We have borne witness to some amazing technology breakthroughs that have generated billions of dollars over the past 10-20 years.  We have also heard about many hyped technologies that have languished in search of mass market acceptance.  In this posting, I’d like to address an issue that sometimes gets short shrift, and impacts speed to market success…the realm of the possible vs the realm of the practical. 

 

As an industry filled with hype, technology loves an acronym.  Remember SMAC…social, mobile, analytics, and cloud…four technologies that fueled big market winners like Facebook, the iPhone, many BI business applications, and Salesforce.  Then we had SMACS with the addition of (cyber) Security.  And Big Data…a logical springboard from Analytics and Business Intelligence (BI), fueled by volumes of structured and unstructured SMACS data, has also managed to drive mass market revenue in both the B2B and B2C world. 

 

Many would argue that the hottest acronym today is IoT (Internet of Things), a rubric term promising huge new sources of revenue.  In 2018, the buzzword is AI.

 

AI, often associated with variants including machine learning, deep learning, and cognitive computing, has had some mass market success in the fields of cybersecurity, insurance and financial services, and speech recognition software. Witness all the buzz around Amazon Echo, Google Assistant, and Apple HomeKit …all of which tie back to IoT.

 

Yes, AI is “hot”, but hidden within all this excitement and hype is the realm of the possible vs the realm of the practical, which leads me to inject the letter Y (“Why”) into this discussion.

 

“Why” is the realm of the practical.  When we speak with AI tech enthusiasts and aspirants across the globe, AI comes across as a panacea with a myriad of practical applications.  Those who are product-centric thinkers marvel at the possibilities, developing “cool” products in search of a market.  Such “cool” products may achieve a measure of initial success, appealing to fellow tech enthusiasts who lack real money but enjoy technology for technology’s sake.  But the path to real revenue in the mass market often proves to be elusive.

 

Market-centric thinkers, on the other hand, start with the realm of the practical.  They ask “why” when developing AI-based solutions, focusing initially on a specific problem or need to be solved before developing an AI-based solution to that specific problem. VCs tell us that their #1 concern when investing is product-market fit…and unless these VCs are willing to take on major investment risk, there had better be a market-validated answer to the question “why”.

 

“Why” asks AI entrepreneurs and big company innovators if there is a critical problem or unmet need can be realistically satisfied by an AI approach.  “Why” demands validated appeal to a specific target customer with a compelling reason to buy.  “Why” dictates what a complete whole product must provide in order to fulfill a compelling need…including complementary products and services.  In sum, “why” starts with a problem in search of a solution…not a solution in search of a problem.  And “why” ends with a complete solution to a problem or opportunity that can scale to acceptance by normal practical buyers seeking safe, proven, complete, low risk answers.

 

This may indeed be the dawn of AI, a field that has been in the making for well over 25 years. With huge advances in computing power, broadband, and data storage, AI may now have the foundation required to build complete whole product solutions to practical market needs.   

 

In our work, we help companies looking to capitalize on AI by asking -- and answering -- Y.  What problem or unmet need are we solving for?  Who cares?  Do they recognize the opportunity and are they willing and able to pay for it?  Can we build it, on a realistictime line, with complementary partners to fill the holes in our whole product solution?  All of these questions demand a market-centric viewpoint…one that we often find lacking in this high potential but nascent arena. 

 

Always ask Y when talking AI.