Unleash the power of generative AI with Amazon Q Business: How CCoEs can scale cloud governance best practices and drive innovation | Amazon Web Services

This post is co-written with Steven Craig from Hearst. 

To maintain their competitive edge, organizations are constantly seeking ways to accelerate cloud adoption, streamline processes, and drive innovation. However, Cloud Center of Excellence (CCoE) teams often can be perceived as bottlenecks to organizational transformation due to limited resources and overwhelming demand for their support.

In this post, we share how Hearst, one of the nation’s largest global, diversified information, services, and media companies, overcame these challenges by creating a self-service generative AI conversational assistant for business units seeking guidance from their CCoE. With Amazon Q Business, Hearst’s CCoE team built a solution to scale cloud best practices by providing employees across multiple business units self-service access to a centralized collection of documents and information. This freed up the CCoE to focus their time on high-value tasks by reducing repetitive requests from each business unit.

Readers will learn the key design decisions, benefits achieved, and lessons learned from Hearst’s innovative CCoE team. This solution can serve as a valuable reference for other organizations looking to scale their cloud governance and enable their CCoE teams to drive greater impact.

The challenge: Enabling self-service cloud governance at scale

Hearst undertook a comprehensive governance transformation for their Amazon Web Services (AWS) infrastructure. The CCoE implemented AWS Organizations across a substantial number of business units. These business units then used AWS best practice guidance from the CCoE by deploying landing zones with AWS Control Tower, managing resource configuration with AWS Config, and reporting the efficacy of controls with AWS Audit Manager. As individual business units sought guidance on adhering to the AWS recommended best practices, the CCoE created written directives and enablement materials to facilitate the scaled adoption across Hearst.

The existing CCoE model had several obstacles slowing adoption by business units:

• Extreme demand – The CCoE team was becoming a bottleneck, unable to keep up with the growing demand for their expertise and guidance. The team was stretched thin, and the traditional approach of relying on human experts to address every question was impeding the pace of cloud adoption for the organization.
• Limited scalability – As the volume of requests increased, the CCoE team couldn’t disseminate updated directives quickly enough. Manually reviewing each request across multiple business units wasn’t sustainable.
• Inconsistent governance – Without a standardized, self-service mechanism to access the CCoE teams’ expertise and disseminate guidance on new policies, compliance practices, or governance controls, it was difficult to maintain consistency based on the CCoE best practices across each business unit.

To address these challenges, Hearst’s CCoE team recognized the need to quickly create a scalable, self-service application that could empower the business units with more access to updated CCoE best practices and patterns to follow.

Overview of solution

To enable self-service cloud governance at scale, Hearst’s CCoE team decided to use the power of generative AI with Amazon Q Business to build a conversational assistant. The following diagram shows the solution architecture:

The key steps Hearst took to implement Amazon Q Business were:

1. Application deployment and authentication – First, the CCoE team deployed Amazon Q Business and integrated AWS IAM Identity Center with their existing identity provider (using Okta in this case) to seamlessly manage user access and permissions between their existing identity provider and Amazon Q Business.
2. Data source curation and authorization – The CCoE team created several Amazon Simple Storage Service (Amazon S3) buckets to store their curated content, including cloud governance best practices, patterns, and guidance. They set up a general bucket for all users and specific buckets tailored to each business unit’s needs. User authorization for documents within the individual S3 buckets were controlled through access control lists (ACLs). You add access control information to a document in an Amazon S3 data source using a metadata file associated with the document. This made sure end users would only receive responses from documents they were authorized to view. With the Amazon Q Business S3 connector, the CCoE team was able to sync and index their data in just a few clicks.
3. User access management – With the data source and access controls in place, the CCoE team then set up user access on a business unit by business unit basis, considering various security, compliance, and custom requirements. As a result, the CCoE could deliver a personalized experience to each business unit.
4. User interface development – To provide a user-friendly experience, Hearst built a custom web interface so employees could interact with the Amazon Q Business assistant through a familiar and intuitive interface. This encouraged widespread adoption and self-service among the business units.
5. Rollout and continuous improvement – Finally, the CCoE team shared the web experience with the various business units, empowering employees to access the guidance and best practices they needed through natural language interactions. Going forward, the team enriched the knowledge base (S3 buckets) and implemented a feedback loop to facilitate continuous improvement of the solution.

