Each year since 2012, the world has seen a new step function advance in AI capabilities. Fundamental building blocks of intelligence like vision (2012), simple video games (2013), machine translation (2014), complex board games (2015), speech synthesis (2016), image generation (2017), robotic control (2018), and writing text (2019) have all been tackled -- and mastered, at superhuman levels. As research continues to churn out new tools, industry scrambles to harness them. According to McKinsey data, AI will add $13tr to the global economy over the next decade, and organizations will be at the center of this propagation. However, only 8% of organizations surveyed in an HBR study have engaged in core practices that support widespread adoption.

Yet there's also no point in accessing richer sources of data unless you have an architecture that can consume it. An AI-ready architecture is able to address different shapes and granularities of data such as transactions, logs, geospatial information, sensors and social. In addition, real-time time-series data is key to the constant feed of input that propels data-driven devices, from smart-home appliances and health devices to self-driving cars. Make sure your AI architecture has the capability to consume different data structures in different time dimensions, especially real time. Is your organization identifying and classifying data at the point of ingestion?

Determining whether your data is up to snuff for AI often requires a new set of metrics. For example, key performance indicators (KPIs) for IT regularly include whether the system is ready when reports need to run, or whether a particular report or data feed was executed within the agreed upon time window. For AI to be successful, however, data metrics need to measure business value and the ability to deliver the desired outcomes. That measure doesn't exist today. Measuring your data IQ will help you hone in on business value.

Finding the answers in AI begins with a strategy for a new data foundation. Making sure the foundation has the computing, storage and analytical capabilities engineered for purpose is only part of the equation. Perhaps more important is the shift in perspective that's needed: Instead of using data to track and measure how business functions perform, the new data foundation lets your organization perform with data. It's at this point in conversations about data that ride-sharing giant Uber typically comes up. And with good reason: Everything Uber does is all about its data.