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Key Considerations When Operationalising an Artificial Intelligence Strategy

Operationalising Artificial Intelligence

Businesses have acknowledged that strategic investments in artificial intelligence technologies are key to their future. AI capabilities provide competitive advantage to enterprises through new business models and digitally enabled products and services. They enable businesses to improve user experience, increase productivity, and innovate for the future. There is little doubt that AI apps and algorithms unlock new opportunities that are impossible to achieve with traditional approaches.

However, operationalising AI is another story altogether. Two recent IDC studies provide some insight. One found that more than 30% of the respondents cited a failure rate of two-thirds for their AI projects. Another found that 80% of the respondents cited an average duration of three months to one year spent on building an AI model for deployment.

As a result, businesses often give up on AI initiatives because of failures that stem from a lack of understanding of infrastructure requirements. Organisations must also understand that successful AI initiatives must factor in the influence of data gravity on deployment choices. Meeting the objective of making AI ubiquitous across a global business means that public cloud (based infrastructure) is not always the best deployment option. To eliminate the barriers to a broad and secure AI deployment, businesses must invest in the right infrastructure stack and, importantly, in the right hybrid IT strategy.

We have partnered with NVIDIA as a DGX-Ready colocation provider to enable enterprises to seamlessly deploy globally distributed AI infrastructure. Businesses can access PlatformDIGITAL® Data Hub solution (which also includes access to NVIDIA's DGX POD™ platforms) across our global data center footprint of 290+ data centers.

Businesses can direct their IT organisations to access PlatformDIGITAL®  making it a turnkey bundle that includes the necessary components and services to rapidly plan, deploy, and scale AI infrastructure. The preconfigured solution accommodates a typical enterprise deployment of a Data Hub to solve placement, connectivity, and hosting of critical data infrastructure in proximity to end users, networks, public and dedicated clouds, and endpoints (which includes IoT devices). It enables enterprises to support their AI workflow, optimising the placement of data and compute at global points of business presence.

Download the full whitepaper to learn how to eliminate the design complexity and lengthy deployment cycle associated with AI infrastructure.

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