Q&A with Cormac Meiners: Operationalizing AI in the Modern Intelligence Landscape — i2 Group - August 6, 2025
By Staff Reports - August 6th, 2025
Artificial intelligence is accelerating the transformation of intelligence operations at a time when the complexity, scale, and urgency of global threats are intensifying. As national security priorities shift from tactical counterterrorism to strategic competition with technologically advanced adversaries, the traditional intelligence cycle — characterized by sequential data collection, human analysis, and post-hoc reporting — is being reengineered.
The intelligence community is increasingly expected to detect adversarial intent across domains such as cyberspace, economics, politics, and information operations — often before physical conflict occurs. AI enables this proactive stance by enhancing the ability to ingest diverse data, identify patterns, and support near-real-time decision-making.
To better understand the opportunities and challenges at stake, we spoke with Cormac Meiners, U.S. Federal Lead at i2 Group, about the strategic, operational, and technological dynamics shaping AI adoption in modern intelligence environments.
Here is what he had to say:
Q: What’s the biggest strategic change the intelligence community is navigating today?
A: The shift from counterterrorism to great power competition has changed the nature of what intelligence is expected to deliver. Instead of focusing on discrete threats with clear timelines, agencies now need to anticipate long-range state-sponsored influence operations, hybrid warfare tactics, and multi-domain disruption campaigns. Intelligence must move from reactive assessments to predictive insight. That shift requires scalable systems capable of correlating subtle signals across sectors, languages, geographies, and platforms — which is where AI plays an essential role. It's not just a tool for faster analysis; it’s a framework for reshaping how insight is generated.
Q: What role does AI play in enabling this predictive approach?
A: AI helps convert vast, fragmented data into structured understanding — at speed. Machine learning models can detect deviations from expected patterns, uncover non-obvious relationships, and surface weak signals that may indicate the early stages of a campaign or coordinated activity. For example, a sudden shift in shipping routes, financial transactions, and social sentiment in a particular region might mean very little when viewed in isolation. But AI can flag that convergence as potentially significant — allowing analysts to dig deeper before an event unfolds. That’s the power of predictive intelligence.
Q: How are human analysts adapting to AI-driven workflows?
A: The analyst's role is evolving. Instead of spending time on manual data sorting or link diagramming, analysts now spend more time interrogating machine-driven findings and validating hypotheses. It’s a shift from data generation to insight curation. But this also places a premium on critical thinking, scenario modeling, and cultural expertise. AI might flag a set of anomalies, but it’s the human analyst who determines their relevance, context, and risk. Training programs must evolve accordingly — not only to teach analysts how to use AI tools but how to question them effectively.
Q: What are the most persistent operational challenges when it comes to AI adoption?
A: There are several, but three stand out: data accessibility, legacy infrastructure, and organizational silos. Many agencies still operate with fragmented systems that make it hard to bring data together in a usable way. Even when data exists, it may be locked behind incompatible formats, outdated security protocols, or classification barriers. AI can't function effectively without access to clean, timely, and relevant information. That makes data modernization and cross-agency interoperability critical operational priorities. Additionally, procurement cycles often lag behind mission needs, making it difficult to onboard and update tools at the pace required by the threat landscape.
Q: What does successful multi-source data fusion look like in practice?
A: In practical terms, it means that analysts can view satellite imagery, SIGINT, financial records, OSINT, and other sources in a single pane of glass — enriched by AI to identify relevance and priority. But it’s not just about aggregation; it’s about meaningful correlation. AI helps normalize and tag data across formats, remove redundancy, and score results based on mission relevance. Done well, data fusion leads to faster assessments, better resource allocation, and earlier threat identification. However, it only works when the underlying data architecture is sound and the analytical models are aligned with operational priorities.
Q: How are agencies addressing the growing demand for transparency and accountability in AI use?
A: Transparency in AI is no longer optional — especially in the intelligence community, where trust and oversight are non-negotiable. Agencies are implementing governance protocols to ensure that AI models are explainable, traceable, and auditable. This includes documenting training data, clarifying the logic behind algorithmic outputs, and providing human analysts with tools to understand — and if necessary, challenge — those outputs. Ethical review boards, red-teaming exercises, and bias mitigation audits are also becoming standard. These measures not only satisfy regulatory requirements but also help build internal confidence in AI tools, which is essential for adoption.
Q: What kind of visualization capabilities are emerging to support analysts?
A: Visual tools are becoming smarter and more integrated. We’re seeing link analysis maps that dynamically update as new data enters the system, geospatial overlays that integrate event timelines with satellite feeds, and dashboards that prioritize high-risk nodes based on AI-generated scoring. These interfaces allow analysts to interact with the data more intuitively, spot trends faster, and communicate findings more effectively. Visualization bridges the gap between machine-driven discovery and human-centered understanding. It helps analysts “see the why,” not just “the what.”
Q: How does AI support faster decision-making without sacrificing rigor?
A: Speed and rigor don’t have to be in conflict. In fact, AI can improve rigor by reducing noise and standardizing how evidence is evaluated. Analysts can focus on high-value findings instead of getting buried in raw data. That said, organizations must build in safeguards — such as confidence thresholds, model validation routines, and escalation protocols — to ensure decisions aren’t made on incomplete or misleading outputs. The objective isn’t just to move faster; it’s to move smarter, with traceable logic and mission-fit outcomes.
Q: Are there risks that the growing reliance on AI could lead to overconfidence or false positives?
A: Absolutely. Like any technology, AI can create blind spots if users assume the system is always right. There’s a risk of what’s called “automation bias,” where human analysts defer to the machine’s output even when it contradicts their own judgment or experience. That’s why we emphasize human-in-the-loop frameworks, continuous training, and cross-validation. Analysts must be empowered to question, contextualize, and even reject AI recommendations when appropriate. Overreliance is just as dangerous as underutilization.
Q: What’s the outlook for AI at the tactical edge — not just in national centers, but in forward-deployed or field environments?
A: We’re already seeing AI models being deployed in tactical environments, where they assist with mission planning, route analysis, and even identifying threats in live sensor feeds. The challenge is ensuring those models are resilient, lightweight, and secure enough to function in disconnected or bandwidth-limited settings. Edge AI requires specialized engineering, but it holds enormous potential for shortening intelligence loops and empowering operators with timely, localized insight. That said, the same governance principles apply — even in austere settings, there must be clarity and accountability around how AI-driven decisions are made.
Q: What should leadership prioritize when integrating AI into intelligence operations?
A: Three things: alignment, adaptability, and accountability. First, align AI capabilities with mission objectives — don’t chase innovation for its own sake. Second, build adaptable systems and teams that can evolve with the threat environment. Third, establish accountability frameworks that govern how AI is used, validated, and improved. This isn’t just a technical evolution — it’s a cultural one. Agencies that embed AI within their strategic mindset, not just their toolsets, will be the ones that thrive in the next era of intelligence.