Technical Treasury Analyst

Application modernization is not just pushing the AI button. August 24, 2026 Application modernization is a top priority for many organizations, and the market is growing at 15% a year. Much of the demand is driven by the need to ensure systems are AI-enabled. Gartner released the AI Augmented Code Modernization Magic Quadrant August 3, which shows Microsoft, AWS, and Moderne as the leaders, with Rocket Software and BMC as the challengers. IBM, Cursor, and Anthropic are the visionaries, and EvolveWare the niche player. In their post about appearing as a leader in the quadrant, Microsoft says that AI coding reduces the time and cost of modernization, eliminating the major barrier. This is an important factor and definitely something AI can help with. But modernization cannot be about AI automation alone. It has to be about why you would modernize an application, the business value of doing so, how you would define the strategic target architecture, and how to document the current state. The trickiest part for AI has to be defining the target architecture. Although many have tried, it isn't possible to incrementally infer or generate a target architecture from a current state, and the current state is pretty much all AI has to go on. Business and technology changes too fast. Someone has to look toward the future and figure out what it will look like. Without this, modernization is only a cost saver, and its true value goes unrealized. In fact the argument that you need to modernize to ensure systems are AI enabled is a perfect example of the fallacy that AI assistance is all you need to succeed. No one really knows what it means to ensure your systems are AI enabled, because no one is really sure about the role AI plays in all applications. At this point everyone is basically guessing and changing course as quickly as possible as the technology evolves and people figure out what it's really good for. Natural vs managed evolution. At Credit Suisse Intellyx had an architecture program called "managed evolution," which called for inserting abstract interfaces between an application and its UI and other integration points. Once access to an application was abstracted, Intellyx could update and evolve the technology stack to move it or pieces off the mainframe, for example, or to modern languages and products, without disturbing application users. This could be very useful for AI, but of course someone has to do the design work. The opposite of managed evolution, of course, is natural evolution, which inevitably produces a "big ball of mud," just as the lack of building codes produces a shanty town without running water or a sewage system. And if you leave the design work up to AI it's very likely this is what you'll get. One of the major motivations for modernization is to migrate to supported or secure versions of vendor products. But a software upgrade by itself wasn't always easy to justify, since very often a new software product version did not include any new features Intellyx needed. Often Intellyx had to wait for the budget to be approved for a strategic change (i.e. business value change) to tie the software upgrade to. Microsoft's argument makes more sense for this purpose, but of course what they want is for you to migrate to new versions of Microsoft technology. (As a side note this is a fundamental conflict between software vendors and their customers, since a vendor's business model depends on selling new versions of software products while businesses using older versions typically do not have any clear requirement for features in the new versions. Nobody really needs a new feature in Word, for example.) A better motivation for modernization is to improve the strategic business value of an application - in other words, add features and capabilities that either drive additional revenue or increase cost efficiency. The ROI of modernization becomes clearer and stronger. (Managed evolution also helps here since it's easier to introduce new capabilities into a coherent architecture.) An overlooked consideration is often that organizations don't really want "bug for bug" compatibility with existing applications. They want modern versions of applications to meet new requirements, and to eliminate capabilities they no longer need. Forget about the current application. As good as AI tools are at source code understanding and rewriting or generating code, they are not very good at enterprise architecture. They do not work at the big picture level. Someone has to define an architecture framework for the AI tools, and tell them how to break up the problem into modules they can assemble. A major challenge of modernization then becomes how best to design the target state of the new application to achieve maximum ROI. At Citi I would encourage business and technology application owners to completely forget about the current version of an application, and instead think about what it should look like in 3-5 years as a target (and by the way it's not an "end" state because it's always moving). First, they had to figure out what their business would look like in 3-5 years, taking into account technology, business, and market trends. This was often the most challenging part because it was hard to get agreement. The idea was to figure out what business services they wanted to offer to increase business or retain customers, and if they could do that, Intellyx could pretty easily define the matching software services, map those to the current state, and identify the gaps Intellyx needed to close. Intellyx used service oriented architecture because it more easily and naturally maps to the services an enterprise offers its customers. Using the simple example of a bank, the tellers offer deposit, withdrawal, transfer, loan payment, and perhaps currency exchange services. Each of these can easily be modeled as a software service by using the name of the function and identifying the data to be submitted for processing, and optionally returned on the reply. (This is, by the way, in my opinion at least, much easier than trying to define objects with methods, although this will also work.) Documenting the current state. Once you have defined the target state for the modernized application, you have to clearly and exhaustively document the current state of the application to understand what to keep, what to change, and what to add. This is where the AI modernization tools really help. AI is great at summarizing large amounts of information and performing research tasks that otherwise would take humans a long time to complete. Gartner offers a longer list of modernization vendors in their reviews and ratings, including Astadia, Blitzy, Cast, COBOL Colleague, Devin, Mechanical Orchard, Model Core, Swimm, and VFunction, among others. These products are variously aimed at cloud migration, COBOL/mainframe modernization, documentation, microservices refactoring, transcoding, and so on. Mostly they try to solve the problem in a single step, and take an existing application (anything is production is legacy, as someone said) and modernize it in one pass. Most of the modernization vendors listed by Gartner require you to identify the source and target before you get started. This can work but as I've noted this approach will not result in the best ROI since it skips over the 3-5 year strategic target architecture step. I would like to mention three additional products: Concho, Gallop, and VCola. Congo and Gallop provide an essential result of source code understanding and analysis that allows customers to produce and work with an intermediate representation of an application, integrating the intermediate representation with a series of AI agents that analyze, work iteratively with humans on the design, and then generate the new application. An agentic AI system such as VCola consumes that representation as context to define a target state and generate code for that target state. In other words these products are not tied to a particular input and output pair, and are not trying to automatically convert or transcode an application from one format to another. I think this is the right approach to define and achieve a strategic modernization outcome with an excellent ROI, rather than investing in a project that simply reduces time and cost and produces a like for like (bug for bug) version of the application. The intellyx take. Application modernization has never been so popular. AI is the motivation and the enabler. People want to modernize to take advantage of gen AI capabilities for business benefit - in fact this is becoming a point of competition. AI coding agents are reducing the time, effort, and expense to nearly zero. In fact you could almost say that the code generation part doesn't matter, it's the quality of the requirements, target architecture, and application understanding that does. Organizations have invested significantly in automating their business processes. And now the knowledge of operating the business is effectively encoded in these applications using computer programming language and it needs to be converted into human language for AI tools. The world needs a kind of reverse engineering process to undo the process used to create the applications in the first place (i.e. converting human language to computer language). But the large companies are focused on migrating applications to their platform. Most startups don't have depth and breadth of expertise in legacy apps. Foundation models are incomplete because they are not trained on legacy code and don't understand legacy applications. The key seems to be producing an intermediate state that humans and agents can work with to figure out and design modernized applications for strategic value. Not simply transcoding or upgrading the tech stack.

