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Intelligent IoT

by Navya Kumar, Sourabh Bumb
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12 December 2017

Intelligent IoT Bringing the power of AI to the Internet of Things

12 December 2017
  • Navya Kumar United States
  • Sourabh Bumb United States
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  • Signals
  • The AI key to unlock IoT potential
  • Implications for enterprises

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

Learn more

Explore the Signals for Strategists collection

With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI.

Signals

  • Venture capital funding of AI-focused IoT start-ups is growing fast: In the first eight months of 2017, this group of start-ups raised $705 million1
  • Acquisitions of AI-focused IoT start-ups are on the rise: 21 in the first eight months of 2017 and 24 in 2016, up from 11 in 20152
  • Vendors of IoT platforms—including Amazon,3 GE,4 IBM,5 Microsoft,6 Oracle,7 PTC,8 and Salesforce9—are integrating AI capabilities
  • Large organizations across industries are already leveraging or exploring the power of AI with IoT to deliver new offerings and operate more efficiently10
  • Gartner predicts that by 2022, more than 80 percent of enterprise IoT projects will include an AI component, up from only 10 percent today11

The AI key to unlock IoT potential

Artificial intelligence is playing a growing role in IoT applications and deployments,12 a shift apparent in the behavior of companies operating in this area. Venture capital investments in IoT start-ups that are using AI are up sharply. Companies have acquired dozens of firms working at the intersection of AI and IoT in the last two years. And major vendors of IoT platform software are now offering integrated AI capabilities such as machine learning-based analytics.

AI is playing a starring role in IoT because of its ability to quickly wring insights from data. Machine learning, an AI technology, brings the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound. Companies are finding that machine learning can have significant advantages over traditional business intelligence tools for analyzing IoT data, including being able to make operational predictions up to 20 times earlier and with greater accuracy than threshold-based monitoring systems.13 And other AI technologies such as speech recognition and computer vision can help extract insight from data that used to require human review.

The powerful combination of AI and 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. For instance, according to one study, such losses average $38 million annually for offshore oil and gas operators.14 Another source estimated that for industrial manufacturing in total, unplanned downtime costs $50 billion per year, with equipment failure being the cause for 42 percent of the outages.15

Predictive maintence—using analytics to predict equipment failure ahead of time in order to schedule orderly maintenance procedures—can mitigate the damaging economics of unplanned downtime. In manufacturing, for instance, Deloitte finds that predictive maintenance can reduce the time required to plan maintenance by 20–50 percent, increase equipment uptime and availability by 10–20 percent, and reduce overall maintenance costs by 5–10 percent.16

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 SK Innovation, for example, expects to save “billions of won” by using machine learning to predict failure of connected compressors.17 Similarly, Italian train operator Trenitalia expects to avoid unplanned downtime and save 8–10 percent on its €1.3 billion annual maintenance costs.18 Meanwhile, French power utility EDF Group has already saved over $1 million with machine learning-driven early warning on equipment failure.19

Increasing operational efficiency

AI-powered IoT can do more than help avoid unplanned downtime. It 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—and to AI technologies’ ability to automate a growing variety of tasks.

For example, for Hershey, managing the weight of their products during the production process is critical: Every 1 percent improvement in weight precision can mean more than $500,000 in savings for a 14,000-gallon batch of product such as Twizzlers.20 The company used IoT and machine learning to significantly reduce weight variability during production. Data is captured and analyzed by the second, and weight variability can be predicted by machine learning models, enabling 240 process adjustments per day, compared to just 12 per day before the ML-powered IoT solution was installed.21

AI-based prediction is also helping Google 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.22

Machine learning produced insights that persuaded one shipping fleet operator to take a counter intuitive action that saved them big money. Data collected from ship-board 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.23

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, for GE’s drone and robot-based industrial inspection services, the company is looking to AI to automate both navigation of inspection devices and identification of defects from the data captured by them. This could result in safer, more precise, and up to 25 percent cheaper inspections for the client.24 In health care, Thomas Jefferson University Hospital in Philadelphia seeks to improve patient experience with natural language processing that will enable patients to control room environment and request various information with voice commands.25

Meanwhile, Rolls-Royce aims to soon introduce a new offering featuring IoT-enabled airplane engine maintenance services. The company plans to use machine learning to help it spot patterns and identify operational insights that will be sold to airlines.26 And automotive manufacturer Navistar 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. According to Navistar technology partner Cloudera, these services have helped cut downtime for nearly 300,000 vehicles by up to 40 percent.27

Enhancing risk management

A number of 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, Fujitsu has piloted the use of machine learning to analyze data from connected wearable devices to estimate its factory workers’ potentially threatening heat stress accumulated over time.28 Banks in India and North America have begun evaluating AI-enabled real-time identification of suspicious activities from connected surveillance cameras at ATMs.29 Vehicle insurer Progressive is using machine learning analysis of data from connected cars to accurately price its usage-based insurance premiums and thus better manage underwriting risk.30 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.31

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.

