The U.S. Intelligence Community's Kodak Moment

The game is changing rapidly. Can Washington's intelligence community keep up?

In 2012, the once-mighty Eastman-Kodak company declared bankruptcy. It was an event that should have reverberated strongly with the United States Intelligence Community (IC)—and not just due to the obvious connection between imaging and spying. Rather, it should have resonated because in Kodak the IC could have glimpsed a reflection of itself: an organization so captivated by its past that it was too slow in changing along with its environment.

To understand the IC’s similar captivation and lethargy—to remain focused on classified collection in an era of increasingly ubiquitous, useful and unclassified data—one must first understand the type of problem around which the modern IC business model remains designed: the Soviet Union. The Soviet Union was fundamentally a collection problem. That is to say, it was a closed system (i.e., a discrete entity) with clear edges and a hierarchical governance structure. Given that nature, knowing what was happening in the Soviet Union required the use of classified means of collection—most of which the IC alone possessed.

Today, however, the IC no longer has the luxury of watching a single discrete entity that demands classified collection in order to obtain relevant data. There is a much more expansive range of interconnected and complex challenges. These challenges—economic contagion, viral political and social instability, resource competition, migration, climate change, transnational organized crime, pandemics, proliferation, cyber security, terrorism, etc.—are interdependent phenomena, not discrete ”things.” As such, they are less collection issues than cognitive ones. To put it differently: relevant data about all these issues is widely available—the real challenge is to make sense of it.

This, of course, is a very different world for the IC, one in which it has little experience. Consequently, the IC—unfortunately, but not surprisingly—does what it knows; it grafts its own legacy experience and expertise—classified collection—onto the new challenges that loom. Accordingly, terrorism (a broad phenomenon that needs to be thought about contextually) becomes—mistakenly—about terrorists (distinct things that need to be targeted for collection). Indeed, the whole slew of complex issues mentioned above get artificially and erroneously reduced to discrete chunks. Not only is this dangerously simplistic, it effectively puts the IC on a divergent path from the increasingly complex world it is tasked to understand.

To finally address this ever-growing divergence, the IC will need to reshape at least eight legacy characteristics:

Cognitive: Intelligence analysts must be capable of thinking creatively—holistically and synthetically across traditional boundaries. The long-held emphasis on reductive thinking that breaks issues into discrete pieces—reinforced by the compartmentalization associated with classified information—is no longer sufficient.

Organizational: Analytic organizations need to be much flatter and more dynamically networked. The traditional fixed and compartmentalized hierarchies—often rooted in secrecy-driven compartmentalism—are not agile and impede holistic thinking. It takes a networked organization to understand a networked world.

Behavioral: Analysts must get to know who they are trying to support and what those policy makers are trying to accomplish. They need to think in terms of clients, not customers; and service, not production. They can no longer just assume relevance based on access to unique, secret information and just “toss” products at policy makers.

Methodological: Analysts must increase their use of synthetic methodologies (wargaming, complex modeling, simulation, etc.) that help make sense of aggregate, often-unclassified knowledge. The ability to “say something” cannot remain beholden to the ability to collect and cite specific—usually secret—information.

Technological: Analysts need to think of and use technology as a cognitive aid and not just as a tool for data management and communication. In particular, they must recognize that visualization technology is a crucial aid to thinking holistically and understanding complex issues.

Linguistic: Analysts must use language that accurately captures and reflects the uncertainty inherent in complex issues. Since language is intricately tied to mindset, the continued misapplication of linear mechanical metaphors (i.e., inertia, momentum, trajectory, leverage, tension, etc.) that promote the illusion of certainty must be abated.

Evaluative: Analytic organizations need to measure their "value-added" to the policy process and its desired outcomes. The traditional metrics—which focused on how much output (i.e., “product”) was churned out, vice the relative utility of work to policy makers—will no longer suffice.

Informational: Analysts need to get past their “secrecy bias”—the notion that classified information is almost always better than open-source. In an open world, this simply cannot remain a fundamental premise.

In the aggregate, changing these eight characteristics amounts to nothing less than a new business model. Of course, such paradigmatic change—from a classified collection model to a cognition (sense-making) model—is scary. Not surprisingly, the IC tends to approach it the way so many organizations do—it takes incremental steps whereby it effectively just nibbles at the list.

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