par with human beings.
- Narrow (focused) AI: This is the type of AI that is currently in use among most major companies, and includes: 1) machine learning, which allows for the program to modify its own code, making programming more efficient than a hard coded one; and 2) deep learning, another outgrowth of machine learning that relies on heavy computation and learning data representations, instead of task-specific algorithms.
Jalal referenced how the Internet of Things (IoT) and AI require one another to progress. AI is primarily built upon large datasets, which makes it both a powerful tool, but also limits its current applications, because it requires many pilot programs. As pointed out in the discussion after the talk, AI has often been found to provide racially- and sexually-biased results, as well as sometimes altogether unintelligent ones. Jalal confirmed the issues inherent in creating ethical AI systems, bringing up both technical hardware problems and back-end data gaps.
Mark Cohen, Director of Retail Studies, Columbia Business School—Human Inefficiencies in Adopting Data-Driven Practices
Mark Cohen spoke about risk- and change-averse leadership, particularly in legacy retail industries, and how the slow adoption of data-driven software is hampering their price optimization abilities and inventory logistics, leading to massive revenue losses. He presented his views on the slow—or complete lack of—adoption of sales and advertising techniques that use large datasets in the retail industry. Drawing on American psychologist Abraham Maslow’s hierarchy of human needs, specifically the physiological needs, including air, water, food, sleep, clothing and shelter, Cohen proposed an additional category: price. Humans naturally become anxious when given the opportunity to make the wrong decision, and how, for most, this situation arises daily in the form of competitive pricing, sales and purchasing.
Cohen recalled his days in advertising, when he used ad software that would check the effects of pricing on sales and help optimize which products should be displayed when and where. Large retailers now have a goldmine of data based on customer expenditures and buying habits, but he believes that many retailers rely on archaic legacy pricing systems that cause massive revenue losses. Risk- and change-averse corporate leadership, according to Cohen, is the major roadblock in adopting more agile pricing programs and creating more efficient logistical practices such as those implemented by digitally-native companies including Amazon.
Furthermore, he argued that consumers have become oblivious to the many one-day sales events, since they have learned to wait until the next one. Sales have become the norm and companies such as Amazon and Walmart have exacerbated consumers’ perception of prices by constantly lowering their price-point expectations. He also noted that retailers, especially those using legacy decision methods and ignoring competitive prices, are selling goods below the levels at which the consumers would value the product.
Roundtable Discussion of the Future of the Retail Industry
The discussion that followed Cohen’s talk raised the issue of how large retailers optimize their pricing and possible methods to make their sales more efficient. Aggregating data and using analytical methods to reveal the optimal price points for individual customers, as well as general machine learning algorithms that could change prices or move inventory, were some examples of solutions. Although most retailers likely use these methods, agile technologies may not be scalable yet and industry practices remain conservative in certain fields.
The question of where the problem in optimized pricing and inventory stocking lies remains unresolved, as does the certain future of AI and its implementation in the retail space. However, this small gathering of industry stakeholders around a breakfast table opened many doors that may just lead to the answers.