Artificial intelligence (AI) refers to the ability of computers to perform tasks usually associated with human intelligence. Today’s AI-enabled computers can recognize images, understand language, and perform complex reasoning while making decisions based on sophisticated mathematical analyses. In business, AI enables better-informed decisions and then automates the tasks that follow.

SwissCognitiveWith time, the systems get smarter at spotting new opportunities and exposing unseen risks. AI augments human intelligence with powerful computing and decision-making informed by precise data analysis.

In the past few years, three factors have led to a dramatic increase in the adoption of AI technologies across the enterprise:

  • Unlimited compute capability on demand. This can be attributed to the power of graphic processing units (GPUs) available in the cloud.
  • The availability of massive amounts of data and the inability of humans to keep up with it. With 90 percent of the world’s data created in just the last two years, the data deluge is bound to continue.
  • Increased competition. Enterprises are looking at innovations in the consumer world and bringing proven technology like sentiment analysis into the enterprise.

These three factors call for augmenting human capabilities with artificial intelligence in order for businesses to remain relevant in a quickly changing world.

Key Considerations for Enterprise AI

Enterprises are uniquely rich in data full of context—both structured and unstructured— and this is distinct from consumer data alone. Unseen and untapped patterns of human, operational, and system activities with strong, predictive ties to key business results are everywhere. Machine learning will uncover these patterns to deliver significant benefits for all functions and extend to an enterprise’s ecosystem.

As companies begin their enterprise AI journey, they should keep these key considerations in mind:

  • More data is better. Enterprises need to use ever-expanding data reservoirs, plus other public data.
  • The recommendations coming from the new systems must be easily understood by all, and must not require specialized skill sets to decipher.
  • Because any decision made by one department always impacts other departments, companies should connect the entire value chain while optimizing locally.
  • Enterprises must have an AI command-and-control center to ensure human oversight and create a traceable audit trail.
  • Context is everything—including the right domain expertise is critical.
  • Rather than replacing existing systems, the AI journey should begin with existing systems and applications.


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