The use of computers to automate human tasks and simulate the human thought process—the field now widely called cognitive computing—is attracting the attention and investment of some of the most well-known names in technology.
More and more often, we are seeing automation and cognitive computing solutions that use statistical modeling, machine learning, natural language processing and other sophisticated capabilities to accomplish complex and learned operations. Google’s RankBrain artificial intelligence system processes search requests to produce increasingly relevant results based on what it has “learned” from past searches, and Twitter has recently announced the acquisition of Magic Pony Technology that uses neural networks and machine learning for visual processing. Jack Dorsey, Twitter CEO and co-founder said, “Machine learning is increasingly at the core of everything we build at Twitter.”
While research in machine learning has been progressing for more than half a century, widespread business application of these cognitive tools is reaching a tipping point now. In the past few years, advances in automation and cognitive computing have been making their mark on the IT and business process services world, making certain repetitive, rules-based and mundane tasks faster and less labor-intensive. Because they can make such a dramatic impact in specific domains, these technologies are changing the way companies operate. Expectations for these solutions are high—and potential returns are great. For many enterprises, robotic automation has delivered direct savings of between 20 percent and 50 percent.
But knowing when and how to implement automation and cognitive computing is a challenge for most enterprises. Different tasks and processes require different levels of complexity and industry-specific use cases require varying levels of control. Meanwhile, the technology is changing rapidly, and it’s difficult to make certain an investment and the change associated with it will pay off.
As with any new technology, an enterprise needs to consider the complexity of the use case—it needs to identify its specific business objectives and how the nature of the technology and its capabilities lend themselves to those goals. Automation and cognitive computing are not a one-size-fits-all solution. Those exploring these technologies in the market today are using a variety of operating models which could be classified under four different operating model archetypes:
- Embedded automation
- Provider-owned point solutions
- Client-incubated innovation labs
Each offers different benefits and levels of commitment. To learn more about how these operating models work, their applicability to different business objectives and the design principles that frame the outcomes of these new technologies, read my recent white paper Planning for the Automation and Cognitive Computing Journey. Or contact me directly to discuss further.