In the last decade, artificial intelligence (AI) has progressed from near-science fiction to common reality across a range of business applications. In intelligence analysis, AI is already being deployed to label imagery and sort through vast troves of data, helping humans see the signal in the noise. But what the intelligence community is now doing with AI is only a glimpse of what is to come. The future will see smartly deployed AI supercharging analysts’ ability to extract value from information.
Exploring new possibilities
We expect several new tasks for AI, which will likely fall into one of these three categories:
– Delivering new models. The rapid pace of modern decision-making is among the biggest challenges leaders face. AI can add value by helping provide new ways to more quickly and effectively deliver information to decision-makers. Our model suggests that by adopting AI at scale, analysts can spend up to 39 percent more time advising decision-makers.
– Developing people. Analysts need to keep abreast of new technologies, new services, and new happenings across the globe—not just in annual trainings, but continuously. AI could help bring continuous learning to the widest scale possible by recommending courseware based on analysts’ work.
– Maintaining the tech itself. Beyond just following up on AI-generated leads, organizations will likely also need to maintain AI tools and to validate their outputs so that analysts can have confidence when using them. Much of this validation can be performed as AI tools are designed or training data is selected.
Intelligence organizations must be clear about their priorities and how AI fits within their overall strategy. Having clarity about the goals of an AI tool can also help leaders communicate their vision for AI to the workforce and alleviate feelings of mistrust or uncertainty about how the tools will be used.
Intelligence organizations should also avoid investing in “empty technology”—using AI without having access to the data it needs to be successful.
Survey results suggest that analysts are most skeptical of AI, compared to technical staff, management, or executives. To overcome this skepticism, management will need to focus on educating the workforce and reconfiguring business processes to seamlessly integrate the tools into workflows. Also, having an interface that allowed the analyst to easily scan the data underpinning a simulated outcome or view a representation of how the model came to its conclusion would go a long way toward that analyst incorporating the technology as part and parcel of his or her workflow.
While having a workforce that lacks confidence in AI’s outputs can be a problem, however, the opposite may also turn out to be a critical challenge. With so much data at their disposal, analysts could start implicitly trusting AI, which can be quite dangerous.
But there are promising ways in which AI could help analysts combat human cognitive limitations. They would be very good at continuously conducting key assumptions checks, analyses of competing hypotheses, and quality of information checks.
How to get started today
Across a government agency or organization, successful adoption at scale would require leaders to harmonize strategy, organizational culture, and business processes. If any of those efforts are misaligned, AI tools could be rejected or could fail to create the desired value. Leaders need to be upfront about their goals for AI projects, ensure those goals support overall strategy, and pass that guidance on to technology designers and managers to ensure it is worked into the tools and business processes. Establishing a clear AI strategy can also help organizations frame decisions about what infrastructure and partners are necessary to access the right AI tools for an organization.
Tackling some of the significant nonanalytical challenges analyst teams face could be a palatable way to introduce AI to analysts and build their confidence in it. Today, analysts are inundated with a variety of tasks, each of which demands different skills, background knowledge, and the ability to communicate with decision-makers. For any manager, assigning these tasks across a team of analysts without overloading any one individual or delaying key products can be daunting. AI could help pair the right analyst to the right task so that analysts can work to their strengths more often, allowing work to get done better and more quickly than before.
AI is not coming to intelligence work; it is already there. But the long-term success of AI in the intelligence community depends as much on how the workforce is prepared to receive and use it as any of the 1s and 0s that make it work.
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