State of Cognitive Survey

2017 Cognitive Survey

By Tom DavenportJeff Loucks and David Schatsky  |  Deloitte Survey  |  November 19, 2017  |  Source


Early adopters speak out on cognitive and AI

​What do the most aggressive adopters of artificial intelligence (AI) and cognitive technologies report based on their efforts to date? On everything from the impact on jobs to their specific goals and exactly which technologies they're using and much more, 250 leaders shared their views on this important business development just as it takes hold in the broader business world.

2017 State of Cognitive Survey


Bullish on the business value of cognitive

Leaders in cognitive and AI weigh in on what’s working and what’s next

With all the talk about cognitive and artificial intelligence (AI) technologies in business circles today, it's natural to wonder whether these capabilities are having any measurable impact.

So we asked some of the most aggressive adopters of cognitive technologies how they have fared to date, focusing on 250 "cognitive-aware" leaders within "cognitive-active" companies.

Why this group in particular? Not only can early-phase signals from such early adopters provide a view from the front lines of these important developments, but many other executives are simply not yet sufficiently knowledgeable about cognitive technologies. So what did these leaders tell us?

"Our survey results indicate that early adopters are bullish on cognitive and AI technologies, with expectations that they will transform both companies and entire industries."

— 2017 Deloitte State of Cognitive Survey

When these technologies are effectively integrated into workflows, they can directly influence how organizations accomplish tasks, make decisions, create engaging interactions, and generate stronger business outcomes.

What cognitive technologies are included in this survey?

Machine Learning
The ability of statistical models to develop capabilities and improve their performance over time without the need to follow explicitly programmed instructions.

Deep learning
A relatively complex form of machine learning involving neural networks with many layers of abstract variables. Deep learning models are excellent for image and speech recognition but are difficult or impossible for humans to interpret.

Natural language processing/generation (NLP/G)
The ability to extract or generate meaning and intent from text in a readable, stylistically natural, and grammatically correct form.

Speech recognition
The ability to automatically and accurately recognize and transcribe human speech.

Rules-based systems
The ability to use databases of knowledge and rules to automate the process of making inferences about information.

Computer vision
The ability to extract meaning and intent out of visual elements, whether characters (in the case of document digitization), or the categorization of content in images such as faces, objects, scenes, and activities.

Physical robots
The broader field of robotics is embracing cognitive technologies to create robots that can work alongside, interact with, assist, or entertain people. Such robots can perform many different tasks in unpredictable environments, often in collaboration with human workers.

Robotic process automation
Software that automates repetitive, rules-based processes usually performed by people sitting in front of computers. By interacting with applications just as humans would, software robots can open email attachments, complete e-forms, record and re-key data, and perform other tasks that mimic human action.

It's working

Ask early leaders in cognitive whether they're seeing results yet and what do they say? Eighty-three percent of respondents told us their companies have already achieved either moderate or substantial benefits from their work with these technologies. Something is working–and it's changing perceptions.

AI: Primary benefits to companies

Download the 2017 Deloitte State of Cognitive Survey

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