It’s hard to read the news or scan social media without seeing examples of artificial intelligence (AI) in action. Some of its most talked about applications include roboadvisors and robotraders in the finance industry, chatbots and personal buying assistants in retail, medical diagnostics, remote patient monitoring and AI tutors for personalized education.
Despite the hype, it’s early days.
Now’s the time to define your AI strategy and assess its impact on business models and customer experience.
AI doesn’t only offer the potential to radically improve existing business activities, but instead creates the potential for data-driven business strategies. This makes data and analytics a primary driver of strategy, which in turn mandates a more expansive examination of the potential for AI.
How to get started
It’s not enough to look at AI in the same way as we have typically created a data and analytics strategy as a byproduct of other strategy work. Rollings recommends focusing on three areas:
- Develop clear line of sight to business value. Start by assessing the relevance of AI to your most important business outcomes and how it can fuel new data-driven capabilities, as well as in relation to specific operational and IT challenges. Many organizations become enamoured with AI capabilities, but in the process they fail to determine the most strategic value drivers.
- Harness disruptive potential in customer experience. A survey of Gartner Research Circle members found that the top three types of AI applications that they used or plan to use all relate to improving customer experience. AI presents unique opportunities for gaining insight and creating personalization. By 2020, 25% of customer service and support operations will integrate smart technology virtual customer assistants across engagement channels.
- Address organizational, governance and technological impacts. Prepare for the organizational, governance and technological challenges imposed by AI. Focus on developing a data-driven culture, data science skills and the ability to “speak data” from a business perspective.
Be mindful of regulatory and ethical considerations. It is possible that the same data with the same analytics may be governed differently based on the use context — one being ethically okay and the other potentially not, and with the same being potentially true for security, privacy, compliance, retention and other once separate questions. As a result, data and analytics leaders will need to raise governance issues as part of normal business discussions.