Generative Artificial Intelligence

Leadership in GenAI

Booz Allen publishes research across all facets of foundation models and is a leader in operationalizing these powerful technologies for the unique, mission-critical requirements of the federal government. Through these efforts, we understand that achieving mission impact with GenAI means addressing a host of risks, including cost, speed, privacy, bias, accuracy, and more, and navigating complex challenges associated with engineering high-performing GenAI applications, such as workflow orchestration, model steering, and grounding.

What sets our approach apart is a blend of unique mission insight, proven risk-management tools, an overriding commitment to responsible AI, and sector-leading expertise in foundation models—from engineering of large-scale enterprise AI/machine learning solutions to technical research and development to secure, fine-tune, evaluate, and optimize these foundation models. Together, these strengths empower us to help clients understand where GenAI is headed, adapt the technology for their specific mission use cases, prepare their workforce to thrive with AI, and operate confidently.

The Power of GenAI, Realized

Booz Allen provides comprehensive support to help agencies use GenAI to transform operations, taking into account its differences from traditional systems and other forms of AI in infrastructure and other areas. Support includes analyzing optimal uses of GenAI, quickly evaluating risk-reward tradeoffs, providing integration with leading platforms, and operationalizing new capabilities in applications central to the mission.

AI Strategy and Evaluation

Drive transformation and operate with confidence

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  • Use Case Identification and Prioritization
  • Enterprise AI and Data Architecture
  • Workforce Training and Change Management

GenAI Mission Applications

Transform mission outcomes with tailored solutions

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  • Intelligent Knowledge Management
  • Conversational AI for Call Centers and Help Desk
  • Generative Software Development
  • AI Agents for Workflow Orchestration

Pipeline and Model Engineering

Automate data-to-decision advantages across cloud to edge technologies

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  • Third-Party LLM Evaluation
  • Mission-Tailored LLM “Tuning” and Optimization
  • Data Pipeline Management
  • Synthetic Data Generation

Commercial and Venture Tech Integration

Accelerate delivery of AI in production environments for operational use and fast-track breakthrough AI

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  • Platform Engineering (Cloud and Hybrid Cloud Designs)
  • Infrastructure Integration
  • Tech Scouting and Integration

GenAI Governance and Assurance

Build and maintain credible and dependable AI solutions

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Federal Use Cases for GenAI

Numerous government use cases can be addressed by building upon GenAI’s core capabilities, which include automated knowledge and data management; text, audio, image, video, and code generation; and enhanced search and summarization. Key federal use cases are rapidly coming into focus:

Customer Service and Help Desk

Autonomous chatbots can be trained to fulfill many time-critical, resource-constrained customer service functions, from gathering information and educating users to answering specific questions and delivering proactive alerts. In some cases, internal help desk operations are emerging as a low-risk test bed for building competency while generating significant return on investment.

Content Analysis and Synthesis

Across government, federal agencies can struggle to transform the vast amount of data and information at their disposal into knowledge and insight. GenAI is a powerful natural language processing tool for search, aggregation, summarization, analysis, and content creation—one that empowers analysts, scientists, and researchers to produce the findings and intelligence needed to advance the mission.

Planning and Scenario Analysis

GenAI can serve as a critical tool for enabling synthesis of complex and interrelated data streams to aid in planning and scenario analysis situations that are critical to the success of defense, intelligence, and civil missions. GenAI can reduce the amount of time spent on mundane, time-consuming tasks and allow decision makers to concentrate on tasks that require higher level judgment. 

Claims Adjudication and Policy Bot

GenAI can dramatically accelerate and improve decision support around claims adjudication and policy assessment. For example, analysts apply GenAI tools to generate and test policy scenarios using open source or proprietary data. LLMs can simplify the assessment of requirements and issuances, accelerate archive searches, and enable prediction and comparison of policy outcomes to guide decisions.

Software Development and Infrastructure Management

GenAI is already demonstrating its potential as an effective “co-pilot” for automating code delivery via text completion. It is poised to play a similar role in IT operations and cybersecurity management, providing real-time analysis of endless logs for anomalies and automating alerting, response, and patching across complex infrastructures.  Learn more about our dedicated AI for Software Development offerings.

Strategic Research and Analysis

Agencies can leverage GenAI to spur innovation through interactive scenario planning, prototype design, and discovery of new models, such as proteins and material compounds. These guided explorations can drive transformative breakthroughs in fields as diverse as climate change, healthcare, and national security.

GenAI Capabilities

As the single largest provider of AI services to the federal government, Booz Allen is uniquely equipped to provide secure and responsible GenAI capabilities to agencies across the defense, national security, and civil sectors. Government leaders can quantify the real-world impact of GenAI and put responsible safeguards in place through our rigorous method for measuring and managing the ethical risk of AI systems.

