To get the most out of your AI initiatives and infuse intelligence into your core business processes, you must first establish an IT environment that is “intelligent” itself. In other words, intelligence breeds intelligence. Without establishing a modernized foundation that is automated, adaptable and anticipatory, your AI initiatives will struggle to scale, adapt, or deliver the ROI you hoped. Just as a successful business strategy hinge on the people, process, and technology, your enterprise IT ecosystem requires embedded intelligence across its key pillars. These pillars are known as infrastructure, operations, development, applications, and governance. Let’s explore these five pillars in greater detail and reimagine how AI can elevate each one.
1. AI-Driven Infrastructure Modernization
The first step is to ensure that your on-prem infrastructure is intelligence ready. This requires hardware prerequisites such as GPUs, latest and compatible hardware, deployment of containers and kubernetes, modern DevOps base layer, and more. Once your infrastructure readiness is achieved, you can introduce AI into your operational and automation tasks that were traditionally done manually or using disparate scripts. These tasks include things such as patching, resource planning, storage management, capacity planning and forecasting. When done in a proactive manner these efforts streamline the right-sizing of virtual machines, making optimization faster, smarter, and more efficient. Businesses must move from a reactive mindset of manual responses to a proactive anticipatory strategy that only AI can deliver. The ability to readily identify usage patterns will enable forward-thinking resource and capacity planning that will then unlock other advanced capabilities:
- Order new infrastructure resources ahead of time so you don’t run into capacity shortages that could disrupt operations.
- Track compute resource performance to identify underutilized or overwhelmed systems and suggest optimizations for better cost management.
- Fully leverage historical trends and usage patterns to achieve smarter long-term planning and budgeting.
2. AI-Driven Operations
Alerts are constantly coming into your NOC or SOC informing you that servers are down or unusual login activity is occurring. How do you keep up with it all? The primary objective of AI enabled operations is to find the underlying cause by correlating all the things that are happening within your enterprise and connecting the dots. Root cause analysis becomes possible only when you can see how different events relate to each other. For instance, a single authentication alert may not mean much on its own, but when you connect it to another event ID, a pattern appears. In other cases, an alert may point to the wrong culprit as a server being down may be a failed network switch upstream. Having a current and accurate CMDB (Configuration Management Database) is critical for generating correlations, and determining other CIs (Configuration Items) that can potentially be affected.
Automatic proactive notifications can be set to trigger when abnormal patterns occur, so that the operations teams get notified and trigger the appropriate and timely response. That may sound simple on the one hand and daunting on the other, but AI makes this possible by removing the guesswork from incident response. This means that your support teams can react faster and more consistently, which directly strengthens both your system’s resilience and overall security. Another emerging feature of AI Ops is the utilization of chatbots, which allows teams to ask questions in normal conversation format and get real answers concerning things such as ticket status or procedural steps around specific workflows.
3. AI-Enhanced Developer Productivity
Many organizations have gained great efficiency for their development teams by using IDE integrations and internal large language models. AI solutions like GitHub Copilot and internal large language models now assist in code validation and linting, processes that could eat up hours of manual work. Developers use AI capabilities embedded into their IDE consoles to gain efficiencies with the programming tasks. The payoff of these automation efforts is almost immediate, as bugs get caught earlier in the process, code quality improves, readability increases, and teams spend far less time on tedious manual reviews. What used to require multiple rounds of human oversight now happens automatically, freeing developers to focus on solving bigger problems.
4. AI Integrated Applications
Whether it is commercial off-the-shelf applications or applications customized for your own organization, AI can take them to the next level. Imagine an application making API calls to the client’s on-premises LLM to solicit AI-generated suggestions for infrastructure optimization that can then be presented to end users as action items. Consider another instance in which an application continuously monitors infrastructure usage patterns, automatically suggesting practical steps like storage deletion, re-tiering, or resource reallocation. AI-enhanced applications are doing far more than just providing data. They deliver context-aware insights that adapt to your specific environment and business needs. That means faster response times, reduced operational overhead and greater strategic use of resources.
5. AI for Governance, Security, and Trust
One of the keys for security today is the ability to identify unanticipated activity. An example could be a privileged user that logs in from a new geographic location, device, or time window. AI detectors can easily identify these abnormalities and initiate alerts that then require validation by the security team. If these events cannot be validated, security policies should then kick in automatically to protect against these events. Essentially, the system learns what “normal” looks like for each user and environment, then acts decisively when patterns break. This level of intelligent governance creates a powerful defense mechanism that stops unauthorized activity in its tracks while maintaining operational flow for legitimate users.
When governance is implemented effectively, it can stop any activity that is not part of the pattern, improving business resiliency and trust. AI-driven governance is especially valuable in industries with strict compliance requirements, like healthcare and finance, where monitoring and enforcing policy adherence is critical.
Keyva as Another Pillar
At Keyva, we focus on the infrastructure and operational backbone that powers your business. While your core business processes may require industry-specific expertise, we ensure your IT infrastructure is smart, responsive, and ready to support whatever your organization builds on top of it. We have proven specialties when it comes to transforming all five pillars of your IT ecosystem and we invite your technology and business leaders to discover how our expertise can help modernize your entire IT landscape to unlock greater agility, performance, and resilience for your enterprise.
![]() | Anuj Tuli, Chief Technology Officer Anuj specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. He has worked on Cloud Automation, DevOps, Cloud Readiness Assessments, and Migration projects for healthcare, banking, ISP, telecommunications, government and other sectors. He leads the development and management of Cloud Automation IP (intellectual property) and related professional services. During his career, he held multiple roles in the Cloud and Automation, and DevOps domains. With certifications in AWS, VMware, HPE, BMC and ITIL, Anuj offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ |