2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020

Despite the global impact of COVID-19, 47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organisations actually planned to increase such investments, according to a Gartner poll. Only 16% had temporarily suspended AI investments, and just 7% had decreased them.

During the pandemic, for example, AI came to the rescue. Chatbots helped answer the flood of pandemic-related questions, computer vision helped maintain social distancing and machine learning (ML) models were indispensable for modelling the effects of reopening economies.

“If AI as a general concept was positioned on this year’s Gartner Hype Cycle, it would be rolling off the Peak of Inflated Expectations. By that we mean that AI is starting to deliver on its potential and its benefits for businesses are becoming a reality,” says Svetlana Sicular, VP Analyst, Gartner.

Five new entrants — small data, generative AI, composite AI, responsible AI and things as customers — make their debut on this year’s AI Hype Cycle, and two megatrends dominate this year’s AI landscape.

Democratisation of Artificial Intelligence

The democratisation of AI means that AI is no longer the exclusive subject matter of experts. Now, organisations want to reach the next level by delivering AI value to more people. In the enterprise, the target for democratisation of AI may include customers, business partners, business executives, salespeople, assembly line workers, application developers and IT operations professionals. 

As AI reaches a larger set of employees and partners, it requires new enterprise roles to deliver it to a wider audience. Along with data scientists and data engineers, developers could also form the future AI teams who put together AI solutions. Gartner foresees developers being the major force in AI.

Data science deals with discovery of the unknown, but engineering provides the stability, reliability and security of what science delivers. Engineering complements data science to deliver AI at scale, and AI developer and teaching kits represent the role on the Hype Cycle.

Industrialisation of AI platforms

The industrialisation of AI platforms enables the reusability, scalability and safety of AI, which accelerates its adoption and growth. This industrialisation aims at getting new adopters of AI on par with early adopters.

According to a recent Gartner survey, the C-suite is steering AI projects, with nearly 30% of projects directed by CEOs. Having the C-suite in the driver’s seat accelerates AI adoption and investment in AI solutions.

For example, decision intelligence indicates that companies want to use AI to make better decisions faster, such as selecting best treatment options for patients or accelerating discovery and prevention of anomalies and vulnerabilities. Moreover, new entrants on this year’s Hype Cycle, such as generative AI, small data and composite AI, indicate that in addition to ML, organisations are considering multiple means of supporting decision making with AI.

Responsible AI and AI governance also become a priority for AI on an industrial scale. They establish and refine processes for handling AI-related business decisions and manage AI risks associated with compliance, privacy and bias. They also address the trustworthiness of AI, which is the top AI challenge today. 

When AI solutions mature, organisations learn a lot and make fewer mistakes. However, they should keep learning, because new challenges such as deep fakes and AI security will arise as AI adoption progresses.