Six design principles for artificial intelligence in the digital enterprise

Artificial intelligence (AI) is able to augment and automate decisions or tasks currently performed by humans, making it an indispensable tool for the digital transformation of businesses. With the help of AI, organizations can hope to reduce labor costs, generate new business models, and generally streamline processes and improve customer service standards. Nevertheless, it is important to remain pragmatic in the approach, because the vast majority of AI technologies are still in their infancy.

To overcome this problem of AI technology immaturity, CIOs must ensure that applications intended to serve a strategic business purpose, such as maximizing revenue or scaling certain services, are designed for a strategic effect.

Gartner has identified and described six design principles to help CIOs evaluate every AI application offering with strategic intent. These are apps meant to help drive business results, not just operational improvements. Applications do not need to adhere to the six principles. However, designs that show less than two principles should be reconsidered.

Design principle no. 1: Anticipate the future

When applied to the digital business, AI generates information that directly leads to business execution. Strategic AI solutions are able to deliver granular information that can suggest how particular customers or markets will behave in specific situations in the future, and what the business can do to influence their decisions. If an application can provide proven and reliable information, it will reap the rewards of being adopted and used by more companies to guide future execution systems.

AI can produce information that is more granular and better suited to individual situations than conventional analytical applications. Therefore, AI applications can reduce false reads – the more reliable the information, the more likely companies are to rely on them to guide execution systems.

Design principle no. 2: Act autonomously

The value of AI applications lies in automating manual processes. However, technology can also enable a business to operate independently. A strategic AI application that acts autonomously does not require human leadership. This autonomy, in turn, produces huge productivity gains by supplementing the work done by humans and freeing up the workforce to perform more personalized tasks.

Those responsible for designing AI applications for autonomous operations should ensure that the applications are located close to the work in progress, that they have a near real-time understanding of the processes and their state, and that they have the capacity to make decisions on the spot. .

Design principle no. 3: Connect to the client

An intimate knowledge of markets and customers is integral to the success of digital businesses. To help digital business initiatives, AI applications should aim to get as close to customers as possible. CIOs would be well advised to follow the lead of the digital behemoths whose popular technologies – driven by AI – lie between businesses and customers.

Relevant examples are Amazon’s Alexa and Apple’s Siri. Consumers use these devices powered by these technologies as intermediaries to access the capabilities of external and third-party platforms. As a result, Amazon and Apple are able to collect better customer data than the companies whose services they access. Likewise, CIOs may consider strategic AI applications that enable their organization to capture critical information, with a view to building more intimate long-term customer relationships.

Design principle no. 4: Elevate the physique

The advent of autonomous mines promises to transform the economy of the mining industry. Robotic surgery aims to improve patient outcomes. Likewise, developers of strategic AI applications should strive to make a difference in the physical world. AI can effect physical change by working in tandem with other advanced technologies, or by facilitating new ways in which things can interact and collaborate. Increasingly sophisticated 3D printing is a prime example: GE Aviation is now creating fan blades for its jet engines using 3D printing technology. Introducing AI into the mix may open up other possibilities and more complex use cases for 3D printing, such as adjusting the printing process to suit manufacturing where a certain many variables must be controlled simultaneously. This is especially important when conditions are unpredictable: extreme weather conditions, battlefield operations, conditions at sea, etc.

Design principle no. 5: Detect the invisible

AI can handle operations at a level human capabilities cannot match and, frankly, cannot even see or detect within a significant timeframe when a rapid disruption event occurs in cyberspace. Mission-critical AI applications should harness the benefits of higher resolution and speed. Digital technology can enable an organization to control things, events and results with incredibly high precision. High speed trading applications are already capable of moving large sums of money in nanoseconds. These applications are powered by algorithms that simultaneously take stock prices, weather and political developments into account. This intelligence allows traders to execute millions of orders in seconds, giving their organizations the edge. In many cases, these capabilities will become the minimum required to enter a business and lead the new capabilities to become competitive.

The digital world is increasingly granular. With 1,000 billion connected objects planned by 2050, AI is the only possible solution to bring decision-making to such a level and face such complexity.

Design principle no. 6: Manage risk

Security, risk and privacy concerns are the main obstacles to developing AI applications. These issues become even more important when AI applications serve a strategic business purpose. A mistake doesn’t just disrupt operations, it damages the heart of a brand or business. In some cases, AI applications themselves, capable of learning, develop their own recipe for accomplishing their mission which may endanger others. As a result, CIOs must design explicit plans to identify and mitigate risks in AI application designs, and set behavioral limits.

These six design principles should be used to evaluate all proposed AI applications. CIOs are advised to prioritize design principles differently, depending on the specific needs and circumstances of each business. For example, conservative businesses should focus on managing risk, while businesses involved in the Internet of Things and blockchain should pay more attention to ‘elevating the physical’ and ‘sensing the unseen’. With a combination of tailored principles, sufficient funding for data quality improvement, and leaders at the helm who master business strategies, organizations will be better equipped to overcome the often daunting obstacles to digital success.

Jorge Lopez, distinguished vice-president, Gartner
Image Credit: John Williams RUS / Shutterstock

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