Two AI megatrends dominating the Gartner Hype Cycle 2020

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While this year’s Hype Cycle includes five new AI solutions, the megatrends of AI democratization and industrialization are unconditionally dominating AI in 2020.

Despite the global impact of COVID-19, 47% investment in artificial intelligence (AI) have been at the same level since the start of the pandemic, and 30% of organizations, according to a Gartner survey, even planned to increase such investments. Only 16% have temporarily suspended their AI investments and 7% have reduced them.

AI is starting to reach its potential, and its business benefits are beginning to materialize.

For example, AI came to the rescue during a pandemic. Chat bots helped answer many questions related to the pandemic, computer vision helped maintain social distancing, and models machine learning (ML) were indispensable for modeling the results of economic recovery.

“If AI had been introduced as a general concept in this year’s Gartner Hype Cycle, it would have come off the peak of inflated expectations. By this we mean that artificial intelligence is starting to realize its potential and its business benefits are being translated into reality, ”says Svetlana Sikulyar, VP of Analysis, Gartner.

Five newcomers – Small Data, Generative AI, Composite AI, Responsible AI, and Things As Customers – are debuting this year in the AI Hype Cycle, with two megatrends dominating.

Find out more about the Gartner Hype Cycle methodology.

Democratizing artificial intelligence

Democratizing artificial intelligence means AI is no longer a topic exclusively for experts. Now organizations want to take it to the next level by bringing AI to more and more people. The goal of democratizing AI in the enterprise can be customers, business partners, business leaders, salespeople, conveyor workers, application developers, and IT operations specialists.

Gartner Envisions Developers to Become the Main Driving Force of AI

As AI reaches more and more employees and partners involved, it needs new corporate roles to extend it to a wider audience. Along with data scientists and data engineers, developers can also form future AI teams that will assemble AI solutions. Gartner believes that developers will be the main driving force behind AI.

Data science is about discovering the unknown, and engineering ensures the stability, reliability, and security of what science achieves. Engineering complements data science by helping AI scale, and AI developer and educator kits play an important role in the Hype Cycle.

Industrialization of artificial intelligence platforms Industrialization of platforms

The industrialization of AI platforms is enabling the reusability, scalability and security of AI to accelerate its adoption and growth. This industrialization aims to attract new AI adopters alongside the field’s pioneers.

According to a recent Gartner survey, C-Suite manages AI projects, with nearly 30% of projects being led by CEOs. Having top management in place of the leader accelerates the adoption of AI and increases investment in AI solutions.

Responsible AI and AI control systems are also becoming a priority for AI on an industrial scale

For example, decision analytics shows that companies want to use AI to make better decisions faster, such as choosing the best treatment options for patients or accelerating the detection and prevention of anomalies and vulnerabilities. What’s more, new contributors to this year’s Hype Cycle, such as generative AI, small data, and composite AI, indicate that beyond machine learning, organizations are looking at many ways to support AI-powered decision-making.

Responsible AI and AI control systems are also becoming a priority for AI on an industrial scale. They establish and improve the processing of AI business decisions and manage AI risks associated with compliance, privacy and bias. They also address the issue of AI reliability, which is the main AI issue today.

As AI solutions mature, organizations gain invaluable experience and make fewer mistakes. However, they must keep learning, because as AI is introduced, new issues will arise, such as deep fakes or AI security.