Manual DevOps Isn’t Keeping Up With India’s Hypercharged App Environment
With around 800 million smartphone users and over a billion citizens with Internet access, India has quickly become one of the world’s largest app consumption markets. In fact, the country saw over 35 billion app installs last year – second overall only to China and leading in categories like music, entertainment and utilities. This has created a scenario where Indians today depend on apps for essential elements of their daily life – whether it’s banking, going places, getting access to public services or simply enjoying a movie at home after a long workday.
Considering the invaluable role apps now play in the way Indians live, continuous, faultless delivery of these services has become a non-negotiable. As a result, development teams today have to:
- Accelerate their development cycles
- Release new features frequently
- Remediate bugs and vulnerabilities quickly
- Scale infrastructure seamlessly
- Be compliant with relevant regulations at all times
- Constantly maintain uptime across potentially millions of user
Manual DevOps struggles to deal with these heightened demands because the process was initially created at a time when there were slower release cycles and simpler architectures. When faced with the scale and complexity of today’s app environments, several Manual DevOps elements simply can’t keep up:
- As applications became complex, manual testing became time-consuming and prone to human error.
- Vulnerabilities and performance issues were identified only after they manifested, leading to a constantly reactive posture.
- Development wasn’t compliance-focused, leading to penalties and reputational damage when violations were discovered late in development cycles.
- All this led to release cycles slowing down at a stage when shortened time-to-market became critical to organizational success.
Embedding AI Across The Entire Development Lifecycle
It is a prevailing thought today that mature DevOps strategies must have AI integration as a core component. Of course, this brings up the entire debate of AI taking over jobs normally reserved for app developers.
Yet, research from several industry leaders shows that, in reality, AI isn’t replacing developers. Instead, it’s changing what developers do day to day – while AI takes on more routine, time-consuming tasks, human roles are expanding towards higher-level initiatives like orchestration, systems thinking, governance and business alignment. Mature DevOps is all about teams orchestrating AI-accelerated systems with human judgment at the core.
Once this is clarified, the way to fully harness the power of AI is not to limit it to isolated use cases. In a survey conducted by McKinsey on the topic, the top performers embed AI across every stage of the application lifecycle:
Let’s now look at how AI optimizes each stage of the lifecycle…
Bringing Predictive Power To The Planning Stage
AI’s biggest role in this stage is to shift the entire process from being intuition-led to data-driven. As an example, Netflix developed a system called ‘Demand Forecasting’ right at the outset of development to predict viewer demand for different types of content. It led to:
- 30% reduction in infrastructure costs
- 40% improvement in streaming quality
- 50% decrease in service outages
By streamlining and analysing all the data required at the requirements gathering stage, AI brings a crystal ball into what development for the project will look like – even before a single line of code is written.
Backlog prioritization becomes more intelligent
NLP techniques constantly analyze parameters like user feedback and production telemetry to deliver dynamic backlog recommendations like:
- Which features deliver the highest user impact
- Which defects affect the most users
- What technical debt items create the most future risk
Predictive analysis informs teams of future risks
By correlating factors like code change patterns, dependency updates and historical failure data, predictive models can generate probabilistic risk scores for upcoming releases. This allows your teams to:
- Flag high-risk features early
- Adjust sprint scope
- Allocate testing effort intelligently
- Sequence releases more safely
Planning becomes integrated with your delivery pipelines
By linking planning tools directly with build systems, testing platforms and production telemetry, this creates a closed planning loop where:
- Delivery capacity informs roadmap decisions
- Past deployment performance influences sprint scope
- Production incidents automatically generate backlog items
Creating Efficiency Through Unification At The Build Stage
In coding environments spanning containers, staging layers and legacy architectures, the lack of complete observability in manual DevOps was leading to lengthy build cycles. AI has brought truly unified governance to builds, setting the stage for more speedy and secure development:
Unified Control Over Development, Staging And Production
- AI-assisted pipeline orchestration helps maintain alignment across these environments in real-time by:
Automatically configuring dependencies
Resolving configuration conflicts
Predicting integration failures
Code Automation
Once unified observability is achieved, you can integrate automation across your pipelines to write relevant, error-free code. A recent case study by Microsoft says that this integration can lead to:
- 30% reduction in coding time for common tasks
- 25% decrease in coding review cycles
- 20% improvement in overall code quality
Shift-Left Security & Compliance Checks
With India facing unprecedented levels of cyberattacks and regulations to combat them, security and compliance can no longer be bolted on after development. Integrating security and compliance checks into your build environments allows for:
- Scanning code for vulnerabilities
- Detecting secrets and misconfigurations
- Strictly enforcing code policy controls
Streamlining Testing Through Automation
In traditional pipelines, testing is usually the most time-intensive and reactive phase of the lifecycle – where defects are discovered late and then teams have to loop back to fix them. The predictive, proactive measures AI integrates in the stages before this significantly reduces the amount of defects found during testing.
Moreover, it brings a level of precision-led quality engineering at this stage through:
- Intelligent test case prioritization during tight release windows, ensuring your teams run the right tests at the right time
- Automated test generation for various types of tests (unit, integration, API, edge, regression, etc.)
- Continuous learning embedded in these testing models that learn from past defects and performance bottlenecks – allowing models to refine predictions over time and further shorten testing cycles
These measures have the capacity to halve change-failure rates and slash post-release defects by about 40%.
Ensuring Seamless Deployment
Finally, once an application officially moves into production, the tolerance for delays or errors drops sharply. This is where real-world impact occurs – latency spikes translate into abandoned transactions, outages halt service access and bugs trigger customer churn.
AI proves to be a gamechanger through real-time ingestion capabilities that span production logs, metrics, traces, usage patterns, performance signals and error rates. The insights from this feed back into the previous stages of the timeline and lead to better outcomes:
- Recurring error patterns automatically generate a prioritized backlog item
- Performance data informs infrastructure scaling or refactoring
- Security anomalies trigger immediate remediation workflows
- Features with low usage can be deprioritised or redesigned
Not only does this loop lead to better user experiences, it also leads to better development when your enterprise is embarking on future projects.
Response AI Governance That Helps Harness All These Benefits
To sum up, integrating AI holistically into every stage of the application lifecycle can bring a wealth of compounding benefits:
- End-to-end visibility
- Built-in security and compliance
- Data-driven, proactive decision making
- Faster time-to-market
- Higher release frequencies without compromising stability
- Cost savings through optimised resource allocation
Yet, it is important to realize that introducing AI brings its own set of novel risks that have to be comprehensively managed. Responsible AI governance is critical to fully realizing all the aforementioned benefits, and is one of the bedrocks behind iValue’s managed enterprise AI services.
