Why ease of use is at the core of AI and advanced analytics success

Despite their sophistication, enterprise AI and advanced analytics systems are not always a resounding success for many organisations, often under-utilised and falling short their promise.

IT leaders already working with AI and data analytics know from first-hand experience the role of hardware and in-built acceleration as a critical component in delivering high performance in this area. They are key drivers of efficiencies, scale, speed and seamless integration. They are also essential to automation and seal the link between the timely delivery of data insights and time to business value.

However, organisations too often find themselves struggling under the burden of managing the infrastructure powering their analytics solutions. Putting the focus on hardware can transform underperforming AI and analytics workflows to power advanced scalability, speed, and intelligence. It’s here cloud and IaaS providers can add the most value to investment in AI and data analytics.

A mixed report card for AI and data analytics

Organisations are seeking efficiencies and cost savings through automation and advanced insights, and the competitive advantage of getting this right. Yet Computing research has found that in many cases they don‘t find it all that easy to carry out AI and analytics tasks across their entire data and workflows for a variety of reasons.

Whether it’s a lack of a coherent view of data or siloed, even fragmented, data, their efforts to date have produced mixed results. The improved reporting and analysis isn’t always translating into increased sales or insights that improve the ability to find new business.
Advanced analytics are a long way from maturity operationally, but while they‘re under-utilised at present, organisational demand should catch up with the value placed on advanced AI and data analytics by IT decision makers and leaders.

Cloud service providers are missing a significant opportunity to show real organisational benefit and build the attendant client loyalty that would follow if they showed the tangible business benefits of advanced AI analytics capabilities.

This shortfall provides fertile ground to improve their offering with advanced analytics and capacity. To seize the opportunities the shortcomings present, infrastructure service providers, and those who utilise them, should look to the role of hardware in delivering platform-wide effective AI and analytics workloads.

Leaning in to the value of hardware investments

To answer these organisational challenges, hardware, both at the edge and in the cloud, is integral to intelligent, insightful AI and analytics programs – and building in AI accelerators will push that workload to the limits of working intelligently with data sets.

Organisations demand scalable, powerful hardware capabilities, with the option of hyperscaling as needs grow, to deliver infrastructure and resources to efficiently handle compute-intensive applications.

And powerful hardware is nothing without sympathetic software, and this is especially true when it comes to AI and data analytics. When advanced capabilities are combined with a great user experience, it shows the true potential of innovation.

By bringing together high-performance computing, AI acceleration and advanced data analytics in a single computing environment provided by their cloud service provider, end users can focus on the work at hand, rather than infrastructure challenges.

Service providers that properly harness the potential of hardware in AI and data analytics – not least when it comes to built-in acceleration – can push the limits of good user experience and ensure organisations deploy this powerful technology with optimum reliability and results.

Learn more about how Intel can help in making hardware the lynchpin in high-performing AI and advanced analytics:

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This post was funded by Intel