According to the Forrester Total Economic Impact (TEI) study commissioned by Microsoft, organizations moved to Azure to tackle a set of recurring challenges with their on-premises environments:

• Aging, expensive infrastructure. Many firms were dealing with legacy systems that were costly and difficult to maintain or replace, especially as hardware approached end-of-life.
• Stability and continuity issues. Infrastructure instability was impacting business continuity and profitability. Teams were spending too much time firefighting instead of enabling the business.
• Limited scalability for AI and ML. On-premises environments often couldn’t scale up and down efficiently. In the study, 47% of respondents who tried and failed to deploy AI on-premises cited lack of scalability as a key factor.
• High capital costs. Scaling for peak demand required large upfront investments in infrastructure that might only be fully used a small fraction of the time.
• Difficulty adapting to changing business needs. Organizations struggled to quickly support new AI and ML use cases, or to iterate on existing ones, because infrastructure changes were slow and complex.

By migrating to Azure, these organizations were able to:

• Reduce operational overhead so IT could focus more on strategic AI initiatives rather than maintenance.
• Improve stability and scalability, especially for AI and ML workloads.
• Leverage a broader Microsoft ecosystem (e.g., Windows Server, Azure SQL, Azure VMware Solution, Azure Arc) to support innovation initiatives.
• Lay a stronger data foundation in the cloud, which made it easier to build and scale AI and ML solutions.

The Forrester TEI study found that migrating AI and ML workloads from on-premises infrastructure to Azure can lower both initial deployment costs and ongoing operating costs.

1. Lower total deployment costs

For the composite enterprise modeled in the study, deploying AI use cases on Azure was $558,000 less over three years than deploying the same use cases on-premises.

Breaking down the initial deployment costs for the composite organization:

• Data and infrastructure costs
  On Azure: $495,000
  On-premises: $565,000
• Professional services and support
  On Azure: $320,000
  On-premises: $410,000
• Internal labor and training
  On Azure: $245,000
  On-premises: $295,000

Overall, the composite’s initial costs to deploy AI and ML were:

• $1.06 million on Azure vs. $1.27 million on-premises.

That translates to:

• 17% lower deployment costs for AI and ML on Azure compared to on-premises.

2. Lower ongoing costs to enable and maintain AI and ML

Beyond the initial deployment, the study found that ongoing costs to enable and maintain AI and ML workloads were also lower on Azure:

• 15% lower ongoing costs on Azure versus on-premises.

These savings come from areas such as:

• Reduced data center and hardware management costs.
• More efficient use of compute and storage for AI and ML workloads.
• Lower reliance on external professional services and premium support.
• More efficient internal labor and training due to Azure’s built-in AI services and tooling.

In short, the study suggests that organizations can reimagine their AI economics by shifting from capital-intensive, on-premises deployments to a more flexible, consumption-based Azure model.

The Forrester TEI study highlights that migrating to Azure does more than reduce costs—it reshapes how organizations innovate with AI and how teams work.

1. Greater flexibility to build and evolve AI and ML applications

Survey data shows a clear difference in confidence between organizations on Azure and those on-premises:

• Flexibility to build new AI/ML applications
  90% of respondents with Azure infrastructure agreed or strongly agreed they have the flexibility to build new AI and ML applications, compared to 43% with on-premises infrastructure.
• Flexibility to change and improve AI/ML applications
  81% of respondents with Azure infrastructure agreed or strongly agreed they can change and improve AI and ML applications easily, versus 25% on-premises.
• Environment makes it easy to innovate with AI/ML
  77% of respondents with Azure infrastructure agreed or strongly agreed their current environment makes it easy to innovate with AI and ML, compared to 34% on-premises.

This flexibility comes from Azure’s AI-infused infrastructure and its broad portfolio of more than 200 products and services that support building, running, and managing applications.

2. Faster path from concept to production

Interviewees described how Azure’s AI services and guardrails helped them move from proof of concept to production more quickly and with less risk. One technology R&D leader noted that building an AI assistant on Azure took about 3–4 months from proof of concept to first production iteration, versus an estimated 14 months without Azure’s capabilities and safeguards.

Azure’s approach—such as providing content filtering, data boundaries, and tenancy controls—helps organizations build real-world AI functionality into existing applications while managing compliance and reputational risk.

3. New opportunities for employees and a culture of innovation

Migrating to Azure also changes how teams are organized and what they work on:

• Infrastructure-focused staff can be retrained and upskilled to work on cloud technology and AI initiatives instead of routine maintenance.
• Azure provides access to tools and services that make it easier for people to test and experiment with new AI technologies, even if they are not deep AI specialists.
• Organizations reported that this shift helped foster a culture of innovation, where teams could focus more on delivering new AI-driven capabilities for the business.

In practice, this means Azure doesn’t just host your AI workloads—it helps you rethink how quickly you can build, iterate, and scale AI solutions, while giving your teams room to grow into higher-value, AI-centric roles.