Enterprise AI is a new category of enterprise software and the latest frontier in the information technology industry. With vast amount of data continuously being generated across the enterprise, AI use cases and deployments can unlock significant economic value for organizations. Enterprise AI applications have the potential to spearhead digital transformation efforts and revolutionize business operations and customer service across every major industry.

But what really is an enterprise AI application? How is it different from typical enterprise software? In this blog post, we aim to shed some light on these questions.

The term “enterprise AI application” is pretty self-explanatory at first. As the name suggests, an enterprise AI application is deployed across the whole enterprise and uses artificial intelligence (AI) and machine learning (ML) techniques to solve a specific business problem. But to go beyond this simplistic answer and attain a fuller understanding of enterprise AI applications requires looking at several major advancements we’ve seen over the past few decades.

The first of these advancements is the field of artificial intelligence and machine learning itself. Although the emergence of this field and the first use of the term “machine learning” was in the 1950s, it wasn’t until the new millennia that we started to witness truly groundbreaking research and advancements. Today, AI is defined as the field of computer science that aims to teach computers how to mimic human intelligence. Machine learning is a subfield of AI, where machines can learn from data to perform certain tasks and functions without being explicitly programmed to do so. Unlike rules-based software, machine learning systems don’t require a long list of pre-defined rules or logic to make decisions, but instead learn patterns from historical training data. ML systems can adapt automatically to changing conditions, business requirements, and circumstances as their underlying training data evolve. ML systems can significantly outperform rules-based software across a variety of business use cases such as in medical diagnostics, operational reliability, customer churn detection, demand forecasting, and many others.

The second major advancement that set the stage for enterprise AI applications is the availability of vast amounts of data across the enterprise. Since AI and ML systems learn from historical data, the performance of these systems increases dramatically with the availability of higher volumes and a more diverse set of data. With this rapid increase in available data volumes and the dramatic expansion in the variety of data sources, AI and machine learning systems are poised for success across enterprise-grade use cases.

The third advancement and a major contributor to the availability and volume of enterprise data is the wide-spread adoption of IoT sensors. Although a futuristic idea only a few decades ago, today IoT sensors are widely used across every major industry, from energy, infrastructure, manufacturing, and telecommunications to logistics, retail, healthcare, and many others. Thanks to the vast number of sensors deployed across value chains, organizations can have real-time visibility and insights across all operations, supply chains, and customer service. While it is impossible to track, monitor, and act on this vast amount of real-time data manually or with rules-based software, enterprise AI applications excel at doing so. This can unlock significant benefits for organizations across additional use cases such as predictive maintenance, quality control, operational safety, logistics management, fraud monitoring, and many others.

The last major advancement is the emergence of the elastic cloud. AI and ML systems learn and improve decision making through a process called training. Training an ML model is the process of finding an optimal set of model weights and parameters that best represent the relationship between the inputs and outputs observed in the training data. While model performance improves significantly with an increase in the size of the training dataset and the number of training iterations, demand for compute and storage resources needed for training can become material. Since a single enterprise AI application may include thousands of ML models, each requiring regular re-training, the need for compute and storage resources can grow exponentially. The availability of elastic, cloud-based, and distributed compute and storage systems at a minimal cost addresses the model training challenge and is a major enabler for enterprise AI applications.

The collection of numerous enterprise AI applications comprise the broader enterprise AI strategy across an organization’s digital transformation journey. To establish a successful enterprise AI strategy, organizations must consider several factors when developing a multi-year roadmap to build, deploy, and operate effective enterprise AI applications. These factors include (but are not limited to) economic value at stake, tractability of the AI problem, implementation complexity, and the user adoption potential of the end solution. Each of these factors are fundamental to ensuring a successful enterprise-wide AI strategy.

Taking shortcuts with any of those factors will usually result in failure, distracted teams, and reduced organizational motivation to explore and advance enterprise AI. If an enterprise AI application is not addressing meaningful economic value or operational improvements, it will not receive the executive attention and sponsorship required to successfully build the application. Without selecting a tractable AI problem or a comprehensive and representative data image to begin with, it will not be possible to move beyond simple prototypes and generate enterprise scale results. Complex and lengthy implementations generally lead to lost momentum and declining excitement, especially if the organization is just starting its enterprise AI journey. An application that was not built around the end user, or one that ignores the existing systems, processes, and practices in place will struggle to gain user adoption. Because these factors are not always properly considered, many organizations face challenges implementing enterprise AI, especially those following a DIY approach.

At C3 AI, we work closely with our customers to define and execute realistic and effective enterprise AI strategies. Over the past decade we have built and enhanced the C3 AI® Platform as a comprehensive enterprise AI platform to develop, operate, and maintain effective enterprise AI applications. Today, the C3 AI Platform is used by some of the largest organizations in the world across manufacturing, energy, financial services, and aerospace & defense, helping them unlock significant economic and operational value from enterprise AI.

About the author

Turker Coskun is a group manager of product marketing at C3 AI where he leads a team of product marketing managers to define, execute, and continuously improve commercial and go-to-market strategies for C3 AI Applications. Turker holds an MBA from Harvard Business School and a bachelor of science in electrical engineering from Bilkent University in Turkey. Prior to C3 AI, Turker was an Engagement Manager at McKinsey’s San Francisco office.