A.I. is powering the future of retail. Aside from being a dramatic-sounding headline, it’s the title of a recent presentation issued by the research firm CB Insights – “The Smarter Store How AI is powering the future of retail

Sure, voice-bots and robots in the aisles are interesting – however, the immediate and significant impact of A.I. is in finding operational efficiencies in areas of the business where it’s humanly impossible for people to measure all the interconnected relationships and trade-offs to make smarter, more profitable decisions. Most retailers will acknowledge that the areas promotions, pricing and demand forecasting are ripe for a more advanced and holistic approach.

Over the past couple of years there’s certainly been no shortage of marketing content, articles, research reports, conference presentations and white papers that purport to tell everyone what A.I. powers… i.e., retail, insurance, medicine, finance, everything… but who is helping us cut through the hype and telling is what A.I. actually is and what it isn’t?

As Daniel Faggella from the consultancy TechEmergence says, the hype is making it hard for business leaders to determine who is providing actual A.I  ‘artificial intelligence’ and ‘machine learning’ are used as buzzwords, mere fiction used to get attention, client inquiries, and investment from VCs.” The stakes are high and the risks are two-fold: one, a retailer may invest in what is thought to be an A.I. solution provider, not see results and write A.I. off as hype; and two, fail to keep pace as A.I. continues to drive prices down.

Through the rise of GPUs, analysis that was computationally impractical just a few years ago is not only possible today — it’s imperative for many companies. Nvidia, a leading GPU manufacturer and pioneer in the spread of A.I. across a wide spectrum of industries, featured Daisy Intelligence in a 2016 report “How AI is Accelerating Retail Transformation” and in 2017 Daisy Intelligence was shortlisted for “Best Application of AI in the Enterprise,” at the AIconics awards at the annual AI Summit in San Francisco. The Alconics is the world’s only independently-judged awards celebrating Artificial Intelligence for Business. Most recently, Gartner named Daisy Intelligence as a Cool Vendor in AI for Retail, 2018. Our CEO, Gary Saarenvirta, has several patents pending for using A.I. powered computers to “take over the highly repetitive and quantitative decision-making tasks.”

Reinforcement Learning is A.I.

Daisy Intelligence’ corporate HQ is in the “A.I. hotbed” city of Toronto. Daisy’s A.I., delivered in a SaaS platform, is based on what is known as “Reinforcement Learning.” MIT Technology Review describes reinforcement learning as “an approach to artificial intelligence that gets computers to learn like people, without explicit instruction.” It looks at a sequence of events and collects data from retail clients over a long period of time and learns new actions without being explicitly programmed. Daisy’s A.I. iteratively adapts – without human intervention – when exposed to any new data. Daisy then utilizes 100% of the data collected to exploit historical patterns, and enables the computer to “practice” by simulating an astronomical number of alternative scenarios. This allows Daisy to evaluate decisions which their clients’ might not have ever made before and offer math-based recommendations to support their decision-making.

Predictive Analytics is not A.I.

A.I. is not predictive analytics. In fact, much of what is described as A.I. today is in actuality predictive analytics. The world’s largest retailers have been using predictive analytics for years in order to forecast demand and set prices. Here is an analogy that helps illustrate the major difference between A.I. and predictive analytics. Imagine two race cars about to race each other around a track. One car is powered by predictive analytics and the other by A.I. The A.I. race car looks out across the track and calculates simulations and learns from the mistakes, constantly optimizing for performance and turns in a decent time. Whereas Team Predictive Analytics says, “how do I get around the track the quickest?” It figures that the long-term objective is having the fastest time across the track, and figures out that the answer is, “put the pedal to the metal”. It doesn’t look at any trade-offs, doesn’t simulate any results, and ends the race by driving into the ditch at the first turn because it hasn’t learned that it needs to slow down during turns. Be wary of traditional analytics providers incorporating machine learning and repackaging themselves as A.I.

Finally, one thing artificial intelligence definitely isn’t is brand-new. In fact, there is a plaque at Dartmouth University commemorating the beginning of A.I. as an academic discipline in 1956. It’s only been very relatively recently that the term artificial intelligence or “A.I.” has been broadly used to describe advanced analytics-based technology solutions and accompanying business use-cases.

So What?

The A.I. community of corporate, government and academic organizations can distinguish between the true A.I. and traditional analytics being marketed as A.I. However, once the message of A.I. and its possibilities reaches a broader audience of business leaders, liberties are taken with regards to marketing, A.I. and “artificial intelligence” become little more than buzzwords, and resources intended to seed and capitalize on an A.I. initiative end up supporting  traditional analytics efforts. For retailers exploring A.I., however the risks of making a bad investment in “Fake A.I.” that delivers zero value are far more serious than a write-off and wasted money and manpower. In retail, it’s an existential risk, because the retailers who do have the ability to recognize and invest in A.I. are going to put them out of business, and fast.

As stated, the stakes are high and keeping pace is going to be as challenging as it’s ever been in the continually evolving landscape that is retail. A.I. is a technology that will prove to be as impactful as the internet was and it has begun to transform virtually every industry on the planet – retail is not exempt. The simplest way for leaders to cut through the hype and validate whether a solution provider is truly A.I. is by the results they’re delivering. If there is not a meaningful, quantifiable and immediate impact on business results, it’s likely not A.I. 

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