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01/12/2021
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Retail 2021: AI Ushers in New Decade

Retail investment in artificial intelligence (AI) is trending upward: According to a study  by Juniper Research, global retail spending on AI will reach $7.3 billion annually by 2022, up from an estimated $2 billion in 2018. Investments in AI-powered predictive and prescriptive analytics will more than double in that time frame. What’s driving the change, and more importantly, how can retailers take a smart approach to implementation? RIS News sat down with executives of NTT DATA to find out.

RIS: Retail is considered a top industry for AI. Why should retailers consider AI, and where can they apply AI to create new business value?

Theresa Kushner is Consultant, AI/Analytics, Telecom/Media, and Data as an Asset at NTT DATA.

Theresa Kushner, Consultant, AI/Analytics, Telecom/Media, and Data as an Asset: AI is already all around us. As a consumer, we use AI-enabled apps such as the camera or maps or voice assistants like Siri or Alexa on smartphones, which makes our lives easier. AI-enabled virtual assistants are ubiquitous and integrated across a range of devices including speakers and connected home devices.

The fact is many retailers were already exploring AI and predictive analytics to stay ahead of disruptive competition over the past few years. With the pandemic, applications of AI have accelerated. For example, AI virtual agents have come from laboratories to kiosks, and retail sales environments as demand for touchless contact increases. Robotic process automation (RPA) has become core to maintaining efficiency, and predictive analytics enables responsiveness in a volatile marketplace.

A recent study by NTT DATA and Oxford Economics on AI and Automation found that nearly three-quarters (70%) of executives say AI has been strategically implemented in key functions to optimize specific processes, but only about 10% have fully implemented the technology across the business at scale.

The study revealed three key things – AI adoption is happening now, with 96% of surveyed executives planning to invest. Secondly, AI is critical for success, with more than 40% of executives believing they will either lose customers or potential employees, and that their bottom line will suffer if they do not implement AI. Lastly, AI improves performance as evidenced by companies that are furthest along in adopting AI reporting stronger business results.

RIS: How can retailers use advanced analytics and AI to improve their business?

Vijay Krishnanji is Director, Customer Experience Innovation at NTT DATA.

Vijay Krishnanji, Director, Digital and Customer Experience Innovation: Retailers that do not use analytics and apply AI to data will be at a disadvantage, as they will lack the insight and foresight of their competitors that do. Predictive analytics lays out the “next best action” given what the data shows — for example, how best to move product, how to rationalize the use of space in-store, what online capabilities should be harnessed, and more. The end-result is a sharper competitive edge and an increased ability to manage risk.

Complete, accurate, unbiased data is the bedrock of all AI. It’s important to note, however, that predictive analytics will be difficult to execute if the data is not collected correctly, refreshed, monitored and curated as an asset. For example, if it relies solely on historical data an anomaly may be interpreted as an ongoing trend. Consider this: During its second quarter ended August 2020, Kroger experienced a 14.6% increase in identical sales (without fuel) and a 127% increase in digital sales. That’s something that won’t be seen again as we return to normal. Consequently, the algorithm needs to change based on current insights.

Making store employees’ life easier is key, especially at a time when retailers must do more with less. As an example, NTT DATA’s Smart Retail Operations data analytics platform is intended to enable proactive communication of exception events — such as out-of-stock situations or compliance issues related to planograms, pricing, or promotions. It processes data from almost any data source, including IoT sensors, and transforms it into actionable intelligence. This helps to accelerate Smart Store pilot projects and subsequently scales across many stores.

Customers want their loyalty rewarded with more flexible programs and differentiated experiences. To strengthen customer loyalty, retailers must engage customers proactively through hyper-personalized benefits tailored to their interests. An AI solution can generate product recommendations and early churn alerts based on past behavior patterns. For example, using the data from customer’s purchases, you could predict which fresh and pre-cooked dishes customers will buy.

Customers expect a quick, easy, and safe shopping experience. NTT DATA is working with stores to provide unmanned shopping experiences. The approach combines AI, machine learning, IoT sensors, and computer-vision-based algorithms to enhance the consumer experience. To use it, customers download a dedicated app, select their preferred payment method, and grab the product in-store. The customer can exit without scanning products.

RIS: How should retailers address the ethical and privacy concerns that continue to be raised by AI?

Kushner: My colleague, Lisa Woodley, reveals in an interview on “Designing Ethical Customer Experiences,” that at times the question of “what should we do” is lost to the question of “what can we do” as technologies like AI become more advanced. And what we can do often raises ethical concerns. When misused, AI provides a real opportunity for companies to manipulate and abuse customers, especially in the retail space. We can drive positive ethical change if we bring a human perspective to technological innovation.

