Before Your Artificial Intelligence Project/Product Fails – Get a Critical Analysis for Buyers of AI Solutions and Technology

Critical Analysis for Buyers & Builders of AI Solutions and Technology

Before you begin, expand, or launch a product, business, or strategy, validate your efforts. Get a second opinion before you fail or risk a fortune. Why, you ask? Because everyone—and I mean everyone—talking about artificial intelligence (AI) is also looking for the dollars and real return on investment (ROI) that AI can either generate or save. Here’s some compelling news from the VentureBeat Summit: "Some IBM customers have achieved ROI within four months, especially in marketing," noted IBM’s Cho Suh. These companies managed to recoup their technology and labor investments within that time by using Watson to increase user engagement and drive cross-selling or upselling. However, he didn’t specify how Watson is achieving this or whether it would work for your business. If you want to analyze the details like a crime scene investigator (CSI), it may bring you some insight—but not necessarily solutions. Chen Peng, head of data science at UberEats, told the VB Summit audience that the company’s initial AI results were immediate and partly explained how UberEats reached a $6 billion gross sales run rate just four years after its founding. This success was largely due to leveraging Uber’s existing AI infrastructure. UberEats employs a team of 40 data scientists and has been data-driven from the start. The company uses AI to recommend restaurants and menu items and to optimize deliveries—each of these efforts is a complex task. "Analytics has played a critical role in driving the growth of the business," Peng said. "We have been using ranking algorithms to tailor the content of the app—for example, menu items and restaurants—which has led to a 10% increase in session conversion rates, directly translating into top-line business growth."

From my research for MindMeld and in the book on business applications for artificial intelligence, I found that even in well-known systems developed for medical applications or sales-ordering processes, quantifying human movement tasks is a complex, if not impossible, problem. Since management decision-making follows few set rules, expert systems are generally used in situations where tedious and repetitive tasks lead to human error—errors that can have disastrous consequences in medical or sales-ordering applications. In other, more complex applications, the process extends beyond simple commands and rule assignments. When thousands or even millions of dollars are at stake in managing certain tasks, the cost of developing an expert system may be justified. Most expert systems—and, in fact, most software programs—are designed by relatively few individuals. While committee decision-making is not always perfect, well-developed and carefully considered decisions often emerge from committees rather than from a single expert. There are numerous corporate policies and procedures that can be automated, including sales-ordering processes, travel voucher processing, purchase order management, management approvals, technology allocations, room size assignments based on management level, and regulations governing the use of executive dining rooms.

Validate your AI Business/Product/Strategy now – in order to make AI pay for itself, provide real ROI or sell by the thousands to customers looking for solutions you a looking for. We recommend at a minimum the following in a market/business validation:

Build a long term view – figure out what you are really trying to do and also realize what AI is today will not be what it is tomorrow. From VB Summit, "AI is advancing quickly, "it's very dynamic and changing, and the industry, and even the academics, underestimate the rate of change."

Focus on compelling business issue aka delivery logistics, complex customer order process, changing customer patterns and other complex issues that you don't have an immediate solution or that your business is changing, and you want a new way to solve the problem.

Explore both historical to new data – neither is important alone but both working together can find weaknesses and opportunities. History is certainly any guide for the future, however, at the very least you may find existing problems that will continue to exist unless something is done about them.

Build for changing data analytics – more than just a better data "algorithm" model build an ever evolving data modeling process. From VB Summit, "You can make some pretty significant errors by assuming that the algorithm will learn itself and do all sorts of wonderful things. So, it's become a reality check and growing hype around AI caused executives to put too much faith in the power of data science alone, but the pendulum has swung back to domain expertise."

Tracking and testing – simulate and then test "live" across customer pockets aka customer who live in warmer climates wear, eat, watch, do things differently than just a few hundred miles north. As one person from the VB Summit say, "We got all these great relevance models, and we were so excited about it. But when we tried to put it in production, it was terrible."

Build an AI business model – what is the goal and how does this AI system or approach fit into an immediate and long-term business model as well is this an internal business or external business to be sold to others.