Gartner Names Four Trends Driving Near-Term AI Innovation
AI innovation is occurring at a fast rate, with a number of technologies reaching mainstream adoption within two to five years. That’s according to Gartner, which today released a report identifying four trends driving near-term AI innovation in the enterprise.
The report has found that while the AI industry remains in an “evolutionary state,” technologies including edge AI, computer vision, decision intelligence, and machine learning are poised to have a transformational impact on markets in coming years. It also indicated a market trend of end-users seeking specific technology capabilities that are often beyond the capabilities of current AI tools.
“AI innovation is happening at a rapid pace, with an above-average number of technologies on the Hype Cycle reaching mainstream adoption within two to five years,” said Shubhangi Vashisth, senior principal research analyst at Gartner. “Innovations including edge AI, computer vision, decision intelligence and machine learning are all poised to have a transformational impact on the market in coming years.”
What are the four trends driving AI innovation according to Gartner?
Responsible AI
The development of AI is creating new opportunities but also raising new questions about the best way to build fairness, interpretability, privacy, and security into these systems. Responsible AI is a governance framework that documents how a specific organisation is addressing the challenges around artificial intelligence (AI) from both an ethical and legal point of view.
“Increased trust, transparency, fairness and auditability of AI technologies continues to be of growing importance to a wide range of stakeholders,” said Svetlana Sicular, research vice president at Gartner. “Responsible AI helps achieve fairness, even though biases are baked into the data; gain trust, although transparency and explainability methods are evolving; and ensure regulatory compliance, while grappling with AI’s probabilistic nature.”
Gartner expects that by 2023, all personnel hired for AI development and training work will have to demonstrate expertise in responsible AI.
Small and Wide Data
Small and wide data approaches reduce organisations’ dependency on big data. According to Gartner, by 2025, 70% of organisations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data hungry.
“Small data is about the application of analytical techniques that require less data but still offer useful insights, while wide data enables the analysis and synergy of a variety of data sources,” said Sicular. “Together, these approaches enable more robust analytics and help attain a more 360-degree view of business problems.”
Operationalisation of AI Platforms
The urgency and criticality of leveraging AI for business transformation is driving the need for operationalisation of AI platforms. This means moving AI projects from concept to production, so that AI solutions can be relied upon to solve enterprise-wide problems.
“Gartner research has found that only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so,” said Sicular. “Innovations such as AI orchestration and automation platforms (AIOAPs) and model operationalisation (ModelOps) are enabling reusability, scalability and governance, accelerating AI adoption and growth.”
Efficient Use of Resources
Given the complexity and scale of the data, models and compute resources involved in AI deployments, AI innovation requires such resources to be used at maximum efficiency. Multiexperience, composite AI, generative AI and transformers are gaining visibility in the AI market for their ability to solve a wide range of business problems in a more efficient manner.