The importance of AISM
Digital transformation isn’t a new concept, and it’s no longer an aspirational goal. The ability to operationalize and rapidly expand digital transformation has become a baseline competitive requirement for every enterprise. That means delivering consumer-grade experiences for employees and customers alike, with the ease of use, reliability, and performance people have come to expect from the best technology companies.
Today, nearly every company is a technology company—regardless of the industry, product, or service they sell. In all types of business and consumer markets, creating and delivering high quality, compelling service innovations has become an essential core competency.
· SaaS and cloud strategy are focused on getting new products to market faster, in addition to the inherent benefits of cost optimization and scale.
· Along with this high-velocity innovation, users place a premium on the experience as well.
However, the speed of digital change also drives new levels of risk. As companies embrace more agile approaches to innovation such as DevOps, the resulting topology—a complex landscape of ever-shifting interdependencies with ephemeral components—is simply too overwhelming for traditional operational paradigms
When it’s impossible for even the most experienced staffer to maintain a full understanding of the environment at a given moment, a new approach to balancing change and risk is needed.
AISM use cases
In 2021, intelligent automation is a top priority for 75% of enterprises. A growing consensus expects that digital experience will be only be compelling, but measurably so. According to Gartner®, “By 2025, 70% of digital business initiatives will require I&O leaders to report on business metrics from digital experience, up from less than 15% today.”
For example, not long ago, chatbots designed with minimal capabilities were seen as a state-of-the-art. With advancements in conversational AI, chatbots are better able to understand language context and extend their capabilities through machine learning requiring less upfront training than the chatbots from just a few years ago.
More than just a new user interface, the chatbot becomes an end-to-end experience that dynamically delivers the customized information people need to maximize productivity. Contextual understanding and natural language processing (NLP) help virtual agents
provide faster, more accurate answers than first-generation chatbots—a wholly improved customer experience.
(Explore enterprise chatbot best practices.)
Key use cases for AISM include: