AI - From big bang to business outcomes:
PAVING THE WAY FOR ARTIFICIAL INTELLIGENCE'S REAL VALUE
Sensors are everywhere.
In 2012, 4.2 billion sensors were shipped for industrial use; in 2014, that figure skyrocketed to 23.6 billion.1 The Internet of Things has changed the industrial landscape, promising improved efficiency and production for everyone from shoe makers to milk processors to refineries to power plants.
It’s clear that companies do not need more data, however. In fact, “70% of captured production data goes unused.”2 Nor do companies want more data. Instead, they want to extract the trapped value that already exists by leveraging collected data to solve specific problems in much more proactive ways. Analyzing static graphs and charts no longer is sufficient in our ever-changing digital economy.
Here is where artificial intelligence (AI) can make IIoT investments pay off ($105 billion globally in manufacturing operations and another $45 billion in production asset management in 20173) — by giving companies dynamic tools to make better business decisions. That’s the beauty and magic of AI.
of captured production data goes unused
Three challenges of AI adoption
We know that using AI can answer business-critical questions on a daily, even hourly, basis, but companies must leap over three current roadblocks:
Data preparation and operationalization
AI, in the form of models, often requires clean data from a diverse set of sources to create the most accurate AI. This is important both at the AI creation stage and through operationalized data pipelines in deployment. Currently, data scientists spend about 60% of their time cleaning up and organizing data8 as “data janitors”9 before they can even think about analyzing that data. This data wrangling imposes on their time to experiment, re-train, and re-deploy models.
Remember that the quality of the model is only as good as the quality of the inputted data. One new signal can change a model’s course drastically, thereby making experimentation essential to ensure the promise of AI comes to fruition with concrete relevance for the business. In “How to Create a Business Case for Data Quality Improvement,” Gartner estimates that “the average financial cost of poor data quality on organizations is $9.7 million a year.”10
AI lifecycle management
As companies increase their investment in AI, the need for AI lifecycle management tools is rapidly becoming critical. Data acquisition and preparation, experimentation, versioning, dependency management, deployment into production systems, monitoring, security, compliance, and model updates all become key elements when running AI-driven systems at scale.
Industry has the potential to automate the full cycle from the point where data are generated and collected to the point that AI models are built and operationalized. Industry can create systems that are more efficient than many other segments because of the level of coverage that can exist throughout its systems. The average project takes 6-9 months to go from concept to production. Once in production, data can become useful the instant data points are generated and fed into trained models that will use that data to make predictions. Re-training based on new data can happen any time, but it often will follow the cycle that fits best with the business depending on the model and situation.
Making AI meaningful across organizations
To realize the full value of AI adoption, companies must expand the discoverability and use of AI from the data scientist to the other roles across the organization. In other words, models need to become much easier for developers, business analysts, and business decision makers to find, understand, and use.
One area we both see as transformative is edge intelligence. Gartner predicts that by 2022, 50% of data will be processed at the edge.11 Together, cloud and connected edge are leading to magnanimous levels of change and acceleration in efficiency and accuracy. When models are deployed to edge devices — that is, closer to the decision making in action — they can be trained to be highly accurate to make decisions more quickly.
A Schneider Electric – Microsoft collaborative case study illustrates this value. Some businesses have remote assets that are not easily cloud-connected. Others may not want to send data outside their own networks. We solved these challenges for the oil and gas industry. Schneider Electric’s Realift™ rod pump control leverages Microsoft machine learning capabilities to monitor and configure pump settings and operations remotely, sending personnel onsite only when necessary for repair or maintenance when Realift indicates that something has gone wrong. Anomalies in temperature and pressure, for instance, can flag potential problems, even issues brewing a mile below the surface. Intelligence edge devices can run analytics locally without having to tap the cloud — a huge deal for expensive, remote assets such as oil pumps.
The right predictive analytics models, moreover, can weed out false positives, which can be the case 99.99% of the time, thereby saving human and financial resources. For example, Schneider Electric can tell through AI when a solar array really has a problem as opposed to just an accumulation of dust or dirt, helping an operator know when to send a squeegee vs. a repair truck.