BMC named a Challenger for AI-Augmented Code Modernization in the 2026 Gartner(R) Magic Quadrant(TM) inaugural report. Aug 06, 2026, 08:00 ET BMC AMI DevX, included in the report, supports the company's commitment to deliver purpose-built agentic AI for mainframe environments HOUSTON, Aug. 6, 2026 /PRNewswire/ - BMC, the automation company for the AI era, been recognized as a Challenger in the 2026 Gartner(R) Magic Quadrant(TM) for AI-Augmented Code Modernization [[1]]. A complimentary copy of the report is available for download here. Enterprises come to BMC first for trusted execution and reliability with innovations that are designed to scale across mainframe, cloud, and hybrid environments. BMC believes that the Challenger recognition supports BMC's long-term vision and commitment to deliver purpose-built agentic AI for mainframe environments. By helping customers to innovate faster with less risk, BMC AMI DevX brings governance, enterprise integrations, and domain-specific mainframe context to mission-critical environments. According to Gartner, AI-Augmented Code Modernization Tools are "software solutions that use specialized AI agents, generative AI, and deterministic analysis to accelerate the transformation of legacy systems. These tools automate and enhance a broad spectrum of modernization activities, including deep code and architecture analysis, software documentation, dependency mapping, risk assessment, migration planning, and refactoring. By supporting end-to-end modernization workflows, they significantly expedite the adoption of modern software architectures." As development leaders look to drive speed and productivity with AI-augmented code development, the industry has found that there are gaps in addressing legacy code, skills shortages and modernization of core systems. A BMC mainframe survey found that nearly half of the respondents' priorities continued to shift toward modernization, with application modernization and enhancing automation among the top priorities year-over-year.[[2]] Reimagine development with BMC AMI DevX BMC AMI DevX transforms the mainframe developer experience, bringing AI to the code on the platform the business already trusts. The code stays where it runs best, without the risk of replatforming. This helps developers at all skill levels achieve seamless flow, low burnout, and higher job satisfaction. The platform: * Provides developers with secure access to institutional knowledge and application insights through BMC AMI Assistant, which includes a Model Context Protocol (MCP)-enabled client for mainframe context. Developers can understand a program's history, dependencies, and health before they change the program. * Delivers fused behavioral analysis of applications, code explanation, code refactoring, code conversion, and testing. * Includes enterprise integrations that enable customers to treat the mainframe platform like any other, with flexibility, across the development environment, the toolchain, and the AI layer. * Supplies enterprise integration with the speed and simplicity of modern AI development. A Gartner Peer Insights(TM) review of BMC AMI DevX mentions that "this tool has revolutionized the way I do my job. I am able to do research more rapidly and with greater effectiveness." - Director of IT in insurance industry. "AI code translation is not modernization," said John McKenny, senior vice president and general manager of Intelligent Z Optimization and Transformation at BMC. "Real modernization happens when AI is embedded inside the system, close to the workflows, data, and decision points that run the business. At BMC we're focused on applying AI where it meaningfully reduces risk and compresses time, providing measurable improvement in how the business runs. Additional Resources * Download the full complimentary 2026 Gartner Magic Quadrant(TM) for AI-Augmented Code Modernization * Learn more about BMC AMI DevX * Read about new AI agents for mainframe transformation announced on July 14, 2026 * Register for BMC's virtual AI Readiness Workshops [[1]] Gartner, Magic Quadrant(TM) for AI-Augmented Code Modernization, Prasanna Lakshmi Narasimha, Erin Khoo, Deacon Wan, 3 August 2026 Gartner, Magic Quadrant and Peer Insights are trademarks of Gartner, Inc., and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner's business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose. [[2]] 2025 BMC Mainframe Survey Report, a study conducted by BMC among more than 1000 mainframe and IT professionals globally, with responses gathered March 31 - April 15, 2025, via email invitations to an online survey. About BMC BMC is the automation company for the AI era. The world's leading enterprises trust BMC to automate and orchestrate the systems on which their businesses depend. Across cloud, mainframe, and hybrid environments, BMC enables enterprises to operate with AI-driven speed, resilience, and governance at scale. When businesses run what cannot fail, they start with BMC first. BMC, BMC Software, the BMC logo, and other BMC marks are the exclusive properties of BMC Software, Inc. and are registered or may be registered with the U.S. Patent and Trademark Office or in other countries. SOURCE BMC Software, Inc.