Generative artificial intelligence (AI) promises to capture the imagination of organizations across all industries, revolutionizing workflows and driving innovation. As public sector organizations explore this innovative technology, key challenges emerge to identify and prioritize high-value use cases that align with specific business objectives and deliver measurable outcomes. I am.
In this post, we introduce the Amazon Web Services (AWS) framework to help public sector organizations advance the adoption of generative AI and realize its true potential. By following a systematic process based on business strategy and value mapping, teams can prioritize high-impact use cases, align stakeholders, and measure the tangible benefits of generative AI initiatives.
In addition to aligning generative AI development priorities with business needs, leaders and technologists must fully understand the capabilities of generative AI to validate whether it is the right tool for their operations. There is a need. According to a study by researchers at Harvard Business School, generative AI can improve the performance of highly skilled workers by 40% on tasks well suited to the technology. Conversely, when generative AI is used beyond its current limits, its use reduces the performance of highly skilled workers by an average of 19%.
Without a clear understanding of both the technology and how to measure business value from the proposed solution, public sector organizations may find that the return on investment in innovation is limited or unspecified.
Successful AI adoption can take many different paths, each depending on an organization’s mission and requirements. Across thousands of customers, we identified a common set of events called the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI (AWS CAF-AI). The following diagram shows the value chain for artificial intelligence cloud transformation.
Figure 1. AWS CAF-AI transformation value chain diagram.
Here are the steps for your AI transformation:
Understand what AI can do and work backwards from there. Define expected long-term business outcomes. Open up the transformation your business has to go through. Develop the foundational capabilities that will enable this journey.
In the following sections, we explore each of these areas in detail through the lens of AnyOrganization, a public sector organization in the field of financial regulation.
Understand what AI can do and then work backwards to understand it.
The Chief Operations Officer (COO) at AnyOrganization recently read an AWS blog post that talked about generative AI for the public sector. COOs believe generative AI has the potential to address some of their organizations’ biggest challenges in customer experience, process improvement, and employee productivity. After consulting with the team, the COO develops proof-of-concept (POC) project recommendations that align with one of the existing objectives and key results (OKRs): improving the agency’s inspection coverage.
Define expected long-term business outcomes
The COO understands that for this POC to be successful, they must accurately predict the expected business value and how it will be achieved. Their OKRs to improve the agency’s inspection coverage stem from the inherent limitations of human inspectors. Due to the limited availability of qualified labor, examiners can only thoroughly review a small portion of documents. Recent events in the financial markets have raised expectations that AnyOrganization can accomplish more with less.
After consulting with the AWS account team, IT, and business stakeholders, the COO approves the following expected business outcomes after implementing the new generative AI solution.
Increase the percentage of documents reviewed using generative AI from 20 percent to 100 percent while maintaining human review at the existing 20 percent level. Effective use of document pre-screening with generative AI results in 20 percent of the most relevant documents being reviewed by humans, resulting in increased examiner job satisfaction and higher levels of 50 percent more findings requiring review.
Unlock the transformation your business must go through
AnyOrganization COO reviewed AWS best practices and found that an organization’s ability to derive measurable business value from AI-driven innovation starts with ensuring the following four transformation domains are in place:
transformation domain
Technology – Does your development team have access to the AI and machine learning (ML) tools and services they need? Are your existing service enablement procedures ready to evaluate, approve, and secure these powerful tools?
Processes – Which long-standing organizational processes need to evolve to optimally use new technologies? Are existing data management practices sufficient to power the AI/ML flywheel? ?
Organization – How are your business and technology teams aligning their efforts to leverage AI to create customer value and achieve strategic intent? Legal and compliance teams are aligning their efforts to leverage AI to create customer value and achieve strategic intent. Need tight integration?
Product – How can you rethink your business model to create new value propositions (products, services) and revenue models that leverage the power of AI? Where will you allocate the new capacity freed up by delivery enhancements? ?
Transforming these domains and enabling them to use AI relies on fundamental business, talent, governance, platform, security, and operational capabilities.
The basic features that make this journey possible
AWS CAF provides six perspectives to see the capabilities needed for successful AI adoption.
Business – This perspective helps ensure that AI investments accelerate digital and AI transformation ambitions and business outcomes. Specifically, we will share how to put AI front and center to reduce risk, increase customer outcomes and outcomes, and effectively enable AI strategy development.
People – This perspective acts as a bridge between AI technology and business, aiming to evolve a culture of continuous growth and learning where change is the norm. AWS offers numerous options to increase your and your team’s generative AI knowledge. AWS Skill Builder has a variety of free, on-demand options for generative AI. Of particular interest to readers of this post may be Generative AI Learning Plans for Decision Makers.
Governance – This perspective helps align AI efforts while maximizing organizational benefits and minimizing transformation-related risks. We address the changing nature of risk, namely the costs associated with developing and scaling AI. Additionally, we are introducing new AWS CAF-AI capabilities around the responsible use of AI.
Platform – This perspective helps you build an enterprise-grade, scalable cloud platform on which you can operate AI-enabled or AI-infused services and products and develop new custom AI solutions. Learn how AI development differs from typical development tasks and how practitioners can adapt to the changes.
Security – This perspective helps ensure confidentiality, integrity, and availability of data and cloud workloads. We extend existing security guidance with this perspective, showing how to infer attack vectors that affect AI systems and how to counter them through the cloud.
Operations – This perspective helps ensure that cloud services, especially AI workloads, are delivered at a level that meets business needs. We provide guidance on how to manage your operational AI workloads, keep them up and running, and ensure reliable value creation.
AWS account teams can assess the structure of their current capabilities through mechanisms such as AWS Cloud Maturity Assessment (CMA), Experience-Based Acceleration (EBA), or Executive Briefing Center (EBC) sessions focused on shaping a generative AI strategy. We can assist you in arranging a standardized assessment. .
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