Four Steps: Use AI to Accelerate Your Product Decision Tempo

By John Sviokla and Paul Blase

In Part 1 of this article we highlighted insights from one of the most prominent military thinkers of the last century, U.S. Air Force Col. John Boyd (a.k.a. 40 second Boyd), that point to ways to improve the odds that products will succeed. He noted that throughout history successful military commanders operate at a higher tempo than their opponent and can change actions or maneuvers more quickly. They were able to observe, orient, decide and act faster than their adversaries. The same holds true for product development today, if you can observe the market, orient the information to inform investment trade-offs, and make timely decisions...you improve your odds of winning. It all boils down to having a faster decision tempo that is more in touch with unexpressed and expressed needs of customers than competitors.

In Part 2 we describe 4 steps product leaders and data scientists can take to design and use innovative, AI driven solutions to accelerate their product decision tempo with informed customer views.

• Give Product Development Teams a Data & Analytics Co-Pilot - every stage of the product development process - from concept identification to post launch measurement - can be informed by more insightful data and analysis to help improve decision making. It requires aligning on the key decisions product teams make at each step of the life-cycle and agreeing on the data needs, types of models and variables that will be measured to pinpoint opportunities and issues.
• Master The Data Outside Your Company - there are billions of data points about products in the market in the form of text, images and videos. It is just as valuable, if not more so, than data inside the company.  While it used to be cost-prohibitive companies can create data lakes with longitudinal market data as a primary source of truth for understanding how products are performing and being used day-to-day by customers.
• Benchmark Your Products Against The Market - companies can combine market data with their internal data to benchmark their products against all of their competitors’ products at the feature level. It is a helpful tool to understand what features are important, differentiated from competitors and resonating with customers so you can pinpoint investments with the highest potential return. This is cost prohibitive using traditional surveys, but not using big data and artificial intelligence.  
• Build Connections with Knowledgeable Customer Enthusiasts - 3rd parties like Amazon and Google increasingly control key customer touch-points that companies used to rely on to understand their customers’ current issues and future product needs. There are no shortage of customer panels to tap for broad market research studies, but they don’t benefit product development teams the way customers do that are enthusiastic and knowledgeable about their product space. Product teams need to identify networks of these types of customers that they can regularly and efficiently connect with to inform and accelerate their discovery efforts.  

As companies start to listen to what their product data is telling them, they will be listening to a whole markets worth of customers, and be on their way to changing the odds on arguably the most important investment companies make - investment in new products. 

Contact: 
Paul Blase - Speciate AI, [email protected]
Dr. John Sviokla - Group Bionic, [email protected]