Please rotate your device.

Our website uses cookies to ensure you get the best experience while you’re here.


Bringing DevOps Into Its Artificial Intelligence Era

By: SEI Team


What’s better than DevOps? DevOps plus AI — here’s why.

Since its inception, DevOps has redefined the development and operational pipeline, transforming sluggish, segmented workflows into high-velocity, integrated workstreams — and it’s safe to say that the businesses that have embraced DevOps processes aren’t going back. According to Puppet, an IT infrastructure platform developer, over 80% of organizations now practice DevOps, with this number expected to grow further.

Now with the prominence of artificial intelligence (AI), DevOps is set to evolve into a cultural and technical movement, revolutionizing the way companies build, test, and deploy applications and services. AI, with its ability to analyze vast amounts of data, identify patterns, and make smart predictions, holds the potential to enhance business intelligence automation, enrich existing DevOps tools, and embolden teams to create better solutions at a competitive pace.

From optimizing business intelligence and automating complex workflows to reimagining the very nature of software development and operations, we are witnessing the dawn of an era where AI-driven DevOps is set to become the new norm.

The Rise of Business Intelligence Automation

Embracing a spectrum of technologies and methodologies, business intelligence automation (BIA) tools help extract, process, analyze, and visualize data to enable stakeholders to derive meaningful insights in real-time. For example, with BIA tools, a retail enterprise can extrapolate sales data and identify seasonality patterns, or an industrial corporation can receive triggers when inventory levels run low. At its core, business intelligence automation represents a paradigm shift in decision-making processes, empowering businesses to harness data-driven approaches for strategic planning, operational optimization, and performance monitoring. 

By embedding BIA capabilities directly into DevOps pipelines, organizations can unlock new efficiencies, enhance decision-making across the entire development lifecycle, and equip DevOps teams with the resources they need to navigate complex environments with confidence and agility. 

Types of Business Intelligence Tools in DevOps

AI-powered business intelligence tools can analyze vast datasets at unprecedented speeds, uncovering hidden patterns, correlations, and insights that may elude human analysts. This lends efficiency across the entire DevOps lifecycle, liberating valuable time and resources for higher-value activities. For example, some tools automate repetitive tasks like data cleansing, aggregation, and reporting, while others proactively monitor key performance metrics for anomalies and bottlenecks.

Here’s a deeper look at the types of business intelligence tools that are helping organizations streamline processes:

  • Data Visualization Tools: These tools offer intuitive interfaces to visualize pipeline performance, track deployment frequencies, and monitor other critical metrics. Plus, they allow DevOps teams to create interactive dashboards and reports that present complex data in an easily digestible format, which helps analysts quickly scan for trends and bottlenecks to make well-informed decisions.
  • Predictive Analytics Tools: Leveraging historical data and machine learning algorithms, predictive analytics tools forecast potential issues within an automated DevOps pipeline. By anticipating bottlenecks or system failures, teams can proactively address challenges before they escalate, helping to ease operations.
  • Self-Service BI Tools: Platforms like Domo democratize data access by enabling non-technical users to independently generate reports and extract insights. This type of accessibility fosters a culture of data-driven decision-making across all levels of the organization, promoting collaboration and innovation.
  • AI-Enhanced BI Tools: Advanced BI tools, such as IBM Watson, leverage AI and machine learning to analyze vast datasets rapidly. These tools provide actionable insights that optimize the DevOps lifecycle, from code development to deployment and monitoring, driving continuous improvement and innovation.

With these resources at their disposal, DevOps teams can allocate more talent and brainpower toward the tasks that matter most.

Artificial Intelligence vs Intelligent Automation

Although they sound similar, artificial intelligence and intelligent automation play complementary yet distinct roles in the DevOps sphere. AI involves technologies that simulate human intelligence, enabling systems to learn from data, adapt to changing conditions, and perform tasks that traditionally require human intervention. In DevOps, AI provides advanced analytics, predictive modeling, and cognitive automation capabilities to augment human skills and tackle complex tasks.

Intelligent automation, on the other hand, focuses on automating repetitive and rule-based tasks, using AI and tools like robotic process automation (RPA) and workflow orchestration to expedite operations and reduce manual effort. Intelligent automation centers on improving productivity by following predefined logic and workflows and, therefore, handling more defined responsibilities. Together, AI and intelligent automation lend DevOps teams a hand in easing manual workloads and better strategizing for business success.

DataOps: A New Frontier in DevOps

A branch of DevOps, DataOps is a methodology that aims to improve the speed, quality, and reliability of data analytics and operations. By applying principles and practices from DevOps to data-related processes, DataOps promotes an agile and iterative approach to data management. Traditional data management involves fragmented and siloed workflows, leading to inefficiencies, errors, and delays. However, DataOps resolves these roadblocks through cross-team collaboration and DevOps automation tools.

Like DevOps, AI also has the capacity to expand upon the role of DataOps in organizations. For example, machine learning and natural language processing can optimize data workflows, automate repetitive tasks, and pinpoint inconsistencies and inaccuracies within incredibly large and complicated datasets — a feat far too impractical for human analysts to commit to on a daily basis. As AI continues to evolve, its integration with DataOps is expected to foster a level of creativity and efficiency that organizations can be proud of.

Delve into AI-Powered DevOps with SEI

As DevOps continues to evolve alongside AI, organizations are poised to unlock new frontiers of optimization, innovation, and competitiveness. The fusion of DevOps and AI promises to revolutionize how assets are developed, deployed, and managed, ushering in a new era of agility, intelligence, and automation. By harnessing the power of AI-driven technologies such as business intelligence automation and DataOps, businesses can unleash their full potential for maximum gain.

At SEI, we’re committed to helping organizations navigate the complexities of DevOps in this AI boom, leveraging our expertise and experience to drive meaningful change that delivers tangible results. Starting with an in-depth assessment of your IT environment, we break down the current state of your infrastructure and outline your desired future state. Then, we get to work until we get the job done. Whether that’s gaining stakeholder approval, gauging maturity levels, or examining resource capacities, we strive to create personalized solutions that meet your business’s unique needs and goals.