If you’ve noticed an uptick in product recommendations based on your Amazon purchases, or GPS services that are increasingly accurate in displaying congested traffic areas, it’s because artificial intelligence (AI) is everywhere. AI adoption in organizations has tripled in the past year, and AI is a top priority for CIOs. Yet early AI initiatives have a high probability of failure due to misalignment with business requirements and lack of agility.
Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated
“Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated,” says Chirag Dekate, senior director analyst at Gartner. “IT leaders should plan early and use agile techniques to increase relevance and success rates.”
Gartner predicts five things that CIOs should consider in the rapid evolution of AI tools and techniques, and how they will play out in their organization.
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AI will drive infrastructure decisions
The use of AI across enterprises is ramping up quickly. In fact, through 2023, AI will be one of the top workloads that drive infrastructure decisions. Accelerating AI adoption requires specific infrastructure resources that can grow and evolve alongside technology. AI models will need to be periodically refined by the enterprise IT team to ensure high success rates.
Manage increasingly complexity of AI techniques through collaboration
“One of the top technology challenges in leveraging AI techniques like machine learning (ML) or deep neural networks (DNN) in edge and IoT environments is the complexity of data and analytics,” says Dekate. Traditional AI use cases that do not involve customer expectations are successful because of the tight collaboration between the business and IT functions, so securing the help of internal engineering teams is a must.
By 2023, 40% of I&O teams will use AI-augmented automation in large enterprises, resulting in higher IT productivity
Simple machine learning techniques sometimes make the most sense
Through 2022, over 75% of organizations will use DNNs for use cases that could be addressed using classical ML techniques. “Classical machine learning techniques are extremely underrated,” says Dekate. “Once you sift through the AI hype, you will realize that many organizations are pushing to apply deep learning techniques without even understanding how they apply to their current initiatives.” As such, simplicity is key, and IT leaders should take the time to learn the spectrum of options to appropriately address their business problems.
Serverless computing will take the stage
Containers and serverless computing will enable ML models to serve as independent functions and, in turn, run more cost-effectively with low overhead. A serverless programming model is particularly appealing in public cloud environments because of its quick scalability, but IT leaders should identify existing ML projects that can benefit from these new computing capabilities.
Adopt automation beyond the surface level
As the amount of data that organizations have to manage increases, so too will the abundance of false alarms and ineffective problem prioritization. With the shortage of digital dexterity talent in I&O to effectively adopt AI, automation is a key solution. By 2023, 40% of I&O teams will use AI-augmented automation in large enterprises, resulting in higher IT productivity with greater agility and scalability.
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