Why AI: AI in the Business Context

All organizations work toward specific outcomes, and they juggle several business metrics and processes to achieve this, such as revenue, costs, time to market, process accuracy, and efficiency. Yet they have limited resources (money, time, people, and other assets). So, the problem boils down to making good decisions about resource allocation (what kind of resources, how many/much of them, what should they do, what capabilities they need, etc), and making those good decisions faster than competitors and faster than the market is changing.

Making these decisions is hard, but clearly, they become a bit easier when data, information, and knowledge are available. Assuming these inputs are available, they need to be aggregated and mined for nuggets. Analysts need time to pull tribal knowledge out of subject matter experts’ heads, adjust to fluctuating business rules, calibrate for personal biases where possible, and to spot patterns and generate insights. Ideally, analysts and managers should (time permitting) assess multiple scenarios and run several experiments to increase confidence in their recommendations and decisions. Finally, the decisions need to be operationalized.

Enter AI, machine learning, and deep learning, which:

• Models the organization based on observations.

• Generates insights by simultaneously reviewing lots of factors and variables (far more than a person can achieve in a reasonable time and cost constraint).

• Learns continuously as new observations are provided.

• Quantifies the likelihood of outcomes (that is, predict what is likely to happen).

• Prescribes specific actions to optimize the business goals and metrics.

• Adjusts rapidly to new business rules through faster retraining versus traditional slower reprogramming.

What makes AI, machine learning, and deep learning possible now is the proliferation of data volume and data types coupled with the lower costs of computing and storage hardware and tools. Web-scale companies (such as Facebook, Google, Amazon, and Netflix) have proven it works, and they are being followed by organizations in all industries. Combined with business intelligence, the trio of artificial intelligence, machine learning, and deep learning overcomes obstacles to decision-making, thereby facilitating organizations to achieve their business goals.

AI, machine learning, and deep learning apply to everyone in metrics-driven organizations and businesses.

In its May 2011 publication “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute stated that the gap between managers and analysts who know how to use the results of analytics stood at 1.5 million, an order of magnitude more than for those who produce the analytics (such as data analysts and data scientists).

Put another way, the chokepoint in the data value chain is not the data or the analytics; it’s the ability to consume the data/analytics in context and in an intelligent way for surgical action. This is an opportunity for business and process professionals to marry AI, machine learning, and deep learning to the business frameworks and concepts already understood so well. It’s a chance to define problems and hypotheses within those frameworks and concepts, and then to use AI, machine learning, and deep learning to find patterns (insights) and to test hypotheses that take too long to test, would otherwise be too expensive to identify and test, or are too difficult for people to carry out,

Organizations and businesses are increasingly turning to AI, machine learning, and deep learning because, quite simply, business is becoming more complex. There are too many things occurring at one time for us people to process; that is, there are too many data points (both relevant and not-so-relevant) for us to synthesize. Looked at it this way, too much data can be a liability (analysis paralysis, anyone?).

But AI, machine learning, and deep learning can turn that pile of data into an asset by systematically determining its importance, predicting outcomes, prescribing specific actions, and automating decision-making. In short, AI, machine learning, and deep learning enable organizations and businesses to take on the factors driving business complexity, among them:

• Value chains and supply chains are more global, intertwined, and focused on microsegments.

• Business rules that rapidly change to keep pace with competitors and customer needs and preferences.

• Correct forecasting and deployment of scarce resources to optimize competing projects/investments and business metrics.

• Need to simultaneously drive towards both increased quality and customer experience while reducing costs.

In many ways, AI, machine learning, and deep learning are superior to explicit programming and traditional statistical analysis:

• The business rules don’t need to be known to achieve the targeted outcome—the machine just needs to be trained on example inputs and outputs.

• If the business rules change such that the same inputs no longer result in the same outputs, the machine just needs to be retrained—not reprogrammed—accelerating response times and alleviating people of the need to learn new business rules.

• Compared to traditional statistical analysis, AI, machine learning, and deep learning models are relatively quick to build, so it’s possible to rapidly iterate through several models in a try-learn-retry approach.