Breaking the Barriers to AI Adoption: Legacy Behind The Lag

Legacy systems and outdated infrastructures. The joy.

They’re some of the most significant roadblocks to successful AI and machine learning (ML) adoption. And it’s safe to say that these ageing setups suck.

Not only do they lack the right computational power and ability to scale. But also the flexibility that you need to support the complex demands of AI and ML workloads. 

Plus, they’re mega inefficient and also ridiculously expensive.

If you, like many others, struggle with trying to fully leverage the benefits of these transformative technologies and can’t seem to progress – then this read is for you.

The Cost of Legacy

Maintaining and upgrading legacy systems is expensive. 

Not only on your financial resources, but also your ability to deliver, respond to markets and your overall competitiveness. 

“Legacy systems are often outdated and may not have the necessary infrastructure to support the latest AI and ML technologies. This may result in slower processing times, increased downtime, and increased maintenance costs,” reveals Deloitte.

Delayed decision-making. Hindered insights. Outdated infrastructure hampers the ability to process and analyse vast amounts of data efficiently. Which prevents you from getting the best out of the volumes of rich data sitting at your fingertips.

If you, like many organisations, find yourself grappling with the limitations of legacy systems, then you’re well aware of losing out on AI-driven opportunities and falling behind your forward-thinking competitors.

According to a survey by Deloitte, 59% of organisations identified legacy systems as a barrier to adopting AI and ML technologies.

Get Familiar With The Cloud

What’s the solution to your legacy issue? 

It lies in the cloud. In hyperscale computing. 

Indeed, there are ways to get your legacy systems to integrate with new toolsets and start operationalising AI. But it’s always going to work out to be a costly and laborious exercise.

Our solution lies in guiding businesses through the transition to cloud-based platforms.

By migrating your AI infrastructure to the cloud, you can unlock a myriad of benefits.

Cloud platforms offer scalable computing resources, enabling you to handle the massive computational requirements of AI and ML workloads effortlessly. 

Additionally, the cloud provides enhanced security measures, ensuring the protection of your valuable data.

  • A report by McKinsey estimated that companies could reduce infrastructure costs by up to 30% by migrating their AI workloads to the cloud.
  • Research conducted by IDC revealed that by 2024, 90% of new enterprise applications deployed will be cloud-native, reflecting the shift towards modern infrastructure.

3 Big Benefits of the Cloud

Cost Savings

One of the main benefits of cloud engineering is the ability to scale resources on demand. 

This means that businesses can easily add or remove computing power, storage, and other resources as needed. Without having to invest in expensive hardware and infrastructure. 

This can lead to significant cost savings. Businesses only pay for what they use and can avoid the upfront costs associated with building and maintaining an on-premises data centre.

In fact, according to a study by Forrester Research, companies that fully migrated to the cloud saved an average of 30% on their IT costs.

Improve Business Agility

In addition to cost savings, cloud engineering can also help businesses improve their agility and responsiveness. 

A study by Capgemini found that companies that fully adopted the cloud saw an average increase of 15% in business agility.

With the ability to quickly and easily scale resources, businesses can better meet the changing needs of their customers and respond to market demands. 

This can be especially important in fast-moving industries where the ability to adapt quickly can be the key to success.

Improve Security

By leveraging the resources and expertise of a cloud provider, businesses can benefit from the latest security technologies and practices. 

This includes the ability to quickly respond to security threats. 

Which is especially important for businesses that handle sensitive data, as it allows them to meet compliance requirements and protect their customers’ information.

A survey by the Cloud Security Alliance found that 77% of respondents experienced improved security after moving to the cloud. 

Move Away From Legacy – And Up To The Cloud

With your solution living in the cloud, it’s important to know all that you can. About this powerful technology and how to implement it effectively. 

As experts in the cloud, data and machine learning engineering spaces, we know exactly what it takes to successfully embrace and leverage the cloud.

And it starts with your architecture.

“Cloud architecture is a critical topic for discussion as you plan your migration to the cloud. A well-architected framework can help you unlock the real business value of the cloud, such as lower operating costs, higher application performance, and better end-user experiences,” – Google.

Our team of specialists have extensive experience in providing the ideal cloud architecture and can help you seamlessly transition your AI infrastructure. 

With our guidance, you can leave behind the limitations of legacy systems and embrace the power and flexibility of modern cloud infrastructure. 

Learn more about our cloud services or about AI and cloud on our blog!

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