For some, the thought of using artificial intelligence in business brings with it nightmares of HAL from “2001: A Space Odyssey” where the 9000 Series computer takes over the spacecraft on its voyage to Jupiter and kills off the entire crew.

In reality, the impact will not be as dramatic — at least we hope not. Instead, AI has the ability to give those businesses that are bold enough to adopt the technology a true advantage.

Like many new technologies, AI will not be available only to the largest corporations. In fact, many emerging businesses have an advantage over larger, more established ones when it comes to the next wave of intelligent automation. They’re less constrained by sunken infrastructure costs, nimbler, and more likely to be able to take advantage of some of the ways AI will reshape the modern business.

It’s no longer, as Rupert Murdoch put it, about big beating small. It’s about the fast beating the slow. And with new intelligent technologies, modern businesses can look ahead and thrive based on decisions made with real-time information that is relevant, timely, accurate, predictive, and actionable.

Today the speed of business, the pace of change, and the growth of data is increasing exponentially. Information lends the insight to move fast. But with the huge growth in data, how can businesses continue to process that information — to find insight that speeds evolution and innovation, without the actual search for the insights slowing them down?

Craig Sullivan

Craig Sullivan

Enter some of today’s biggest buzzwords: AI, machine learning, bots, and other intelligent forms of automation. The spectrum of services they aim to provide — from easing data processing, to presenting users with decisions, to educating a machine to act on those decisions — can be dizzying. How does a company that has relied on its business and financial software, which for years has been helping it automate processes and provide retrospective business intelligence, take a step into the intelligent world?

Specifically, there are three major ways in which businesses are likely to take advantage of AI in the future:

Intelligent Analysis: Instead of the rear-view perspective that business intelligence affords, AI analyzes large amounts of data to recommend decisions, or, in the case of machine learning, actually act on the data. With AI, outliers and trends that affect business can be identified in real time.

For example, a projected delay in a purchase order for a cheap raw material will affect the on-time delivery of high-value orders in a month’s time and have a ripple effect. It will cause customer satisfaction issues, which in turn cause payment delays, credits, increased discounts, lower repeat orders and impact revenue six months later.

Knowing about the issue immediately will allow the buyer to find a replacement rather than accept a short delay in delivery, avoiding the revenue impact in the future. Armed with this analysis, users can predict outcomes and make the appropriate decision sooner to avoid costly mistakes.

Intelligent Interaction: Applications can have hundreds of role-based dashboards that are delivered out of the box. These are based on years of developing and implementing in multiple industries with functions for specific roles in order for employees to do their jobs effectively. In the future, intelligent interaction will dynamically construct dashboards based on what it thinks the user needs to see based on current activities and the behavior of thousands of similar roles.

We are also now seeing the emergence of intelligent voice-activated solutions. The intelligence here is taking unstructured instructions, determining what the user means, and then ensuring the right information is delivered back.

Intelligent Automation: Enterprise solutions can have very powerful workflow engines that allow companies to change and automate business processes without writing code. These are rules-based engines: If A = B then C = D. New intelligent systems will learn, suggest, and automate processes based on learning business patterns and behaviors. For example, the machine can automatically place holds on customers based on payment and ordering behaviors.

Now, here are four potential use cases of how CFOs can extract value from intelligent automation to determine future trends in customer or employee behavior.

Predictive Analytics: For CFOs, the real value of predictive analytics will be around determining future trends of customer behavior. The first big opportunity is to identify customers that the company is at risk of losing the account, affecting the top line. Payment behavior, data collected from sales interactions, etc., could be aggregated to understand future behavior.

Second, this represents an opportunity to identify product/offering gaps that might be missed by traditional thinking or analysis. Take for example the insight that early retirement leads to earlier death. It seems counterintuitive until big data revealed that early retirees begin to engage in riskier behavior like consuming more alcohol.

Predictive Accounting: This is about transitioning from the traditional historical financial reporting model into a predictive management accounting and analysis model. The goal here is to provide decision makers with real-time insights that can be used to make real-time decisions. This would enable the system to monitor transactions as they are posted to look for specific patterns that could be important to solve business issues, and automatically suggest journal entries.

Predictive Auditing: Predictive auditing moves beyond the traditional or continuous auditing model. While continually testing data, predictive auditing would begin to understand the flow of data and identify when things are out of the ordinary. In theory, it would catch potential fraud as it was just emerging instead of when sizable enough for detection through traditional means. Such capabilities could significantly reduce the cost and length of audits.

Predictive Risk Management: This works to identify patterns in large data sets that are indicative of fraud or other concerns. Many financial institutions are increasingly looking to deploy machine learning to manage and mine regulatory reporting data and unstructured information that can significantly improve analytical capabilities across risk management and compliance areas, such as money laundering, credit risk modeling, and regulations.

Financial firms are also combining structured and unstructured data such as social media and web monitoring, email messages, word processing documents, videos, photos, and audio files to identify rogue activities, patterns, and trends, and mitigate risks such as fraud or cyber breaches.

Unlike the chaos wrought by HAL in 2001: A Space Odyssey, companies that leverage intelligent technologies will be well placed to outmaneuver their competition, reduce waste, and grow revenue and profits.

Craig Sullivan is senior vice president of of enterprise and international products for Oracle NetSuite Global Business Unit.

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