What is New in Artificial Intelligence from the 2022 Gartner Hype Cycle

The wide range of AI innovations is expected to impact people and processes within and outside an enterprise context, making them important to understand for many stakeholders, from business leaders to the enterprise engineering teams tasked with deploying and operationalising AI systems. 

Data and analytics (D&A) leaders have the most to gain, however, from using the Hype Cycle outlook to craft their AI strategies for the future and use technologies that offer high impact in the present.

The AI innovations on the Hype Cycle reflect complementary and sometimes conflicting priorities across four main categories:

1. data-centric AI;

2. model-centric AI;

3. applications-centric AI; and

4. human-centric AI.

Find out more: Executives’ Guide to AI

Causal AI includes different techniques, like causal graphs and simulation, that help uncover causal relationships to improve decision making. Though it will take 5 to 10 years for causal AI to reach mainstream adoption, the business benefits are expected to be high, enabling new ways of performing horizontal or vertical processes that will result in significantly increased revenue or cost savings for an enterprise. Causal AI benefits include:

• efficiencies from adding domain-knowledge to bootstrap causal AI models with smaller datasets;

• greater decision augmentation and autonomy in AI systems;

• better explainability by capturing easy-to-interpret cause-and-effect relationships;

• more robustness and adaptability by leveraging causal relationships that remain valid in changing environments; and

• reduced bias in AI systems by making causal links more explicit.

Innovations here include AI engineering, decision intelligence, operational AI systems, ModelOps, AI cloud services, smart robots, natural language processing (NLP), autonomous vehicles, intelligent applications and computer vision.

Decision intelligence and edge AI are both expected to reach mainstream adoption in two to five years and have transformational business benefits.

Decision intelligence is a practical discipline used to improve decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.​ 

Decision intelligence helps to:

• reduce technical debt and increase visibility, and improve the impact of business processes by materially enhancing the sustainability of organisations’ decision models based on the power of their relevance and the quality of their transparency, making decisions more transparent and auditable; and

• reduce the unpredictability of decision outcomes by properly capturing and accounting for the uncertainty factors in the business context and making decision models more resilient.

Download now: How to Set Strategy for Your Decision-making Process

Edge AI refers to the use of AI techniques embedded in Internet of Things (IoT) endpoints, gateways and edge servers, in applications ranging from autonomous vehicles to streaming analytics. Business benefits include:

• improved operational efficiency, such as manufacturing visual inspection systems;

• enhanced customer experience;

• reduced latency in decision making, with the use of local analytics;

• connectivity cost reduction, with less data traffic between the edge and the cloud; and

• persistent solution availability, irrespective of network connectivity.

This group of innovations includes AI trust, risk and security management (TRiSM), responsible AI, digital ethics, and AI maker and teaching kits.

When AI replaces human decisions, it amplifies good and bad outcomes alike. Responsible AI enables the right outcomes by resolving dilemmas rooted in delivering value versus tolerating risks. Responsible AI is an umbrella term for aspects of making appropriate business and ethical choices when adopting AI, including business and societal value, risk, trust, transparency, fairness, bias mitigation, explainability, accountability, safety, privacy and regulatory compliance. Responsible AI will take 5 to 10 years to reach mainstream adoption but will ultimately have a transformational impact on business. 

Digital ethics are a nearer-term trend (two to five years) likely to have a high business impact. Digital ethics comprise the systems of values and moral principles for the conduct of electronic interactions among people, organisations and things. These issues, especially as they relate to privacy and bias, remain of concern to many. People, increasingly aware that their information is valuable, are frustrated by lack of transparency, misuse and breaches. Organisations are acting to mitigate risks around managing and securing personal data, while governments are implementing stricter legislation.

Many organisations still ignore digital ethics, because they think it does not apply to their industry or domain, but Gartner predicts that by 2024, 30% of major organisations will use a new “voice of society” metric to act on societal issues and assess the impact on their business performance. Organisations will need to integrate digital ethics into their AI strategies to bolster their influence and reputation among customers, employees, partners and society.

In short:

• The 2022 Gartner Hype Cycle™ for Artificial Intelligence features “must-know” innovations expected to drive extensive benefits to any organisation.

• These innovations go beyond everyday AI techniques already being used to add intelligence to previously static business applications, devices and productivity tools.

• Pay early attention to innovations expected to hit mainstream adoption in two to five years, including composite AI, decision intelligence and edge AI.

Afraz Jaffri is a Research Director at Gartner, where Mr. Jaffri focuses on Analytics, Data Science and AI. He advises Data and Analytics leaders on making the most from their investments in modern data science, machine learning and analytics platforms.