Once every decade or so, an innovation seemingly overtakes society, completely transforming the way we function at work and in our lives. In the 80’s it was the personal computer, in the 90’s it was the early days of the Internet, the late 2000’s were marked by the smartphone explosion and much of the 2010’s were about the power of the cloud and 5G.  As we go deeper into the 2020’s, it has become abundantly clear that the era of AI is upon us. While it’s early days and the AI revolution will undoubtedly have various ups and downs, it’s clear we are at the beginning of a total transformation of the way and speed at which both individuals and enterprises work.

Much as with the web and mobile, AI is well represented in our day-to-day lives, in things as innocuous as our cars, voice assistants, search engines and mobile devices.  Behind the scenes, AI is also changing how we work and likely nowhere is that impact more prevalent than in the way we plan, build, test, secure, deliver and monitor software. Today, in companies big and small, AI-powered tools are being leveraged to streamline and accelerate the development and delivery of software through AI augmentation. From AI-assisted coding to AI-augmented monitoring (and everything in between), there is little doubt the traditional approaches of the past are giving way to the future. In fact, according to a recent Gartner[1] report, by 2025, Gartner expects 95% of developers will regularly use generative AI to assist with code creation, up from 50% in 2023.

Unpacking Fears: The Risks and Barriers Holding AI Innovation Back

While the excitement and potential impact of AI-augmented software development and delivery is clear, the barriers to adoption, especially in the enterprise, are very real.  For all the benefits, there are risks.  For every productivity gain, there is a potential security, compliance, governance and quality issue waiting around the corner.  As an example of the tradeoffs, a recent McKinsey study shows software developers can complete coding tasks up to twice as fast with generative AI. At the same time, a study by NYU found that 40% of reviewed AI-generated code included bugs or design flaws that made it vulnerable to threats. Unfortunately, we are seeing that the same AI-assisted tools that are revolutionizing and accelerating workflows for developers  also expose organizations to a myriad of threats and risks when adopted at scale.  So how do we balance speed/productivity with security/quality/compliance? 

Looking Back to Look Forward – DevSecOps as a Foundation 

While AI can provide game-changing benefits, companies need guardrails to use it responsibly. To find part of the solution we need to look back, before looking forward. Many of the same DevSecOps practices of the past have quickly shifted from “nice to haves” towards “must have” status in the age of AI-augmented software development. For years, enterprise DevSecOps has amplified innovation across the enterprise by improving software delivery automation, governing code creation, scanning for vulnerabilities, scaling quality, ensuring compliance and providing predictive insights into unseen risks. In fact, according to InfoSec, 60% of engineers release code twice as quickly, thanks to DevSecOps principles.

These same principles of process definition, increased automation, automated governance/compliance/quality, shifting quality/security left, and driving predictive measurement of the business process of building and delivering software are key parts of how the enterprise will take advantage of the benefits of AI-assisted while managing the risks.  But what should leading enterprises be doing today to prepare for the pending revolution? 

AI in the Enterprise – Key Steps Enterprises Should Take Now to Prepare

There are key steps enterprises are and should be taking to prepare for the transition in how they will deliver software. These steps should be taken whether a company is just starting to think about AI, or if it has started to leverage AI-augmented solutions.

  1. Formulate a GenAI Strategy Group: Establish a dedicated strategy group comprising internal stakeholders and engage external experts. Define a comprehensive strategy that includes risk assessment, objectives and the best approach for integrating generative AI effectively across a business, at scale.  Focus both on how you are going to use AI to improve your goods and services, while also looking at how AI will change the way your teams work.
  1. Accelerate Automation Adoption: Expedite the implementation of automation across the entire development lifecycle to facilitate consistent productivity improvements, end-to-end.  Automated DevSecOps, automated testing at scale, and automated code scanning and compliance testing are must-haves in this new world.
  1. Democratize and Leverage End-to-End Data: Tap the full potential of end-to-end data, both in development and production. Harmonizing and utilizing this data will help establish a productivity baseline, and also enhance predictability and transparency throughout the development process.
  1. Conduct Pilot Programs: Initiate pilot programs to test the waters with generative AI. Enterprises can gauge the effectiveness of generative AI tools, identify challenges, and fine-tune their approach before widespread implementation.

These steps will help enterprises drive measurable improvements today while establishing a strong foundation for the pending generative AI revolution in software development and delivery, ensuring a smoother and more successful adoption. 

The journey of enterprise companies harnessing AI to enhance productivity and consistency in software development is just beginning. This transformation is sure to unfold over time, mirroring the progression of the most significant technological revolutions.  As they say, “The revolution will happen very evolutionarily.”

But the imperative is clear: The time to prepare is now. As AI continues to redefine possibilities, those who are preparing for and embracing the potential will be better positioned to thrive in tomorrow’s technological landscape. With a strategic approach, an open mindset and a commitment to change, enterprises can set themselves up for scalable success and protect themselves from threats throughout the revolution.

[1] Gartner: Emerging Tech: Generative AI Adoption Trends and Future Opportunities, September 2023