TLDR: Datacom is revolutionizing legacy application modernization by deploying autonomous AI engineering teams. These AI agents, leveraging large language models, can automate up to 70% of coding tasks, leading to significant cost savings (30-50%) and accelerated project timelines. This shift redefines developer roles, focusing on AI orchestration and engineering, and promises enhanced productivity, cybersecurity, and regulatory compliance.
Datacom is at the forefront of a transformative shift in enterprise software modernization, introducing autonomous AI engineering teams designed to rebuild legacy systems with unprecedented speed and scale. This innovative approach, spearheaded by Datacom’s Director of Data & Engineering, Joel Macfarlane, a 28-year veteran in software development, is being hailed as ‘the largest change management exercise in the history of IT.’
Leveraging advanced AI agents and large language models (LLMs), Datacom’s methodology streamlines legacy system upgrades by automating a substantial portion of the development process. The company reports that AI agents can write up to 70% of the necessary code, resulting in remarkable cost reductions of 30-50% for their clients, and in some cases, even more. This automation also drastically cuts down project timelines, compressing months-long feature development into mere weeks.
Macfarlane emphasizes that while AI tools and copilots have seen widespread adoption, their application in app modernization—the process of updating outdated applications to utilize newer technologies and platforms—is particularly impactful. He notes, ‘The developers aren’t developing to the degree they used to, the testers aren’t testing as much. What they’re increasingly doing is wrangling large teams of AIs to do those jobs, or they’re sitting down trying to crack challenges with them.’
The core of Datacom’s strategy treats application modernization as an assembly line process. Instead of manual code translation, AI agents, powered by various LLMs, execute discrete tasks to achieve dramatic efficiency improvements. These include:
Automated Documentation: AI agents can analyze decades-old systems and generate comprehensive specifications in hours, a task that traditionally took months.
Self-Managing Developer Teams: AI ‘tech leads’ are deployed to review code commits and ensure consistency across distributed work, mimicking human team dynamics.
Continuous Validation: AI testers perform hundreds of test cases across both old and new systems simultaneously, ensuring robust quality control.
Macfarlane explains, ‘We emulate the way that human teams work. We give the AI agents access to things like code repositories, documentation, and project management software so they can collaborate together and break up challenges.’ These AI agents even update their progress on digital kanban boards, integrating seamlessly with human workflows.
This AI revolution is not about eliminating roles but redefining them. Senior developers are transitioning from extensive coding to AI orchestration, utilizing tools to manage AI agent teams. New graduates are entering the workforce as ‘AI engineers’ rather than traditional programmers, and project managers are now overseeing both human and AI agent workflows. Datacom is actively addressing this evolution through comprehensive reskilling programs, recognizing that cultural adaptation is as crucial as technological implementation.
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The efficiency gains are already making a significant impression on customers, particularly those grappling with systems from the 1990s. Beyond speed and cost, AI in software development also enhances cybersecurity by enabling rapid rebuilding of vulnerable systems on secure platforms. Furthermore, it strengthens regulatory compliance through automated documentation, ensuring audit readiness. Macfarlane concludes, ‘For businesses concerned with competitive agility but weighed down with dated technology, this capability couldn’t emerge at a more critical time.’
