CTO Revamps Engineering Lifecycle—and Its Impact on His Team
Discover how a fintech CTO embedded AI across the engineering lifecycle to accelerate delivery, reduce operational overhead, and reshape team workflows.
Sergey Terentyev
CTO, Banking and Fintech - UAE
AI Integration: Sergey Terentyev integrated AI at every step to enhance speed, delivery, and productivity.
Cultural Shift: AI has become a standard part of engineering workflows, moving beyond being an experimental tool.
Regulatory Balance: Balancing innovation and regulation is crucial in a highly regulated banking environment.
Human-AI Balance: Some tasks remain human-driven; AI can't fully replace engineering judgment in regulated fields.
Operational Model: CTOs should treat AI as an operating-model shift rather than merely a tooling upgrade.
Sergey Terentyev leads technology and engineering within a licensed digital banking environment. In the past year, he has overhauled the entire engineering lifecycle so that AI is integrated into every step.
We caught up with Sergey to learn more. He told us what he changed and how it affected his team.
AI Is Changing More Than Tooling
I’m Sergey, currently CTO in the banking and fintech space in the UAE. I started as a software engineer and moved into leadership by building products, scaling teams, and solving complex technology problems.
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My path to AI transformation was simple: Traditional methods for building and running technology teams are becoming too slow. Teams still spend too much time on repetitive work, coordination, documentation, support, and manual processes.
Now, I focus on practical AI adoption within engineering and product organizations. Not AI as hype, but as a way to move faster, improve delivery, reduce waste, and help smaller teams achieve much more.
I see this moment as a time to rethink how technology organizations work. AI is changing not only tools, but also teams, leadership, and how companies build products.
Balancing Innovation and Regulation
The organization where I lead technology and engineering operates in a highly regulated space, so the technology landscape combines modern product development with enterprise-grade security, compliance, and operational requirements.
The engineering organization covers mobile and web platforms, backend services, cloud infrastructure, cybersecurity, integrations, data platforms, and vendor management. We work across both in-house development and strategic external partners, while gradually moving more core capabilities internally.
From an architecture perspective, we are moving toward a more modular and API-driven model with stronger ownership inside product engineering teams. The environment is fairly complex because we operate across banking systems, fintech integrations, payment infrastructure, customer-facing applications, and internal operational platforms.
Deployment and delivery are highly iterative with a strong focus on automation, CI/CD, cloud infrastructure, observability, and operational resilience. A major priority is reducing dependency on traditional delivery models and increasing engineering velocity through AI-assisted development and automation.
A big part of my role is balancing innovation speed with the realities of operating in a regulated financial environment where reliability, security, and governance are critical.
How Mandatory AI Changes Engineering Workflows
We introduced AI directly into the engineering delivery workflow, rather than treating it as an optional individual tool.
Before that, engineers spent a lot of time on repetitive tasks like boilerplate implementation, documentation, code analysis, debugging, test preparation, reviewing vendor code, and translating business requirements into technical tasks. Operational overhead overloaded senior engineers, preventing them from focusing on architecture and product decisions.
Over the last year, we systematically integrated AI-assisted development into daily engineering processes. This included code generation, code review support, documentation generation, impact analysis, incident investigation, and faster prototyping.
The change affected not only speed but also how teams work. Engineers now spend less time on low-value repetitive activities and more time on system design, product thinking, and decision-making. It also reduced dependency on a small number of senior people by making knowledge easier to distribute across teams.
The biggest shift was probably cultural. Teams stopped viewing AI as an experimental tool, and it became part of the normal engineering workflow. They now expect automation by default instead of assuming every process must be manual.
How AI Can Touch Every Part of the Engineering Lifecycle
Let's look at the path from business requirement to production-ready engineering implementation.
It usually starts with a product or business request written in high-level language. AI helps translate that into structured technical requirements, identifies dependencies, suggests API contracts, and highlights possible edge cases or integration risks early in the process.
From there, engineers use AI-assisted development tools during implementation to scaffold services, generate repetitive code patterns, create test cases, and accelerate documentation. The goal is not fully autonomous coding, but reducing low-value manual work so engineers can focus on architecture and logic.
During development, AI also assists with code reviews, explains unfamiliar parts of the codebase, identifies possible vulnerabilities, and validates implementation approaches. For incident-prone systems, engineers use AI to analyze logs and summarize abnormal behavior much faster than traditional manual investigation.
Once changes move into deployment pipelines, automation handles testing, validation, and observability. AI becomes useful again after deployment by helping teams interpret alerts, correlate incidents, and reduce investigation time during operational issues.
As you can see, AI is now embedded across the entire engineering lifecycle instead of only as an isolated chatbot or coding assistant. It became part of the daily workflow from planning to operations.
Why CTOs Must Improve Machine Readability
We've been reducing dependency on centralized knowledge and manual coordination. Instead of critical information residing only with specific people or teams, we introduced workflows where AI helped engineers navigate systems, understand architecture, analyze incidents, and accelerate onboarding independently.
