This post is co-written with Hearst's Steven Craig.
To maintain their competitive advantage, organizations are constantly looking for ways to accelerate cloud adoption, optimize processes, and drive innovation. However, Cloud Center of Excellence (CCoE) teams can often be perceived as bottlenecks to organizational transformation due to limited resources and overwhelming demand for their support.
In this post, we share how Hearstone of the country'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 created a solution to scale cloud best practices by giving employees across multiple business units self-service access to a centralized collection of documents and information. This freed the CCoE to focus its time on high-value tasks by reducing repetitive requests from each business unit.
Readers will learn about key design decisions, benefits realized, 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 of its amazon Web Services (AWS) infrastructure. The CCoE implemented AWS Organizations in a substantial number of business units. These business units then used the CCoE's AWS best practices guide by implementing landing zones with AWS Control Tower, managing resource configuration with AWS Config, and reporting control effectiveness with AWS Audit Manager. As individual business units sought guidance on how to adhere to AWS recommended best practices, the CCoE created written policies and enablement materials to facilitate adoption at scale across Hearst.
The existing CCoE model had several obstacles that slowed its 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 was unable to disseminate updated directives quickly enough. Manually reviewing each request across multiple business units was not sustainable.
- Inconsistent governance – Without a standardized self-service mechanism to access the expertise of CCoE teams and disseminate guidance on new policies, compliance practices or governance controls, it was difficult to maintain consistency based on 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 give business units more access to updated CCoE best practices and patterns to follow.
Solution Overview
To enable self-service cloud governance at scale, Hearst's CCoE team decided to harness the power of generative ai with amazon Q Business to create a conversational assistant. The following diagram shows the architecture of the solution:
The key steps Hearst took to implement amazon Q Business were:
- Application deployment and authentication – First, the CCoE team implemented 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 QBusiness.
- Curation and authorization of data sources – 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 configured a general group for all users and specific groups adapted to the needs of each business unit. User authorization for documents within individual S3 buckets was controlled through access control lists (ACLs). Adds access control information to a document in an amazon S3 data source by using a metadata file associated with the document. This ensured that end users only received responses from documents they were authorized to view. Using the amazon Q Business S3 Connector, the CCoE team was able to sync and index their data with just a few clicks.
- User access management – With the data source and access controls in place, the CCoE team then configured user access on a business unit by business unit basis, considering various security, compliance, and custom requirements. As a result, the CCoE could offer a personalized experience to each business unit.
- UI Development – To provide an easy-to-use experience, Hearst created 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 across business units.
- Implementation and continuous improvement – Finally, the CCoE team shared the web experience with the different business units, allowing 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 fastest 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 put these powerful generative ai capabilities in the hands of employees as quickly as possible, unlocking new levels of scalability, efficiency and innovation for consistent cloud governance across the organization.
- This strategic decision to use a managed service at the application layer, such as amazon Q Business, allowed the CCoE to deliver tangible value to business units in a matter of weeks. By taking the accelerated path to using generative ai on AWS, Hearst was never bogged down in the technical complexities of developing and managing its own generative ai application.
The results: decreased support requests and greater consistency in cloud governance
Using amazon Q Business, Hearst's CCoE team achieved notable results by empowering self-service cloud governance across the organization. The initial impact was immediate: within the first month, the CCoE team experienced a 70% reduction in the volume of guidance and support requests from various business units. This freed the team to focus on higher-value initiatives rather than getting bogged down in routine, repetitive requests. The following month, the number of CCoE support requests decreased by 76%, demonstrating the power of a self-service assistant with amazon Q Business. The benefits went beyond simply reducing the volume of applications. The CCoE team also saw significant improvement in the consistency and quality of cloud governance practices across Hearst, improving the organization's overall cloud security, compliance posture, and cloud adoption.
Conclusion
Cloud governance is a fundamental 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. Implementing amazon Q Business enabled Hearst's CCoE team to scale the governance and security that business units depend on through a generative ai assistant. By disseminating best practices and guidance throughout 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 core team. If your CCoE team is looking to expand your impact and empower your workforce, consider harnessing the power of conversational ai through services like amazon Q Business, which can position your team as a strategic enabler of cloud transformation.
Hear Steven Craig share how Hearst leveraged amazon Q Business to scale the Cloud Center of Excellence
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About the authors
Steve Craig is Senior Director of the Cloud Center of Excellence. He oversees cloud economics, cloud enablement, and cloud governance for all Hearst-owned companies. Previously, as vice president of operations and product strategy 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 to achieving sequential AWS Managed Service Provider certifications. Steven has been AWS Professional Certified for over 8 years.
Oleg Chugaev is a Principal Solutions Architect and Serverless Evangelist with over 20 years in IT and multiple AWS certifications. At AWS, he guides customers on their cloud transformation journeys by turning complex challenges into actionable roadmaps for technical and business audiences.
Rohit Chaudhary is a Senior Client Solutions Manager with over 15 years of diverse technology experience. His experience spans customer success, product management, digital transformation coaching, engineering and consulting. At AWS, Rohit serves as a trusted advisor for clients to work backward from their business goals, accelerate their cloud journey, 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. It specializes in helping enterprise customers harness the power of amazon Q, an ai-powered generative assistant, to overcome complex challenges and unlock new business opportunities.
