AWS Partner Network (APN) Blog
Accelerate Mainframe Modernization with Accenture, AWS, and Generative AI
By Joel Rosenberger, Mainframe Modernization WW Technical Lead – Accenture
By Mahesh Jadhav, Principal Partner Solutions Architect – AWS
By Paulo Coutinho, Principal Partner Solutions Architect – AWS
By Howard Hinman, Principal Partner Development Manager – AWS
By Dan Spillane, Principal Partner Development Manager – AWS
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| Accenture |
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A vast majority of large enterprises today (including an estimated 71 percent of Fortune 500 companies) still rely on mainframes to run critical applications and support massive business databases. Although these systems served well for decades, maintaining mainframe systems is increasingly challenging due to a growing skills shortage and slow pace of innovation.
As organizations chart a path off the mainframe, it may be helpful to understand key drivers for modernization, including the influence of generative AI.
The Amazon Web Services (AWS) Cloud offers comparable benefits to traditional mainframe systems, along with notable cost savings, AI-driven innovation, and enhanced efficiency. Such advantages are no doubt helping fuel projected growth in the mainframe modernization services market, from US$23.5 billion in 2021 to US$108.9 billion by 2031.
Mainframe modernization in the cloud improves agility and scalability by migrating challenging legacy systems to more flexible and open solutions. The transition enables faster updates, better integration with modern applications, and strengthened security, ensuring enterprises stay competitive in an evolving digital landscape.
Even if the benefits of mainframe modernization are clear, the complexity of the process can be challenging. For organizations navigating the process alone, it is understandable why maintaining the status quo may seem “good enough” for now.
This blog post explores compelling benefits of mainframe modernization, how generative AI can simplify the process, and how Accenture and Amazon Web Services (AWS) can accelerate implementation.
Agility and performance are more important than ever, which is why Amazon Web Services (AWS) and Accenture are working jointly on a strategic initiative to raise the bar even higher on mainframe modernization solutions and managed services.
Accenture, an AWS Premier Tier Services Partner and Managed Services Provider (MSP), offers comprehensive solutions to migrate and manage operations on AWS.
AWS has been recognized as a leader in mainframe application modernization software, providing businesses powerful tools and infrastructure for modernizing, migrating, testing, and running mainframe applications.
Introducing Generative AI in Mainframe Modernization
Generative AI for mainframe modernization is in its early days, however the technology is quickly showing promise for reducing complexity, timeline, and risk in this process.
Emerging services and capabilities add value to legacy systems documentation, rules extraction and modeling, user stories generation, test case creation, and code transformation assistance.
Other noteworthy use cases in which generative AI can streamline mainframe modernization:
- Provable Security: Mainframe applications for core business uphold stringent security, compliance, and regulatory standards. Security administrators have finely tuned thousands of security rules over decades, controlling various aspects of application operation and resource access. During modernization efforts, meeting or surpassing these security requirements is crucial for compliance and security enhancement. Automated reasoning, a discipline of AI, can provide equivalent security controls during the security modernization phase. For example, one security definition can control which users can submit a batch job, while another security definition can control which users can execute a specific transaction.
- Reimagine Code and Data: During the reimagination of business functions, customers often face the challenge of manually extracting and interpreting mainframe business logic and data models. Given a user story, a generative AI model can generate a high-level outline or skeleton of the code required to implement the described functionality. Building on the code outline, the generative AI model can generate more detailed code snippets to implement the various components of the user story.
- Test Case Generation: Modernizing mainframe applications involves extensive testing, consuming up to 70 percent of project time and budget. A lack of test cases can translate to significant manual effort to validate functional and non-functional requirements. Generative AI can streamline test-case creation and synthetic test-data generation across various dimensions, optimizing testing efficiency throughout the project lifecycle. For example, a generative AI model can learn the end-user interfaces (3270 “green screens” or web interfaces) in order to execute test scenarios. Additionally, proper test case creation combined with data augmentation can identify dead code and logic no longer executed (technical debt). Another generative AI assistant can use automated reasoning to identify parameters to reach hard-to-access conditions and code. Finally, it can orchestrate the process of generating test cases.
After understanding more about how generative AI can streamline the mainframe modernization process, let’s now consider key reasons for initiating modernization efforts.
