Six Design Principles for AI in Digital Business
Artificial intelligence (AI) can augment or automate decisions and tasks today performed by humans, making it indispensable for digital business transformation. With AI, organisations can reduce labour costs, generate new business models, and improve processes or customer service. However, most AI technologies remain immature.
“To overcome this hurdle, CIOs must ensure that applications intended to serve a strategic business purpose, such as increasing revenue or scaling services, are designed for strategic plans,” says Jorge Lopez, Distinguished Vice President Analyst, Gartner.
Lopez outlines six design principles that will help CIOs and organisations evaluate all proposed AI applications with strategic intent — that is, applications intended to help achieve business results, not just operational improvements. Applications do not have to follow all six principles; however, designs that show two or fewer principles should be reconsidered.
Design principle No. 1: Anticipate the future
In digital business, AI generates insights that lead directly to business execution. A strategic AI application can produce granular insights into what customers, markets or other entities are likely to do in specific future situations and what the enterprise can do to influence them. The more trustworthy the insights, the more enterprises will rely on them to guide future execution systems.
Design principle No. 2: Act autonomously
AI applications provide value by automating existing manual processes, but can also go a step further by enabling autonomous operation of the business. A strategic AI application that acts autonomously can operate without human direction, producing significant productivity gains as it augments the work done by humans and frees them for more personalised tasks.
When designing AI applications for autonomous operations, ensure the AI applications are located as close as possible to the work being done, have near-real-time understanding of what’s going on and have the intelligence to make decisions on the spot.
Design principle No. 3: Connect to the customer
Digital businesses thrive on knowledge of markets and customers. To support digital business initiatives, AI applications must get as close to customers as possible. CIOs should take cues from digital giants that use their popular technologies powered by AI to get between companies and their customers.
For example, consumers often use Amazon’s Alexa and Apple’s Siri to access the capabilities of platforms from other companies. As a result, Amazon and Apple can gather better data about customers than the companies that provide the service. Similarly, CIOs should think about strategic AI applications that enable their organisation to capture critical information to help build more intimate customer relationships overtime.
Design principle No. 4: Elevate the physical
Strategic AI applications should make a difference in the physical world. AI can have a physical impact by enhancing the power of other advanced technologies. For example, 3D printing continues to grow in sophistication. GE Aviation now creates fan blades, a critical part for jet engines, using 3D printing. Adding AI can extend 3D printing to even more complex use cases, such as adjusting the printing process to accommodate manufacturing where many variables must be controlled.
Design principle No. 5: Detect the invisible
AI can manage operations in ways that humans cannot, and strategic AI applications should take advantage of this ability. Strategic AI applications can make decisions much faster than humans about increasingly complex situations. For example, high-speed trading applications can already move money around in nanoseconds. They are powered by algorithms that take into account variables such as stock prices, weather and political developments. This enables traders to execute millions of orders in a matter of seconds, giving their organisation a huge advantage.
Design principle No. 6: Manage risk
Security, risk and privacy form the biggest barriers to the development of AI applications and are even more of an issue when AI applications serve a strategic business purpose. A mistake doesn’t just disrupt operations, it harms the brand or the enterprise. As a result, CIOs should define behaviour limits. These limits reduce the risk of concept drift and prevents any damage the application could do.