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FEATUREThe future of intelligence analysisA task-level view of the impact of artificial intelligence on intel analysisKwasi Mitchell,Joe Mariani,Adam Routh,Akash Keyal,and Alex MirkowTHE DELOITTE CENTER FOR GOVERNMENT INSIGHTS2The future is already hereIN THE LAST decade,artificial intelligence(AI)has progressed from nearscience 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.1 But what the intelligence community(IC)is now doing with AI is only a glimpse of what is to come.These early applications point to a future in which smartly deployed AI will supercharge analysts ability to extract value from information.The adoption of AI has been driven not only by increased computational power and new algorithms but also the explosion of data now available.By 2020,the World Economic Forum expects there to be 40 times more bytes of digital data than there are stars in the observable universe.2 For intelligence analysts,that proliferation of data means surefire information overload.Human analysts simply cannot cope with that much data.They need help.Intelligence leaders know that AI can help cope with this data deluge but they may also wonder what impact AI will have on their work and workforce.According to surveys of private sector companies,there is a significant gap between the introduction of AI and understanding its impact.Nearly 20 percent of workers report experiencing a change in roles,tasks,or ways of working as a result of implementing AI,yet nearly 50 percent of companies have not measured how workers are being impacted by AI implementation.3 This article begins to tackle those questions,offering a tasks-level look at how AI may change work for intel analysts.It will also offer ideas for organizations seeking to speed adoption rates and move from pilots to full scale.AI is already here;lets see how it will shape the future of intelligence analysis.AI in the intel cycleIntelligence flows through a five-step“cycle”carried out by specialists,analysts,and management across the IC:planning and direction;collection;processing;analysis and production;and dissemination(figure 2).The value of outputs throughout the cycle,including the finished intelligence that analysts put into the hands of decision-makers,is shaped to an important degree by the technology and processes used,including those that leverage AI.Technologies such as unmanned aerial systems,remote sensors,advanced reconnaissance airplanes,the internet,computers,and other systems have supercharged the collection process to such an extent that analysts often have more data than they can process.4 Complicating matters,the data collected often resides in different systems and comes in different mediums,requiring analysts to spend time piecing together related informationor fusing databefore deeper analysis can begin.How will artificial intelligence impact intel analysis and,specifically,the intelligence community workforce?Learn what organizations can do to integrate AI most effectively and play to the strengths of humans and machines.The future of intelligence analysis:A task-level view of the impact of artificial intelligence on intel analysis3WHAT DO WE MEAN BY AI?The term“artificial intelligence”can mean a huge variety of things depending on the context.To help leaders understand such a wide landscape,it is helpful to distinguish between the types of model classes of AI,and the applications of AI.The first are the classifications based on how AI works;the second is based on what tasks AI is set to do.Source:Deloitte analysis.loitte Insights deloitteoinsightsles enginesRulesased soware,oen in the rm o if-then statements,that automate predened ocesses.Intelligent rules enginesRulesased soware,oen in the rm o if-then statements,that automate predened ocesses and can learn and ada.Machine learningA set ostatistical technies that automate analytical modeluildinusinalrithms that learn om data without exicit oamminDeep learningA more soisticated rm omachine learninthat delo multie hidden layers oanalysis to make edictions.PROAMS TT AER TMSEESCognitive languageA set ostatistical technies that enae the analysis,understandin and neration ohuman lanas to cilitate intercinwith machines.Computer visionAutomatic extraction,analysis,and understandinoinrmation om a sine ima or a seence oimas that models,reicates,and sursses human sion.PASoware that rrms routine ocesses mimickinhow oe interact with alications throu a user interce and llowinsime rules to make decisions.Predictive analyticsAnalys data comninmodel classes,escially machine learnin to edict outcomes and understand key riaes.Technique examples Potential usesNatural lana rocessin LPNatural lana neration LGSemantic comtinSeech recoitionSeech synthesisIma recoitionVideo analysisHandwritinrecoitionVoice recoitionOical character recoitionProcess automation and congurationGraical user interce UI automationAdnced decision systemsPredicti statistical modelsNae Bayes and other rolistic modelsNeural networksEluatinhuman source reliality n other rms orertinAnalynthe syntax osocial media or other sts to identi outliers that may e adrsary communicationsIdentiinand trackinhicles,oects,and eoe in hotos or deosIdentiinoects and linkinto aroiate ou and indidualsAutomatinmissionelated and back-oce reporting tasksFillinin common rmsAutomatinlatrm schedulin deconiction for collection manamentAnalynadrsary courses o action Modelinadrsary oess on nuclear or other technolo deloentProdinleaders with realime decision suortFIGURE 1Articial intelligence:Model classes and sample applicationsThe future of intelligence analysis:A task-level view of the impact of artificial intelligence on intel analysis4Access to more data should be a good thing.But without the ability to fuse and process it,it can inundate analysts with mountains of incoherent data to piece together.The director of the National Geospatial Intelligence Agency said that if trends hold,intelligence organizations could soon need more than 8 million imagery analysts alone,which is more than five times the total number of people with top secret clearances in all of government.5 In the modern digitized age,where success in warfare depends on a nations ability to analyze information faster and more accurately than adversaries,data cannot go unanalyzed.6 But given the pace at which humans operate,there simply isnt enough time to make sense of all the data and perform the other necessary intelligence cycle tasks.AI can provide much-needed support.Intelligence agencies are already using AIs power to sort through volumes of data to pull out critical“knowns”for further analysis.For example,agencies have used AI to automatically identify and label patterns of vehicles to identify SA-21 surface-to-air missile batteries or sift through millions of financial transactions to identify patterns consistent with illicit weapons smuggling.Similarly,the Joint Artificial Intelligence Center(the Department of Defenses focal point for AI)is already working to develop products across“operations intelligence fusion,joint all-domain command and control,accelerated sensor-to-shooter timelines,autonomous and swarming systems,target development,and operations center workflows.”Our analysis suggests AI operating in these capacities can save analysts time and enhance output.While exact time savings will depend on the type of work performed,an all source analyst who has the support of AI-enabled systems could save as much as 364 hours or more than 45 working The future of intelligence analysis:A task-level view of the impact of artificial intelligence on intel analysis
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