Mission

After an extended period out of the limelight, artificial intelligence, or AI, has returned to the public consciousness in a big way. AI’s virtues and vices are now discussed daily in the popular press. While the societal implications of AI remain a topic of debate, it is broadly accepted that its business implications will be significant.

Our hope is that AiPath will help illuminate where and how enterprises can apply AI to drive greater operational agility and performance across the set of use cases we call “industrial AI.” We also discuss the challenges in and impediments to doing so, and offer some pointers for those organizations just getting started.

AI for Industrial applications

Among those who track such trends, AI is expected to be a large driver of enterprise competitiveness in the not-so-distant future. The views shared in a recent report by investment bank Goldman Sachs are representative of this sentiment. The paper states that “the ability to leverage AI technologies will become one of the major defining attributes of competitive advantage across all major industries in the coming years. While the strategy will differ by company size and industry, management teams that don’t focus on leading in AI and benefiting from the resulting product innovation, labor efficiencies, and capital leverage risk being left behind.”

IT industry research firm Gartner anticipates this impact as well, and projects it to take root sooner than later. They foresee that “by 2018, more than half of large organizations around the globe will compete using advanced analytics and proprietary algorithms, causing disruption on a grand scale.”

The question for enterprises then is not when and if, but how. Unfortunately, answering even this basic question can be difficult today, because doing so requires a broad and clear understanding of the various ways that AI can impact the business. While much information on the topic exists, it tends to be very fragmented, poorly organized, and limited to only a small subset of use cases.

For better or worse, contemporary discussion of enterprise AI use cases has focused on applications in the digital domain. These are applications like getting people to click on ads, making recommendations, personalizing the customer experience, predicting customer churn, and detecting fraud of various sorts.

But what about those parts of an organization whose operations extend beyond the digital domain? Surely they need AI too?

The answer, we believe, is a resounding yes.

In fact, AI presents a unique and compelling opportunity for those businesses whose operations span the virtual and physical worlds. To compete effectively, these firms must drive towards increased operational efficiency and asset utilization, and they must aspire to the same level of agility in the physical aspects of their business as they have sought in its virtual aspects.

It has become clear that some of the world’s largest enterprises believe this as well. They are betting big on AI to create competitive advantage through increased situational awareness, greater efficiency, and higher quality.

Our hope is that this will help illuminate where and how enterprises can apply AI to drive greater operational agility and performance across the set of use cases we call “industrial AI.” We also discuss the challenges in and impediments to doing so, and offer some pointers for those organizations just getting started.

We begin by defining AI and Industrial AI.