AI is a truly transformative technology, on par with the microchip, the PC, the Internet, the Web, the smartphone, and the cloud. The best-known uses of AI today have been the ones that have been reported by mainstream media that, include controversy-generating apps that write essays, create presentations, and compose musical scores.

This 3-part blog series introduces an AI use case that has generated less publicity but will have far more impact on the way we work and live.

Pioneered by, it is called AI-powered DevSecOps. It is important because it can radically increase the efficiency of DevSecOps and accelerate innovation at major enterprises worldwide.

Simply put, AI-powered DevSecOps for the enterprise is not just another innovation but the catalyst for scaling up innovation across the enterprise. The solution combines improving software delivery automation with the help of AI, the ability to govern AI-assisted code writing, and predictive problem-solving. These come together to empower developers, yet with the right governance required of large enterprises, cut operational complexity, and accelerate time-to-market for new products and services with AI, but leverage AI safely and responsibly.

Some of you may have noticed the tongue-in-cheek title of a recent podcast: “Artificial Intelligence—Threat or Menace?” Be assured that AI-Powered DevSecOps for the enterprise is a win for all stakeholders in the development process. It is a prime example of the potential of AI to benefit rather than threaten the future of humanity.

Read on to discover the opportunities and unique capabilities of the AI-powered DevSecOps solution. We will also look at some of the challenges and milestones enterprises face on the journey to full business value.

What a Difference Scalability Makes

AI-powered DevSecOps for the enterprise merges multiple AI capabilities that until now have evolved separately:

  • AI-powered software delivery workflows enable enterprises to automate further software delivery processes that improve velocity, limit risk, and automate large-scale programs like app modernization or cloud migrations by utilizing the power of AI.
  • AI-based code governance helps developers use code-assist and other generative AI tools to speed up the writing, checking, and optimizing of traditional code but ensure AI is leveraged responsibly. This empowers developer innovation while allowing the organization to ensure the overall quality, security, and governance of their applications is where they need to be.
  • Predictive intelligence applies machine learning algorithms to data across the entire software development and delivery lifecycle so managers gain earlier software delivery insights in order to forecast capacity, foresee risks, predict, and respond to changes. It will help them proactively improve processes to increase overall software quality and time to market.

Previously, AI solutions have been implemented in a piecemeal fashion, typically among small teams. This approach limits the benefits and adds to the general clutter of siloed, fragmented tools & technologies, methodologies, and operational processes.

AI-powered DevSecOps for the enterprise is a truly holistic approach used to create efficient, automated, intelligent, continuous software delivery processes across developers and teams, and most importantly, it merges these AI capabilities and concepts to be leveraged at the scale required of the world’s largest enterprises.

Quantifying the Opportunities

Analysts agree that scalable AI solutions are capable of delivering substantial and quantifiable benefits to development teams and workflows.

For example, a recent Gartner report¹ found that as AI accelerates coding tasks, developers become “x-times multipliers,” allowing the rapid scale-up of development processes. The report summarized that product leaders “must capitalize on generative AI that can be used to scale their product development.”

Our own analysis and customer experiences show a 2-3x productivity improvement per developer, and external studies have revealed the following tangible benefits²:

  • Better developer productivity: Generative AI coding tools can save developers 20–50% of the time usually spent on repetitive coding tasks
  • Faster software delivery: Development programs infused with AI improve code optimization by 10-30% so citizen and pro developers can identify inefficiencies faster
  • More creativity and innovation: Generative AI tools can dramatically improve the onboarding process for developers at an average of 20-40% faster

However, challenges remain. According to recent studies:

  • 40% of generative AI code is proven to have security vulnerabilities³
  • 39% of customers feel limited AI expertise and knowledge is the main barrier to AI adoption4
  • 74% of organizations are concerned with data privacy of AI-based products or services5


In the next post, we’ll describe how solutions transform the limitations of previous AI solutions into new sources of business value for the enterprise, and in our final post, you will learn the key steps to scale AI in DevSecOps processes post.


¹ Source: “Emerging Tech: Generative AI Code Assistants Are Becoming Essential to Developer Experience,” Gartner, May 2023.
² Source: Open.aI: improvements of generative AI.
Source: NYU-Center For Cybersecurity
4 Source: Tabnine: how to make software eng beam better with AI
5 Source: EnterpriseAppsToday: Unfolding Google Bard


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