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As digital platforms expand and customer demands grow, enterprise cloud strategies are undergoing a fundamental shift. Traditional migrations, which focused on cost and scale, are being replaced by intelligent, adaptive systems built to optimize operations in real-time and support continuous innovation
Reflecting this shift, PwC’s latest Cloud and AI Business Survey reports that 92% of top-performing companies plan to increase their cloud budgets, with 63% citing AI capabilities as the primary driver of their investments. As enterprises accelerate the integration of AI into cloud architectures, the focus is moving beyond infrastructure management toward creating dynamic, self-optimizing ecosystems designed for resilience, agility, and growth.
To explore this transformation in depth, we spoke with Kishore Jeeri, Senior Engineering Manager with over 16 years of experience leading software development and DevOps initiatives at top-tier firms including Charles Schwab, Oracle, and My Compliance Office. Known for delivering scalable SaaS solutions and driving complex technology integrations, Kishore specializes in full-service ownership models, cloud infrastructure, and automation. He played a pivotal role in the successful merger of Schwab Compliance Technologies and My Compliance Office, leading critical modernization, disaster recovery, and business continuity initiatives. A continuous learner, he holds advanced training in AI-driven decision-making from Wharton and is pursuing certifications in Generative AI and project management. In our conversation, he shares how AI is reshaping automation strategies, what technical missteps can limit growth, and why early engineering discipline is becoming a key predictor of long-term platform success.
Kishore, as cloud strategies evolve beyond basic infrastructure, what are the most significant shifts you’re seeing in how enterprises approach modernization, and where does AI have the most influence?
Enterprises are shifting from static cloud models to architectures that are adaptable by design. Modernization no longer ends with migration; it involves creating systems capable of reacting to real-time operational, security, and performance signals. AI supports this shift by introducing predictive capabilities, helping platforms allocate resources dynamically, optimize performance under changing loads, and anticipate infrastructure risks before they become incidents.
One of AI’s most tangible impacts is its enhancement of operational decision-making. AI models process telemetry data to detect anomalies earlier, recommend adjustments, and guide automation workflows. This enhances a platform’s ability to remain resilient under varying conditions without requiring constant human oversight. Enterprises that integrate these AI-driven feedback loops into their cloud modernization strategies are positioned to deliver better service continuity, responsiveness, and long-term agility.
At Charles Schwab, you played a key role in developing automation frameworks for large-scale systems, including Ansible, Terraform, and LoadRunner. How is automation evolving in AI-driven engineering, and what priorities should organizations focus on?
Automation is moving beyond scripted responses toward systems that adapt their workflows based on environmental and operational data. Traditional CI/CD pipelines remain important, but AI-enhanced automation is starting to optimize deployment timing and operational adjustments after code is shipped. This makes automation a dynamic component of system resilience rather than a static process.
For organizations building AI-driven platforms, clarity and observability must be prioritized. It is crucial to design services that expose meaningful metrics, proactively monitor system behaviors, and maintain consistency under evolving loads. Automation should be designed with built-in oversight, allowing engineers to understand and trust autonomous decisions. This foundation enables systems to scale without sacrificing reliability or operational transparency.
While leading resilience engineering at MyComplianceOffice, you significantly reduced recovery times for critical applications from four to under one hour. As AI systems grow more complex, how should companies rethink resilience?
Resilience today requires a design assumption that instability will occur, and that systems must continue to function gracefully through disruption. AI-based platforms introduce shifting data models, user behaviors, and integration points, which can destabilize traditional recovery models. Building resilience now means engineering platforms that enable self-assessment, independent recovery, and operation under degraded conditions without service loss.
This shift calls for embedding real-time observability, automated failover, and predictive anomaly detection into the platform’s core. Systems must identify early indicators of risk and initiate mitigation steps autonomously. Resilient architecture is no longer optional; it is the mechanism through which services maintain credibility and compliance in increasingly dynamic environments.
In your experience, what technical decisions are often overlooked during early modernization efforts but later become bottlenecks?
Teams often underestimate the long-term impact of inconsistent environment management during early growth. When configurations, scripts, and operational processes are handled manually or evolve informally, technical debt accumulates. Over time, these inconsistencies obstruct scaling efforts, delay deployments, and increase operational risk.
Embedding infrastructure-as-code practices early helps establish a foundation of consistency and repeatability. Versioning environment specifications, deployment configurations, and monitoring standards makes it easier to scale, secure, and audit a system. This discipline allows platforms to grow organically without facing disruptive technical bottlenecks during expansion.
You’ve led globally distributed engineering teams at Charles Schwab and MyComplianceOffice. What leadership practices matter most today as organizations scale AI-driven platforms?
Leading distributed teams requires reinforcing a shared technical and operational framework. Engineers must work from a common understanding of system behaviors, standards, and priorities to ensure that decentralized development does not create fragmented architectures. Regular communication, clear roadmaps, and visible success metrics help align distributed teams around common goals.
Adaptability is equally critical. AI-driven systems evolve rapidly, and engineering teams must have room to experiment and integrate lessons into their practices. Encouraging learning sprints, collaborative reviews, and decentralized decision-making builds teams capable of adapting with the technology, rather than falling behind it.
Your recent research highlighted how fragmented CI/CD practices in distributed teams can limit reliability and slow down delivery at scale. Given this, what structural changes should engineering leaders prioritize as they build AI-driven platforms?
One of the core findings was that fragmented deployment pipelines undermine platform reliability at scale. When workflows, testing practices, or release validations vary between teams, gaps are introduced that are difficult to detect until they impact users or security posture. AI-driven platforms, where services evolve independently, amplify this risk.
Engineering leaders should establish unified CI/CD frameworks that are observable, standardized, and version-controlled across teams. Pipelines should enable full traceability from code to production, support rapid rollback mechanisms, and integrate compliance checks automatically. Treating delivery infrastructure as a product of its own ensures that systems remain auditable, reliable, and ready to support accelerated growth.
Looking ahead, what shifts do you expect in how engineering teams design and operate AI-native systems, and how can organizations prepare?
AI-native platforms will be designed to function under continuous change. Engineers will need to create systems that monitor evolving data inputs, adapt service behaviors based on environmental signals, and maintain performance even as models or workflows are retrained in production. Stability will come from flexibility, not rigid control.
Organizations preparing for this shift should invest in modular architectures, strong telemetry infrastructures, and operational models that support dynamic adaptation. They must prioritize engineering cultures that reward experimentation, transparent documentation, and rapid feedback cycles. Future success will favor teams that view change as an operational constant rather than an exception.
As enterprises evolve toward AI-native platforms, Kishore Jeeri emphasizes that success hinges on both technology and disciplined engineering, as well as intelligent automation and resilient design. In an era defined by continuous change, the platforms that will thrive are those built to learn, adapt, and scale from the start.
