Warsaw, Poland and Palo Alto, CA, USA - August 30, 2018 - Deloitte estimates that in 2021 enterprise spending on artificial intelligence and machine learning projects will reach 57 billion dollars, four times more than in 2017. These technologies are now in everyday use, and not only among innovation leaders. Thanks to digitalization of business processes, organizations command ever greater amounts of data, which, with the help of machine learning, can be used to automate work.
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Warsaw, Poland and Palo Alto, CA, USA - August 30, 2018
by Konrad Budek, deepsense.ai
Deloitte estimates that in 2021 enterprise spending on artificial intelligence and machine learning projects will reach 57 billion dollars, four times more than in 2017.
These technologies are now in everyday use, and not only among innovation leaders.
https://deepsense.ai/wp-content/uploads/2018/08/gx-deloitte-tmt-2018-predictions-full-infographic-1.pdf
Thanks to digitalization of business processes, organizations command ever greater amounts of data, which, with the help of machine learning, can be used to automate work.
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Building a Matrix with reinforcement learning and artificial imagination.
Photo courtesy of deepsense.ai |
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At the same time, spending on the following five areas can be limited:
1. maintenance, thanks to reduced energy consumption
2. payroll costs, thanks to task automation
3. raw material and quality assurance costs, thanks to the automation and tightening of quality control
4. equipment and machinery costs, thanks to the automation of control systems for operations and maintenance
5. operating costs including marketing and sales
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More and more of the business community is catching on to the savings they can harness with artificial intelligence.
The evidence for this is clear from the steps individual enterprises are taking, as well as the development of numerous machine learning business examples and the entire ecosystem of companies offering products based on these technologies and support in implementing them.
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1. Reducing the costs associated with maintaining and using the energy
As a system’s complexity grows, so too does the challenge of supervising it.
Consider, for example, the cooling of large server rooms. In terms of energy consumption and CO2 generation, the ICT sector (communication technologies, including telecommunications and IT services) produces two percent of global emissions, which is on a par with the airlines.
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To reduce its electricity expenses, Google decided to entrust energy management in one of its server rooms to AI, which “learned” the structure of the center and reduced cooling costs by 40 percent.
https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/
No new equipment was needed – it was enough to develop new software that leveraged AI.
Ultimately, the system is going to be used in all Google server rooms.
The British national energy supplier National Grid has also expressed interested in the solution.
https://www.ft.com/content/27c8aea0-06a9-11e7-97d1-5e720a26771b
Related: Five trends for business to surf the big data wave
https://deepsense.ai/five-trends-for-business-to-surf-the-big-data-wave/
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2. Reduction of human costs through automation
Machine learning enables the automation of repetitive, often time-consuming activities, freeing up the teams that had been doing them to take up more profitable tasks.
We produced a program for the international research company Nielsen that could find, read and save in a database the composition of the a product’s contents using only a photo of its packaging.
https://press.deepsense.ai/deepsense-io-shares-first-look-on-deep-learning-application-for-retail-analytics/
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This shortened the working time from several minutes dedicated to manually rewriting the composition from the label, to the few seconds required to take a picture of the packaging.
If you need more convincing, consider these figures: If a company employs 46,000 people, helping even half of them save five minutes a day translates into 314 full-time positions each day.
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3. Predictive maintenance 4.0 – optimizing machine maintenance costs
Because any hardware failure involves both repair costs and production downtime, what company wouldn’t look for tools that can predict failures and prevent them?
Another solution deepsense.ai has prepared, this time for a manufacturer, used data from sensors mounted on machines.
https://deepsense.ai/machine-learning-for-applications-in-manufacturing/
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By reviewing and analyzing the signals, the solution can predict upcoming failures up to two weeks before they occur.
Another example of predictive maintenance comes thanks to the OneWatt company, which tests the sounds issued by industrial machines.
AI steps in where the human ear would be helpless: It detect changes in the sounds the machines produce to predict potential failures.
https://thenextweb.com/artificial-intelligence/2018/07/04/machine-failure-predictive-analytics/
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Related: Three reasons why data analysts make the perfect data scientists
https://deepsense.ai/three-reasons-why-data-analysts-make-the-perfect-data-scientists/
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4. Quality control – fewer mistakes with machine learning
In many industries, quality control comes with huge costs.
For semiconductor manufacturers, huge means up to 30 percent of costs.
https://www.mckinsey.com/industries/semiconductors/our-insights/smartening-up-with-artificial-intelligence
Automating quality control with image recognition tools increases the percentage of defects detected by up to 90 percent.
https://deepsense.ai/spot-the-flaw-visual-quality-control-in-manufacturing/
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| Photo courtesy of deepsense.ai |
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Unlike automated systems, machine learning-based vision systems can continuously evolve and adapt to new product specifications.
Fujitsu implemented a system that both catches defective products and prepares each one for automated assembly at the next production stage.
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Applying machine learning, the system not only automatically recognizes the parts of the machine but also assesses their compliance with standards in more than 97 percent of cases.
https://deepsense.ai/spot-the-flaw-visual-quality-control-in-manufacturing/
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| Photo courtesy of deepsense.ai |
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5. The power of data in sales, marketing and customer service
Machine learning is able to process data sets faster and more efficiently than even the most expert analysts.
This makes it possible to constantly analyze what is happening, for example, in the company’s sales or transaction system, and also to regularly monitor customer activity.
To understand just how beneficial machine learning can be here, consider the customer loyalty survey.
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Only one in every 26 clients expresses their dissatisfaction before looking to the competition for what they need.
https://www.superoffice.com/blog/customer-experience-statistics/
Data science can help you capture the behavior patterns of a dissatisfied customer and react in advance.
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Using data more effectively benefits not only business, but the whole of society.
A solution developed by deepsense.ai for the city of Portland, Oregon enabled the police to predict in which parts of the city crime would take place.
https://deepsense.ai/crime-forecasting-minority-report-realized/
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| Photo courtesy of deepsense.ai |
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Machine learning in practice
Machine learning is giving enterprise more opportunities to look for savings and generate additional revenue.
AI helps people accomplish complex tasks that under normal conditions would overwhelm them, so great is their complexity.
Machine learning also makes it possible to automate activities that, though repetitive and schematic, require maximum focus, so employee productivity tends to fall quickly.
AI helps people do their work more effectively and devote more energy to those activities that bring the most value.
Konrad Budek, deepsense.ai
Source: deepsense.ai
https://deepsense.ai
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