A Publication of WTVP

Artificial intelligence is now a reality, and its most promising application is not replacing workers, but augmenting their capabilities.

Today’s information age could be affectionately called “the rise of the machines.” The foundations of data management, business intelligence and reporting have created a massive demand for advanced analytics, predictive modeling, machine learning and artificial intelligence. In near-real time, we are now capable of unleashing complex queries and statistical methods, performed on vast volumes of heterogeneous information.

But for all of its promise, big data left unbounded can be a source of financial and intellectual frustration, confusion and exhaustion. The digital universe is expected to grow to 40 zettabytes by 2020 through a 50x explosion in enterprise data. Advanced techniques can be distracting if they aren’t properly focused. Leading companies have flipped the script; they are focusing on concrete, bounded questions with meaningful business implications—and using those implications to guide data, tools and technique. The potential of the machine is harnessed around measurable insights. But true impact comes from putting those insights to work and changing behavior at the point where decisions are made and processes are performed. That’s where amplified intelligence comes in.

Open the Pod Bay Doors…
Debate rages around the ethical and sociological implications of artificial intelligence and advanced analytics. Entrepreneur and futurist Elon Musk told CBS News: “We need to be super-careful with AI. Potentially more dangerous than nukes.” At a minimum, entire career paths could be replaced by intelligent automation and made extinct. As researchers pursue general-purpose intelligence capable of unsupervised learning, the long-term implications are anything but clear. But in the meantime, these techniques can be used to supplement the awareness, analysis and conviction with which an individual performs his or her duty—be it an employee, business partner or even a customer.

The motives aren’t entirely altruistic… or self-preserving. As Albert Einstein famously pointed out, “Not everything that can be counted counts. And not everything that counts can be counted.” Business semantics, cultural idiosyncrasies and sparks of creativity remain difficult to codify. Thus, while the silicon and iron (machine layer) of advanced computational horsepower and analytics techniques evolve, the carbon (human) element remains critical to discovering new patterns and identifying the questions that should be asked. Just as autopilot technologies haven’t replaced the need for pilots to fly planes, the world of amplified intelligence allows workers to do what they do best: interpreting and reacting to broader context versus focusing on applying standard rules that can be codified and automated by a machine.

This requires a strong commitment to the usability of analytics. For example, how can insights be delivered to a specific individual performing a specific role at a specific time to increase his or her intelligence, efficiency or judgment? Can signals from mobile devices, wearables or ambient computing be incorporated into decision-making? And can the resulting analysis be seamlessly and contextually delivered to the individual based on who and where they are, as well as what they are doing? Can text, speech and video analytics offer new ways to interact with systems? Could virtual or augmented reality solutions bring insights to life? How could advanced visualization support data exploration and pattern discovery when it is most needed? Where could natural language processing be used to not just understand semi-structured and unstructured data (extracting meaning and forming hypotheses), but to encourage conversational interaction with systems instead of via queries, scripts, algorithms or report configurators?

Amplified intelligence creates the potential for significant operational efficiencies and competitive advantage for an organization. Discovery, scenario planning and modeling can be delivered to the frontlines, informed by contextual cues such as location, historical behavior and real-time intent. It moves the purview of analytics away from a small number of specialists in back-office functions who act according to theoretical, approximate models of how business occurs. Instead, intelligence is put to use in real time—potentially in the hands of everyone—at the point where it may matter most. The result can be a systemic shift from reactive “sense-and-respond” behaviors to predictive, proactive solutions. The shift could create less dependency on legacy operating procedures and instinct. The emphasis becomes fact-based decisions informed by sophisticated tools and complex data made simple by machine intelligence that can provide insights.

Bold New Heights
Amplified intelligence is in its early days, but the potential use cases are extensive. The medical community can now analyze billions of web links to predict the spread of a virus. The intelligence community can now inspect global calls, texts and emails to identify possible terrorists. Farmers can use data collected by their equipment, from almost every foot of each planting row, to increase crop yields. Companies in fields such as accounting, law and healthcare could let frontline specialists harness research, diagnostics and case histories, which could arm all practitioners with the knowledge of their organization’s leading practices, as well as the whole of academic, clinical and practical experience. Risk and fraud detection, preventative maintenance and productivity plays across the supply chain are also viable candidates. Next-generation soldier programs are being designed for enhanced vision, hearing and augmented situational awareness delivered in real time in the midst of battle—from maps to facial recognition to advanced weapon system controls.

In these and other areas, exciting opportunities abound. For the IT department, amplified intelligence offers a chance to emphasize the role it could play in driving the broader analytics journey and directing advances toward use cases with real, measurable impact. Technically, these advances require data, tools and processes to perform core data management, modeling and analysis functions. But it also means moving beyond historical aggregation to a platform for learning, prediction and exploration. Amplified intelligence allows workers to focus on the broader context while allowing technology to address standard rules that can be codified and executed autonomously.

