Artificial Intelligence
Understanding AI
Several organizations that wasted time and money pursuing the wrong technology for the job at hand. But if they’re armed with a good understanding of the different technologies, companies are better positioned to determine which might best address specific needs, which vendors to work with, and how quickly a system can be implemented. Acquiring this understanding requires ongoing research and education, usually within IT or an innovation group
Process Automation
The automation of digital and physical tasks, typically back-office administrative and financial activities, using robotic process automation technologies. RPA is more advanced than earlier business-process automation tools because the “robots” (that is, code on a server) act like a human inputting and consuming information from multiple IT systems.
RPA is the least expensive and easiest to implement of the cognitive technologies we’ll discuss here, and typically brings a quick and high return on investment. It is particularly well suited to working across multiple back-end systems.
Actionable Data Insights
Using Machine Learning algorithms to detect patterns in vast volumes of data and interpret their meaning. These are usually much more data-intensive and detailed, the models typically are trained on some part of the data set, and the models get better, that is, their ability to use new data to make predictions or put things into categories improves over time.
Versions of machine learning, that is, deep learning, in particular, which attempts to mimic the activity in the human brain in order to recognize patterns, can perform feats such as recognizing images and speech.
Cognitive insight applications are typically used to improve performance on jobs only machines can do, tasks such as programmatic ad buying that involve such high-speed data crunching and automation that they’ve long been beyond human ability, so they’re not generally a threat to human jobs.
Cognitive Engagement
Engaging employees and customers using natural language processing chatbots, intelligent agents, and machine learning.
- intelligent agents that offer 24/7 customer service addressing a broad and growing array of issues from password requests to technical support questions, all in the customer’s natural language
- internal sites for answering employee questions on topics including IT, employee benefits, and HR policy
- product and service recommendation systems for retailers that increase personalization, engagement, and sales—typically including rich language or images, and
- health treatment recommendation systems that help providers create customized care plans that take into account individual patients’ health status and previous treatments
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With our AI Advisory, Experimentation and Engineering Services, working closely with your domain experts, we help you prioritize use cases to unlock true business value.
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