Ethical considerations center around bias. As human beings, we are naturally biased, but with AI, those biases can surface faster and with greater harm than ever before. Facial recognition may be important for retailers that want to prevent theft and loss of inventory. However, implementing AI programs associated with this data necessitates being cognizant of subtle implications. For instance, a recent article in Wired online opens with this sentence: “Men often judge women by their appearance. Turns out, computers do too.” It reports that researchers had sent images of congressmembers through Google’s image recognition service, which applied annotations to the individuals’ physical appearances, then labeled the male images as “official” or “businessperson” and the female images as “smile” or “chin.” This is a good example of perpetuating a long-time gender bias. 

AI represents challenges to business because privacy concerns as well. Privacy has been a topic for as long as the Internet has existed. The General Data Protection Act (GDPA) in Europe, the California Privacy Rights Act (CPRA), and the Brazilian Lei Geral de Proteção de Dados (LGPD) all lay out how an individual’s privacy must be protected and stipulate some heavy fines for non-compliant companies. These new laws also apply to the use of data in algorithms, including algorithms that harness AI, making it impossible to apply data that could identify an individual in any way. So, AI data scientists must be very aware of privacy laws in the countries where they develop AI programs, as well as in the countries where these programs are deployed.

To avoid bias and violating customer privacy, AI applications need to be created by diverse teams. That means diversity in thought as much as diversity in ethnicity, gender, etc. The first task of this team should be to evaluate the data that will be used with the algorithm to ensure that it was collected, managed, and curated without bias and in line with privacy considerations.

RIS: It has been said that people are the real key to digital transformation. How does this play out in a digital transformation that involves embracing AI, and what role does change management play in this scenario?

Krishnanji: My colleague, Kim Curley, insightfully noted in a recent article that having a growth mindset is key at the organizational and individual levels alike. While being good at implementing and using a tool is important, success is dependent on how well you can handle change. A human-centric approach and change management are critical to AI success, particularly at scale.

The culture of the organization will influence AI adoption. A culture that is data-driven, analytical, collaborative, vulnerable, curious, and — most importantly — nimble enough to take a ‘test and learn’ approach is vital. AI can generate constant, significant change within an organization, and only those who can go with that kind of flow will be successful.

AI projects must involve business stakeholders from the beginning to frame the right problem to solve, to build trust and credibility, and to set clear intentions as to how the organization will digest the kinds of change created by AI. Listening to and gathering feedback from users is key. Algorithms — unlike humans — don’t consider what happens when the result is applied. So, upskilling your workforce to become AI coaches enables more rapid improvements to AI solutions and broader application.

RIS: What do retailers need to keep in mind to successfully build, deploy and scale AI capabilities

Kushner: As retailers prepare for successful adoption of AI into their operational environments, there are a few important things to keep in mind:

Find a sponsor. AI requires support from the most senior executive levels to truly gain a position within a retail operation. The sponsor should be able to provide the necessary funding, along with much-needed organizational support.

Identify a good problem. A good, first AI problem to solve impacts the business, is somewhat easy to explain, and addresses a customer or employee need. Business impact means increased revenue, decreased costs, or increase in customer loyalty. Problems in these areas can require different types of AI applications, so zeroing in on one that can be easily explained to your sellers and marketing teams is imperative. Most operations begin with a straightforward machine learning algorithm that "learns" patterns from the data it gathers as it is applied. For example, if your goal is to increase revenue, you might create a cross-sell, up-sell algorithm that deduces from previous customer transactions what the “next best” purchase may be.

Ensure your data is solid. Most AI applications fail because of poor data. Either the data is incomplete, inaccurate, biased, or is totally missing the variables that are most predictive or required for solving the problem. Data is the foundation of any good AI solution, so break down data silos to understand where data will be useful and how to make it available to your algorithms in timely fashion.

Think scale at the start. A good many data scientists have been hired across corporate America to create AI algorithms. Unfortunately, not all of them understand that the “science” is the first part but deploying the science is the important part. This is why many AI proofs-of-concept or pilots never make a difference in the business. The key to success with AI is to apply it to the most appropriate stage of the business process. AI operations becomes very important here. Why? AI operations simply takes the data scientist-created algorithms and embeds them into application systems. But these applications must be monitored and governed as they are deployed because they learn with each new piece of data that is processed. The AI algorithms will need adjustment to ensure success over time — AI is not a one-time project.

Before launching any AI project, remember that this is a journey, not a destination. AI is designed to keep learning within the environment. Consequently, business processes may have to be redesigned so that the algorithm can be applied in a beneficial way. Or, data may need to be collected in a way that makes it easier to use. These situations may require some additional work to ensure the success of the AI project.

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NTT DATA Services, a global digital business and IT services leader, is the largest business unit outside of Japan of NTT DATA Corporation and part of NTT Group. With our consultative approach, we leverage deep industry expertise and leading-edge technologies powered by AI, automation and cloud to create practical and scalable solutions that contribute to society and help clients worldwide accelerate their digital journeys.

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