Nowadays, the talk of the town is using artificial intelligence ( AI ) or machine learning ( ML ) to optimize business processes, increase revenue, and improve overall customer experience. However, no real progress and impact could be possible without implementing a series of AI quality drivers. At Cerebri AI, we look at these drivers in four disciplines:
- Time management
These core disciplines must be mastered to deliver the best insights from AI/ML on a consistent basis. This whitepaper will focus on our favorite, Timing, and subsequent papers will look at the other AI quality drivers.
Cerebri AI helps large enterprises and organizations make better decisions by applying the computational power of the cloud ( or high-performance clusters ) to leading-edge AI/ML algorithms, methods, and techniques, to a wide variety of business problems that traditionally required extensive domain experience gathered over a long period of time.
AI/ML sounds complicated, but it’s not. Let’s break it down. A business expects employees to use their experience ( the body of knowledge, patterns, intuition they accumulate over time ) to make decisions driving positive results for the business.
In addition, the business expects employees to use machines to help them make even better decisions by leveraging additional insights ( generated through data-driven pattern analysis out of reach to humans ). These insights are then used to make predictions ( weighted probabilities based on millions of events ) about what will happen and when to take the best actions ( time-based interventions ) to achieve the desired results. We use the term AI/ML, or just AI, to cover all three aspects of making decisions using machines.
Quality is essential
in the decision-making process, from the moment we collect data, how we handle it, how it is used when it is updated, and what actions are taken. All aspects of quality have a direct impact on the customer experience ( “CX” ). A slip in quality poses the risk of losing a sale or losing a customer. Customers are costly to acquire, easy to lose, and can be impossible to re-acquire.
Humans react instinctively to problems, thanks to their experience; machines don’t have the experience, so they have to be taught. Before we worry about data, modeling, and other AI concepts that everyone immediately gravitates to, we should settle on how “timing” applies to AI and machine learning in our frame of reference. Why? Because as we all know, timing is everything.
The long-standing timing reference in large enterprises when dealing with machines has been “batch” – transactions that are processed as a group on a regular schedule. Batch makes processing efficient and less costly, a legacy of a time when computational resources were limited, static, and expectations on time were different.
In the omnipresent “digital” world we live in, timing is anything but batch. Timing is streaming. Timing is “right now.” In today’s omnichannel world, customers expect to interact with their vendors in real-time, in the channel of their choice ( e.g., digital, bricks & mortar, call center ). They expect to be treated as a unique individual or unique customer with the same actions and offers presented to them whenever and however, they interact. Whenever is 24/7 and 365.