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Artificial Intelligence: Trends Driving AI Adoption in the Enterprise
Artificial Intelligence (AI) is still in its early days, but change and innovation in the field are happening at a blistering pace. A problem is that while many companies see great potential in the newest developments in AI, operationally they often lag far behind in terms of their skillsets and capabilities.
The four short-term trends in AI that Garter predicts will have an impact on how enterprises adopt AI are as follows:
Responsible AI
Svetlana Sicular, research vice president at Gartner, said that “increased trust, transparency, fairness and auditability of AI technologies continues to be of growing importance to a wide range of stakeholders.”
David Leslie, Ethics Team Lead at the Alan Turing Institute, wrote that “responsible data science is good data science—data science with and for society and worthy of public trust.”
Gaining Useful Insights from Smaller Data Sets
Rita Sallam, distinguished research vice president at Gartner, said that “small and wide data approaches provide robust analytics and AI, while reducing organizations’ large data set dependency. Using wide data, organizations attain a richer, more complete situational awareness or 360-degree view, enabling them to apply analytics for better decision making.”
Shrinking the Timeline from AI Concept to Enterprise Deployment
Garnter estimates that less than half of AI pilot projects ever advance to being put into production. Thomas Davenport, professor at Babson College, wrote in MIT Sloan management Review that “implementation of AI projects is the only way that organizations will realize tangible business value from their AI investments. Companies are spending considerable sums on AI technology, and it should not be viewed exclusively as an experiment. It is only when companies step up to production status with AI that it will deliver ROI and productivity for their organizations.
Improved efficiency in Development and Deployment of AI
Gartner analysis comments that “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.”
