Are retailers set up to scale the value of AI investments?


According to McKinsey, the retail industry has the potential to create $1.7 trillion of value, or 12.39 percent of total sales, from artificial intelligence.
How much value do you think AI creates for a retail business?
Before answering such a question, you must first have established goals and the capacity to track use case performance, plus a way to translate it into metrics that matter: sales, margin, customer value, retention, productivity or efficiency.
This is important because companies that achieve outsized benefits from AI fund their use case roadmaps on the backs of successful projects. You simply cannot do this if you approach AI within silos and buried within business processes.
AI is most often employed to automate tasks within processes. This means you have to isolate the impact of AI on the process in order to measure effectiveness.
Fortunately, for those early in their AI journeys, most initial use cases that pass muster during prioritization are based on lots of historical data just ripe for machine learning. So, the baselines for use cases targeting improved personalization, recommendation or recognition should be readily available.
Retailers already proficient with AI have built up the technical, organizational and governance foundations necessary for significant value scale. Your goal should be the same, but you cannot get there overnight.
That is why before you head down any AI use case path you should:
- Ensure you have the means to establish a baseline from which you expect to improve.
- Have the data to support the baseline analysis — data that is updated at the right frequency and is available in the right format; data that connects actions performed by AI to business metrics.
- Be capable of conveying use case results in business metric terms to the executives who fund and depend on the AI.
- Plan for next steps: If results fail to meet expectations, make changes or retire the use case and leverage the learnings into the next one. If results are positive and your executives seek to improve further, build upon what you have.
- How to set expectations for your AI project – LinkedIn/ Gib Bassett
- The Executive’s AI Playbook – McKinsey
- How to Choose Your First AI Project – Harvard Business Review
- AI, ML and Deep Learning – A very quick primer for curious executives – LinkedIn/Rama Ramakrishnan
- What are the biggest barriers to AI adoption for retailers? – RetailWire
DISCUSSION QUESTIONS: How aware do you think retail executives are today of the impact AI is having or could have on their business? What do you see as the main barriers to retailers tracking the performance of AI use cases?
Join the Discussion!
18 Comments on "Are retailers set up to scale the value of AI investments?"
You must be logged in to post a comment.
You must be logged in to post a comment.
CEO/Founder, Crobox
Execs may note the importance of AI but not understand how best to use it. And yet it’s true that leveraging AI should be a mandate from the C-level down. Without a culture of experimentation at the core of a brand’s strategy, it’ll be more difficult to reap the benefits of AI. In order for this to stick (and work), retailers need to be data-driven, flexible, and IT-first – often a hard sell in an industry dominated by legacy issues and interdepartmental silos.
Director, Solutions Marketing with Alteryx
Well said Rodger, I agree!
Managing Partner, Advanced Simulations
They are probably aware and probably unsure as to how it is best used. They need to differentiate it from automation, which is where productivity gains might be found. Hard to believe there’s trillions in value being left on the table – sales are a relatively zero-sum game so if somebody is picking up value with AI, somebody is losing. And that is the biggest obstacle – unrealistic expectations about what AI can do for you short term and long term.
Director, Solutions Marketing with Alteryx
I also thought about the zero sum thing Dr. Needel, there are definitely winners at the expense of others when it comes to this value.
President of FutureProof Retail
Often, when there is a new technology solution in the market, there’s a lot of media attention centered around the theory of technology instead of its practical use. AI can do many things, but it’s only helpful to retailers when used to solve an existing priority business problem faster, better, and cheaper. For example, AI is great for personalized product recommendations. Amazon generates 35 percent of its sales through its recommendations engine. Similarly, Halla’s AI engine is trained using recipe data and natural languages to easily plug into a retailer’s e-commerce and/or scan and go platforms.
Director, Solutions Marketing with Alteryx
Agree about product recommendations, probably the #1 most talked about AI use case in retail, but also the least understood when it comes to what makes a great one, versus a less great one.
Vice President, Research at IDC
Retailers are not aware of the potential impact because few use cases exist that show that AI adds tremendous value. It’s challenging to measure the impact and costly to implement solutions, especially data-focused solutions because the systems are not in place to enable it. Outside the standard e-commerce recommendations, demand forecasting, intelligent routing scenarios, and some robotics, there haven’t been major strides in applying AI to retail. The big drawbacks are tied to lack of data infrastructure, real-time data access and AI/data talent in the retail space. Regardless of retail exec awareness, retailers are unable to move towards major AI powered results without a data science foundation. For retailers it’s a matter of practicality.
Director, Solutions Marketing with Alteryx
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.
CEO, Currency Alliance
AI deserves the attention it is getting but, for the algorithms to work, they need clean data – and too few organizations are dedicating the effort to clean up their data and generating single customer profiles. Without those foundations in place, a great deal of money will be wasted on AI as the ROI will be depressed by poor data management practices.
Director, Solutions Marketing with Alteryx
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.
Chief Marketing Officer, PerimeterX
In many businesses, AI is still a solution looking for a problem, so its potential to drive improved business outcomes is not well understood. Analytics teams need a C-suite sponsor behind a strategic initiative that puts AI’s potential to work in a visible way. They will also need to take the black box off of AI to help explain the “why” behind the outcomes it drives in a way that can be broadly understood and ultimately increase its leverage. Only then will AI be recognized for the impact it is having and be fully embraced as a meaningful business tool.
Director, Solutions Marketing with Alteryx
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.
CEO Antuit AI
Retailers are undergoing rapid omnichannel transformation and AI can be a key weapon. Given the need for speed, retailers should prioritize use cases and leverage proven, ready-to-go SaaS solutions wherever possible. Internal data science teams should focus on high impact use cases not readily available.
In our current environment, AI speed will eat the competition.
Director, Solutions Marketing with Alteryx
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.
Founder | CEO, Female Brain Ai & Prefeye - Preference Science Technologies Inc.
Director, Solutions Marketing with Alteryx
Global Industry Architect, Microsoft Retail
Director, Solutions Marketing with Alteryx