CIOs wish for simpler ways to wrangle data and experiment with business models – but change remains hard to scale. Also, it may be time to stop chasing “alignment.”
AI in retail: Two data priorities for CIOs
How can retail industry CIOs capitalize on AI’s transformative potential? Focus on data completeness and collaborate on data sharing
A Gartner study earlier this year found that 37 percent of CIOs said their enterprises had deployed artificial intelligence or would do so shortly, compared to just 10 percent four years ago. While AI technology has been around for years, more recent use cases seek to transform a wide range of business processes, from sales reporting to medical diagnostics.
If AI ramps up as anticipated, consumers will benefit from more personalized experiences. Today, we see “Marty” the robot at Stop & Shop grocery stores alerting you to spills and hazards. Tomorrow, AI aims to aid the shopper wherever they are on their path to purchase, with tailored recommendations and upsell suggestions being pushed in real time and pulled from multiple data sources.
Retail CIOs investigating the potential of AI for these myriad use cases are really evaluating the enrichment of the data they’re collecting, and how to structure it to be put to work in more effective ways.
Given AI’s critical dependence on structured data, there must be careful consideration given to data standardization and business process consistency to help AI function as intended.
[ Is your data house in order? Read the related article: Getting started with AI in 2019: Prioritize data management. ]
The foundational data required to make algorithms “smarter” ultimately rely on the common global format that standards provide. There are two ways that CIOs can capitalize on AI’s real potential to transform an organization – focus on data completeness and collaborate on data sharing.
Focus on data completeness
Today new technology pilots are moving fast – but it’s important to consider the condition and quality of your data before piloting. Applying widely recognized data standards can mean setting up AI to have all the relevant information it needs to efficiently perform.
For example, online retailers like Amazon and eBay are looking to AI for more personalization to secure customer loyalty, and want to take advantage of the time consumers spend on their sites to cross-sell and upsell more products. Amazon alone has applied for over 35 US patents related to “search results” since 2002, according to CB Insights. A recent Accenture study shed light on the emerging use case where AI can provide a continuous loop of information about a product’s quality and performance to help supply chain partners better incorporate the voice of the consumer into product listings.
Those key pieces of data featured in product listings can be processed by AI to surface more relevant search results for the consumer. Product attribution – things like size, color, heel height, collar type, fabric type and more – is increasingly important to digital-savvy consumers researching products on their phones prior to purchase. CIOs interested in leveraging AI algorithms to address consumers’ appetite for information need to start with how they are feeding the most complete, accurate, and consistent data sets into AI.
[ Read also: AI vs. machine learning: What’s the difference? ]
With online research becoming the make-or-break moment to securing a sale (whether it is in store, online, or a combination of both such as click-and-collect), the trend toward more extensive attribution has exposed a need for more data and fewer human hours spent chasing down data.
For example, retailers may spend valuable time and resources filling in missing attributes if suppliers provide incomplete information. This last minute scramble can affect operations in a number of ways – the data is less trustworthy because it is not sourced directly from a brand, leading to potential consumer dissatisfaction. Or, the data could be out of synch with product shipments, causing a delay in the product’s speed-to-market. Data completeness is going to be key to taking truly consumer-centric strategies to the next level.
Collaborate on data sharing
Not only must more data be collected, it must be governed and standardized to help AI draw the right inferences in real time. Setting up systems interoperability in preparation for AI applications can ensure efficient data sharing. On a basic level, this means Organization A’s system must interoperate smoothly with Organization B’s system.
For example, Ocado, the UK-based online grocer, has implemented effective use of AI to speed warehouse and logistics processes to meet consumer demand. Ocado is now expanding operations through partnership with Kroger, the largest grocer in the U.S. Such a partnership illustrates how standards can enable two partners to more easily allow cross-functional teams to solve business problems through the use of technology.
A CIO who recognizes that data is a competitive advantage might say “I don’t need more data. I need to get more value out of my existing data.” Algorithms can automate tasks using data – standards can help organizations and systems interoperate to do something more valuable with the data when AI is applied.
Does this mean that humans are obsolete after a company standardizes its data and runs AI to complete designated tasks? Not at all. In fact, AI functions better with employee engagement to supplement the decision-making skills that AI cannot make alone. A recent study by IBM described “intelligent automation,” which is how AI is being used to automate processes along the supply chain. With AI, retailers can start to automate supply chain processes, like rerouting trucks due to developing bad weather conditions, based on the consumption of massive amounts of data.
In these types of scenarios, supply chain, store operations, merchandising, product design, finance, and sales teams benefit from AI so that they, as humans, can make smarter decisions that have a real impact on customer experience. Therefore, as AI goes mainstream, CIOs will need to align and collaborate cross-functionally with internal and external partners to ensure that all parties are knowledgeable of what types of data AI is best at processing, and what still requires human context.
With a renewed focus on data quality and collaboration through industry standards, companies who explore and adopt these emerging technologies can successfully harness the power of data through AI.
[ Want lessons learned from CIOs applying AI? Get the new HBR Analytic Services report, An Executive's Guide to Real-World AI. ]