That brings me to another important change we've made: designing systems for machine readability, not just human readability.
Clean APIs, structured documentation, observability, modular services, and well-defined interfaces dramatically improve both human productivity and AI effectiveness.
Why AI Can't Handle Judgment In a Regulated Industry
AI especially accelerates delivery and reduces repetitive engineering work. For example, during incident response, AI helps engineers analyze logs, identify possible root causes, summarize findings, and speed up investigation.
At the same time, some areas remain explicitly human-driven. Final architecture decisions, security governance, risk acceptance, prioritization, and production accountability still require experienced people to make judgment calls.
Simply put, AI excels at acceleration and pattern recognition, but leadership decisions in complex systems still depend on context, tradeoffs, business priorities, regulation, and long-term thinking. Especially in financial services, you cannot fully automate responsibility.
AI excels at acceleration and pattern recognition, but leadership decisions in complex systems still depend on context, tradeoffs, business priorities, regulation, and long-term thinking.
How AI Can Negatively Impact Engineering Teams
The biggest positive result has been engineering speed. Tasks that previously took days now sometimes take hours. Operational friction has also been reduced, improving overall delivery flow and allowing senior people to focus more on architecture and decision-making instead of constant firefighting.
And we saw faster onboarding because AI support helps engineers understand unfamiliar systems much quicker.
However, there has been a negative side:
- We learned AI can create a false sense of confidence. Generated code often looks correct even when the underlying assumptions are wrong. Without strong engineering fundamentals and review processes, teams can move faster in the wrong direction.
- We noticed that weaker engineers sometimes become overly dependent on AI tools without fully understanding the systems they build. So, an unexpected challenge was ensuring AI increases capability instead of reducing technical depth.
- Governance and security have become more of a challenge. In regulated environments, you need clear boundaries around what data external AI systems can access, how to review generated code, and how to ensure decisions remain auditable.
Where AI Is Underdelivering
AI has yet to deliver on autonomous software engineering. Excitement surrounds the idea of fully AI-generated systems or replacing engineering teams, but in practice, we are still far from that in complex enterprise environments.
AI accelerates, prototypes, and assists experienced engineers very well. But once systems become large, interconnected, regulated, and operationally critical, the limitations become obvious. Context handling, long-term consistency, architectural tradeoffs, and deep system understanding are still weak compared to those of experienced humans.
Reliability in production-grade outputs also disappointed me. AI-generated code often looks impressive initially, but the quality can degrade quickly in edge cases, scalability concerns, security requirements, or maintainability. The last 20 percent of engineering work is still very human.
How AI Challenges Assumptions About Teams and Architecture
Engineering scale no longer comes mainly from adding more people.
Smaller, highly technical teams with strong AI tooling can move much faster than much larger organizations with heavy process overhead. In some cases, coordination itself became a bigger bottleneck than actual engineering work.
That's why we moved to smaller, product-oriented engineering teams with stronger ownership and more automation around delivery, operations, and documentation.
AI also changed how I think about seniority. Previously, access to information and experience created strong bottlenecks around a few senior engineers or architects. AI reduces some of that asymmetry because knowledge, documentation, and technical exploration become much more accessible across the organization.
And architecturally, it also pushed me toward simpler and more modular systems. AI performs much better when systems are well-structured, observable, and clearly separated. Complex, undocumented environments create friction both for humans and AI systems.
Why Humans Require More Focus Than Tools in AI Adoption
I underestimated the human side of adoption.
Initially, we focused mostly on tooling and capability. We assumed that once engineers had access to strong AI systems, adoption would happen naturally. Teams reacted very differently. Some engineers immediately became dramatically more productive, while others barely changed their workflows at all.
If I could redo part of the rollout, I would spend less time evaluating dozens of AI tools and more time redesigning workflows, training teams, and defining clear operational patterns for how AI should be used within engineering.
I underestimated the human side of adoption…We assumed that once engineers had access to strong AI systems, adoption would happen naturally. Teams reacted very differently…Treat AI as an operating-model shift, not just a tooling upgrade.
Why CTOs Must View This As an Operating-Model Shift
My advice to CTOs is to treat AI as an operating-model shift, not just a tooling upgrade.
Many companies still approach AI like they approached previous software waves — buying platforms, running pilots, and adding isolated use cases. This misses the bigger change. AI reshapes how engineering teams work, how products are built, and how organizations scale.
Second, stay pragmatic. Right now, huge hype exists, and many companies try to force AI into problems where it adds little value. Best results usually come from improving existing workflows first instead of chasing futuristic ideas.
Third, strongly invest in engineering fundamentals instead of assuming AI replaces them. Strong architecture, good engineering culture, clear ownership, operational discipline, and experienced people are even more important in an AI-driven environment.
And finally, CTOs should personally use these systems directly — not through reports or vendor demos, but hands-on. The pace of change is too fast to manage this topic only strategically without direct exposure to the technology itself.
Follow Along
You can follow Sergey Terentyev's work on LinkedIn.
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