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6 Trends in AI and Intelligent Automation

Research firm Omdia shared insight into trends that will shape AI and intelligent automation in 2023.

It is no secret that artificial intelligence and intelligent automation are gathering momentum. The main driver for the increased adopt of these technologies is their contribution to business value on many fronts – as a competitive advantage, productivity boost, efficiency enhancer and prediction engine.

Here are six trends for 2023 and beyond from our sister research firm Omdia.

1. AI rubber hits the road, aka operationalizing AI

AI market adoption has reached critical mass, with the number of deployments likely to double in the next two to three years. But these early majority buyers still need to overcome many internal challenges to adopt and scale AI successfully, including budgets, literacy, organizational structure, KPIs, sustainability, risk and lifecycle management, etc.

Technology vendors are building solutions for AI responsibility (privacy, transparency, bias, etc.), repeatability, delivery, and governance. Best practices are also emerging from the early adopters. Also, new consumption models such as AI as a service, pre-built AI, and embedded AI will help not just to operationalize AI but do so rapidly and at scale across the business.

2. Democratization foreshadows coming AI ubiquity

High demand for AI and advanced analytics in the enterprise has revealed a significant technological skills gap, one that may never be filled through human talent alone. Yet companies are beginning to see solutions to bridging this chasm through a rapidly evolving set of technologies and practices laser-focused on democratizing AI.

New AI-driven automated workflows and low/no-code AI development tools, along with large-scale pre-trained AI models, embedded AI business apps, and even end-to-end AI solutions spanning software to silicon, all promise to turn AI into a more readily consumable enterprise resource with far fewer requirements for specialist skills.

Still, many questions remain. Can AI be trusted to build responsible AI outcomes? Will AI specialization vanish beneath a few, massive, vertically integrated platforms?

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