This post was co-authored with Hearst’s Steven Craig.
To stay competitive, organizations are constantly looking for ways to accelerate cloud adoption, streamline processes, and drive innovation. However, cloud centers of excellence (CCoE) teams are often perceived as the bottleneck for organizational transformation due to limited resources and overwhelming demand for support.
In this post, Hearst, one of the largest global and diverse information, services, and media companies in the U.S., is building a self-service generative AI conversational assistant for business units seeking guidance from CCoE. Introducing how we overcame these challenges. With Amazon Q Business, Hearst’s CCoE team is developing a solution that extends cloud best practices by giving employees across business units self-service access to a central collection of documents and information. built. This allows CCoE to reduce repetitive requests from each business unit and free up time to focus on high-value tasks.
Readers learn the key design decisions, benefits achieved, and lessons learned from Hearst’s innovative CCoE team. This solution serves as a valuable reference for other organizations looking to scale their cloud governance and empower their CCoE teams to have greater impact.
The challenge: Enabling self-service cloud governance at scale
Hearst undertook a comprehensive governance transformation of Amazon Web Services (AWS) infrastructure. CCoE has implemented AWS Organizations across a significant number of business units. These business units used AWS best practices guidance from CCoE by deploying landing zones with AWS Control Tower, managing resource configurations with AWS Config, and reporting control effectiveness with AWS Audit Manager. . As individual business units seek guidance to adhere to AWS recommended best practices, CCoE has developed written instructions and enablement materials to facilitate large-scale adoption across Hearst.
Existing CCoE models had several obstacles that slowed adoption by business units.
- extreme demand – The CCoE team had become a bottleneck, unable to keep up with the growing demand for expertise and guidance. Teams were understaffed, and traditional approaches that relied on human experts to address all questions were hindering the pace of organizations’ cloud adoption.
- limited scalability – As the volume of requests increased, the CCoE team could no longer distribute updated directives fast 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 the CCoE team and disseminate guidance on new policies, compliance practices, or governance controls, consistency based on CCoE best practices across each business unit is difficult to achieve. It was difficult to maintain.
To address these challenges, Hearst’s CCoE team recognized the need to rapidly create scalable, self-service applications that give business units greater access to the latest CCoE best practices and patterns to follow. did.
Solution overview
To enable self-service cloud governance at scale, Hearst’s CCoE team turned to Amazon Q Business to harness the power of generative AI to build conversational assistants. The following diagram shows the solution architecture.
Here are the main steps Hearst took to implement Amazon Q Business:
- Application deployment and authentication – First, the CCoE team deploys Amazon Q Business and integrates AWS IAM Identity Center with an existing identity provider (in this case using Okta) to manage user access and permissions between the existing identity provider and Amazon Q Business. managed seamlessly.
- Data source curation and authorization – The CCoE team created several Amazon Simple Storage Service (Amazon S3) buckets to store selected content, including cloud governance best practices, patterns, and guidance. Set up general buckets for all users and specific buckets for each business unit’s needs. User authorization to documents within individual S3 buckets was controlled through access control lists (ACLs). Add access control information to documents in an Amazon S3 data source using the metadata file associated with the document. This allows end users to only receive responses from documents they are authorized to view. Using the Amazon Q Business S3 connector, the CCoE team was able to synchronize and index data in just a few clicks.
- User access management – Once data sources and access controls are in place, the CCoE team configures user access by business unit, taking into account various security, compliance, and custom requirements. As a result, CCoE can deliver a personalized experience to each business unit.
- User interface development – To provide a user-friendly experience, Hearst built a custom web interface that allows employees to interact with the Amazon Q Business Assistant through a familiar and intuitive interface. This has increased adoption and self-service across business units.
- Deployment and continuous improvement – Finally, the CCoE team shared the web experience with various business units, allowing employees to access the guidance and best practices they need through natural language interactions. Moving forward, the team has strengthened the knowledge base (S3 buckets) and implemented a feedback loop that facilitates continuous improvement of the solution.
For Hearst’s CCoE team, Amazon Q Business was the quickest way to use generated AI on AWS with minimal risk and reduced up-front technical complexity.
- Speed ​​to value was a key advantage. This allows CCoE to get these powerful generative AI capabilities into the hands of employees as quickly as possible, enabling new levels of scalability, efficiency, and innovation for consistent cloud governance across the organization. Now it looks like this.
- This strategic decision to use managed services such as Amazon Q Business at the application layer has enabled CCoE to deliver tangible value to business units in a matter of weeks. By choosing to take the fast-track path to using generative AI on AWS, Hearst avoided getting mired in the technical complexity of developing and managing its own generative AI applications.
Results: Fewer support requests and more consistent cloud governance.
Using Amazon Q Business, Hearst’s CCoE team achieved remarkable results in strengthening self-service cloud governance across the organization. The initial effects were immediate: within the first month, the CCoE team noticed a 70% decrease in the volume of requests for guidance and support from various business units. This allows the team to focus on higher-value initiatives instead of getting bogged down with repetitive, day-to-day requests. Over the next month, the number of CCoE support requests decreased by 76%, demonstrating the power of Amazon Q Business’s self-service assistant. The benefits went beyond reducing request volume. The CCoE team also saw significant improvements in the consistency and quality of cloud governance practices across Hearst, strengthening cloud security, compliance posture, and cloud adoption across the organization.
conclusion
Cloud governance is a set of important rules, processes, and reports that guide organizations to follow best practices across their IT estate. At Hearst, the CCoE team sets the policies and cloud governance standards that each business unit follows. With the implementation of Amazon Q Business, Hearst’s CCoE team can now scale governance and security to support business units through generative AI assistants. Disseminating best practices and guidance throughout the organization frees up resources for CCoE teams to focus on strategic initiatives, gives employees access to self-service applications, and reduces the burden on central teams. I did. If your CCoE team is looking to expand its impact and empower your workforce, consider harnessing the power of conversational AI through services like Amazon Q Business. This positions your team as a strategic enabler of cloud transformation.
Steven Craig talks about how Hearst leveraged Amazon Q Business to scale its Cloud Center of Excellence.
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About the author
stephen craig I am the Senior Director of the Cloud Center of Excellence. He oversees cloud economics, cloud enablement, and cloud governance for all Hearst-owned companies. Previously, he was Vice President of Product Strategy and Operations at Innova Solutions, where he helped migrate applications to public cloud platforms and create IT operations managed services. His leadership and technical solutions were key in achieving successive AWS Managed Service Provider certifications. Steven has been AWS Professional Certified for over 8 years.
Oleg Chugaev He is a Principal Solutions Architect and Serverless Evangelist with over 20 years of experience in IT and holds multiple AWS certifications. At AWS, we power our customers’ cloud transformation efforts by translating complex challenges into actionable roadmaps for both the technical and business layers.
Rohit Chaudhary is a Senior Customer Solutions Manager with over 15 years of diverse technology 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 to accelerate their cloud migration and implement innovative solutions.
Al DeStefano is an AWS generative AI specialist based in New York City. Al leverages his expertise in the AI/ML domain to develop and execute global go-to-market strategies that drive transformative results at scale for AWS customers. He 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.