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Ranked 1st in AI Services for the Federal Government, with More Than 3 Times the Federal AI-Related FY20-22 Contract Obligations of Nearest Competitor

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2,100+ Data Scientists and Engineers Supporting 200+ Active AI Projects for U.S. Government Agencies

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Strategic Partnerships with AI Leaders, including Amazon Web Services, Google, Microsoft, and NVIDIA, and Innovators, such as Credo AI, Latent AI, and Synthetaic, Alike

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15+ Peer-Reviewed Publications on GenAI Since 2021 and a Dedicated AI Adoption Studio at The Helix, Booz Allen’s Center for Innovation

Getting Started with GenAI

Alison Smith, Director of GenAI for Booz Allen, discusses how federal leaders can take advantage of GenAI and how to avoid potential pitfalls.  

Full Transcript of Video

My name is Alison Smith, and I'm a director of Generative AI at Booz Allen. We have some clients asking us, what is it? You know, they want to understand what generative AI is and how it's different from some of its preceding technologies, right? But then you have other clients who are kind of ready and wanting to know what other organizations are already doing. And then finally, you have this other subset of clients, typically ones that are a little bit more advanced, who've done some pretty large kind of deployments of large language models where they might ask, you know, how is this going to change the platform that I already have? You know, what tweaks do I need to do to actually use it? And then lastly, all of them are always asking, you know, all my leadership is super excited about generative AI. How is it different than traditional AI and where does it actually make sense? Because I hear it's very expensive. Generative AI introduces a lot of new challenges that traditional AI didn't really have. The first one that really comes top of mind is interpretability. So the outputs of the generative AI system can be different every time, even if you ask it the same question. And so those outputs, you know, aren't always interpretable. In fact, it's really hard to explain why a generative AI system produced or created the sequence of words that it did. And in use cases where it's really important to explain why a decision was made or why, you know, a report looks the way it does, it's going to be really hard for generative AI system to explain that or for even a human to try to explain it. There are a few other challenges that include the bias in data. Generative AI systems have been trained on a whole lot of data, and you can't even imagine what types of biases are inherent to that data. And that's not something you can just pull out from a foundation model. And so it would be important to think about the guardrails that you need in place and having a human in the loop to make sure that we're not perpetuating especially some harmful biases. Another one would be thinking about the security. So generative AI systems are very expensive to build, and you have these tech companies that are pouring in millions and billions of dollars into building these foundation models. And so you can't expect your average organization to build one for themselves. And so they're going to be using other people's tools and foundation models. And so we really need to think about how to keep that sensitive or proprietary data that these firms have secure when they're using these external tools. And then lastly, we have to think about IP rights. All of that still remains very uncertain. A lot of the training data, some might argue, included some copyrighted or even patented information. And so it's hard to say whether it's okay for a foundation model to have that information. And if it accidentally produces output that was partially copyrighted, then, you know, is it okay to use it? And then lastly, the outputs that it does create, if that's going to be some form of intellectual property for your enterprise or for any organization, you have to think about where policy is going on that and seeing whether you know, you can actually have rights to something that was created not by a human or your organization, but a generative AI system. The government holds itself to a much higher standard, than you have to necessarily for some companies, right? You have companies that can use generative AI for recommendation engines. In terms of you know, what movie to watch next or what clothing to buy. And those are relatively lower risk. And when you're working with the federal government who have missions that really impact people's entire lives, they have to maintain a standard that is much higher in terms of security and accuracy. And so while the government may be slower to act on generative AI, it's going to be paving the way in terms of how it's thinking about policy for AI, how to maintain security and how to think about it from a very responsible and ethical framework. And I think those are all areas where you're going to see the government innovate potentially even faster than industry because they have to. It's sort of limitless at this point, and it's really exciting to be at the precipice of something totally new that's going to create a huge shift in how we do work. 

Explore Our Latest Thought Leadership

Explore AI Insights for Federal Innovators

 

Velocity, an annual Booz Allen publication, studies the complex issues that are emerging for mission and technology leaders on the front lines of government innovation.

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In a new whitepaper, our experts discuss how ChatGPT redefines the LLM marketplace, the technology’s current strengths, potential use cases, and gaps, and how federal agencies can get started with it today.  Explore What to Know About ChatGPT and AI’s Future to learn how your mission might benefit from ChatGPT.

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Discovering What’s Possible with Targeted Research

Booz Allen has been at the forefront of LLM research since the technology’s emergence, exploring and documenting strengths and capabilities as well as potential risks and gaps. For example, we have produced over 15 peer-reviewed publications on GenAI since 2021, many accompanied by source code and data, and have presented our findings at leading scientific conferences, including:

  • Conference on Neural Information Processing Systems (NeurIPS)
  • Conference on Empirical Methods in Natural Language Processing (EMNLP)
  • Association for Computing Machinery (ACM) Conference on Fairness, Accountability, and Transparency (FAccT)
  • ACM International Conference on Information and Knowledge Management (CIKM)

This research and insight help us train and fine-tune popular LLMs for uniquely governmental tasks and roles. Specific focuses of our published research to date include:

Published Research

Image Generation and Editing with Natural Language Guidance
Understanding How Memory Works in LLMs

Contact us to learn more about harnessing the power of GenAI to transform critical missions.