All Together Now
The emphasis on usability and deployment moves the information agenda from isolated data scientists to multidisciplinary teams. The agenda should focus on helping end users by understanding their journey, their context, and how to enhance and reshape their jobs. Like the revolution in user engagement that transactional systems have recently experienced, amplified intelligence solutions start from the user down, not from the data model and analytics up. To start the process with users, organizations should identify a crunchy question that, if answered, could significantly improve how a specific individual does his or her job. The process should also understand how an answer could affect how the individual conducts business—where he or she would likely need the information, in what format, when and via what channel.

Company leaders interested in improving their decision-making can use machine learning and other amplified intelligence approaches to generate new growth ideas for their organizations. Amplified intelligence is becoming critical for competitive success around the world, across industries. U.S.-based Uber uses big data to match passengers with car services. European grocer Tesco leverages big data to capture a disproportionate share of sales from new families and parents. Effective scenarios should be designed to be deployed for high impact. That impact should inform scope, solutions and iterative development in which incremental solutions are tested in real-life scenarios.

Cyber Implications
Cyber security and data privacy considerations should be a part of analytics conversations, especially as amplified intelligence moves insights more directly into the heart of how and where business occurs. Information should be monitored and protected when it is at rest, in flight and in use. These three scenarios feature different actors using different platforms and require different cyber-security techniques. Moreover, for each scenario, you must know how to manage misuse, respond to breaches and circle back with better security and vigilance.

“At rest” is the traditional view of information security: How does one protect assets from being compromised or stolen? Firewalls, antivirus software, intrusion detection and intrusion prevention systems are still needed, but are increasingly less effective as attackers rapidly evolve their tools and move from “smash-and-grab” ploys to long-dwell cybercrimes. Instead of an outright offense that may leave telltale signs, attackers gain access and lie dormant—launching incremental, almost imperceptible activities to discover vulnerabilities and gain access to valuable IP.

The additional emphasis on “in flight” and “in use” reflects a shift in how organizations put their underlying data to use. Information is increasingly consumed in the field via mobile, potentially on personally-owned consumer devices. Encryption can help with transmission and data retention. Identity, access and entitlement management can help properly control user actions, especially when coupled with two-factor authentication. Application, data and/or device-level containers can protect against attacks on the network, hardware or other resident apps. Again, though, these techniques may not be enough, given the growing sophistication of criminal products, services and markets.

Organizations should couple traditional techniques with advanced analytics, amplifying the intelligence of cyber-security personnel. Leading cyber initiatives balance reactionary methods with advanced techniques to identify the coming threat and respond proactively. They take a fusion of information from a range of sources with differing conceptual and contextual scope, and combine it with human-centered signals such as locations, identity and social interactions of groups and individuals. This approach has a number of implications. First, it creates the need to adopt a broader cyber-intelligence mindset—one that leverages intel from both internal and external sources. Insight pulled from new signals of potentially hostile activities in the network can point to areas where security professionals should focus. Similar to how amplified intelligence informs approaches to business operations, this raw security data should be analyzed and presented in ways to augment an individual’s ability to take action.

Machine learning and predictive analytics can take cyber security a step farther. If normal “at rest,” “in flight” and “in use” behavior can be baselined, advanced analytics can be applied to detect deviations from the norm. With training to define sensitivities and thresholds, security teams’ capabilities can be amplified with real-time visibility into potential risks when or before they occur. At first, this ability is likely to simply guide manual investigation and response, but eventually it could move to prescriptive handling—potentially enabling security systems to automatically respond to threat intelligence and take action to predict and prevent or promptly detect, isolate and contain an event when it occurs.

Where Do You Start?
The information agenda is not without baggage, but hopefully that baggage includes the foundations needed for strides in amplified intelligence. One size likely won’t fit all. Organizations will probably need a variety of approaches, tools and techniques suitable to the question asked and the end users affected. They should also accommodate each scenario’s requirements around data velocity, structure, analytics complexity and user interface/deployment vehicles. While individual mileage will vary, some overarching steps can help guide the journey.

The best outcomes will likely be from scenarios where technology or analytics were seen as infeasible or too difficult to take advantage of. New opportunities exist when companies expand information-based decisions beyond just the executive suite’s purview into the field by giving managers, sales teams, service techs, case workers and other frontline employees simple tools that harness exceptionally complex intelligence. Ideally, computational intelligence will be refined and extended by collective intelligence, creating a feedback loop where people are also augmenting the advanced tools and models. Individual creativity and resourcefulness can and should continue to flourish. The goal, however, should be mutual elevation: As machine analytics are enhanced, users have the opportunity for more nuanced and valuable pursuits, putting important feedback into the system. The overall outcome: Artificial intelligence amplifies human intelligence to transform business intelligence. iBi

This is an extract of Deloitte Consulting LLP’s original Tech Trends 2015: Amplified Intelligence chapter, available at Copyright © 2015 Deloitte Development LLC. All rights reserved. Reprinted with permission.