The 2023 Gartner Hype Cycle identifies 25 must-know emerging technologies designed to help enterprise architecture and technology innovation leaders: 

• Evaluate the business impact of emerging technologies

• Examine and explore potentially transformative technologies 

• Strategize how to benefit from these technologies

These technologies are expected to greatly impact business and society over the next two to 10 years, and will especially enable CIOs and IT leaders to deliver on the promise of digital business transformation. 

Because emerging technologies are disruptive by nature, it’s critical to understand the potential use cases and paths to mainstream adoption.

“The technologies in this Hype Cycle are at an early or embryonic stage,” says Gartner Distinguished VP Analyst Arun Chandrasekaran. “Great uncertainty exists about how they will evolve, so there are greater risks for deployment, but potentially greater benefits for early adopters.”

Four Gartner Hype Cycle themes to think about in 2023 and beyond

These technologies provide opportunities for sustainable differentiation and greater workforce productivity. While generative AI has great potential to enable competitive differentiation, several other emerging AI techniques also offer immense potential to enhance digital customer experiences, make better business decisions and distinguish yourself among your competition.

An example of emergent AI, generative AI can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. It will continue to have profound business impacts, including on content and product development; automation of human work; and in enhancing customer and employee experiences as it reaches mainstream adoption in two to five years. 

Other critical technologies in emergent AI include:

• AI simulation is the combined application of AI and simulation technologies to jointly develop AI agents and the simulated environments in which they can be trained, tested and sometimes deployed.

• Causal AI identifies and uses cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously.

• Federated machine learning aims to train a machine learning algorithm without explicitly sharing data samples, enabling better privacy and security. 

• Graph data science (GDS) is a discipline in