With the data economy amplifying demand for all things digital, many organizations are examining their systems and wondering whether their data and technology assets are up to snuff. Can they scale to meet the challenges and the promise of AI?
Companies are looking for straightforward results: a tailored, Uber-like customer experience, an adaptive supply chain like Amazon’s, responsive products like Netflix’s.
Straightforward, yes; simple, no. Even if your organization has AI initiatives underway, the path to AI is long and winding. Unlike other scalable technologies such as cloud and analytics, AI requires a fresh look at every use case. Modernizing your organization’s assets to take advantage of AI means constant renewal.
Guidelines for AI Preparation
Our recent webinar explored the data disconnect that needs to be addressed when prepping your organization and systems to scale for AI. The experts who joined our panel came ready with first-person experiences and suggestions you can put to work. Here’s a recap:
1 Data modernization isn’t just about technology.
In addition to smart tech strategies, data modernization requires a full-scale corporate shift that’s already underway. New research from MIT underscores the extent of the changes taking place. In the past, organizations put data analytics to use for internal gains, such as beefing up business processes or boosting efficiencies, noted Barbara Wixom, principal research scientist for MIT Sloan Center for Information Systems Research. While internal projects still account for 51% of economic returns from data analytics assets, Wixom said market-facing activities represent 49% of the data-based revenues, according to her research. It’s a dramatic ascendance for data and one that ripples through every business function.