By Bryan Hill, Director of Platforms, Interxion
In recent years, artificial intelligence (AI) has grown in popularity as a key component of business growth and success. In fact, the latest McKinsey Global AI Survey revealed a 25 percent year-over-year increase in the use of AI for standard business processes.
The study suggests that the majority of companies are applying AI capabilities to single areas that can directly create value in their industry, like a telecom company using AI-enabled virtual agents to improve customer service. But the companies that have found the most success up to this point are deep adopters, incorporating AI into many of their business processes, ranging from marketing to product development and corporate strategy.
The benefits of incorporating AI across business processes are numerous. The same McKinsey survey found that 63 percent of companies that have adopted AI across their business increase their revenue as a result. Creating new AI-based products and enhancements, or using AI to model effective pricing, brings new opportunities for revenue.
In addition to revenue growth, AI adoption also helps 44 percent of companies realise cost savings. Manufacturing and supply-chain management are key areas for potential savings. AI can enable businesses to optimise yield, energy and speed during manufacturing processes, as well as analyse spend within the supply-chain and enhance the logistics network.
As AI adoption continues to increase, what will it take for companies to realise the potential benefits and capture value at scale? On the business operations side, the most successful adopters capture value from AI by aligning business, analytics and IT stakeholders, investing in the necessary talent, and making sure both business and technical teams have the skills needed to scale the project.
But in order to truly reap the benefits of deep AI adoption, enterprises also need to invest in the powerful, connected, highly-performant infrastructure required to support it. The technology and infrastructure needed to implement AI strategies are unique and complex, from massive amounts of processing power to the ability to transfer large amounts of data. Deploying AI at scale demands state-of-the-art facilities with high-density compute infrastructure that can run nearly continuously, as well as the associated power and cooling capabilities.
Connectivity is also an essential element of successful implementation. A secure and performant hybrid-cloud architecture enables enterprises to transport data from on-premise and ‘edge locations’ across public cloud providers, to core processing. In addition, stitching together workloads from inference to deep learning will require high levels of interconnection; optimising data transport and reducing time from data ingestion to training, simulation and model optimisation. Cloud and content hyperscalers have been leveraging the value of such an architecture for years, and the next wave is for enterprises to do the same, creating value from their own data to drive competitive advantage.
Most of today’s enterprises don’t have the resources to build this kind of environment on their own and public cloud is not always the optimum environment. As a result, many enterprises are turning to third party colocation data centres for infrastructure that can support AI. As a colocation provider for the NVIDIA DGX-Ready Data Centre Program, Interxion offers businesses everything they need to develop and scale their AI programmes, with facilities built to support AI, deep learning and hybrid cloud workflows. Together with NVIDIA, Interxion provides the scalable and flexible environments enterprises need to innovate in their industries and future-proof their AI strategy.
To learn more about how Interxion can help enterprises accelerate their AI mission, click here.