For Hearst’s CCoE team, Amazon Q Business was the quickest way to use generative AI on AWS, with minimal risk and less upfront technical complexity.

• Speed to value was an important advantage because it allowed the CCoE to get these powerful generative AI capabilities into the hands of employees as quickly as possible, unlocking new levels of scalability, efficiency, and innovation for cloud governance consistency across the organization.
• This strategic decision to use a managed service at the application layer, such as Amazon Q Business, enabled the CCoE to deliver tangible value for the business units in a matter of weeks. By opting for the expedited path to using generative AI on AWS, Hearst was never bogged down in the technical complexities of developing and managing their own generative AI application.

The results: Decreased support requests and increased cloud governance consistency

By using Amazon Q Business, Hearst’s CCoE team achieved remarkable results in empowering self-service cloud governance across the organization. The initial impact was immediate—within the first month, the CCoE team saw a 70% reduction in the volume of requests for guidance and support from the various business units. This freed up the team to focus on higher-value initiatives instead of getting bogged down in repetitive, routine requests. The following month, the number of requests for CCoE support dropped by 76%, demonstrating the power of a self-service assistant with Amazon Q Business. The benefits went beyond just reduced request volume. The CCoE team also saw a significant improvement in the consistency and quality of cloud governance practices across Hearst, enhancing the organization’s overall cloud security, compliance posture, and cloud adoption.

Conclusion

Cloud governance is a critical set of rules, processes, and reports that guide organizations to follow best practices across their IT estate. For Hearst, the CCoE team sets the tone and cloud governance standards that each business unit follows. The implementation of Amazon Q Business allowed Hearst’s CCoE team to scale the governance and security that support business units depend on through a generative AI assistant. By disseminating best practices and guidance across the organization, the CCoE team freed up resources to focus on strategic initiatives, while employees gained access to a self-service application, reducing the burden on the central team. If your CCoE team is looking to scale its impact and enable your workforce, consider using the power of conversational AI through services like Amazon Q Business, which can position your team as a strategic enabler of cloud transformation.

Listen to Steven Craig share how Hearst leveraged Amazon Q Business to scale the Cloud Center of Excellence

Reading References:

About the Authors

Steven Craig is a Sr. Director, Cloud Center of Excellence. He oversees Cloud Economics, Cloud Enablement, and Cloud Governance for all Hearst-owned companies. Previously, as VP Product Strategy and Ops at Innova Solutions, he was instrumental in migrating applications to public cloud platforms and creating IT Operations Managed Service offerings. His leadership and technical solutions were key in achieving sequential AWS Managed Services Provider certifications. Steven has been AWS Professionally certified for over 8 years.

Oleg Chugaev is a Principal Solutions Architect and Serverless evangelist with 20+ years in IT, holding multiple AWS certifications. At AWS, he drives customers through their cloud transformation journeys by converting complex challenges into actionable roadmaps for both technical and business audiences.

Rohit Chaudhari is a Senior Customer Solutions Manager with over 15 years of diverse tech experience. His background spans customer success, product management, digital transformation coaching, engineering, and consulting. At AWS, Rohit serves as a trusted advisor for customers to work backwards from their business goals, accelerate their journey to the cloud, and implement innovative solutions.

Al Destefano is a Generative AI Specialist at AWS based in New York City. Leveraging his AI/ML domain expertise, Al develops and executes global go-to-market strategies that drive transformative results for AWS customers at scale. He specializes in helping enterprise customers harness the power of Amazon Q, a generative AI-powered assistant, to overcome complex challenges and unlock new business opportunities.