As with most projects, starting with AI-enabled capabilities requires a good foundation. For AI, that foundation is data — training data. Successful AI deployments require good training data to power supervised and unsupervised learning. Ironically, the best implementations will apply human judgment to the data used in the machine learning models.
Successful AI deployments require good training data to power supervised and unsupervised learning.
When it comes to prioritizing AI-enabled capabilities, organizations need to be super-targeted on the highest value that can be delivered quickly with the existing data quality and data types the organization has available. The learning curve is steep and the journey can be very expensive, but certainly worthwhile. Starting this journey in 2021 will also make it possible to learn a lot quicker and to become more graceful and capable over the next few years, which may provide a competitive advantage.
As with any significant investment of resources (financial, people, technical, and information), there needs to be a clear business case and it needs to include how AI is expected to impact people, activities, and roles in the organization. For example, will the
AI-enabled capabilities replace existing human roles?
Organizations adopting AI cannot be successful without a set of core digital technologies. AI is also reliant on sufficient, relevant, clean, and well-managed data, so solid data governance and management is a prerequisite. Organizations should consider starting their journey with defining the data strategy and thinking about how the Data Catalog can facilitate the achievement of their AI objectives.
AI is transforming how we process and experience information, and it means that a lot more information is collected about us, which can then be used to provide customized products, services, and experiences. However, organizations need to consider the different customer expectations as well as legal and regulatory obligations for different parts of the world and different business contexts related to data capture, retention, use, and privacy.
AI is reliant on sufficient, relevant, clean, and well-managed data, so solid data governance and management is a prerequisite.
The following is an extract from a longer AI-focused ITSM.tools article by John:
Stay focused on value
One of the challenges with any technology is that we get enamoured with the technology and everything it
can do and forget the basics of any