The promise of artificial intelligence streamlining software development is running into the realities of enterprise IT, according to industry experts. While AI tools are demonstrably accelerating the creation of code, the ongoing costs and complexities of running that software – security, compliance, and 24/7 support – are often underestimated, potentially negating any savings on software as a service (SaaS) licenses. This debate centers on the evolving role of AI agent platforms and their impact on the software landscape.
“AI makes it dramatically easier to write software. It does not make it easier to run enterprise software,” said Maya Mikhailov, CEO and founder of SAVVI AI, a company focused on helping financial services and fintech firms deploy custom AI/ML models. “Those are two incredibly different problems, and most of the cost lives in the latter.” Mikhailov, who previously led an AI division at Synchrony after her company was acquired in 2017, argues that internalizing software development with AI doesn’t eliminate operational burdens; it simply shifts them. She has been featured in publications like Bloomberg and The New York Times for her work in the AI space.
The allure of AI-driven development is understandable. Businesses are eager to reduce reliance on expensive SaaS subscriptions and gain greater control over their technology. However, Mikhailov cautions that this approach often overlooks the hidden costs. “The moment you internalize building software, you also inherit security, compliance, uptime, integrations, and 24/7 support. It sounds good in theory, but costs and complexity will squarely land on the bottom line,” she explained.
The Reliability Question: OpenClaw as a Cautionary Tale
Beyond operational costs, concerns about the reliability and security of AI-generated code are gaining traction. Collin Hogue-Spears, a technical expert at Black Duck Software, points to the rapid proliferation of vulnerabilities in the OpenClaw automation tool as a stark warning. OpenClaw, a workflow automation tool, quickly exposed a significant number of instances due to its speed of execution, but lacked the necessary safeguards for responsible deployment.
“OpenClaw went from zero to 135,000 exposed instances in weeks because it executes workflows fast. It does not produce audit evidence, satisfy license obligations, or generate the compliance documentation that a regulator demands before that code ships,” Hogue-Spears stated. This highlights a critical gap: AI can accelerate development, but it doesn’t inherently address the rigorous requirements of enterprise-grade software governance. The incident underscores the need for robust security protocols and compliance measures, even – and especially – when leveraging AI tools.
SAVVI AI’s Approach to Bridging the Gap
SAVVI AI is attempting to address this challenge by focusing on a platform that helps FinServ and FinTech companies build and deploy custom AI/ML models and AI agents. According to information presented at the FinovateSpring conference in San Diego, California, scheduled for May 5-7, 2026, the platform is patented and designed to enhance existing products and streamline operations. Mikhailov is slated to speak at the event, further detailing her vision for responsible AI implementation.
The company’s focus on the financial sector is deliberate. FinTech and financial services are heavily regulated industries, demanding a high degree of security and compliance. SAVVI AI’s approach aims to provide the tools necessary to meet these requirements while still harnessing the power of AI. The platform’s emphasis on custom models suggests a move away from generic AI solutions towards tailored applications that address specific business needs.
Implications for SaaS and the Future of Software Development
The debate over AI’s impact on SaaS costs extends beyond simple license fees. The total cost of ownership (TCO) for software includes not only the initial purchase price but also ongoing maintenance, support, security updates, and compliance efforts. If AI-driven development leads to increased complexity in these areas, the potential savings from reduced SaaS subscriptions could be offset – or even outweighed – by higher operational expenses.
The long-term implications of these trends are still unfolding. It’s likely that we’ll see a bifurcation in the market, with some organizations opting for fully managed SaaS solutions for simplicity and security, while others embrace AI-driven development with a willingness to invest in the necessary infrastructure and expertise. The key will be a realistic assessment of the trade-offs involved and a clear understanding of the hidden costs associated with internalizing software development.
The industry will be closely watching the developments at events like FinovateSpring 2026, where companies like SAVVI AI will showcase their solutions for navigating this complex landscape. The next major checkpoint will be the release of further details regarding the platform’s capabilities and its impact on real-world deployments within the financial services sector.
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