Three Steps to Implement AI
A recent #MITSMRChat suggests three best practices for organizations implementing AI.
Topics
Artificial Intelligence and Business Strategy
In collaboration with
BCG
Our recent Twitter chat exploring AI implementation connected more than 150 people wrestling with tough questions surrounding the technology. What do their organizations use AI for? What are the biggest challenges to implementation? And what lessons can we take away from this conversation? Three clear best practices emerged.
Our readers — those of you on the front lines of these efforts — put it best, so we’ve assembled a roundup of some comments that best encapsulate these issues.
1. Consider the “Why”
“Let’s be honest. Most organizations are focusing their AI efforts on things that matter only to them, hardly things that matter to their customers. Best to find the sweet spot where both interests meet.”
As Wim observes, organizations often focus on using AI to streamline their internal processes before they start thinking about what problems artificial intelligence could solve for their customers. Consider using the technology to enhance your company’s existing differentiators, which could provide an opportunity to create new products and services to interest your customers and generate new revenue.
Another chatter adds:
“Many companies look to use AI to optimize decision making ([by adding] transparency) which lowers risk/cost and improves [revenue because] AI can help find opportunities that [are] otherwise overlooked.”
— Sisi
Our survey data shows that, while less-advanced organizations focus most AI initiatives on cost reduction, more-advanced companies see revenue increases from the technology, indicating a shift to more strategic — and hopefully customer-centric — AI deployments.
2. Organize for AI
Three years into our collaborative research with Boston Consulting Group on the adoption of AI, we still see the market struggling to align organizations around AI. As one chatter notes, it’s first about developing a shared understanding:
“The biggest challenge is the misconception about what AI is: There’s no one definition. Many companies are excited about it but don’t have resources to consider if it’s feasible for them, i.e., if they have sufficient data.”
Having the proper infrastructure in place is another prerequisite. AI leaders, whom we call Pioneers, place an emphasis on data management and access, laying the building blocks for AI implementation. (More on Pioneers’ distinctive characteristics can be found in our 2018 report “Artificial Intelligence in Business Gets Real.”)