Creating The Intelligent Enterprise In Retail (Part 1)

Marc Teerlink and Steve Mauchline

Part 1 of the 3-part series “Retail and Intelligent Technology

Do you recognize this photograph? It’s a picture of a robot from Bossa Nova that has been “patrolling” the aisles of some Walmart stores in the United States.

Bossa Nova at Walmart

There have been attempts for several years to bring automation to retail stores – with mixed success. A popular back-office example is the “picking robots” used in Amazon and Ocado distribution centers. Even Costco’s pizza-making bot provides an eye-catching example of automation. And like the Roomba, these innovations are meeting people’s expectations because the bot does one thing and does it really well.

But the Bossa Nova is a different take on the robot story: It’s beginning to change the tasks carried out by team leads, supervisors, and managers in retail stores around the world. Why? Because this AI-powered robot is capable of not only visually recognizing what is on the shelves but also feeding that information back into the retailer’s ERP and other backend systems to create notifications for necessary actions.

The Bossa Nova can directly interject itself into processes that have traditionally been considered “human.” No longer must we rely on the duty manager to walk around the shop and prioritize the team’s actions. The robot – which makes three laps of the stores where it is deployed each day – can spot the things that need to be done. Plus, the robot doesn’t get distracted, go to lunch, or go home at the end of the day.

Consider the level of detail a robot like this can accomplish. The on-duty manager or team leader likely won’t spot a shelf label with an incorrect price or an item misplaced by a customer who changed their mind. (Don’t we all do that sometimes?) And a duty manager is unlikely to know the stock level of every item in the backroom.

The robot doesn’t have to hand over information at shift change, as it’s always on (except when it needs to charge its battery). A visual-recognition robot doesn’t need time to learn its role, unlike new duty managers (who come onboard often due to attrition in retail stores). This creates a greater level of consistency and operational standards – at least based on standards that the robot is capable of “seeing.”

A robot has the potential to improve efficiency across key elements of store operations, relieving team leaders of repetitive, mundane tasks and allowing them to focus on customers. We haven’t replaced the duty manager. Instead, we are moving the heavy lifting to AI-enabled robotics, enabling humans to do more creative thinking and use their human skills to improve a customer’s in-store experience.

Maximizing human resources with AI

Many retailers have started their journey to digitize processes in order to be more agile and grant greater visibility into their processes. This allows them to direct scarce resources to focused areas for maximum impact, for example, in hopes of digitally transforming their organization with game-changing outcomes. These retailers strive to become intelligent enterprises by effectively using their data assets to achieve their desired outcomes faster – and with less risk and margin exposure.

This comes at the same time millennial employees have accelerated the demand for more purpose from their work and are bringing new digital skillsets to the workforce. Currently, most work tasks are accomplished through a combination of automation and manual labor. AI and ML can augment or automate human tasks and support human decision-making. After all, AI is at its best when a decision can be seamlessly automated or augmented to support an existing business process.

Let’s take the example of allocating tasks to the people who are best suited for a specific job. One employee (let’s call him Fred) is more effective at task #1, while his coworker, Wilma, is better at task #2. Traditionally, duty managers or team leaders would assign work to each employee manually. However, if we can track Fred’s and Wilma’s location in the store with through their wearable devices, the technology can allocate tasks based on the employee’s proximity to their next priority task, which can change dynamically as the robot rounds the next corner and spots something more urgent.

In this scenario, the co-workers’ tasks are assigned based on their efficiency, but it can be extended naturally to use AI intermediaries to assign tasks based on Fred’s and Wilma’s preferred daily duties. And from there, it’s a short leap to tie customer wants and needs to the skills that Fred, Wilma, and their colleagues possess.

If we capture customers’ interests along with their opt-in preferences, why couldn’t we align these capabilities to the store associates’ opt-in or opt-out equivalences?

Increasingly, customers are choosing experiences that have meaning and are delegating the rest to AI. Wouldn’t customers enjoy their experience more if they were served by associates with similar passions and interests? Without AI, it’s too complex, time-consuming, and dynamic to make these connections. It requires re-creating processes enabled by intelligent technology and backed by integrated applications.

Robots like the Bossa Nova, powered by agile, adaptive AI, allow us to think about attaining wins more easily than ever before. Because these models challenge the status quo, we need to engage in open and candid discussion among leadership teams and reflect on the business strategy, risk of action, and disruption to the company.

The commercial benefits of using logic or algorithms are certain to support their business justification. With low unemployment rates and a digital millennial generation demanding purpose, retaining and growing key team members will be more important than ever. Emerging technology provides a mechanism to give individuals worthwhile work that decreases the likelihood they will leave your organization. Let the robots do the heavy lifting, the brain-numbing repetitive work, and enable the humans to use their human skills to their best ability.

While we began by talking about an almost seven-foot-tall robot in Walmart stores, it’s not much of a leap to see the huge opportunity in presenting a completely connected circle of data that allows us to create not only a step change in productivity, costs, and in-store efficiency but also meaningful change in the customer experience.

And that might just be the difference between success and failure in the competitive retail world.

In future blogs, we will cover how the retail use of AI and ML gives enormous potential to both simplify store management and help new leaders become more effective at their roles much quicker.

Attending NRF in New York City January 13–15, 2019? Join Steve Mauchline at the SAP booth with his new proof of concept Bossa Nova robot and discover its potential for retail operations.

This article is part of a series covering how intelligent technology, such as artificial intelligence, is likely to impact many industries and lines of business. Visit here to read the full series to date.

SAP worked with more than a dozen industry experts to uncover five trends that will determine the customer experience over the next decade. The Future Customer Experience: 5 Essential Trends report examines each of these trends and offers recommendations for how brands should respond now to prepare.


Marc Teerlink

About Marc Teerlink

Marc Teerlink is Global Vice President of Intelligent Enterprise Solutions at SAP. He drives the strategy, vision, and production of AI and machine learning technologies delivered through SAP Leonardo. Prior to his current role, Marc was IBM Watson’s Chief Business Strategist, where he oversaw the new offerings portfolio for the Watson platform during IBM's formative years of artificial intelligence. During his time at IBM, Marc executed a number of successful transformational projects and created and delivered cognitive computing solutions and services offerings. Before IBM, he built expertise as a banker, consumer products business manager, consultant, and change leader within nine countries across three continents.

Steve Mauchline

About Steve Mauchline

Steve Mauchline is a Business Architect in SAP North America's Presales unit. He helps customers rationalize business capabilities to ensure clear understanding of their customer vision & strategy and provides business capability recommendations to operationalize the future business and operational model. Prior to his current role, Steve spent 10 years with IBM, focused on software and services solutions in Retail, Consumer Products and Transport & Travel industries. He spent his formative years working for a large UK Retailer, building his expertise across stores, supply chain, merchandising and business analytics.