Adopting Amazon Q Developer in Enterprise Environments

Increasing developer productivity has been a persistent challenge for senior leaders over the past decades. With the rise of generative artificial intelligence (AI), a new wave of innovation is transforming how software teams work. Generative AI tools like Amazon Q Developer are emerging as game-changers, supporting developers across the entire software development lifecycle. But how are large-scale organizations successfully adopting this AI-assisted approach? This post shares good practices I have discovered through working with enterprise customers navigating this technological transformation.

A common misperception still is that a developer is more productive when they write code faster. But a median developer spends less than one hour per day writing code, as a study conducted by software.com in 2022 shows. This makes clear that there are other aspects to consider when it comes to building an application and running it in production. Generative AI tools for developers, such as Amazon Q Developer, started as coding companions that provided inline completions. As the technology evolved, Amazon Q Developer is now able to support developers across the entire software development life cycle.

The Change Challenge

Generative AI offers new and interesting ways for developers to solve challenges and to support them in their daily work. But taking advantage of those opportunities takes some time. It introduces a noticeable change to their familiar ways of working and therefore it is much about forming a new habit. This, according to science, usually takes a minimum of two months. Teams need the space and permission to play with this new approach and to find out what works best for them. Expecting them to adopt a new way of working while expecting the same (or higher) level of output at the same time will only lead to teams falling back to what they know works. For customers that successfully have adopted Amazon Q Developer I have seen them reducing the delivery expectations to give space to learn, and having required teams to share learnings in return.

Additionally, as in any other large change project there is a significant cultural aspect to consider. If people feel intrinsically convinced by the value and benefits of an AI-supported approach to software engineering they will use it. “Simply ordering from above” will not help making an adoption successful. But building community, experimenting, learning, and sharing successes will. “Culture eats strategy for breakfast”, as the management visionary Peter Drucker said.

Keeping that in mind it is clear that there is no one-size-fits-all prescriptive way of successfully introducing generative AI tools for developers in your organization. It very much depends on your individual culture, goals, challenges, people, skills, and technology stack for example. However, there are a number of principles and good practices that work well with several of our large-scale customers who have successfully adopted Amazon Q Developer.

Best Practices for Successful Adoption

The following illustration describes the change management cycle and recommended activities for adopting AI-assisted software development.

Figure 1. The change management cycle

1. Secure Top-Down Commitment

Secure executive buy-in for adopting AI-assisted software development because this is a powerful sign for the organization. It helps to remove roadblocks and to promote successes across the organization. An executive sponsor links the adoption of AI-assisted software development to the strategic goals of the organization, will help resolving prioritization and capacity conflicts. Invite the sponsor to a kick-off meeting. Include them for the discussion of goals and success criteria, and keep them updated on progress, success stories, and challenges.

2. Become Clear on Goals and Success Criteria

Simply rolling out Generative AI tools to a large developer base won’t be enough. You need to be clear what you expect from such an adoption for your organization, for your developers, and for your business. It is important for you to understand your organization’s pain points from a developer experience perspective and how they affect your development productivity. These likely differ between different personas, like Software Developers and DevOps Engineers. For example, are your applications lacking test coverage or is your code lacking documentation, which creates a maintenance burden, and makes it difficult to onboard new developers? Is handling legacy code risky and time-consuming so that you avoid necessary upgrades because of the complexity and effort? Is troubleshooting applications locally or in production eating up your developers’ time? Or are you facing challenges caused by hard-to-find security issues in the code? What is it that you want to achieve by introducing an AI-assisted development approach, and why?

3. Establish Ownership

Adopting a new approach like the use of generative AI in software development at scale leads to many change management and coordination tasks. Those are related to getting access to the tool, enablement for new users, budget planning, measuring success and creating transparency on problems, creating momentum and making sure the adoption sustains, amongst others. Therefore, introducing a Customer Champion as a leader who coordinates the business and technology related aspects of the adoption is a common approach. They will connect the strategic goals of the organization with the tactical activities necessary for the implementation. If your adoption is spanning multiple different business units with larger developer bases consider establishing this role for each of them, forming a team that collaborates across your whole organization. Key responsibilities of the Customer Champion are to bring the right people together to successfully work on the adoption, to create transparency on status, and to address impediments early on.

4. Introduce Metrics

Once you have gained clarity on the specific pain points and goals for your organization, the next step is to determine the appropriate metrics to measure the success of your efforts. For example, you can measure the impact of using Amazon Q Developer on onboarding new developers by tracking the time to first commit against a previously established baseline average. Comparing the time it takes to complete certain tasks – like writing and integrating code, fixing bugs, setting up or upgrading environments, the time it takes to build a new feature, or by comparing sprint velocity before and after the introduction of Amazon Q Developer will give you further indications. But keep in mind that there is ambiguity in these metrics because they are impacted by different factors.

