AI is not a crutch, it is a bridge. Part 1 -Boost.AI

About a month ago, I was invited to participate in the panel discussion at the 2019 AI World Summit in Amsterdam. This was an event of a global magnitude, attended by "who-is-who" of the AI community. Some of luminaries included the likes of Werner Vogels and Stuart Russell delivering keynote speeches.

In Amsterdam, I have presented my views on the AI opportunities and the state of AI in retail, absorbed tons of information, and have corroborated my key conviction that AI is NOT a Crutch, it is a Bridge. This, not to be cute, is paraphrasing of a well-known quote by Jaggi Vasudev -"a Guru is not a crutch, he is a bridge"

Truly, AI is a means to an end - it is not a goal, an objective, nor it is a silver bullet. In fact, 90% of real-life problems are best solved with a common sense and good-old descriptive BI. Using AI to answer most run-the-business questions, to validate linear hypothesis, is like going duck-hunting with a cannon. Moreover, AI does not substitute an intuition, nor it can drive decision on limited, incomplete datasets.

So, why then AI, by far, is the most exciting intersection of technology, data science, and business today? Why so much energy, interest, development, and investment pouring into the field of Machine Learning(ML)? The reason is clear - it is that remaining elusive 10% of business problems that can be answered with the help of AI that is going to separate winners from losers. No company can remain competitive without a full-scale AI adoption. And, I am not only talking about the technology and data science, of course - the whole company must evolve to with this adoption.

Since more and more companies realize its significance, AI market today is over-hyped, full with exaggerated claims, and PowerPoint-ware, leaving many corporate observers bewildered and confused, unable to fathom the true relevance, benefits, approaches, and complexities of AI implementation. Incidentally, this is true across all the market verticals, not only just retail. One of my favorite quotes that I cite in the Quotes page of my website is from Sophocles: “Nothing vast enters the life of mortals without a curse.” The AI certainly fits the bill. So, lets explore some true immediate and strategic opportunities and applications for AI today.

When I think of a “blue ocean” state for a modern large retailer, I think of the 5 essential criteria:

1. Consistency of Intelligence an User Experience at Scale. It would be super cool for a large company to locally have a customer insight and enjoy a good-will of a neighborhood “mom & pop” store where an owner knows its customers, their tastes, children names, dog names, wedding anniversaries, etc. These businesses are often perceived not as a for-profit concern, but as an indelible part of the local community. And very often they truly are;
2. Economy of Scale that includes well-integrated service modalities, multi-presence capabilities, purchasing power, brand leverage, and reach of a national retail brand, operating thousands of stores across the country;
3. Corporate Culture of a Software or Internet Company. Don't think as a traditional retailer! You are not there to sell stuff. You're after to change the world. Do away with the traditional separation of IT and business, “IT for business” type of mentality. IT is business! The retailers who are going to dominate are indistinguishable in the way they think from the leading high-tech companies; In fact, the most successful retailer in the world is the software and SaaS/PaaS company. So, this is not really debatable;
4. Deliver Hyper-Personalized Experience that includes transparent and consistent Services, relevant Assortment, appealing Promotional Offers. These are to be accompanied by a low total cost of ownership (TCO), reduced friction, and product lifecycle strategies that incentivize seamless subscription or upgrade cycles.
5. Transformation to a Direct-to-Consumer(D2C) model, building digitally-native vertical brands that are distinguished by a complete control of customer experience and product lifecycle. These brands experience explosive growth, greater customer affinity and deliver 5x to 10x margin of traditional retailers.

This is not a quixotic state of affairs. For some of the visionary retailers, like Warby Parker, Thread, Dollar Shave Club, MTailor, StitchFix it is a reality.

Their secret sauce – a. maniacal focus on customer and b. pervasive and increasing adoption of AI to drive all aspects of business workflows. Let's expose then the real-life applications for AI for retailers. From the top, there are two primary domains of AI implementation:

1. To gain incremental improvements, find new operational efficiencies, create better customer-relevancy in the scope of the current business model. I am going to refer to it as Boost.AI
2. To discover game-changing, disruptive opportunities, to re-invent and to transform the current business model - Disrupt.AI

The Boost.AI

In our first “bucket” we find the most typical applications of AI today, generating a multitude of scoring models (also referred as prediction models.) They all convert Input data into Output, based on the predictions (scoring) model assigned to the Output data. The latency may differ from batch to near-real-time to real-time. This is where 99% of current implementation and AI adoption takes place. Representative ML models use-cases are built for the following AI problem set:

• Customer Propensity to Buy;
• Customer Propensity to Churn;
• Fraud Detection Pattern;
• SPAM/SCAM/Fishing Patterns;
• SKU-level Assortment, Merchandise Planning, Demand Forecasting;
• Anomaly condition detection: outlier performers, etc.
• Static Customer Segmentation;

Then, somewhat further down the line, most forward-thinking companies use ML to:

• Create ontologies between items and categories to drive sales;
• Gauge data source signal accuracy;
• Improve Data Quality, including reduction of data duplication, creating inferred definitions on poorly defined datasets

These outputs are typically visualized in content-rich dashboards, providing data scientists and business users with an innate insight missing from the legacy metric-driven reports. The state-of-the-art of AI in the enterprise is realization that wide and deep-level operationalization of ML is totally mandatory to maintaining a long-term competitive edge. I am convinced that any company under-investing in AI will be out-competed. And NOT in the long-term - the rate of change is ever accelerating, and AI is going to be like turning-on afterburners to get away from the bad-guys, aka, competition.

Then, good, we have our recipe. Not really. A simple run-down through the use-cases above suggests that none of them, taken separately, creates profound process changes that could lead to uncovering long-term opportunities leading to strategic transformation of the business model, to “blue ocean”. Here is where our second “bucket” comes in. That will be a story for the next post.

A complete post can be found at: https://www.kraychik.net/post/ai-is-not-a-crutch-it-is-a-bridge