A picture of the capitol building in Washington, D.C.

A picture of the capitol building in Washington, D.C. Caleb Perez

AI is no longer just a technology or a business case, it’s a key enabler of an agency’s mission and strategy

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Artificial intelligence (AI) is one of the most significant contextual changes for the U.S. federal government in the past decade. Trained to follow specific rules, do a particular job, and do it well, traditional AI doesn’t create anything new, it’s instead designed to respond to a specific set of data inputs within the guardrails of a predefined strategy. The technology has enabled important advancements for many federal agencies in operational efficiency, productivity and decision making. For example, traditional AI has helped the Internal Revenue Service (IRS) speed up the processing of paper tax returns (and the delivery of tax refunds to citizens), the Department of Veterans Affairs decrease the time it takes to process veteran’s claims, and the Navy’s Fleet Forces Command better plan and balance food supplies while also reducing related supply chain risks. 

Whereas traditional AI excels at pattern recognition, generative AI excels at pattern creation. Generative AI is a form of AI that can create something new – including content and images – based on a large volume of training data. With generative AI, large datasets can even be pre-trained without a specific task in mind. Federal agencies for example, could later specialize these AI models, known as foundation models, with much less data to perform a specific task. Foundation models offer an opportunity to accelerate and scale generative AI adoption. The effects of generative AI are wide-ranging and can potentially be applied to many use cases including:

  • Talent

Human resources (HR) teams are constantly being asked to do more with less. They need to drive engaging experiences, provide data-driven insights, and operate more efficiently. By leveraging generative AI and training foundation models with agency-specific HR data, teams could augment and enhance productivity by leveraging AI to help complete tasks such as sorting and classifying written content and creating job postings. 

  • Citizen services

Citizen expectations that government services can operate with the agility and efficiency they’re accustomed to continues to rise. Using natural language processing (NLP) and generative AI agencies could uplevel the citizen experience with advanced AI chat bots and AI-powered agents for basic questions and quicker streamlined delivery of important public information. 

  • Application modernization

Developers could enhance their productivity using generative AI to create starter code and playbooks that they can build on.

As with any disruptive technology, generative AI also comes with potential risks. Trust, cybersecurity, privacy, accuracy, transparency and governance are no longer just buzz words – they’re prerequisites. Agencies must be able to correctly identify, quantify, or manage potential risks, when it comes to both AI data sources and the final output. As federal agencies continue to explore their options for implementing AI, enterprise-ready platforms, end-to-end tooling and technical expertise can support their journey, but there are still factors to keep in mind when adopting AI.

  • Building trustworthy AI is critical

As agencies develop and deploy AI, they need assurance that the AI they’re using for mission-critical decisions is built responsibly. AI must be designed to be explainable, fair, robust and transparent, all while prioritizing and protecting citizens’ privacy and data rights to help promote trust. 

  • Solutions should be tailored to federal agencies’ unique needs

The key to governments’ effective use of AI is the ability to customize and adapt to an audience’s specific needs and priorities. The advantage of generative AI and foundation models is rooted in the ability to customize by tuning insights and outputs based on unique data and domain knowledge with specificity that was previously very difficult and labor intensive.

  • AI environments should have governance and flexibility at their core

AI initiatives must evolve based on changing demands and opportunities. At the same time, the technology must not only promote trust and uphold governance for mission-critical decision making, but also offer the flexibility to effectively navigate the complexities of regulatory and compliance demands. A secured hybrid cloud approach, for example, enables data integration, scalability and adoption of new processes, workflows and technologies (like generative AI). 

IBM is empowering the federal government in the age of AI

IBM collaborates with federal agencies to transform programs and optimize operations with consulting services and cutting-edge software, turning technologies into business results and helping agencies achieve mission outcomes with AI and automation. We and our ecosystem of business partners help drive digital transformation of government by building and orchestrating open, secured hybrid cloud environments, improving data integration, and developing trustworthy AI. IBM watsonx, our enterprise AI and data platform, embodies these principles by offering an efficient and governed approach to AI development and deployment across a variety of environments. IBM stands ready to empower federal agencies in the age of AI.

Click here to learn more about how IBM supports federal agencies' drive toward responsible AI development. 

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