We’re entering a new era of enterprise AI. For decades, the expectations of how dramatically AI could affect enterprises and operationalize change have outpaced reality. But with greater volume, variety, and velocity of data now available, along with massive, on-demand computing power, AI has become more intelligent than ever and is transforming businesses across all industries.
But there is one amazing difference between AI and many other technologies that have had a transforming effect. Because AI is so tied to the unique data at each company, and because there are so many techniques to choose from, it is highly likely that, when successful, AI will result in significant amounts of differentiation between the businesses that really put the technology to work and those who fail to capitalize on its potential.
Now, I understand the skepticism business leaders can have towards claims about technology being “transformational” or “disruptive.” However, AI truly is different. With enterprise software like ERP or CRM, a company can become more efficient. But it’s rare for a company to become differentiated from using such software.
Compare that to enterprise AI. With enterprise AI, we will gain the ability to benefit from a high volume of insights. As Gartner’s Whit Andrews recently wrote, “What makes the best AI projects stand out is that they allow for solutions that previously would have been impossible to conceive, because they include what seems like human insights but at a volume humans could never achieve.” In other words, enterprise AI will provide us with never before seen scale.
I believe there will be two main ways that AI will lead to high degrees of differentiation: 1) improving existing use cases and 2) uncovering new value and making new use cases possible.
Building a better mousetrap: How AI will improve what you’re already doing
One of the key ways AI will lead to differentiation is by tackling what you’re already doing and then doing it much better. AI allows companies to take their data and extract every ounce of value from it. That’s what you see already happening at industry stalwarts like Amazon and Google -- data is mined to the nth degree. Because of the advances in big data collection and AI technology, that type of AI-empowered analysis is now possible for a wider range and size of businesses that don’t have a Google level of engineering resources. Companies are going to be able to leverage all of their high-value data because AI will detect signal within data that previously would have remained invisible.
This will immediately bolster how well companies can perform existing operations because they’ll be using data that only their organization has access to. Take, for instance, financial services. In the realm of fraud detection, companies have to combine credit card transaction data, mobile location data, and digital network data to make a determination about whether a transaction is indeed fraudulent. With traditional analytic techniques, this was extremely difficult and too often produced false positives. Using AI, the process becomes much easier, in part because AI reduces false positives, allowing people to investigate where they are really needed. Combining deep neural networks and representation learning with traditional machine learning techniques, companies can come to more reliable conclusions about fraud and anomalous behaviors at a faster pace than ever before. AI simply can look at more correlations than any human could on his or her own.
Companies have to monitor immense volumes of transactions to understand their customers’ behaviors in depth. AI can automate these processes and make them more efficient. The result, for the companies that harness AI in this way, will be immediate returns financially and in the security, regulatory, and compliance arenas.