IT leaders and business executives around the world recognize the strategic importance of operationalizing AI, yet surprisingly few have moved beyond experimentation. A recent Capgemini survey finds that only 13% of companies have moved beyond proofs of concept (POC) to scaling AI across the enterprise. The struggle to operationalize AI is painful because it represents lost time and resources and unrealized potential. Articles abound full of suggestions, frameworks and manifestos, shared with the intent of closing the gap between AI concept and enterprise delivery (including one proposal to eliminate the POC altogether). Many of these are smart and worthwhile.

This year, despite the challenges from the Covid-19 pandemic, large corporations in the financial industry are operationalizing their AI initiatives. Many mature organizations already have established processes. In the last few years, they've been implementing process workflows, software tools, and frameworks to quickly operationalize their models to capitalize on the changing business landscape. However, as the business environment changed during the Covid-19 pandemic, organizations observed changes in their models' underlying assumptions. The urgency to rapidly deploy new models in a controlled environment to account for the market risks and take advantage of new opportunities proved to be challenging.

IT leaders and business executives around the world recognize the strategic importance of operationalizing AI, yet surprisingly few have moved beyond experimentation. A recent Capgemini survey finds that only 13% of companies have moved beyond proofs of concept (POC) to scaling AI across the enterprise. The struggle to operationalize AI is painful because it represents lost time and resources and unrealized potential. Articles abound full of suggestions, frameworks and manifestos, shared with the intent of closing the gap between AI concept and enterprise delivery (including one proposal to eliminate the POC altogether). Many of these are smart and worthwhile.

MONTREAL, Quebec, Feb. 12, 2020 -- Element AI, a global developer of artificial intelligence-powered (AI) services and software that helps enterprises'operationalize AI', today announced Element AI Orkestrator, the latest addition to the company's portfolio of AI-powered software solutions. Designed for AI practitioners and the IT teams that support them, Element AI Orkestrator is a workload scheduling tool built as a result of the company's need to optimize its own computing resources. A software-as-a-service (SaaS) version is available now, with an on-premises version expected for Spring 2020. Element AI Orkestrator helps customers future-proof their IT infrastructures through optimized utilization of GPU clusters, allowing AI practitioners to build quality models and remove most of the engineering heavy lifting required to efficiently use GPUs. By maximizing these specialized and in-demand resources, Orkestrator will play a pivotal role in supporting the structural backbone of organizations deploying AI solutions for increased overall productivity.

All types of artificial intelligence (AI) technology – from machine learning to natural language processing to cognitive computing – are being leveraged by enterprises to drive better customer experiences and process efficiency. Based on our market research, more than one-third of enterprises have prioritized adoption of AI-powered customer experience management (CXM) solutions in the next two to three years. Despite these challenges, AI can be a key contributor to upping organizations' competitive capabilities in the contact center space. But, to derive real, tangible, sustainable benefits from AI, we recommend enterprises carefully address the following considerations when attempting to operationalize their AI deployments. There's no question that AI is a key enabler in driving personalized, targeted customer service. But how enterprises embrace it will mean the difference between also-ran and game-changer status.