The chief data officer’s guide to an AI strategy

Artificial intelligence (AI) is set to be a priority for more than 30 percent of CIOs by 2020, according to Gartner. While AI promises game changing capabilities, this is only going to happen if your organisation applies it effectively.

If you’re a chief data officer (CDO) trying to realise the full potential of AI, now’s the time to broaden your strategy, assess the impact on both business models and customer experiences, and prepare for other strategic challenges.

Much of the current wave of attention is the result of gains in advanced analytics and machine learning. This current shift is partially attributable to the emergence of inexpensive, massive and readily available computing power, as well as the mountains of data available to train machines, form patterns and produce insights.

Although top of mind, many organisations are just beginning their AI journey — gathering knowledge and developing strategies for applying it. If you’re like many data and analytics leaders, the need to define an AI strategy and identify uses is a real challenge.

An increasing number of organisations are finding that AI doesn’t simply offer the potential to radically improve existing business activities, but instead creates the potential for data-driven business strategies like never before. This potential makes data and analytics a primary driver of strategy, which in turn mandates a more expansive examination of the potential for AI.

It’s not enough to assess the potential for AI in the same way we’ve typically assessed data and analytics strategy as a by-product of other strategy work. We certainly need to understand the appropriate and emerging uses of AI, but we also should consider the business-changing potential by becoming familiar with new strategy development practices.

There are three areas you should focus on:

Start by assessing the relevance of AI from a business value and governance perspective, as well as in relation to specific operations and IT challenges.

Business value is an imperative for gaining focus for AI initiatives. Many organisations become enamoured with AI capabilities, but in the process they fail to determine the most strategic value drivers. This lens clarifies where to apply critical resources such as data scientists; new solutions would benefit from AI; and crystallises the resolve to build capabilities where longer-term business outcomes are desired.

Expand your strategy repertoire with frameworks that help you determine AI‘s applicability to business model components and their interrelationships. Business model assessment frameworks establish a common language for describing your organisation’s existing business model. It also aids in assessing and proposing changes to individual components — improving cost structures, enabling data-driven revenue streams, or identifying new key partnerships where data and analytics play a prime role. It also can help identify changes to interrelated components that support potential extensive business model changes.