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The chief data officer's guide to an AI strategy

The chief data officer's guide to an AI strategy

Develop a data-driven culture but be mindful of regulatory and ethical considerations

Many organisations become enamoured with AI capabilities, but fail to determine the most strategic value drivers

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.

AI doesn't simply offer the potential to radically improve existing business activities, it also creates the potential for data-driven business strategies like never before.

Mike Rollings, Gartner

Realising the full potential of AI

There are three areas you should focus on:

  • Develop clear line of sight to business value

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.

  • Harness disruptive potential in customer experiences

AI presents several opportunities for gaining insight, creating personalisation and enhancing the customer experience, which is one of the top opportunities for the use of AI and machine learning. Assessing its disruptive potential gives you the opportunity to engage customers in new ways, deepen your understanding of customer behaviour and shape the future of customer experience in digital business.

There are many opportunities to improve customer experience with AI, including developing customer insights and customising their journey, chatbots and virtual agents, and predictive analytics for marketing. Leverage approaches such as journey mapping and outcome-driven innovation to identify unmet customer needs and opportunities for use.

  • Address organisational, governance and technological impacts

Prepare for the organisational, governance and technological challenges imposed by AI. Lack of necessary skills is often seen as a primary hurdle to AI adoption, so developing necessary competencies will be critical. The obvious impact is with the development of data science skills and refactoring the CDO’s organisation to foster the creation and use of intelligence.

Many of the benefits of AI will come from the predictions rendered by machine learning. Yet, organisations are woefully ill-prepared to use this data rather than going with their gut instinct, much less being able to evaluate and use probabilistic assessments of outcomes in decision making.  This underscores the equal, if not greater importance to develop a data-driven culture and the ability to "speak data" from a business perspective.

Using AI to gain insight into areas that humans cannot, underlies advancements in predictive analytics, natural-language processing, computer vision, image recognition and many other displays of seeming intelligence. Numerous business scenarios will certainly benefit from AI-generated insights and capabilities, but governing them may be a challenge due to a lack of transparency in how some of these approaches attain their results, a lack of processes to ensure quality results and appropriate use. 

For example, it’s possible that the same data with the same analytics may be governed differently based on the use — one being ethically okay and the other potentially not. The same may also be true for security, privacy, compliance and retention.

To address these challenges, develop a data-driven culture; be mindful of regulatory and ethical considerations; and steer clear of dangerous myths – all while also fostering a learning laboratory for AI capabilities.

Mike Rollings is a research vice president at Gartner, focused on the strategic use and governance of data; how advanced analytics is transforming business; and how to drive enterprise behaviour change. Mike will be presenting on data and analytics strategy at the Gartner Data & Analytics Summit, taking place in Sydney, 26-27 February 2018.

Related reading:

The data scientist in the age of AI and IoT

The chief data officer who decided to ‘look forward, not back’

The CIO who quit his job to work on open data

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Tags dataGartnerbig dataanalyticsbusiness strategyartificial intelligenceAImachine learningdigital transformationMike Rollings

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