Intelligent IoT: Bringing the Power of AI to IoT Deployments

David Schatsky and Sourabh Bumb
January 10, 2019

 

The Internet of Things (IoT) is getting smarter: companies are incorporating Artificial Intelligence (AI) — in particular, machine learning — into their IoT applications and seeing capabilities grow, including improving operational efficiency and helping avoid unplanned downtime. The key: finding insights in data.

Companies are finding that machine learning can have significant advantages over traditional business intelligence tools for analyzing IoT data. With AI-enabled analytics, businesses across industries can gain benefits from IoT deployments through:

  • Utilizing new types of sensor inputs such as voice or visual, extracting insight from data that used to require human review: for example, a leading conglomerate is leveraging computer vision to analyze data from cameras and infrared detectors to detect cracks and other problems in airplane engine blades [1]. Meanwhile, in healthcare, a hospital is piloting a solution to allow patients to use voice commands to control their environment [2].
  • Generating real-time insights to drive adaptive, optimal responses for dynamic environment: for example, AI-based prediction is helping a leading tech player cut 40 percent of data center cooling costs. The solution, trained on data from sensors in the facility, predicts temperature and pressure over the next hour to guide actions for limiting power consumption [3].
  • Enabling earlier discovery of upcoming challenges: for example, a machine learning-enabled solution used for industrial operations by a machinery and equipment provider could predict pump failures 5 to 6 days in advance, versus a mere 12-hour heads-up by the previous solution, with the same sensor data [4].
  • Facilitating identification of influencers or variables previously not realized: for example, a leading European oil & gas company used machine learning capabilities on top of existing IoT systems to identify key variables affecting their diesel refining process — not only enhancing existing data models, but determining new models. This continues to deliver savings of more than $600,000 per year [5].

Commercial Benefits of AI-Powered IoT

With the above capabilities enabled by AI, its powerful combination with IoT technology is helping companies avoid unplanned downtime, increase operating efficiency, enable new products and services, and enhance risk management.

Avoiding Costly Unplanned Downtime

In a number of sectors, unplanned downtime resulting from equipment breakdown can cause heavy losses. And “predictive maintenance” can greatly help mitigate or reduce such losses.

Because AI technologies — particularly machine learning — can help identify patterns and anomalies and make predictions based on large sets of data, they are proving to be particularly useful in implementing predictive maintenance. Leading South Korean oil refiner, for example, expects to save “billions of won” by using machine learning to predict failure of connected compressors [6].

Increasing Operational Efficiency

Not just avoiding unplanned downtime, AI-powered IoT can also help improve operational efficiency. This is due in part to the power of machine learning to generate fast and precise predictions and deep insights.

In one case, machine learning produced insights that persuaded one shipping fleet operator to take a counter-intuitive action, generating significant savings. Data collected from shipboard sensors was used to identify the correlation between the amount of money spent on cleaning the ships’ hulls and fuel efficiency. The analysis showed that by cleaning their ships hulls twice a year rather than every two years — and thereby quadrupling their cleaning budget — they would end up saving $400,000 due to greater fuel efficiency [7].

Enabling New and Improved Products and Services

IoT technology coupled with AI can form the foundation of improved and eventually entirely new products and services as well. For instance, an automotive manufacturer is looking to machine learning analysis of real-time connected vehicle data to enable a new revenue stream, in-vehicle health diagnostics, and predictive maintenance services. These services are claimed to have helped cut downtime for nearly 300,000 vehicles by up to 40 percent [8].

Enhancing Risk Management

Several applications pairing IoT with AI are helping organizations better understand and predict a variety of risks as well as automate for rapid response, enabling them to better manage worker safety, financial loss, and cyber threats.

For instance, a leading tech equipment provider has piloted the use of machine learning to analyze data from connected wearables to estimate its factory workers’ potentially threatening heat stress accumulated over time [9]. One vehicle insurer is using machine learning analysis of data from connected cars to accurately price its usage-based insurance premiums and thus better manage underwriting risk [10]. And the city of Las Vegas has turned to a machine learning solution, to secure its smart city initiatives, aimed at automatically detecting and responding to threats in real time [11].

Implications for Enterprises

For enterprises across industries, AI has the potential to boost the value created by IoT deployments, enabling better offerings and operations to give a competitive edge in business performance. It may soon become rare to find an IoT implementation that does not make some use of AI. Refer to Deloitte Insights’ piece for detailed analysis of the convergence IoT and AI, and the significant implications the development has for enterprises [12].

Further Readings

  1. https://www.technologyreview.com/s/600986/ai-hits-the-mainstream/
  2. https://www.ibm.com/blogs/internet-of-things/harman-health/
  3. https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/
  4. http://cdn.osisoft.com/osi/presentations/2016-users-conference-emea-berlin/2016-users-conference-emea-berlin-d2-Industrial-IT-E060-SparkCognition-Flowserve-Gillen-FlowserveSparkCognition-Industrial-Intelligence--Cognitive-Analytics-in-Action.pdf
  5. http://cdn.osisoft.com/osi/presentations/2016-rs-houston-iiot/2016-rs-houston-iiot-040-OSIsoft-Harclerode-The-MOL-Story--A-Journey-with-IIOT-Advanced-Analytics-Big-Data--$1B-EBITDA-enabled-by-the-PI-System.pdf
  6. http://pulsenews.co.kr/view.php?year=2017&no=404314
  7. https://www.forbes.com/sites/bernardmarr/2017/02/07/iot-and-big-data-at-caterpillar-how-predictive-maintenance-saves-millions-of-dollars/2/#6afd7f086f5c
  8. http://www.prnewswire.com/news-releases/navistars-iot-deployment-on-cloudera-wins-tdwi-2017-best-practices-award-300492324.html
  9. http://www.fujitsu.com/global/about/resources/news/press-releases/2017/0712-02.html
  10. https://internetofbusiness.com/10-examples-iot-insurance/
  11. http://www.nextgov.com/cybersecurity/2017/09/how-city-las-vegas-uses-ai-protect-against-hackers/140739/
  12. https://www2.deloitte.com/insights/us/en/focus/signals-for-strategists/intelligent-iot-internet-of-things-artificial-intelligence.html

 


 

David SchatskyDavid Schatsky analyzes emerging technology and business trends for Deloitte’s leaders and clients. His recent published works include Signals for Strategists: Sensing Emerging Trends in Business and Technology (Rosetta Books 2015), “Demystifying artificial intelligence: What business leaders need to know about cognitive technologies,” and “Cognitive technologies: The real opportunities for business” (Deloitte Insights 2014-15). Before joining Deloitte, David led two research and advisory firms.

 

Sourabh BumbSourabh Bumb tracks and analyzes emerging technology and business trends, with a primary focus on the Internet of Things, for Deloitte’s leaders and its clients. He is also involved in assessing and identifying promising startups in various areas including artificial intelligence, blockchain, AR/VR, among others. Prior to Deloitte, Sourabh worked with multiple companies as part of technology and business research teams.

 

 


 

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