Aim higher, reach further.
CIO Journal
CIO Insights and Analysis from Deloitte

Machine Intelligence Mimics Cognition

Emerging machine intelligence capabilities and exploding data volumes could enable IT systems to make inferences and predictions, ushering in a new era of cognitive advances.

Artificial intelligence (AI) technologies capable of performing tasks normally requiring human intelligence are increasingly being incorporated into enterprise analytics efforts. Yet the bigger story in cognitive computing is machine intelligence, a collection of advances representing machine learning, deep learning, advanced cognitive analytics, robotic process automation (RPA), and bots, to name a few.

Spending on various aspects of machine intelligence is projected to reach nearly $31.3 billion in 2019. It is also becoming a priority for CIOs: In Deloitte’s 2016 Global CIO Survey, 1,200 IT executives were asked to identify emerging technologies in which they plan to invest significantly in the next two years, and 64 percent intended to invest in cognitive technologies.

A confluence of three powerful forces is driving machine intelligence:

Exponential data growth. The digital universe—comprising all data created and stored—is doubling in size every 12 months,¹ and will likely grow more rapidly as new signals from the internet of things (IoT), dark analytics, and other sources proliferate. The more data these systems consume, the “smarter” they become by discovering relationships, patterns, and potential implications. Effectively managing rapidly growing data volumes is a crucial building block of machine intelligence ambitions.

Faster distributed systems. Networks that make data accessible to individual users have become exponentially more powerful—making possible advanced system designs such as those supporting multicore and parallel processing, and data storage techniques that support rapid retrieval and analysis of archived data. Distributed networks can now interface seamlessly with infrastructure, platforms, and applications residing in the cloud, and can digest and analyze ever-growing data volumes within it. They can also provide the power needed to analyze and actuate streamed data from IoT sensors and embedded intelligent devices.

Smarter algorithms. In recent years, increasingly powerful machine intelligence algorithms have advanced steadily toward simulating human thought processes. The following capabilities will likely see broader adoption during the next 18 to 24 months:²

  • Optimization, planning, and scheduling: Optimization automates complex decisions and tradeoffs about limited resources. Similarly, planning and scheduling algorithms devise a sequence of actions to meet processing goals and observe constraints.
  • Machine learning: Machine learning enables automatic discovery of patterns in data without following explicitly programmed instructions. Once identified, patterns can be used to make predictions.
  • Deep learning: Developers are working on deep learning algorithms involving artificial neural networks inspired by the structure and function of the brain. Interconnected modules run mathematical models continuously tuned based on processing a large number of inputs.
  • Probabilistic inference: New AI capabilities use graph analytics and models to identify the conditional dependencies of random variables and infer likely outcomes.
  • Semantic computing: Computer vision (the ability to analyze images), voice recognition, and various text analytics capabilities can uncover naturally expressed intention and the semantics of computational content. The information can then be used to support data categorization, mapping, and retrieval.
  • Natural language engines: These engines understand written text as humans do but can manipulate that text in sophisticated ways, such as automatically identifying all of the people and places mentioned in a document; recognizing the main topic of a document; or extracting and tabulating the terms and conditions in a stack of contracts.
  • Robotic process automation: Software robots, or “bots,” can perform routine business processes by mimicking how people interact with software applications. Many enterprises are beginning to employ RPA in tandem with cognitive technologies to automate perceptual and judgment-based tasks once reserved for humans.

How Machine Intelligence Can Create Value

For CIOs, machine intelligence calls for reimagining data analysis as a way to automate tasks and achieve efficiencies. There is a spectrum of opportunities CIOs can consider:

Cognitive insights. Machine intelligence can provide visibility into what is happening now and likely to happen next. For example, call center service representatives use multifunction customer support programs to answer product questions, take orders, investigate billing problems, and address other concerns. In many such systems, workers jump back and forth between screens to access information needed to answer specific queries. Machine intelligence can streamline this process and help business leaders prescribe actions to augment worker performance.

Cognitive engagement. An emerging field of cognitive applications will likely provide access to complex information, perform digital tasks such as admitting patients to the hospital, or recommend products and services. They may offer even greater business potential in customer service, where cognitive agents could replace some human agents by handling billing or account interactions, fielding tech support questions, and answering HR-related questions from employees.

Cognitive automation. Machine learning, RPA, and other cognitive tools could develop deep domain-specific expertise and then automate related tasks. For example, one health care startup is already applying deep learning technology to analyze radiology images. In testing, its system has been up to 50 percent better than expert human radiologists at judging malignant tumors. In education, machine intelligence capabilities embedded in online learning programs mimic the benefits of one-on-one tutoring by tracking the learner’s “mental steps” during problem-solving tasks to diagnose misconceptions, then providing timely guidance, feedback, and explanations.

Where to Start

Machine intelligence offers new approaches and technologies that may help enterprises finally overcome some longstanding data challenges.

Curating data. Machine intelligence can be applied in a largely automated fashion to define, rationalize, and maintain master data. Likewise, it can potentially provide a means for remedying content or context issues that arise.

Finding value in data. Machine intelligence can provide insight into business issues that, if resolved, could deliver meaningful value. The scope of the problem can inform the required data inputs, appropriate techniques, and surrounding architectural and data management needs.

Working with partners. Consider partnering with established vendors willing to co-invest in machine intelligence efforts. Likewise, collaborating with academics, startups, and thought leaders can provide valuable insight.

Industrializing analytics. Driving consistent and repeatable approaches, platforms, tools, and talent for all dimensions of data across the enterprise—including machine intelligence—will likely lead to services for data ingestion, integration, archiving, access, entitlement, encryption, and management.


Machine intelligence can help organizations move from the legacy world of retrospective data analysis to one in which systems make inferences and predictions. The ability to use these insights to automate tasks and responses represents the beginning of a new cognitive era.

—by Nitin Mittal, principal; Peter Lowes, principal; and Rajeev Ronanki, principal, Deloitte Consulting LLP

    1. Dan Vesset and David Schubmehl, “IDC FutureScape: Worldwide big data, business analytics, and cognitive soft­ware 2017 predictions,” International Data Corp., December 2016.

    2. David Schatsky, Craig Muraskin, and Ragu Gurumurthy, Demystifying artificial intelligence.

About Deloitte Insights