Executives contemplating new IoT-based projects should be aware that machine learning for predictive capabilities is now integrated with most major horizontal (in other words, general-purpose) and industrial IoT platforms, such as Microsoft Azure IoT,32 IBM Watson IoT,33 Amazon AWS IoT,34 GE Predix,35 and PTC ThingWorx.36

A growing number of turnkey, bundled, or vertical IoT solutions take advantage of AI technologies such as machine learning.37 For instance, for connected-car use cases, BMW’s CarData platform gives access to data shared by vehicle owners and AI capabilities from IBM Watson IoT.38 In consumer products and retail, a number of replenishment automation and optimization solutions use machine learning to predict demand and optimize inventory levels.39 Providers of telematics solutions for the auto insurance industry are integrating machine learning to create more accurate risk models and predict claims behavior.40

It may be possible to use AI technology to wring more value from IoT deployments that were not designed with the use of AI in mind.41 For instance, a Hungarian oil and gas company applied machine learning to sensor data that was already being collected during diesel fuel production. The analysis allowed the company to more accurately predict the fuel’s sulfur content and helped identify process improvements that are now saving the company more than $600,000 per year.42 The major horizontal and industrial IoT platforms—which enterprises may already be using—are offering new AI-based capabilities that might help boost the value of existing deployments.

The future of IoT is AI

It may soon become rare to find an IoT implementation that does not make some use of AI. The International Data Corp. predicts that by 2019, AI will support “all effective” IoT efforts and without AI, data from the deployments will have “limited value.”43 A growing number of IoT vendors are offering at least basic AI support. Vanguard companies across industries are already reaping the benefits of AI in their IoT deployments. If your company has plans for implementing IoT-based solutions, those plans should probably include AI as well.

Authors

David Schatsky analyzes emerging technology and business trends for Deloitte’s leaders and clients. He is based in New York.

Navya Kumar works with Deloitte Services India Pvt. Ltd. She is based in Mumbai.

Sourabh Bumb is a senior analyst at Deloitte Services India Pvt. Ltd. He is based in Mumbai.

Acknowledgements

The authors would like to thank: Ragu Gurumurthy, chief innovation officer and chief digital officer, and Craig Muraskin, managing director of Deloitte US Innovation, Deloitte LLP; and Aniket Dongre of Deloitte Support Services India Pvt Ltd.

 

Cover image by: Molly Woodworth

Endnotes
    1. Deloitte analysis of CB Insights data. View in article

    2. Ibid. View in article

    3. Amazon Web Services, “AWS IoT now supports integration with Amazon Machine Learning and AWS CloudTrail,” April 11, 2016. View in article

    4. Reuters, “GE acquires two artificial intelligence startups,” November 15, 2016. View in article

    5. Stacey Higginbotham, “IBM is bringing in Watson to conquer the Internet of Things,” Fortune, December 15, 2015. View in article

    6. Kevin McLaughlin, “Microsoft starts selling Azure Internet-of-Things suite, teases new container management tech for enterprises,” CRN, September 29, 2015. View in article

    7. Oracle, “Oracle expands IoT cloud portfolio, enabling customers to accelerate intelligence and ROI from connected assets,” August 31, 2017. View in article

    8. PTC, “PTC to acquire big data machine learning and predictive analytics leader ColdLight,” May 5, 2015. View in article

    9. Woodson Martin, “IoT Cloud Einstein: A farewell to the Internet of (Dumb) Things,” Salesforce Blog, September 18, 2016. View in article

    10. See, for instance, Mike Dano, “AT&T Labs working to combine drone video footage with artificial intelligence monitoring,” FierceWireless, May 1, 2017; Richard Evans and Jim Gao, “DeepMind AI reduces Google data centre cooling bill by 40%,” DeepMind, July 20, 2016; Scott Carey, “Rolls-Royce uses Microsoft IoT tools to cut down on engine faults and fuel costs, and wants to sell the insights back to airlines,” ComputerWorld UK, November 3, 2016; Lynne Slowey, “Harman and IBM Watson IoT team up to help improve patients’ lives,” IBM Internet of Things blog, October 25, 2016. View in article

    11. Mark Hung and Tom Austin, AI on the edge: Fusing artificial intelligence and IoT will catalyze new digital value creation, Gartner, June 5, 2017. View in article

    12. For an introduction to the Internet of Things, see Jonathan Holdowsky et al., Inside the Internet of Things (IoT): A primer on the technologies building the IoT, Deloitte University Press, August 21, 2015. For an introduction to artificial intelligence, see David Schatsky, Craig Muraskin, and Ragu Gurumurthy, Demystifying artificial intelligence: What business leaders need to know about cognitive technologies, Deloitte University Press, November 4, 2014. View in article

    13. Greg Herr, Josh Lyon, and Stuart Gillen, “Industrial intelligence: Cognitive analytics in action,” presentation at EMEA Users Conference, Berlin, 2016. View in article

    14. GE Oil & Gas, “The impact of digital on unplanned downtime: An offshore oil and gas perspective,” October 2016. View in article

    15. IndustryWeek in collaboration with Emerson, “How manufacturers achieve top quartile performance,” WSJ Custom Studios, accessed December 7, 2017. View in article

