Thursday, June 6, 2024
Reflections on SPADE 2024

As noted in a recent IBM Institute for Business Value research brief, “Artificial intelligence (AI) has become a critical and strategic capability for defense organizations around the world, offering immense benefits such as improved efficiency, accuracy, and decision making.

It has the potential to revolutionize military operations, improve mission outcomes, and gain decision advantage.”

Recently, I had the opportunity to explore how AI can enhance “decision advantage” across national security, defense, intelligence, and joint force operations. Terry Halvorsen joined me on The Business of Government Hour to discuss this topic and reflect on the themes and insights shared during the SPADE 2024 conference. The following offers an overview of our discussion.

These insights highlight the strategic importance of AI in modern defense operations, the need for high-quality data, and the critical role of international cooperation and ethical considerations in advancing AI applications in the defense sector.

SPADE is annual thought leadership conference, hosted by IBM and AFCEA, tailored for senior defense and intelligence leadership taking place in May 2024. This year’s event brought together key leaders from across U.S. Defense Departments and Services to discuss and exchange ideas on how artificial intelligence (AI) can power “decision advantage” across the defense, security, intelligence, and joint force operations.

Powering Decision Advantage

Decision advantage refers to the ability to make decisions faster and more accurately than one's adversary. 

AI can power decision advantage by eliminating time-consuming manual processes, presenting data in a more digestible format, and providing insights that inform decision-making. Decision advantage involves making impactful decisions faster than adversaries, thereby influencing the battlefield more efficiently. AI can enhance decision-making speed and accuracy. It helps by quickly analyzing large volumes of data and providing actionable insights, thus allowing for faster and more refined decisions.

AI in Defense

The adoption of AI in defense varies by nation and specific focus areas. AI in defense is predominantly in its early stages, with significant use on the business side (e.g., logistics, cybersecurity) and gradually moving to operational applications.

AI is currently being used as a force multiplier, helping to analyze large amounts of data and freeing up analysts to focus on higher-level decision-making.

The quality of data is key to successful AI applications, and the industry and government need to work together to mature the data and ensure its accuracy and auditability.

There is consensus on the need to maintain human oversight in AI-driven decision-making, especially in lethal operations. Ensuring the integrity and ethical use of data is paramount. The process must uphold responsible and ethical standards in AI deployment.

The quality of AI has improved significantly, with better ability to verify and audit data, and to understand intent behind questions. The training of AI models has also become faster and more accurate. The industry has moved from novelty to more practical applications of AI, with a focus on ensuring the accuracy and reliability of AI-generated data.

Decision-Making Speed and Data Presentation

AI is being used to assist critical decision-making, but not replace human judgment.

The application of AI can drastically reduce the time taken to analyze data, potentially cutting it down to one-third of what a human would require. This efficiency gives a significant advantage, enabling faster and more accurate decision-making. AI can present data in a digestible format for commanders, moving beyond inundating them with raw data to providing actionable insights.

For example, AI can quickly process images to identify specific types of vehicles and weapons, which would take a human significantly longer. The AI can then refine this data further to identify which vehicles are in a state of readiness to engage. This processed and visualized data can be reviewed by analysts who can add their experience to the AI-generated insights, significantly speeding up the overall process from hours to potentially just an hour. In critical battlefield situations, such speed can be a matter of life or death, giving a strategic advantage by allowing quicker and more informed reactions to enemy actions.

Data Integration and Open-Source Data Use

The data fabric now referred to as the data mesh is a critical advancement in data integration.

The data mesh enables the integration of data from various sources, including defense, economics, and politics, to provide a more comprehensive picture for commanders.  

The concept of all-domain refers to the need for commanders to consider multiple domains, including defense, economics, politics, and more, in their decision-making. AI can help with integrating data from these various domains to provide a more comprehensive picture. It can help with data analysis, distribution, and prioritization, but it also requires careful consideration of data value and security.

One significant advancement is the use of open-source data for defense purposes. This includes using publicly available information, such as traffic data or social media, to gain tactical advantages. In Ukraine, open-source data has been used effectively to anticipate enemy movements and plan responses.

This integration is powered by machine learning and AI, highlighting the practical applications of these technologies in real-world scenarios. One of the challenges is ensuring the reliability of open source data. AI helps by verifying the integrity of this data, checking for consistency, and presenting it in a trusted format to commanders.

Successful Partnerships and Collaborations

Collaborative efforts between various stakeholders, including defense organizations, industry partners, and academia, have led to the development of robust AI ethics and principles. These guidelines serve as a foundation for responsible AI use across defense sectors. Collaborative discussions have helped in addressing ethical concerns related to AI, such as bias, fairness, and transparency.

Collaboration has also been instrumental in initiatives aimed at improving data quality and integrity. By working together, stakeholders can implement processes and technologies to enhance the reliability and trustworthiness of data used for AI applications.

Challenges and Strategic Considerations

The conversation at the conference included discussion of supply chain security, which is critical in defense operations.

Secure access to supply chains is essential, as well as ensuring that equipment is not corrupted.  AI can help with supply chain management, including identifying potential risks and vulnerabilities.

A major challenge discussed was the security classification of data. Determining what data can be shared and with whom, without compromising security, is a complex issue that AI can help manage by prioritizing and categorizing data based on its importance and time sensitivity.

The strategic use of AI involves not just making tactical decisions but also ensuring that these decisions do not compromise broader strategic advantages.

This includes knowing when to act on certain data and when to withhold it to avoid revealing capabilities to adversaries.

Challenges in Scaling AI

The conference identified several key challenges to scaling AI that include:

  • Workforce: A shortage of people with the necessary skills to understand and implement AI effectively.

  • Funding: Shifting funding from legacy programs to AI programs, while maintaining bipartisan support.

  • Trust: Educating users (military and civilian) about AI capabilities and limitations to build trust.

Future Trends in AI for Defense

The conference identified several key trends that include:

  • Logistics: AI is expected to play a significant role in improving logistics processes.

  • Mission Planning: AI can help analyze data faster, facilitating higher quality mission planning and scenario simulations.

  • Cost Reduction: AI is expected to reduce costs associated with logistics and mission planning.

Key Takeaways

  • Consensus on the Importance of AI: There was widespread agreement among participants that AI will play a crucial role in future defense operations. Decision superiority was highlighted as a key factor in determining success.

  • Emphasis on Data Quality: There was a recognition of the essential link between AI and the quality of data. Participants acknowledged the need to invest time and resources in ensuring the reliability and accuracy of data before deploying AI solutions.

  • Areas for Advancement: Logistics and mission planning were identified as areas where AI can be rapidly advanced. These domains offer opportunities to build trust, educate stakeholders, and accelerate adoption.

  • Collaboration and Education: Collaborative efforts between industry, academia, and government are crucial for advancing AI in defense. Additionally, there's a need for ongoing education at all levels to ensure stakeholders understand the capabilities and limitations of AI.