Enterprise AI has emerged as a new category of enterprise software, unlocking significant economic value and spearheading digital transformation across major industries. By leveraging artificial intelligence (AI) and machine learning (ML) techniques, enterprise AI applications are revolutionizing business operations and customer service. In this LinkedIn article, we explore what enterprise AI applications are and how they differ from traditional enterprise software, as well as the major advancements that have set the stage for their success.

A Deeper Look into Enterprise AI Applications

To fully understand enterprise AI applications, we must examine the key advancements that have occurred over the past few decades:

  1. AI and ML Research and Development: Although AI and ML emerged in the 1950s, it wasn’t until the new millennium that groundbreaking advancements took place. Today, AI and ML systems can automatically adapt to changing conditions, outperforming rules-based software in various use cases such as medical diagnostics, customer churn detection, and demand forecasting.
  2. Availability of Vast Amounts of Data: The performance of AI and ML systems increases dramatically with higher volumes and diverse sets of data. With the rapid expansion of available data, AI and ML systems are poised for success across enterprise-grade use cases.
  3. Widespread Adoption of IoT Sensors: IoT sensors are now widely used across major industries, providing real-time visibility and insights across operations, supply chains, and customer service. Enterprise AI applications excel at managing this vast amount of data, unlocking benefits for organizations across use cases like predictive maintenance, quality control, and operational safety.
  4. The emergence of the Elastic Cloud: Elastic, cloud-based, and distributed compute and storage systems at minimal cost have addressed the model training challenge and enabled the growth of enterprise AI applications.

Building a Successful Enterprise AI Strategy

When developing a multi-year roadmap for building, deploying, and operating effective enterprise AI applications, organizations must consider factors such as economic value, the tractability of the AI problem, implementation complexity, and user adoption potential. These factors are fundamental to ensuring a successful enterprise-wide AI strategy.

Neglecting any of these factors can result in failure, distracting teams, and reduced motivation to explore and advance enterprise AI. To avoid these pitfalls, organizations should carefully evaluate their AI initiatives and work closely with experienced partners.

iForels: Your Partner in Enterprise AI

At iForels, we collaborate with our customers to define and execute realistic and practical enterprise AI strategies. Over the past decade, we have built and enhanced our comprehensive enterprise AI platform to develop, operate, and maintain effective enterprise AI applications. Today, our 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.