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Developers are experiencing an evolution in the way they complete their work. With the advent of generative AI, a race for AI-augmented programming has begun. Several technology vendors are introducing new and improved tools that provide an immersive AI coding experience and help developers increase productivity.
Gen AI code generation has the potential to revolutionize the software development workflow and developer experience. Generative assistants can augment the work of developers by helping with tasks such as boilerplate code generation, legacy code refactoring, test case writing, vulnerability checking, and more. Gartner predicts that by 2025, 80% of the product development lifecycle will use build AI code generation, with developers acting as validators and orchestrators of back-end and front-end components and integrations.
For companies, a superior development experience is key to attracting and retaining top engineering talent. It also ensures that development teams are productive and engaged in their work, which helps accelerate innovation. In a recent Gartner survey, 58% of software engineering leaders said the developer experience is “very” or “extremely” critical to their organization’s C suite.
Tech vendors will lead the charge both in experimenting with AI code assistants to build software faster, as well as integrating them as part of the experience they want to deliver to their customers – coders and citizen developers. . Therefore, business leaders in these organizations need to understand the potential of AI coding assistants and plan for the impact of these solutions on results across the organization.
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Developers will become orchestrators of software development
AI code assistants will provide two important benefits to technology companies, the first being productivity. Software engineering teams will be able to scale their productivity, and therefore their ability to iterate and improve features at a faster rate. In the near future, developers will increasingly take on the role of orchestrators of coding tasks, with code helpers doing the vast majority of the work.
The second advantage will be a faster response to competitive pressure. AI code assistants will significantly lower the barriers to entry in software development, which means new entrants into the competitive space will add pressure on the pace of innovation and margin of existing players. Development teams that do not adopt code helpers into their software lifecycle will be left behind in their ability to run and deliver in a rapidly changing competitive landscape.
AI code assistants will increase the personalities of developers
Many technology vendor organizations also need to consider the impact of gen AI code assistants on their product offerings. For companies providing developer-facing software, product teams need to capitalize on changing expectations around the developer experience.
Augmented integrated development environments (IDEs) with code helpers will replace basic code editors, becoming near-term table stakes. Targeted developers expect a superior experience in the apps and platforms they use.
If the platform does not offer native options or options for integration with approved AI code assistant services, developers will either choose competitors that offer this option or they will deploy their development efforts outside the platforms. proposed designated forms. Business leaders at companies looking to deliver a competitive experience for software targeting developers should work with product teams to incorporate augmented IDE services into their offerings.
Low-code and no-code generative apps will accelerate citizen technologist personas
Finally, business leaders should also consider how gen AI code helpers can impact development activities outside of IT. Gartner predicts that by 2025, 80% of custom technology solutions within enterprises will be created by people who are not full-time technical professionals, up from 20% in 2020. Generative work will be a natural progression from task-based management. code generation.
Process metadata will be the basis for training and guiding generative processes that orchestrate blocks of generative code tasks. This generation AI application will fuel the productivity wave of low-code and no-code citizen developers. They will be able to use text-to-process generative assistants that produce processes and workflows with multiple code tasks.
This will allow citizen developers to invite generative assistants to design and build complete applications that combine front-end and back-end services. Voice-to-text-process examples are already emerging for building basic functional web applications and will continue to progress into more complex tasks.
Using gen AI coding helpers to support the developer experience is just the start. The low-code and no-code authoring experience will increase the value of gen AI coding assistants, enabling organizations to increase productivity and results beyond the development team. Business leaders need to help citizen technologists within their organizations use generation coding solutions to build apps and speed up processes.
How to start integrating AI code assistants into the enterprise
To attract and retain critical software engineering talent, stay ahead of the competition, and drive digital transformation through citizen technologists, companies must embrace AI code wizard offerings in all aspects of workflow. software development work. This will require business leaders to commit to making the right supplier and talent management decisions, as well as taking the appropriate risk mitigation actions.
From a vendor management perspective, gen AI coding assistants are evolving rapidly, with commercial offerings currently more mature than open source versions. Vendor offerings use a range of different models, which means developers may prefer different products. When evaluating code helper offerings, focus on vendors that make the exploratory experience for developers easy and accessible. Look for vendors that provide enterprise-grade services that focus on security and privacy, as well as the continuous learning and feedback loop of codebases in generative models powering the tools.
Business leaders can start by working with IT and software engineering leaders to pilot solutions for rapid deployments to maximize developer productivity. Facilitate the use of approved products by interested developers and encourage the sharing of best practices between engineering teams. Best practices should cover not only appropriate tools for certain tasks, for rapid engineering, with documented examples to improve code generation results.
While the responsibility for mitigating the risk of using AI code helpers is shared by the vendor and the buying companies, organizations using generation AI tools for software development should actively take aware of the risks associated with these tools. Remain vigilant throughout the evaluation, activation, and full operationalization of AI Code Helpers. Potential risks to watch out for include intellectual property risks, software bugs and security vulnerabilities, code quality impacts, and the overall pace of change in the vendor space, among others.
AI coding assistants will improve developer productivity, but they won’t replace developers in the short to medium term. However, the long-term outlook remains to be determined. Technology leaders must act now to evolve their development teams to harness the power of these offerings while planning for the long-term evolution of the software engineering experience.
Radu Miclaus is a senior analyst director at Gartner, Inc.
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