As the digital landscape rapidly evolves, AI stands at the forefront, driving significant innovation across industries. However, to fully harness the power of AI, businesses must be AI-ready; this means having defined use-cases for their AI apps, being equipped with modernized databases that seamlessly integrate with AI models, and most importantly, having the right infrastructure in place to power and realize their AI ambitions. When we talk to our customers, many have expressed that traditional on-premises systems often fall short in providing the necessary scalability, stability, and flexibility required for modern AI applications.
A recent Forrester study, commissioned by Microsoft, surveyed over 300 IT leaders and interviewed representatives from organizations globally to learn about their experience migrating to Azure and if that enhanced their AI impact. The results showed that migrating from on-premises infrastructure to Azure can support AI-readiness in organizations, with lower costs to stand up and consume AI services plus improved flexibility and ability to innovate with AI. Here’s what you should know before you start leveraging AI in the cloud.
Challenges faced by customers with on-premises infrastructure
Many organizations who attempted to implement AI on-premises encountered significant challenges with their existing infrastructure. The top challenges with on-premises infrastructure cited were:
- Aging and costly infrastructure: Maintaining or replacing aging on-premises systems is both expensive and complex, diverting resources from strategic initiatives.
- Infrastructure instability: Unreliable infrastructure impacts business operations and profitability, creating an urgent need for a more stable solution.
- Lack of scalability: Traditional systems often lack the scalability required for AI and machine learning (ML) workloads, necessitating substantial investments for infrequent peak capacity needs.
- High capital costs: The substantial upfront costs of on-premises infrastructure limit flexibility and can be a barrier to adopting new technologies.
Forrester’s study highlights that migrating to Azure effectively addresses these issues, enabling organizations to focus on innovation and business growth rather than infrastructure maintenance.
Key Benefits
1. Improved AI-readiness: When asked whether being on Azure helped with AI-readiness, 75% of survey respondents with Azure infrastructure reported that migrating to the cloud was essential or significantly reduced barriers to AI and ML adoption. Interviewees noted that the AI services are readily available in Azure, and colocation of data and infrastructure that is billed only on consumption helps teams test and deploy faster with less upfront costs. This was summarized well by an interviewee who was the head of cloud and DevOps for a banking company:
We didn’t have to go and build an AI capability. It’s up there, and most of our data is in the cloud as well. And from a hardware-specific standpoint, we don’t have to go procure special hardware to run AI models. Azure provides that hardware today.”—Head of cloud and DevOps for global banking company
2. Cost Efficiency: Migrating to Azure significantly reduces the initial costs of deploying AI and the cost to maintain AI, compared to on-premises infrastructure. The study estimates that organizations experience financial benefits of USD $500 thousand plus over three years and 15% lower costs to maintain AI/ML in Azure compared to on-premises infrastructure.
3. Flexibility and scalability to build and maintain AI: As mentioned above, lack of scalability was a common challenge for survey respondents with on-premises infrastructure as well. Respondents with on-premises infrastructure cited lack of scalability with existing systems as a challenge when deploying AI and ML at 1.5 times the rate of those with Azure cloud infrastructure.
Interviewees shared that migrating to Azure gave them easy access to new AI services and the scalability they needed to test and build them out without worrying about infrastructure. 90% of survey respondents with Azure cloud infrastructure agreed or strongly agreed they have the flexibility to build new AI and ML applications. This is compared to 43% of respondents with on-premises infrastructure. A CTO for a healthcare organization said:
After migrating to Azure all the infrastructure problems have disappeared, and that’s generally been the problem when you’re looking at new technologies historically.”—CTO for a healthcare organization
They explained that now, “The scalability [of Azure] is unsurpassed, so it adds to that scale and reactiveness we can provide to the organization.” They also said: “When we were running on-prem, AI was not as easily accessible as it is from a cloud perspective. It’s a lot more available, accessible, and easy to start consuming as well. It allowed the business to start thinking outside of the box because the capabilities were there.”
4. Holistic organizational improvement: Beyond the cost and performance benefits, the study found that migration to Azure accelerated innovation with AI by having an impact on the people at all levels of an organization:
◉ Bottoms-up: skilling and reinvestment in employees. Forrester has found that investing in employees to build understanding, skills, and ethics is critical to successfully using AI. Both interviewees and survey respondents expressed difficulty finding skilled resources to support AI and ML initiatives at their organizations.
◉ Migrating to the cloud freed up resources and changed the types of work needed, allowing organizations to upskill employees and reinvest resources in new initiatives like AI. A VP of AI for a financial services organization shared: “As we have gone along this journey, we have not reduced the number of engineers as we have gotten more efficient, but we’re doing more. You could say we’ve invested in AI, but everything we have invested—my entire team—none of these people were new additions. These are people we could redeploy because we’re doing everything else more efficiently.”
◉ Top-down: created a larger culture of innovation at organizations. As new technologies—like AI—disrupt entire industries, companies need to excel at all levels of innovation to succeed, including embracing platforms and ecosystems that help drive innovation. For interviewees, migrating to the cloud meant that new resources and capabilities were readily available, making it easier for organizations to take advantage of new technologies and opportunities with reduced risk.
◉ Survey data indicates that 77% of respondents with Azure cloud infrastructure find it easier to innovate with AI and ML, compared to only 34% of those with on-premises infrastructure. An executive head of cloud and DevOps for a banking organization said: “Migrating to Azure changes the mindset from an organization perspective when it comes to innovation, because services are easily available in the cloud. You don’t have to go out to the market and look for them. If you look at AI, originally only our data space worked on it, whereas today, it’s being used across the organization because we were already in the cloud and it’s readily available.”
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