How DevOps Benefits from Artificial Intelligence (AI)

How DevOps Benefits from Artificial Intelligence (AI)

by Vijay Kumar

Artificial Intelligence (AI) will be business-critical for companies looking to gain a competitive edge in the marketplace. DevOps teams must, therefore, integrate AI into their daily work.

AI and its potential as a driving force for the digital economy are now the focus of strategic considerations at companies around the world.

At the same time, DevOps, which function as an active collaboration between development teams and the IT operations, opens up the opportunity for companies to tap into the latest technologies with agility and flexibility as well as to implement them swiftly.

Digital Transformation Leads to AI Adoption

AI is forcing companies that make the digital transformation to rethink. Today, we see a rapid advance in technology based on AI, big data, and predictive analytics. AI enables developers and DevOps teams to leverage these technologies directly for intelligent decision-making based on real-time data.

However, according to a study by IDG Research, only 28 percent of all companies have an active digital-first strategy. As AI continues to evolve, companies need to integrate these forward-looking technologies into their overall approach to compete in the digital economy.

IDG’s report stated that “technology leaders are fueling digital transformation to survive and thrive in the face of market disruption. The research shows that they are collaborating with their line of business counterparts on business strategy, but IT remains in control of the vendor shortlist and is the primary budget holder.”

Another report from market research leader IDC reported that at least half of global value added would be digitized by 2021. In all industries, growth will be driven by digitization, work ethic, and relationships. Hopefully, the clock can drive every company to push forward its own digital transformation.

Companies that are sluggish in digitizing their offerings and workflow will increasingly be marginalized by the competition and unable to seize the opportunities in their market segment. Unfortunately, the timeframe is very tight; seemingly, within the next three years, companies need to be able to make significant progress toward the digital native.

The IDC report further stated that by 2019, 40 percent of all digital transformation initiatives would use one form or another of AI. By 2021, AI will be deployed in 75 percent of commercial enterprise applications.

AI will be essential for companies looking to gain a competitive edge and build their digital business success. For DevOps, this means nothing less than the indispensability of involving AI in their daily work.

Adopting New Technologies

Companies using DevOps methods to accelerate innovation must also adopt more modern technologies such as Google Talk, Amazon’s Alexa, and other voice-controlled digital assistants. Context-sensitive digital assistants can automate individual functional tasks right down to integrated process automation (IPA).

AI not only contributes to increasing productivity. Instead, it can also minimize the risks and increased time that results from human error, as it enables developers to correct the problem very quickly.

Another benefit of digital assistants is that developers no longer have to interact with many different platforms. Instead, developers and DevOps teams can direct digital assistants to retrieve information from various applications.

These assistants can answer questions, make requests, and deliver analytics as they are customized to the user. The goal behind using assistants is to free up resources for developers and DevOps teams so they can focus on tasks that increase and maximize value for end users.

DevOps Approach

With the DevOps approach, there is no panacea for the implementation of AI in the enterprise. Developers and DevOps teams must scrutinize existing traditional methods and rethink how they can leverage AI’s potential. For instance, AI provides actionable data that allows immediate, goal-oriented customizations, creating a new basis for decision-making where well-tried reports or intuition used to reign.

While DevOps teams can rapidly scale up, developers need to keep track of how fast they are getting started and find ways to use AI to ship multiple releases a year. The more teams can safely automate their operation, the more successful the development. Developers who do not acquire this new way of thinking will quickly lose their relevance.

Stay Agile

Companies in all industries need to innovate constantly. Developers need to provide end-user applications with functionality and features, and the same applies to their systems and processes. New versions, as well as entirely new products, must be delivered on time and must not exceed the assigned budget.

In this regard, other methods may not be up to par. For example, in Agile software development, a business will not always know how to exactly deploy new software. Further, there is no way to know what stress testing the software will require before it is developed, which makes it impossible to predict the impact the software will have on a business’ operations.

In turn, this problem makes it almost impossible to calculate the ROI during the planning, approval, and development phases. Companies are, metaphorically, jumping into the cold water.

Meaning, they do not know how much a project will cost, how long it will take, and what value it will deliver. Starting a project without a clear picture of the costs and resources needed to determine if it can be carried out and when it will be completed is irresponsible, if not grossly negligent.

A real-world example of this problem is Samsung’s crisis after the recall of the Samsung Galaxy 7 Note where it was publicly reported that the device has been spontaneously exploding. The logical explanation is that development and Q&A had been rushed to keep up with the release of the latest iPhone. But, this cost Samsung billions and their customers’ trust.

Possible unknowns, like these, in product development, are where DevOps and AI are clearly superior to older methods. DevOps focuses on short, two- or three-day sprints within a 30-day time frame for a project.

These short periods offer multiple advantages. First, they enable the frequent rollout of features, software, feedback cycles, and bug fixes. With customer and business requirements evolving daily, these short, dependable development times are becoming ever more necessary.

