In mid-September, The Parker Avery Group (PAG) will be sponsoring and presenting at Synergy, the Mi9 Retail customer conference in Las Vegas, Nevada. PAG Associate Partner, Amanda Astrologo, will be attending the conference and leading a discussion on ‘future-proofing’ retail—how retailers must ensure foundational systems and capabilities, as well as a solid change management program, are in place before embarking on new initiatives and innovations.
This week, as Parker Avery prepares for the conference, Amanda shares a few of her perspectives on how to chase new technologies now available to retailers, what it means to ‘future-proof,’ and the related implications of both for retailers.
What does ‘future-proofing’ retail mean?
To me, it means making sure you’re foundationally ready. There are so many ‘shiny objects’ in today’s software arena like artificial intelligence (AI), machine learning (ML), or even just analytics in general. Every retailer is trying to get ahead—and in many cases just keep up or stay viable. It’s easy to get caught up in the whirlwind that is today’s retail, but we see so many of our clients not prepared for the fast lane and not taking the critical initial steps to truly assess if they are ready, not only with necessary supporting systems in place, but also from an organizational perspective—the people and roles necessary for success.
It’s also important to have the foundational processes in a good place. If the ‘simple’ things—basic retail block and tackle business activities like merchandise financial planning (MFP) or buying—are done differently in each area or the solutions are heavily customized, there is a significant risk in being able to do these well in an omnichannel world where everything is connected and customers expect seamless, immediate experiences. It’s great to want analytics, but you need to have a place for the data to go and be utilized easily and across your entire business. Otherwise it’s just data…and potentially expensive data.
Machine learning and artificial intelligence have been buzzwords for several years now. Why have retailers traditionally been slow to adopt?
Many leaders think that if they invest in the science, it is the silver bullet. However, the more they research or talk to experts in this space, they start to understand it’s a much larger undertaking. There is still a human factor that must be taken into consideration, as well as understanding where the data comes from (and ensuring it’s clean), where it is housed, and how it will be utilized in current solutions. This is where the people, roles, and responsibilities come into play and are extremely critical for success.
What must retailers do to get ready for the ‘shiny objects’ like ML and AI?
To successfully adopt any new technology, your data needs to be solid (accurate, clean, and well-governed) and so do the core business processes. Standardizing and streamlining across the business units where it makes sense will help with user adoption and ease training. I would also mention having a solid implementation plan, skilled implementation partners, and strong change management (OCM) strategy. Change management is one piece most say, “We can cut OCM out and still be ok.” I would 100% disagree—while you may be able to implement an AI or ML solution out of the gate, there is a huge sustainability risk with respect to user engagement and adoption.
Is there a process or area of the business that you would recommend starting if someone was looking to get their “toes wet” in this space?
My recommendation would be to start somewhere like merchandise planning where you can give visibility to the user. In most solutions you can configure a measure that is the analytical or demand forecast. The user can choose whether or not to accept it. Starting with MFP is far easier than trying the ‘black box’ approach which you may find with some allocation or replenishment solutions. Those solutions also have other levers to pull that can muddy the waters to a user when you start talking forecast accuracy. I’ve implemented demand forecast capabilities across several solutions, and the hardest part is always user adoption. You can have the best data and the most advanced software, but if the user (and leaders for that matter) don’t adopt…it’s dead in the water.