Leveraging Artificial Intelligence Requires Real Intelligence

So Why AI?

How productive would you be if you had the information you needed on demand and it was being pushed to you versus having to ask for it? Artificial Intelligence (AI) promises to deliver what we perceive as a competitive advantage, regardless of your industry.

Your ability to translate enterprise data into action IS the true competitive advantage. This competitive advantage is given to whichever organization has access to their enterprise data, in context, the quickest. The faster an organization can harvest the power of its collective enterprise data, the quicker it can take intentional actions on precise targets.

This journey to gain a competitive advantage is hinged on an organization’s ability to integrate and correlate data across its respective IT-enabled ecosystems, including external integrations. This requires the execution of a carefully thought-out and detailed plan. However, the path to AI integration is often overlooked or minimized.

This data integration and correlation continuum is based on establishing context and understanding. This journey includes:

• Data: This is simply the 1’s and 0’s
• Information: Initial context provides “Who, What, When, and Where”
• Knowledge: Additional context provides the “How”
• Wisdom: Ultimate context provides the “Why”

There are a few challenges we face along the journey to deliver knowledge and wisdom to decision makers and people delivering the last step mission actions. The timeline for gaining the competitive advantage is directly associated with an organizations ability to navigate these challenges. These are not ranked in order of importance.

• Procurement Hurdles: Complex acquisition processes delay the adoption of AI tools. ​
• Data Silos & Quality: Fragmented and unstructured data impede effective AI training and deployment. ​
• Legacy Systems: Outdated infrastructure complicates the integration of modern AI solutions.
• Cultural Resistance: Institutional inactivity and skepticism towards new technologies slow adoption. ​
• Talent Shortage: A deficit of skilled AI professionals hampers development and implementation efforts.
• Funding Constraints: Limited budgets and rigid funding cycles limit the scope of AI initiatives. ​
• Ethical & Legal Issues: Ensuring AI systems operate within legal frameworks and ethical guidelines.​
• Interoperability Issues: Difficulties in ensuring AI systems work seamlessly across different platforms and agencies. ​
• Trust & Explainability: Concerns about AI decision-making transparency affect user trust and acceptance.
• Security Concerns: The potential for AI systems to be manipulated or to inadvertently leak sensitive information raises caution.

Recommendations for Leveraging AI:

1. Policy Reform, with the goal to enable agility, trust, and compliance.

• Modernize procurement processes: Streamline acquisition to support iterative, flexible tech adoption (e.g., adopting “other transaction authority” methods).
• Establish clear AI governance at the highest level: Define responsibility, risk frameworks, ethical standards, and accountability across agencies.
• Promote interagency collaboration: Mandate shared data standards and AI practices through federal directives or National AI Strategies.
• Support data-sharing: Create secure, lawful pathways for intelligence agencies to access and share data needed for AI.

2. Investment in Infrastructure and Talent, with the goal to build a technical foundation and workforce capable of sustained innovation.

• Modernize data infrastructure: Implement cloud-native architectures, scalable storage, and edge computing to support real-time AI.
• Fund AI R&D programs: Invest in secure, mission-focused AI/ML tools, emphasizing adversarial robustness and explainability.
• Develop AI talent pipelines:
  • Expand fellowships and exchange programs with academia and industry.
  • Incentivize STEM careers in national security (e.g., scholarships, fast-track hiring).
  • Reskill the current workforce in AI fluency, including prompting

3. Foster a Culture Open to Innovation, with the goal of encouraging experimentation, learning, and cross-disciplinary collaboration.

• Create internal AI incubators: Like DIU (Defense Innovation Unit) or IARPA, foster “safe zones” for piloting new tech with lower risk tolerance.
• Reward experimentation and learning: Shift from a “failure-averse” to a “fail-fast, learn-fast” mindset through leadership training and performance incentives.

Agency objectives and corresponding policies have to be translated into requirements for each new program/project during the Service Strategy and Service Design phases. Critical existing projects should be evaluated and impacted for future funding requirements to ensure compliance and ultimately alignment with the agency’s journey.

Gaining a competitive advantage is a journey, and it has to be done with intentionality and hard decisions. Artificial Intelligence is not a destination, but an IT-enabling capability to assist with delivering a competitive advantage. The next new capability is on the horizon, but the challenges and desires of obtaining access to enterprise data will remain until we collectively address them.

Chenega Agile Real-Time Solutions, LLC (CARS), an Alaska Native-Owned subsidiary company of Chenega Corporation, has found ways to address all of the AI intragrain concerns. CARS works with our customers to review their individual needs to help them create a plan to implement AI and give them the competitive advantage. With most of our customers within the Department of Defense (DoD) and Intelligence Community (IC), CARS has a history of providing professional support services and IT solutions in custom and complicated environments.