These sprints also help with budgeting and product expectations. Shorter projects with defined goals are inherently easier to estimate. This applies to both the return and the cost of required resources.

Further, sprints can also be made on-demand rather than on a random schedule. This way the needs of specific customers or businesses that depend on constant innovation can be taken into account.

DevOps provides companies with a foundation for producing solutions without breaking cost and timeframe requirements. These solutions are exactly what companies need to develop efficiently and collaboratively.

Flexibility Is the Key

The overwhelming power of DevOps is to use bugs as a basis for improvement, to iterate quickly, and to make changes immediately. It’s a departure from the waterfall model where a single phase of software development is completed before the next one can begin; with current IT demands, this method often feels like a rigid approach for developers.

With DevOps, quality management can be automated so that errors can be found quickly and iterated rapidly. It means fast-paced development without sacrificing quality, and this is how AI comes into its own. Here, AI enables developers and DevOps teams to add value and flexibility, fundamentally changing how they work through successful automation.

The DevOps approach itself has also changed in structure, tempo, and iterations. These changes also affect which products the developers need to rethink. Machine Learning (ML) is a part of AI that has clearly gained importance in the industry.

ML can be considered a classic technology, but DevOps teams need to understand it to use it effectively. ML is a learning-based AI; it teaches computers how to perform tasks using data without the explicit, rule-based programming traditionally used in robotic process automation (RPA).

The rapid increase in the use of ML in software is impressive and has positively impacted efficiency. ML-based systems can assist DevOps teams in real-time with problem-solving suggestions.

AI as an Opportunity

AI does not pose a threat to developers or DevOps teams. Instead, it gives them time for new tasks and innovation. When companies realize the potential of AI, their employees can work more efficiently.

For example, Gartner predicts that if companies are using AI-based systems by 2021, around 6.2 billion man-hours will be recovered for more productive work by employees. AI relieves the DevOps teams of recurring administrative activities, so they can devote their time to more important, strategic tasks. It begins with developing concepts for integrating AI and focusing on the business and technical value of the goal.

By using this method, an efficiency level that allows for higher performance is achieved. DevOps teams can bring business process experts and AI experts together to distribute the workload optimally. Ultimately, this helps everyone: the developers, the DevOps teams, the business, and the end users.

Higher ROI Due to AI and DevOps

In the traditional approach, developers add new code line by line. Today, in the cloud, developers can work far more efficiently. AI enables optimization and automation throughout the software development lifecycle,

Overall, AI has tremendous potential to increase developer productivity, the overall success of DevOps collaboration, and ultimately, the bottom line. Also, the profitability of using AI can be demonstrated in many areas. An example is reducing the time spent on certain activities.

DevOps offers a broad application to the field for AI. Therefore, it is essiential to find out which parts of the cycle could benefit most from AI in terms of process optimization, productivity enhancement, and minimizing human-error risks. By examining these criteria before implementing an AI-based solution, teams can measure their impact from the outset and present the results more convincingly in terms of contributing to business performance.

Integrating AI and ML Capabilities

AI makes it possible to connect more and more devices with each other. Thus, companies are always creating new services that deliver real value to their customers. Such services can be found, for example, in the context of smart home and car entertainment.

However, the target group will only accept the new offers if they consistently work smoothly. If companies have additional insights into the usage behavior of their customers and can see which services are particularly in demand, their new value-added services can generate economic success.

Those who want commercial success have to keep an eye on the increasingly complex world of business solutions, and complexity is a vital word when it comes to ML. Finally, more and more devices are producing more and more data. To be able to process this data within a reasonable timeframe, new intelligent solutions are needed.

The further development of the classic IT operations toward DevOps is only the beginning. In the future, IT operations will increasingly take on a BizDevOps role. This means that IT operations will assume more responsibility.

The contribution IT makes to business success in the digital world will become more visible. This will also benefit employees in the IT department, as their reputation in the business increases.

A study by Gartner shows that only five percent of big companies combine big data and ML. But by the year 2020, this number will be about two-fifths. This increasing acceptance illustrates the importance of preparing for this change today.

To do so, companies need to know what important data they own and what processes they can automate. They also need access to the expertise that is required to implement and operate AI. Then, they are prepared for the future of AI.

Conclusion

For DevOps teams who want to keep their business growing in the digital economy, AI will be critical for success. To make it a victory for all stakeholders, AI should be implemented in the DevOps cycle and throughout the software development lifecycle.

Only then can AI’s value be visibly displayed to the outside world. If widely deployed across enterprises, AI can enable developers to deliver high performance and help companies meet the increasingly complex needs of their customers.

Vijay is a Senior DevOps Engineer and Systems Architect at Practical Logix

Leave a Reply

Your email address will not be published. Required fields are marked *