Mainframe Modernization Driver #1: Cost and Scalability
Unsurprisingly, cost and scalability remain key business drivers for moving away from mainframes and onto the cloud—and they are inextricably linked.
The “scale-up” architecture of mainframes can be rigid and expensive. Managing the hardware and software is complex, and significant downtime and planning may be required to manage maintenance around critical business functions.
By contrast, cloud-based systems have a “scale-out” architecture, which allows businesses to quickly match resources with real-time demand. Consumption-based pricing in the cloud means businesses avoid outsized, upfront investment.
Mainframes require adding more capacity to a single, fixed system, often increasing upfront costs and limiting flexibility. Third-party software costs are also increasing as the market shrinks, with clients reporting licensing cost increases up to 4x.
Importantly, migrating and modernizing applications in the cloud removes hardware and staffing costs of maintaining traditional systems. Clients report cost savings of up to 85 percent on infrastructure alone. Performance increases are also being realized due to the scale-out capabilities of the cloud.
Although mainframes are renowned for reliability and robust performance, the AWS Cloud availability goal is 99.99 percent, and it is designed to replicate applications and data across both Availability Zones and AWS Regions.
Mainframe Modernization Driver #2: Innovation
As more companies consider future applications for AI tools and frameworks, modern cloud environments provide resources needed to dynamically adjust and incorporate the latest developments in technology—unlike mainframe systems.
Cloud-based architectures offer increased agility to adapt to and integrate new technologies as they emerge. Moving data to the cloud opens up new opportunities for leveraging machine learning, AI, and generative AI.
For example, Accenture worked with one luxury automaker to create a new platform that leverages generative AI across many business use cases, including accelerating productivity on the sales floor, optimizing supply chain processes, and supporting marketing campaigns with real-time data and customer insights. The solution delivered a 30-40 percent productivity surge to the automaker’s business users.
Emerging technologies such as generative AI appear on modern cloud platforms before becoming available on mainframes and at significantly lower costs given the massive data I/O and CPU utilization to fuel such advancements.
AWS and Accenture also help accelerate and reduce risk of mainframe modernization programs leveraging years of proven experience. For example, AWS Mainframe Modernization offers a robust set of managed tools for a broad range of mainframe modernization use cases, including:
- Automated refactor:
- Replatform:
- Code conversion
- Data replication and file transfer
- Application testing
Mainframe Modernization Driver #3: Efficiency
Looking to achieve results faster, large organizations often take a hybrid approach to mainframe modernization, replacing some legacy systems with software as a service (SaaS) solution and replatform, refactor, or reimagine other systems. However, drawbacks to this approach may surface in the long run.
SaaS solutions may force a business to mold its needs around sometimes limited solution capabilities as opposed to molding systems around the organization’s needs.
Relying on SaaS solutions that are available to anyone can also prevent organizations from differentiating themselves in their respective marketplaces. Additionally, it may lock in clients to architectures that over time make it more difficult to innovate.
Mainframe modernization can improve performance, reliability, and scalability, helping companies meet business demands. By replicating and augmenting data, businesses can migrate processes from mainframes to more cost-effective cloud services, benefiting from the cloud’s scalability across regions; tape libraries and archives can be transferred to the cloud and accessed by the mainframe as needed, enhancing data warehousing strategies. This approach maximizes cost-controls and enhances scalability, making operations more agile, responsive, and efficient.
Conclusion
Mainframe modernization is compelling for its potential to reduce operating costs, increase performance, leverage modern skill sets, and support integration with newer technologies that foster business agility and competitive advantage.
Although mainframe modernization is a complex process that may take years to complete, innovations in generative AI are quickly rewriting that story.
That is why AWS and Accenture are all in on mainframe modernization, working jointly to combine Accenture’s massive network of certified professionals with scalable, affordable, and flexible cloud solutions.
Together, AWS and Accenture can assist customers like you in tackling complex mainframe modernization efforts more quickly and successfully while reducing risk.
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Accenture – AWS Partner Spotlight
Accenture is an AWS Premier Tier Services Partner and MSP that provides end-to-end solutions to migrate to and manage operations on AWS. By working with the Accenture AWS Business Group (AABG), a strategic collaboration by Accenture and AWS, organizations can accelerate the pace of innovation to deliver disruptive products and services.