Up to 50% reduction in energy costs in first season, as reported by farmers
For Schneider Electric, any AI application that delivers tangible business outcomes is a breakthrough. Our goal is to turn data into actionable insights. Powered by the Microsoft Azure platform and Schneider Electric’s EcoStruxure™ Industry IoT architecture, SCADAfarm is an integrated automation and information management solution developed for WaterForce, an irrigation solutions builder and water management company in New Zealand.
Schneider Electric, in collaboration with AVEVA, and Microsoft
- increased visibility of irrigation system performance and status – for both farmers and WaterForce;
- more efficient and effective water use;
- up to 50% reduction in energy costs in first season, as reported by farmers; and
- remote monitoring capabilities that reduce the time farmers have to spend driving to inspect assets.
As a solution builder, WaterForce now can offer additional value-add services such as fault diagnosis and performance benchmarking, driving forward its own digital transformation.
For Microsoft, offering lifecycle management of AI is a true breakthrough as well. Microsoft solutions can significantly cut time wasted on cleaning up and preparing data, enabling data scientists to do what they do best: data science. Security and compliance workflows (e.g., safety and regulatory reviews) can be integrated between experimentation and deployment of models, thereby feeding a most-valuable AI loop.
Up to 50% reduction in energy costs in first season, as reported by farmers
30+ years of Schneider Electric and Microsoft co-innovation
Together, we're helping customers benefit from Schneider Electric's deep domain expertise and Microsoft's trusted, secure cloud.
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1 Elfrink, Wim. “The Internet of Things: Capturing the Accelerated Opportunity.” Cisco Blog, October 15, 2014. http://blogs.cisco.com/ioe/ the-internet-of-things-capturing-the-accelerated-opportunity. Cited in World Economic Forum, in collaboration with Accenture, “Industrial Internet of Things: Unleashing the Potential of Connected Products and Services,” January 2015. http://www3.weforum.org/docs/WEFUSA_IndustrialInternet_Report2015.pdf
2 Technology and Innovation for the Future of Production: Accelerating Value Creation, World Economic Forum, March 2017, http://www3.weforum.org/docs/WEF_White_Paper_Technology_Innovation_Future_of_Production_2017.pdf
3 IDC, “Worldwide IoT spending in 2021,” IDC’s Semiannual Worldwide Internet of Things Spending Guide, 2H16 update, May 2017.
4 “How AI Boosts Industry Profits and Innovation,” by Mark Purdy and Paul Daugherty. Accenture Research, June 2017. https://www.accenture.com/t20170620T055506__w__/us-en/_acnmedia/Accenture/next-gen-5/insight-ai-industry-growth/pdf/Accenture-AI-Industry-Growth-Full-Report.pdf?la=en
5 Jeff Leek, “The key word in ‘data science’ is not data, it is science,” December 2013. https://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/
6 George Anadiotis, “Data to analytics to AI: From descriptive to predictive analytics,” ZDNet, November 23, 2016 http://www.zdnet.com/article/data-to-analytics-to-ai-from-descriptive-to-predictive-analytics/
7 Michael Schrage, “4 Models for Using AI to Make Decisions,” Harvard Business Review , January 27, 2017. Business Review. https://hbr.org/2017/01/4-models-for-using-ai-to-make-decisions
8 CrowdFlower, “2016 Data Science Report,” http://visit.crowdflower.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport_2016.pdf
9 “Data janitors” used by Josh Wills, as cited in Jessica, Leber, “In a Data Deluge, Companies Seek to Fill a New Role,” MIT Technology Review, May 22, 2013. https://www.technologyreview.com/s/513866/in-a-data-deluge-companies-seek-to-fill-a-new-role/
10 Susan More, Gartner, “How to Create a Business Case for Data Quality Improvement,” January 9, 2017. http://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement/
11 Rob van der Meulen, "What Edge Computing Means for Infrastructure and Operations Leaders," Gartner. October 18, 2017. https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/