Progressive ways of implementing Artificial Intelligence to Grow your Business

Digital Transformation has spilled on the new opportunities of artificial intelligence and machine learning. A lot of the coverage has been thought-provoking pieces on the long-term possibilities for "cognitive computing," which allows computers to reason and simulate human thought processes.

In the meantime, robust machine learning algorithms are proving their worth in three ways that can easily be implemented in your business today.

Process automation

As a pattern-recognition engine, machine learning enables more automation in existing business processes.

Machine learning requires a great deal of high-quality data. In most organizations, this is found in existing business applications such as finance, logistics, and sales. The data in these systems has already been collected, cleansed, and stored over a long period of time, so there's plenty of data available to create meaningful, useful predictive models.

Machine learning works best where there's a tightly defined decision to be made, thousands of times a day, using a small number of variables, and where errors are clear and can be quickly corrected to further improve the algorithm.

Machine learning is easiest to implement when the decision can be seamlessly automated as part of an existing business process, rather than requiring new processes or cultural changes.

Some examples of automatable processes:

Extracting relevant payment or order data from unstructured invoices, forms, emails (such as product names, amount, currency, payee, address, etc.)
Classifying transactions for tax compliance
Predicting when contracts based on usage will need to be renewed
Predicting and acting on stock-in-transit delays
Calculating the optimal length of time between physical inventories to ensure that it's in line with automated systems
Routing customer service requests to the most appropriate teams

These "boring" uses of machine learning are by far the biggest real opportunity for business value today. McKinsey calculates that around 43% of financial processes can be automated using AI, and Gartner believes that by reducing the need for action and choices, AI will save half a billion people two hours a day this year alone. There is vast potential if you combine the power of machine learning with sensors, IoT, and other technologies.

More intuitive interfaces

Recent advances in machine learning have dramatically improved the ability of computers to decipher and understand human speech, writing, and commands.

New service chatbots can make it easier for customers to find information and do simple transactions via voice or chat interfaces. Machine learning algorithms can scan large amounts of product and technical documentation and automatically create answers to frequently asked questions. Initial deployments show that using chatbots to answer basic questions results in quicker customer conversations, increased customer satisfaction, and much lower costs.

Inside organizations, new enterprise digital assistants can assist you throughout your working day, especially in the context of core business processes such as procurement, HR, and budgeting. Instead of clicking around in a complex interface, you could tell the digital assistant, "I'd like to book a week's vacation next week" or ask, "what's the current actual vs. budget for my department?"

In a work environment, digital assistants have access to a vast amount of context that can be used to simplify or even anticipate the exchanges. The system knows how many vacation days you have to remain, which budget you are part of, and so on. Using machine learning, the system could even start identifying and alerting you to unusual circumstances without having to hunt down the information: "Based on your current reservations and forecasted trips, you will exceed your travel budget by 30% this quarter �?? would you like to review your trips or alert finance?"

Reveal and optimize processes in ways that previously weren't possible

Machine learning can help efficiently process data that was too complex or expensive to analyze before. This gives insights into processes that can be optimized in new ways.

Examples include:

Predictive maintenance. Using detailed sensor data and algorithms to deter the first real signs of problems in parts or machinery. This can save huge sums of money compared to changing them on a regular schedule regardless of whether or not they are worn or -worse -waiting until the parts have failed and stopped production. These technologies are even being used for expensive human assets such as professional athletes. By keeping track of the detailed vital signs of players during training, coaches can ensure they are performing optimally while minimizing injuries that would keep them on the sidelines.

Image analysis and tracking. There has been a particularly sharp rise in the ability of new "deep learning" algorithms to interpret and understand complex images. This has opened up a wide range of business opportunities. For example, an oil company can scan barrels to ensure they are correctly and clearly labeled, or sponsors of sporting events can get detailed analytics of how often their logo appears during video coverage of a sporting event, for how long, and where on the screen. This helps them optimize coverage and determine if they are getting a good return on their sponsorship investments. For companies that have complex catalogs of many different products and variations, these algorithms can be used to quickly identify the right product from a photo, whether it's a company selling office supplies, tires, or crystal jewelry.

Text analysis and classification. Machine learning can be used to extract text and images from electronic documents, then classify that information so it can be analyzed more easily than ever before. Uses include analyzing insurance claim texts for possible fraud, sentiment analysis for customer retention, classification of drug interactions based on research documents and much, much more.

Source: HOB

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