Very good comments Oliver, I totally agree with the value of the use cases you cite here and the necessity of the real or right time technical requirements. I can't remember exactly but I think a company like Kroger perhaps with the support of 8451 does the things you describe. Even if I'm not recalling this example correctly, the solution scope and expertise necessary unless available truly turnkey and highly packaged, is arguably hard to justify as the initial use cases in a retailer's AI roadmap. The research and best practices I'm aware of suggest that the value of AI use cases must accrue and be recognized by executives as a means to fund ever higher value and possibly risker but more valuable use cases. I think this makes complete sense but the way most retailers think about data, IT, management and processes I fear flies in the face of what's needed to really achieve scale value of AI investments. I think most retailers interested in real time IoT enabled adjustments in price and promotion at the shelf must test what this buys them versus the current state to justify the project. To get there, a small market test done more manually, but leveraging ML to derive or leverage unrecognized factors, might prove the utility of a broader and real time architecture investment.
Very thoughtful comments Cynthia. You describe the bespoke "craft" data science-led use case path that few inexperienced leaders in any industry have time or the inclination to risk exploring. It's unfortunate given the potential opportunity and as you say, bringing the industry and business knowledge heat to solving the right problems can pay off. The flip side are the use case opportunities to accelerate AI projects within packaged software, which I happen to be familiar with. The best ones allow the retailer to tailor AI to their specific needs, and done right, allow the retailer and the associated business, technical and analytical teams to really learn in practice how AI use cases become reality and are different from how a process unfolds today - the skills, data, ethical considerations, governance issues, and technical architecture and integration requirements. Risk is lower, time to value faster, but if you don't manage these use cases as a group IMO and have a thoughtful strategy to expand upon win after win, then the retailer may never realize any significant value from AI investments, be they bespoke data science fueled or otherwise.
Thanks Craig, your wording here is exactly as I have described it as well -- balancing what you can do fast that has value via packaged means, while exploring cases that are maybe more complex and specific to your business such that you may need data science skills. The challenge I've observed is that use cases across these two scenarios tend to live in silos so there is no reconciliation of business value relative to priorities, resources and budgets. I think those that do however stand to get a chunk of that potential AI value reported by McKinsey.
Well said Kim. It would be great if there were an Executive Accelerator to get over the hurdle of understanding and trusting the potential, such that an Exec had the confidence to stick their head out a bit into an unfamiliar area.
Thanks Chuck - agree about data quality and foundation as important. What I have seen is too much of this data quality conversation confined in a small group, usually IT, without connection or direction from the business. This persists and over time unfortunately I am sure it hurts the company's performance and it struggles on many fronts with fewer resources due to COVID and general market headwinds.
Thanks Ananda. I think retailers are in a tough spot. On one hand as you say, there are many AI use cases used today but the value may not be the breakthrough execs expect or desire. The problem is that these use cases, IMO, were done in relative isolation and not part of a thoughtful plan for AI across the retail organization.
There is some good research from Tom Davenport showing that the transformation value from AI for most companies in any industry, at their initial stages, comes from adding up the value of many use cases and leveraging these into successively more valuable and potentially more complex use cases that have massive value potential. If execs don't recognize and believe this, status quo results and unfortunately probably poor financial results too.
What AI represents to the retail business is a function of its challenges and the business outcomes potentially improved through intelligent automation. It is as simple as that to begin with, and so you need to land on the use cases most material for your business. So it's helpful to know what an AI use case looks like.
Beyond that, there are considerations for prioritizing use cases, governance, ethics, oversight, technology products, technology architecture, data science, training, education, and re-skilling. It sounds big and scary, which is why I think so few retailers can articulate a vision for AI in their business.
The easiest path I think is to view AI as an extension of the long-standing analytics function in your business and build out from there with an eye on maximizing the value of investments across technology, people and processes.
I would not fixate on building models and deploying them versus purchasing tech products or services off the shelf. Instead, I would view these as part of one “whole” approach to leveraging data and intelligent automation across your business. In this way, you can home in on the business value achieved, while maximizing the learnings and developing reusable assets to scale AI into the future.
Mr. Tritton's quote in this discussion signals what looks like a great plan structure, so if executed well things should continue to improve. Rather than focus on just digital, e-commerce and related omnichannel, he mentions the customer-inspired assortment. You can have all the greatest omnichannel this/that, but if your experience and offerings fail to connect or are not priced right then it's all wasted. It shows also the underlying inference here - that the omnichannel digital work and assortment decisions feed off one another based on the underlying data and analytics.
Seems to me that whether it’s building tech or building a storefront, you create unique things when it must be core to your value proposition – something your stakeholders highly value and associate only with you. You instead buy things that tend to be common among your peers, and tune these to your business needs around the edges of the core use case.
The things you build uniquely, of course, must be architected to work smoothly with what you buy. You must have flexibility and interoperability.
Easier said than than done, that's the point. Every retailer comes at this problem with different baggage and considerations. It requires leadership and frankly a great analytics capability.
I'll bet there are few retailers who actually know factually what truly matters to their business such that they must build it themselves. Wouldn’t it be great to focus tech development investments on the edges of the core business processes that every retailer needs, tune them to meet the needs of your customers, and create new solutions that your customers appreciate, recognize and value as uniquely "you"?
Going out on a limb here, but depending on how long it takes for a vaccine or proven treatment, I expect Target and other essential retailers with large square footage to shift at least half their space to shipping and fulfillment operations. I say this because you see lines of people outside waiting their turn to enter the store (this was yesterday, six feet a part). Metering the customers allowed in the store means less shopping space is required, and there will be customers who continue to shop in person. You can't easily sublease space to other businesses or open smaller footprint stores so you may as well re-design the space to support the new shopping experience.
Robotics is a use case for Artificial Intelligence. Even if a retailer or food service company were to outsource this, which would be necessary except for maybe Amazon or Walmart, you want to control, own and leverage this data - on customer orders, routes, scheduling, and the feedback loop. Also - every justification to integrate with existing customer data programs for loyalty, marketing and service.
Having written that - as a use case - I would ensure to prioritize it alongside others within your analytics portfolio. If you look at this solely as a quick gut reaction add on, versus a part of your overall customer experience, you will be disappointed in the results.
Last and most important points: Of course not every community will support this - either legally or for safety reasons. However, long-term, it's going to happen. That alone may not justify this. But think about this.
Signaling to your customers and the market you are investing in making it easier to order and receive products can only create good will. Will delivery jobs be lost? Maybe, or more likely jobs will be created to build and maintain the machines, or operate the service for the retailer.
Also - at this time today, and maybe into the foreseeable future, consumers will limit their in-store shopping to only those destinations that represent the largest variety of goods - to minimize trips and possible exposure. It may sound silly, but I think millions of consumers will behave this way.
Unless you are a one stop shop like Amazon or Walmart, you best be ready to serve demand without requiring your customers to visit your store.
At this point in time, I think retail workers fear for their jobs due to COVID-19 more than any AI.
To answer this question, if retail workers fear AI it's because their leadership has not communicated a strategy for how AI and analytics supports the business. People drive processes, processes are improved through AI, and you will not get adoption and value if your people are not clear on the strategy. All the research into what works/does not validates that this is what happens in cases when companies report failed AI initiatives.
Collective intelligence is a good way to frame how people and machines working together create better outcomes than they can individually. In cases when jobs change due to AI, it is often because repeatable tasks within a specific job can be automated. The job itself is not lost, but changes becomes more efficient. As a result, you may need fewer new people to do the "old" job, but more people to do the "new" job that leverages human talents that AI cannot replace.