Focusing on developer experience and productivity, monitor the development of your established measurement framework, like DORA, SPACE, or GSM. SPACE in particular, with its “Satisfaction & Well-Being” dimension, pays close attention to how software developers perceive their work and how satisfied they are with their own productivity. Tools like Amazon Q Developer have shown a positive effect here, as for example a McKinsey study shows. They help to free developers from tasks that are perceived as toil, like repetitive and boring grunt work that doesn’t necessarily bring business value. To measure this impact on perceived productivity, customers sometimes design simple surveys, asking developers questions like “in percentage, how would you estimate the impact of using Amazon Q Developer on your productivity” or “on a scale from 1-5, how is Amazon Q Developer impacting the satisfaction with your day-to-day work?”

As a last dimension, understanding the general usage of Amazon Q Developer across your organization is important. Monitor the number active subscriptions, accepted code recommendations, or the number of executed security scans from the Amazon Q Developer dashboard. That way you will get an understanding for the acceptance of the tool in your developer base. Correlate this information with the success metrics you defined.

5. Start Small

When “rolling out” an AI-assisted software development approach, keep the technology adoption lifecycle and Everett Roger’s bell curve in mind. It describes that adoption of a new technology usually starts with a few “innovators”, followed by a small fraction of “early adopters”. Only if those early groups demonstrate convincing success, the majority of users will follow the adoption. To settle on this model will help you making the adoption of AI-assisted development successful in your organization.

Start small. Identify your tech innovators, or champions, who are enthusiastic to support the introduction of Amazon Q Developer and to advocate the approach in your organization. With them, build a team or a Center of Excellence (COE) that will help with identifying early adopters across your development teams, building technical and enablement foundations for onboarding users, creating roll-out plans, and evangelizing and sustaining the adoption. The champions will act as a bridge between your adoption program team and your users. They can provide insights and feedback on the adoption and come up with recommendations.

6. Create Momentum

Now that you have your foundations in place it is time to create momentum and onboard your early adopters to Amazon Q Developer. Start with a communication plan to raise awareness, to share updates and success stories, and to collect feedback from the users. You will need to create training material covering organization-specific resources (how to get access, for example, or which communication channels and contact points exist), user guides, tutorials, and pointers to relevant documentation and online resources. Consider creating your own prompt library, documenting prompts for Amazon Q Developer that your users find particularly helpful. There are community projects like promptz.dev that can deliver inspiration. Especially for new users this will have a lot of value for getting up to speed.

Consider how to integrate learning pathways with your organization’s learning platform moving forward. These should include the company-specific experience and knowledge you collect. In addition, it might be helpful to integrate external offers, like classroom trainings or workshop formats offered by AWS.

Hands-on technical enablement workshops will help your early adopters jumpstarting their experience. AWS offers multiple formats that can be tailored to your individual context. These include service deep-dives with Amazon Q Developer specialists, Immersion Days and hackathon support for teams to hands-on experience the tool in a guided, interactive format, or joined proof-of-value engagements. Your AWS account team will provide you more information.

7. Sustain and Scale Adoption

At that point you will have established Amazon Q Developer among an initial segment of your developer base. However, keeping Roger’s adoption curve in mind the largest part of the adoption still lies ahead of you. To facilitate the it across your whole organization, focus on delivering enablement workshops for the teams to be onboarded. These will be led by your champions and incorporate your organization-specific practices and knowledge.

To sustain the adoption, make sure the existing user base stays active. Continue offering exchange and support, collecting feedback, updating your enablement material, sharing updates on the latest developments for Amazon Q Developer, and reviewing your metrics. Create visibility across your organization for the accomplishments and positive experiences. Let user talk about the impact Amazon Q Developer has on their work. This will help building momentum. Nurture community building around your users of AI-assisted development.

Establish regular office hours with your champions for providing support and enablement to your users of Amazon Q Developer. This will facilitate the continuous gathering of feedback to improve documentation and enablement materials, collect and promote success stories, and validate the adoption approach. Additionally, establish a consistent communications and reporting cadence to keep all relevant stakeholders informed of key metrics, updates, feedback, and success stories. This ensures the alignment of the adoption of AI-assisted development with your strategic goals and expectations.

8. Inspect and Adapt

In addition, keep reflecting on the goals and metrics you came up with from a strategic perspective. Are they developing in the right direction? Are your pain points still the same? Should you move your focus to different aspects of the software development life cycle? And how might new capabilities of Amazon Q Developer support your goals?

Conclusion

By following the outlined approach, large organizations can successfully navigate the challenges of adopting Amazon Q Developer. The approach addresses technical, cultural, and organizational aspects of the adoption, increasing the likelihood of realizing the full potential of AI-assisted development across the enterprise.

If you are looking for advice or support during your adoption journey, your AWS Account Team will connect you with experts on Amazon Q Developer, provide you information on training and enablement, and support you with setting up a successful rollout program for your organization. To learn more about how Amazon Q Developer’s capabilities support the whole software development lifecycle, visit the product page or have a look at the documentation.

About the Author

Rene-Martin Tudyka is a Senior Customer Solutions Manager at AWS and provides guidance and support for enterprise customers on their cloud maturity journey. He has a long background in developing highly performant IT organizations and in successful large-scale cloud adoption.