AI focus shifts to ‘small and wide’ data

▲圖片標題(來源： Shutterstock / Gorodenkoff )

AI innovation is occurring at a fast clip, with a number of technologies on the “hype cycle” 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. It finds 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.

Gartner sees evidence of a trend of companies seeking capabilities beyond what current AI tools can often accomplish. Organizations are focusing on implementation, risk management, and ethics as they look to scale AI initiatives. But data leaders run the risk of failing to realize value from these initiatives if they don’t “prioritize and accelerate” investments in AI technologies at various stages of maturity, Gartner warns.

Responsible AI

Increased trust, transparency, fairness, and auditability of AI technologies continues to be of growing importance to a range of stakeholders, according to Gartner. “Responsible AI” can help to achieve a semblance of fairness, trust, and regulatory compliance — even if biases are baked into the data and explainability methods fall short. For this reason, Gartner expects that all experts hired for AI development and training work will have to demonstrate competence in responsible AI by 2023.

At the same time, Gartner predicts that emerging “small and wide data” approaches will enable more robust analytics and AI, reducing organizations’ dependency on big data. Wide data allows analysts to examine and combine a variety of small and large, unstructured and structured data, while small data is focused on applying analytical techniques that look for useful information within small, individual sets of data.

According to Gartner, by 2025, 70% of organizations 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.

Operationalizing AI

The need for enterprise digital transformation during the pandemic has bolstered investments in AI. AI startups raised a collective $73.4 billion in Q4 2020, a $15 billion year-over-year increase. And according to a recent report from ManageEngine, 80% of companies in the U.S. accelerated their AI adoption over the past two years.

The report finds that the urgency of leveraging AI for business transformation is driving the need to operationalize AI platforms. This means moving AI projects from concept to production, so that AI solutions can be leveraged to solve enterprise-wide problems like customer service automation. Given the complexity and scale of the data and compute resources involved in AI deployments, AI innovation will require these resources to be used at maximum efficiency, Gartner notes.

“[Our] 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,” Svetlana Sicular, research VP at Gartner, said in a statement. “Innovations such as AI orchestration and automation platforms and model operationalization are enabling reusability, scalability, and governance, accelerating AI adoption and growth.”

轉貼自： VentureBeat

若喜歡本文，請關注我們的臉書 Please Like our Facebook Page： 　　　Big Data In Finance