AWS Public Sector Blog
Working backwards from generative AI business value in the public sector
Generative artificial intelligence (AI) has captured the imagination of organizations across industries, promising to revolutionize workflows and drive innovation. As public sector entities explore this transformative technology, a critical challenge emerges: identifying and prioritizing high-value use cases that align with specific business objectives and delivering measurable outcomes.
In this post, we present an Amazon Web Services (AWS) framework to help public sector organizations navigate generative AI adoption and unlock its true potential. By following a systematic process rooted in business strategy and value mapping, teams can prioritize high-impact use cases, align stakeholders, and measure the tangible benefits of their generative AI initiatives.
In addition to matching generative AI development priorities to business needs, leaders and technologists must sufficiently understand the capabilities of generative AI in order to validate if it is the right tool for the job. According to a study by researchers at Harvard Business School, generative AI can raise the performance of highly skilled workers by 40 percent on tasks well-suited to this technology. Conversely, when generative AI is used outside the frontier of current limitations, its use reduces highly skilled worker performance by an average of 19 percent.
Without a clear understanding of both the technology and how to measure business value from the proposed solution, public sector organizations may see their innovation investment return limited or unidentifiable.
Successful AI adoption follows many different paths, each of which is unique to the organization’s mission and requirements. Across thousands of customers, we’ve identified a common sequence of events we call the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI (AWS CAF-AI). The following diagram shows the Artificial Intelligence cloud transformation value chain.
Figure 1. Diagram of the AWS CAF-AI transformation value chain.
The AI transformation steps are as follows:
- Work backward from your understanding of what AI enables you to do.
- Define what your expected business outcomes are over time.
- Carve out the transformation that your business has to go through.
- Develop the foundational capabilities that enable this journey.
In the following sections, we provide a deeper look into each of these areas through the lens of AnyOrganization a public sector organization in the financial regulation space.
Work backward from your understanding of what AI enables you to do
The AnyOrganization chief operations officer (COO) recently read an AWS blog post that addressed generative AI for the public sector. The COO thinks that generative AI has the potential to address some of their organization’s largest challenges in customer experience, process improvement, and employee productivity. After consulting with their team, the COO prepares a recommendation for a proof of concept (POC) project aligned to one of their existing objectives and key results (OKRs): improve coverage of agency examinations.
Define what your expected business outcomes are over time
The COO knows that in order for this POC to succeed, they must accurately forecast the anticipated business value and how they will achieve it. Their OKR, improve coverage of agency examinations, stems from an inherent human examiner limitation. Given their limited qualified workforce, examiners can only thoroughly review a small percentage of documentation. Recent events in the financial markets have placed a higher expectation on AnyOrganization to achieve more with fewer resources.
After consulting with their AWS account team, IT, and business stakeholders, the COO approves the following expected business outcomes after the implementation of the new generative AI solution.
- Increase the percentage of reviewed documentation from 20 to 100 percent using generative AI, while maintaining human review at the existing 20 percent level.
- Effective use of generative AI prescreening of documentation results in humans reviewing the most relevant 20 percent of documents, resulting in increased examiner job satisfaction and 50 percent more findings that require higher-level review.
Carve out the transformation that your business has to go through
The AnyOrganization COO reviewed AWS best practices and saw that an organization’s ability to derive measurable business value from AI-driven innovation starts with ensuring the following four transformational domains are in place.
Transformational domains
Technology – Do your development teams have access to the AI and machine learning (ML) tools and services needed? Are your existing service enablement procedures ready to evaluate, approve, and safeguard these powerful tools?
Process – Which longstanding organizational processes need to evolve to make optimal use of the new technology? Are existing data management practices sufficient to fuel your AI/ML flywheel?
Organization – How do your business and technology teams orchestrate their efforts to create customer value and meet your strategic intent, driven by AI? Do your legal and compliance teams need to integrate with development more tightly?
Product – How can you reimagine your business model to create new value propositions (products, services) and revenue models that capitalize on the capabilities of AI? Where will you allocate new capacity unlocked by enhancements in delivery?
Transforming these domains and enabling them to use AI depends on your foundational capabilities in business, people, governance, platform, security, and operations.
Foundational capabilities that enable this journey
The AWS CAF provides six perspectives from which to view the capabilities required for successful AI adoption.
Business – This perspective helps ensure that your AI investments accelerate your digital and AI-transformation ambitions and business outcomes. In particular, we share how to make AI center-stage, reduce risks, and increase outputs and outcomes for customers, effectively enabling the formulation of an AI strategy.
People – This perspective serves as a bridge between AI technology and business and aims to evolve a culture of continual growth and learning, where change becomes standard. AWS provides numerous options for boosting the generative AI knowledge of you and your teams. AWS Skill Builder has a variety of no-cost, on-demand options for generative AI. Of specific interest for readers of this post might be our Generative AI Learning Plan for Decision Makers.
Governance – This perspective helps you orchestrate your AI initiatives while maximizing organizational benefits and minimizing transformation-related risks. We address the changing nature of the risk and, therefore, the cost that is associated with the development and scaling of AI. Additionally, we introduce a new AWS CAF-AI capability to this perspective: The responsible use of AI.
Platform – This perspective helps you build an enterprise-grade, scalable cloud platform that enables you to operate AI-enabled or -infused services and products and to develop new, custom AI solutions. We illustrate how AI development is different from typical development tasks and how practitioners can adapt to that change.
Security – This perspective helps you achieve the confidentiality, integrity, and availability of your data and cloud workloads. We extend our existing security guidance in this perspective to show how you can reason about the attack vectors affecting AI systems and how to address them through the cloud.
Operations – This perspective helps ensure that your cloud services, particularly your AI workloads, are delivered at a level that meets the needs of your business. We provide guidance on how to manage operational AI workloads, how to keep them operational, and how to ensure reliable value creation.
Your AWS account team can assist with arranging structured assessments of your current capabilities through mechanisms such as the AWS Cloud Maturity Assessment (CMA), Experience Based Acceleration (EBA), or an Executive Briefing Center (EBC) session focused on forming your generative AI strategy.