Artificial Intelligence (AI) is an unavoidable concept in the digital age, if not for its pervasive application, then by virtue of the fact that it provides a technological solution to information overload and the quest for context. Enterprise interest in AI has been driven by its promises for efficiency in the form of speed, accuracy, agility, and access to insights embedded in ‘dark data’—a reference to the estimated 80% of unused enterprise data.
Innovators and change agents see the hype of AI everywhere but struggle to know where to begin
In our ongoing research of the artificial intelligence market, we assess how enterprises are preparing their organizations for AI. What we see and hear are answers associated with data, such as data cleansing, pipeline integrations, and on occasion, use case prioritization. While these are practical starting points for AI, our research finds true readiness requires a broader swath than data alone.
Upcoming research by Kaleido Insights’ industry analyst, Jessica Groopman, examines the need for preparation and introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethics, governance, and strategy. This research is the culmination of some 25 interviews, direct client experience, countless industry gatherings, and briefings and presents practical considerations needed to deploy effective and sustainable machine and deep learning programs.
- 6 critical ways organizations must prepare for AI in the enterprise
- Case examples, anecdotes, and real-world findings
- Frameworks for preparation
- Best practices and recommendations from companies that have built AI programs