Enterprise AI For Dummies. Zachary Jarvinen
some ways, AI is more powerful than the human brain, especially in how fast it can match certain patterns. JP Morgan Chase developed a machine-learning system that processed loans that took lawyers and loan officers a total of 360,000 hours to complete; it did this in less than a minute and with fewer mistakes.
In other ways, the human brain is more powerful than current AI implementations. Humans can use all the pattern matching processes that they have learned before to contextualize new pattern matching processes. This ability allows them to be far more adaptable than AI, for now. For example, if you take a photo of a chihuahua from a certain angle, it can look surprisingly like a blueberry muffin. A human can quickly identify which photos are chihuahuas and which are muffins. AI, not so much.
Understanding the Demand for AI
If there is a universal constant in commerce throughout the ages, it is competition. Always changing, always expanding, always looking for a foothold, an advantage — whether from reducing costs, increasing revenue, or unlocking new, innovative business models.
Similarly, while much discussion has taken place in the last few decades about the challenges posed by a global economy, international trade is not a recent phenomenon. It dates back at least to the Assyrians.
Four millennia later, the goal is the same for the modern enterprise: establish a competitive advantage. However, the specific challenges to tackle are new.
Converting big data into actionable information
As data increased in volume, variety, and velocity (known as the three Vs of data), data processing departments experienced an increasing challenge in turning that data into information.
Enter big-data analytics, which is a collection of analytical methods that provide increasing levels of understanding and value.
Descriptive analytics = information
Diagnostic analytics = hindsight
Predictive analytics = insight
Prescriptive analytics = foresight
Descriptive analytics
Descriptive analytics reveal what happened. Sometimes called business intelligence, this tool turns historical data into information in the form of simple reports, visualizations, and decision trees to show what occurred at a point in time or over a period of time. In the larger landscape of big-data analytics, it performs a basic but essential function useful for improving performance.
Diagnostic analytics
Diagnostic analytics reveal why something happened. More advanced than descriptive reporting tools, they allow a deep dive into the historical data, apply big-data modelling, and determine the root causes for a given situation.
Predictive analytics
Predictive analytics present what will likely happen next. Based on the same historical data used by descriptive and diagnostic analytics, this tool uses data, analytical algorithms, and machine-learning techniques to identify patterns and trends within the data that suggest how machines, parts, and people will behave in the future.
Prescriptive analytics
Prescriptive analytics recommend what to do next. This tool builds on the predictive function to show the implications of each course of action and identify the optimum alternative in real time.
AI-powered analytics
AI-powered analytics expose the context in vast amounts of structured and unstructured data to reveal underlying patterns and relationships. Sometimes called cognitive computing, this tool combines advanced analytics capabilities with comprehensive AI techniques such as deep learning, machine learning, and natural-language recognition.
Figure 1-1 shows the relationship between business value and difficulty of an analytic method.
All these tools combine to bring a fourth “V” to the table: visualization.
FIGURE 1-1: Business value versus difficulty in analytics.
Relieving global cost pressure
More than a decade ago in his book The World Is Flat, Pulitzer Prize-winning New York Times columnist Thomas Friedman posited three eras of globalization:
Globalization 1.0, circa 1400-1940: The globalization of countries and governments, beginning with Vasco da Gama and Christopher Columbus. It included the invention of the steamship, the railroad, and the telegraph.
Globalization 2.0, circa 1940-2000: The globalization of multinational companies, beginning with World War II through to Y2K, intensifying in the final two decades. It included the popularization of air travel, computers, and telecommunications.
Globalization 3.0, circa 2000 forward: The globalization of the individual, powered by the Internet and instantaneous, continuous connection with every market.
Globalization is a one-way train that left the station centuries ago and is still putting on steam. From the perspective of developed countries, globalization exerts a downward pressure: lower material costs, lower wages, lower prices, lower margins.
Accelerating product development and delivery
Despite all the best efforts of product managers and their associated professional organizations and certifications, product development can be chaotic and unpredictable.
One study by McKinsey showed that 70 percent of the software projects analyzed failed to meet their original delivery deadline, and 20 percent of the projects that did meet the deadline did so by dropping or delaying planned features. The average overrun was 25 percent of the original schedule. A study of IC design projects revealed that 80 percent were late, and the projects were equally likely to overrun the schedule by 80 percent as they were to finish on time. Cost overruns were also common.
Facilitating mass customization
Studies show that you can boost sales by reducing the range of choices. And if those limited choices are targeted to the customer’s preferences, you can boost them even more. Accenture found that 75 percent of consumers are more likely to buy from a retailer that recognizes them by name and can recommend options based on past purchases.