Leveraging AI and Machine Learning As Competitive Business Drivers

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Click to learn more about author Dr. Michael Zeller.

As volume, velocity and variety of data – often referred to as the “three V’s of Big Data” – continue- to increase, many businesses are unprepared to match the influx of this data with tools for collection and analysis. Even more surprising is that most organizations are not in a position to truly leverage their own operational data for more intelligent decision making, which is the means by which data converts into business value.

Basically, the current, standard business processes mostly leverage hard-coded rules and make fixed, pre-defined decisions. This classical “rules-based” approach is in many cases ill-suited for today’s Cloud-centric, data-driven enterprise environment where customers demand a more personalized response of IT systems.

A smarter, data-driven way of automating certain decisions is the logical next step for many enterprises who have collected vast quantities of data on processes, customers, and other integral information key to their business. AI and Machine Learning technologies embody such data-driven approaches and are ideal to make faster, better, and more nuanced decisions. Once you have the right data, leveraging Data Scientists to apply well-known Machine Learning algorithms will allow businesses to make smarter, more automated, real-time decisions in milliseconds.

Don’t Fall Behind the Data Disruptors

Organizations that seek to use data to drive business value should consider a strategy which combines a Cloud-based IT infrastructure with Artificial Intelligence and Machine Learning. While the Cloud will ignite agility with rapid prototyping of new technologies on one side, AI has the power to dramatically improve the business outcome by applying predictive models and Machine Learning algorithms. Both AI and Machine Learning — the current frontier of “smart” enterprise automation — can deliver more precise, adaptive, and intelligent decisions than the inefficient, rigid processes in place today at many Global 2000 businesses.

Many enterprises are realizing the impact and disruption that AI and Machine Learning can bring to their industries. Early movers are leveraging the Cloud and AI, not only to transform their internal business processes to improve efficiency but also to disrupt their competition with entirely new business models. Unfortunately, many well-established industry leaders today are missing the opportunities to benefit from these data-automation advancements.

The laggards in implementing data-driven process automation as the spear tip of their digital transformation strategy will likely see themselves at the bottom rungs of their industry in the future. Those businesses who are aggressive in investing in AI and Machine Learning will be poised to become the leaders in certain industries simply because of their foresight.

Enterprises that wait too long to implement AI and Machine Learning will put their businesses at significant risk as nimble competitors find new ways of disrupting the industry status quo.  Those involved in key industries such as the Internet of Things, financial services, insurance, marketing and sales technologies will feel these repercussions of late adoption the most.

Take the financial industry, for example, where Equifax has launched InterConnect as a secure, Cloud-centric decision management platform which global clients leverage to integrate, audit and deploy predictive models. In the case of InterConnect, streamlining the integration of AI into core business operations allows for more intelligent decision making when undertaking the data-heavy processes of risk scoring and fraud detection.

The threat posed by innovative upstarts who optimize their data operations through AI and Machine Learning early is something that should be of concern for the established Global 2000 leaders, regardless of industry. Early adopters of AI will quickly become the disruptors in their respective markets and most likely even beyond.

Implementing AI and Machine Learning

While there is a lot of hype around AI and Machine Learning as the next evolution for data-reliant enterprises, the use of both technologies is actually quite practical and easier than ever to implement across the enterprise.

Both of these automation technologies can enable enterprises in a number of ways. But there are challenges involved in doing it right.

First, enterprises need to understand that both the IT framework and the Data Science solutions need to be flexible, scalable and based on industry standards; they need to be able to work together seamlessly. You shouldn’t have to revamp an enterprise’s entire IT infrastructure to take advantage of AI and Machine Learning, and vice-versa. You also need to select an open platform in which different AI algorithms can be plugged into the existing IT solutions – just like Lego blocks – without major efforts or disruption to existing processes.

As Gartner outlined, a digital business technology platform based on established industry standards is key to deriving value from AI and Machine Learning as it allows to quickly integrate new decision processes without the need for complex IT projects or elaborate custom code development. The openness and flexibility allow for the implementation of enhanced business processes which leverage AI, especially advanced Machine Learning algorithms that may continue to change frequently.

Poor enterprise implementations of AI and unsuccessful Machine Learning projects do occur; again, these are frontier technologies so a steep learning curve for organizations is to be expected. The most common mistakes include poor project management or unrealistic expectations by business executives or IT management. Be aware that hiring for expertise with data science and AI automation can be difficult and often quite expensive.

Just by taking the first steps to leverage AI and Machine Learning, businesses are already separating themselves from industry competitors. By investing in these technologies, enterprises will see the expertise of the Data Science and IT teams who use these tools expand. That in and of itself is worth the investment risk of leveraging data for more intelligent automation and smarter decisions.

When Big Data was the biggest trend for enterprises, the competitive advantages were the quantity of data collected and building the most scalable software and hardware infrastructure. Today, however, we realize that it is not collecting that data but what we do with the data that creates true business value. Organizations that leverage data best — especially those who rely on smarter algorithms for intelligent automation — will assume the lead positions in their industries.

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