The Future Is AI, but AI Has a Software Delivery Problem
In the annals of transformative technologies, few can rival the disruptive potential of artificial intelligence (AI). Like the rise of the internet and mobile technology, AI is already proving to be the next frontier in innovation.
However, while the potential is staggering, AI development confronts a significant challenge: actually getting it into products.
Think back to the dawn of the internet — a time filled with overhyped promises that nonetheless harbored game-changing realities for those who could harness that new technology. Today, engineering teams are at a similar crossroads, grappling with the pressure to leverage AI against the uncertainty of where to begin.
Readers of Sequoia Capital’s article Generative AI’s Act Two may conclude that GenAI’s next chapter is near. The early half of 2023, Act One, was a race to build foundational models from scratch, and Act Two is about figuring out how to incorporate existing models into more comprehensive solutions.
But what about beyond Act Two? As more developers become comfortable building AI-powered software, Act Three will trigger a new race: the ability to build, deploy and manage AI-powered software at scale, which requires continuous monitoring and validation at unprecedented levels.
This is why crucial DevOps practices for delivering software at scale, like continuous integration and continuous delivery (CI/CD), will play a central role in providing a robust framework for engineering leaders to navigate the complexities of delivering AI-powered software — therefore turning these technological challenges into opportunities for innovation and competitive advantage.
From Yes/No to Infinite Gray: The Testing Maze of AI
Just as software teams have honed practices for getting reliable, observable, available applications safely and quickly into customers’ hands at scale, AI-powered software is yet again evolving these methods. We’re experiencing a paradigm shift from the deterministic outcomes we’ve built software development practices around to a world with probabilistic outcomes.
This complexity throws a wrench in the conventional yes-or-no logic that has been foundational to how we’ve tested software, requiring developers to navigate a variety of subjective outcomes. Manually testing such systems becomes an arduous, time-consuming endeavor, as it demands not only verifying a vast array of potential interactions but also assessing the subjective quality of decisions made by the AI.
Today, the work of validating quality answers is often done by subject matter experts, but in order to scale, teams will need to look to CI/CD tools that seamlessly integrate with evaluation platforms to automate this process. This underscores the need for innovative approaches in testing and validating AI, building on everything we’ve learned about CI/CD and what it takes to effectively and safely deliver applications to customers in this new world.
Using Today’s CI/CD Pipeline to Deliver AI’s Act Three
CI/CD is pivotal in helping teams manage the complexity of developing AI-powered software. These methodologies offer a structured, automated pipeline spanning from building and testing to training and deploying AI-enabled applications.
Think of it as supercharged delivery, ensuring that computational resources are both scalable and efficient. Automated testing and fast feedback loops allow for the rapid identification of issues, mitigating the risk of AI-related challenges like model drift.
CI/CD enhances team collaboration, accelerates development timelines, and refines AI-powered software quality through process automation. This automation minimizes manual errors and bolsters reproducibility, empowering teams to swiftly and confidently deliver reliable AI-powered applications. By integrating AI software development with automated testing and continuous deployment, CI/CD pipelines facilitate a seamless workflow where any and all changes are consistently built, trained, tested, deployed, and monitored.
This system acts as a quality gatekeeper, ensuring only AI-enabled applications that meet the stringent standards make it to production. Furthermore, if performance wanes due to model drift, the pipeline can safely roll back, retrain and redeploy updated AI-enabled applications, ensuring deployed AI/ML applications remain robust and functional over time.
Aligning AI and ML Projects with Business Objectives
Strategic business alignment is crucial when investing in AI-powered software, extending far beyond the engineering team’s purview. It requires a synergistic effort, where stakeholders across various departments — such as product management, marketing, sales and customer service — collaborate to define clear objectives that AI can achieve.
The key is ensuring that AI initiatives are closely tied to core business goals, such as enhancing customer experiences, streamlining operations or unlocking new revenue streams. This cross-functional alignment ensures that AI projects are technically viable and commercially strategic, maximizing ROI and ensuring that the technology serves broader business objectives rather than existing in a silo.
Accelerate Your AI-Powered Innovation to Win Tomorrow’s Market
The future may indeed belong to AI, but realizing its full potential hinges on our ability to solve the software delivery conundrum. It demands a combination of strategic business alignment, technical preparation and the right tools and processes.
By integrating AI with robust CI/CD practices, engineering leaders can navigate the complexities of delivering AI-powered software, transforming potential into performance and vision into reality. As AI reshapes the landscape, the readiness to evolve and adapt software delivery practices will separate the pioneers from the rest, ensuring a competitive edge in the ever-evolving technology arena.