    16. Chris Coleman et al., Making maintenance smarter: Predictive maintenance and the digital supply network, Deloitte University Press, May 9, 2017. View in article

    17. Jung Wook, “SK Innovation to run IoT-incorporated plant,” Maeil Business News Korea, June 16, 2017. View in article

    18. Matthew Finnegan, “Trenitalia to cut train maintenance costs with SAP IoT and big data project,” ComputerWorld UK, October 4, 2016. View in article

    19. Kim Custeau, “Reduce maintenance costs with the IIoT and predictive asset analytics,” Schneider Electric Blog, December 1, 2016. View in article

    20. Teena Maddox, “How Hershey used IoT to save $500K for every 1% of improved efficiency in making Twizzlers,” TechRepublic, February 24, 2017. View in article

    21. Alec Shirkey, “Sweet IoT journey: How one solution provider helped implement Microsoft Azure Machine Learning at Hershey,” CRN, September 19, 2017. View in article

    22. Evans and Gao, “DeepMind AI reduces Google data centre cooling bill by 40%.” View in article

    23. Bernard Marr, “IoT and big data at Caterpillar: How predictive maintenance saves millions of dollars,” Forbes, February 7, 2017. View in article

    24. Wylie Wong, “NVIDIA’s AI supercomputers help ‘augment’ human site inspectors,” DataCenter Knowledge, September 7, 2017. View in article

    25. IBM, “Thomas Jefferson University Hospitals plans cognitive hospital rooms powered by IBM Watson Internet of Things,” October 4, 2016. View in article

    26. Carey, “Rolls-Royce uses Microsoft IoT tools to cut down on engine faults and fuel costs, and wants to sell the insights back to airlines.” View in article

    27. Cloudera, “Navistar’s IoT deployment on Cloudera wins TDWI 2017 best practices award,” July 21, 2017. View in article

    28. Fujitsu, “Fujitsu estimates workers’ heat stress levels with new AI-based algorithm,” July 12, 2017. View in article

    29. Harshith Mallya, “With AI-powered ATM cameras, Uncanny Vision aims to prevent tampering and theft,” YourStory, February 3, 2017. View in article

    30. Doug Drinkwater, “10 real-life examples of IoT in insurance,” Internet of Business, May 24, 2016. View in article

    31. Caitlin Fairchild, “How the city of Las Vegas uses AI to protect against hackers,” Nextgov, September 5, 2017. View in article

    32. Microsoft Azure, “Azure IoT Suite,” accessed December 7, 2017. View in article

    33. Greg Knowles, “Watson IoT platform analytics—covering al your IoT analytics needs,” IBM, March 15, 2017. View in article

    34. Amazon Web Services, “AWS IoT now supports integration with Amazon Machine Learning and AWS CloudTrail.” View in article

    35. GE, “Predix,” accessed December 7, 2017. View in article

    36. Linda Seid Frembes, “Unlock the power of IoT analytics,” PTC, accessed December 7, 2017. View in article

    37. For more information on the growing importance of turnkey IoT solutions, see Avinav Trigunait, Steve Atkins, and David Schatsky, Turnkey IoT: Bundled solutions promise to reduce complexity and accelerate ROI, Deloitte University Press, July 15, 2016. View in article

    38. IBM, “IBM integrates with BMW CarData to enable new and innovative services for drivers,” June 14, 2017. View in article

    39. See, for instance, BusinessWire, “Kwik to demonstrate industry’s first IoT automatic replenishment solution at Shoptalk Europe,” October 4, 2017, and Blue Yonder, “Blue Yonder launches machine-learning-based replenishment optimization for distribution centers,” January 16, 2017. View in article

    40. See, for instance, Richard Harmon, “The data don’t lie: Using machine learning to fight insurance fraud,” Computer Business Review, August 8, 2017. View in article

    41. See, for instance, Microsoft New Centre Europe, “Refining oil, in the cloud,” May 5, 2017. View in article

    42. Craig Harclerode, “The MOL story: A journey with IIoT, advanced analytics, & big data,” OSIsoft, presented at HRS 2016. View in article

    43. IDC, “IDC sees the dawn of the DX economy and the rise of the digital-native enterprise,” November 1, 2016. View in article

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Navya Kumar

Navya Kumar

Associate | Deloitte Services India Pvt. Ltd

Navya works with Deloitte Services India Pvt. Ltd., tracking and analyzing emerging technology and business trends, with a primary focus on enterprise digital transformation and the Internet of Things. She has written on macroeconomic themes and worked in life sciences business research.

  • kunavya@deloitte.com
  • +91 9820 246 274
Sourabh Bumb

Sourabh Bumb

Senior Analyst | Deloitte Services India Pvt. Ltd

Sourabh is a senior analyst at Deloitte Services India Pvt. Ltd. He tracks and analyzes emerging technology and business trends, with a primary focus on the Internet of Things, for Deloitte’s leaders and its clients. Prior to Deloitte, Sourabh worked with multiple companies as part of technology and business research teams.

  • sbumb@deloitte.com
  • +1 615 209 6968

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