AI News, The Top 5 Impacts of Artificial Intelligence (AI) in Logistics! artificial intelligence

Global AI Survey: AI proves its worth, but few scale impact

We define artificial intelligence (AI) as the ability of a machine to perform cognitive functions that we associate with human minds (such as perceiving, reasoning, learning, and problem solving) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work).

The online survey was in the field from March 26 to April 5, 2019, and garnered responses from 2,360 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures.

Of these respondents, 1,872 work at companies they say have piloted AI in at least one function or business unit, embedded at least one AI capability in at least one product or business process for at least one function or business unit, or embedded at least one AI capability in products or business processes across multiple functions or business units.

The results also show that a small share of companies—from a variety of sectors—are attaining outsize business results from AI, potentially widening the gap between AI power users and adoption laggards.

We define an AI high performer as a company that, according to respondents, has adopted AI in five or more business activities (is in the top quartile for the number of activities using AI), seen an average revenue increase of 5 percent or more from AI adoption in the business units where AI is used, and seen an average cost decrease of 5 percent or more from AI adoption in the business units where AI is used.

The findings, however, provide a potential road map for laggards, showing that the AI high performers are more likely to apply core practices for using AI to drive value across the organization, mitigate risks associated with the technology, and retrain workers to prepare them for AI adoption.

year, about one-third of respondents say they expect AI adoption to lead to a decrease in their workforce in the next three years, compared with one-fifth who expect an increase, and AI high performers are doing more retraining.

In this year’s survey, we asked respondents about 33 AI use cases across eight business functions, including how adoption of AI for each of these activities has affected revenue and cost in the business units where AI is used.

Aggregating across all of the use cases, 63 percent of respondents report revenue increases from AI adoption in the business units where their companies use AI, with respondents from high performers nearly three times likelier than those from other companies to report revenue gains of more than 10 percent.

Overall, 44 percent of respondents report cost savings from AI adoption in the business units where it’s deployed, with respondents from high performers more than four times likelier than others to say AI adoption has decreased business units’

use of the following AI capabilities: natural language text understanding, natural language speech understanding, natural language generation, virtual agents or conversational interfaces, computer vision, robotic process automation, machine learning, physical robotics, and autonomous vehicles.

Fifty-eight percent of respondents report that their organizations have embedded at least one AI capability into a process or product in at least one function or business unit, up from 47 percent in 2018—a sign that AI adoption in general is becoming more mainstream.

What’s more, responses show an increase in the share of companies using AI in products or processes across multiple business units and functions: 30 percent of this year’s respondents report doing so, compared with 21 percent in the previous survey.

While this seems to indicate that more companies are beginning to scale AI, high performers are much further along in these efforts, averaging 11 reported AI use cases across the organization versus about three among other companies.

However, the increases put all of these regions, as well as China, at similar aggregate reported levels of adoption, suggesting that while there is considerable variation at the level of individual companies, the adoption of AI is a global phenomenon.

But the survey results indicate that AI high performers plan to invest more, with nearly 30 percent of respondents from these companies saying their organizations will increase investment in AI by 50 percent or more in the next three years, compared with just 9 percent of others who say the same.

The survey results suggest these core practices hold true for scaling AI, given that respondents at AI high performers are far more likely than others to say their organizations apply these practices (Exhibit 3).

For example, only 36 percent of respondents from these companies say their frontline employees use AI insights in real time for daily decision making, and just 42 percent systematically track a comprehensive set of well-defined key performance indicators for AI—two practices, in our experience, that are crucial for achieving end-user adoption and value.

Despite extensive dialogue across industries about the potential risks of AIand highly publicized incidents of privacy violations, unintended bias, and other negative outcomes, the survey findings suggest that a minority of companies recognize many of the risks of AI use.

When asked about internal controls aimed at reducing privacy risks, 89 percent of respondents at high-performing companies say their organizations adopt and enforce enterprise-wide privacy policies, compared with 68 percent of other respondents.

Similarly, 80 percent of respondents at AI high performers report that their organizations implement tech-enabled access restrictions to sensitive data, versus 59 percent of those at other companies.

While respondents from a handful of industries, including automotive and assembly, are more likely to report a workforce reduction than an increase in the past year because of AI (Exhibit 5), more respondents overall report job increases of 3 percent or more at their companies in the past year than report decreases of the same magnitude (17 percent and 13 percent, respectively).

Thirty-four percent of respondents from organizations that have adopted or plan to adopt AI expect it to drive a decrease in the number of employees, versus 21 percent who expect an increase—although most predict the change to be less than 10 percent in either direction.

Respondents reporting that their companies have piloted or embedded one or more AI capabilities, or plan to do so in the next three years, were asked how they expect the adoption of AI to affect the number of employees relative to the number if the organizations had not adopted AI.

Artificial Intelligence in eCommerce – Comparing the Top 5 Largest Firms

eCommerce is a bustling segment of the retail industry representing an estimated $102.7 billion or 8.3 percent of total U.S. retail sales in 2016.

To gauge the impact of AI among leading eCommerce firms across the globe, we researched this sector in depth to help answer questions business leaders are asking today, including: This article aims to present a comprehensive look at the four leading eCommerce firms and their use of AI based on 2016 sales revenue sourced from company financial reports.

Machine learning drives the algorithms which are core to Amazon’s targeted marketing strategy, allowing the company to predict what products will most likely interest customers and to provide customized recommendations based on customer searches.

After Amazon’s estimated $775 million acquisition of warehouse robot manufacturer Kiva Systems, Inc (now Amazon Robotics) back in 2012, to date, the company has a reported 100,000 robots in operation across its warehouse locations worldwide.

According the company’s 2016 annual report, from 2014 to 2016, Amazon’s net product shipping costs steadily increased from approximately $4.2 billion to $7.2 billion.

The pilot started with just two customers and is expected gradually expand to more customers over time in other countries including the U.S. Current government restrictions on the use of drones may pose challenges to deploying Prime Air in the U.S. The company is reportedly in working with regulatory agencies both in the U.S. and other countries to expand Prime Air.

Jeff Bezos, Amazon CEO, Letter to Shareholders (2016) (For readers with a strong interest in drone applications, refer to our article on drone delivery, and our other article on commercial drone regulations.) Amazon Go is an emerging initiative which is part of the company’s heavier push into the grocery industry, evidenced by the estimated $13.7 billion acquisition of Whole Foods Market.

The company claims that it leverages deep learning combined with computer vision and sensor technologies to track “when products are taken from or returned to the shelves.” Products are tracked in a virtual cart and customers are charged via their Amazon accounts.

As Amazon works to improve its model, the ideal customer capacity for this technology may require a smaller store design and a certain number of human staff on site to monitor operations and provide tech support.

Reports claim that the number of online orders hit a total of 1.26 billion in 2015 (double the amount of orders in 2014) and approximately 85 percent of those orders were delivered within two days.

According to reports, November 2018 is the company’s target date for the debut of its “first unmanned warehouse” and AI and robots will be responsible for handling jobs related to “parcel sorting, packaging and categorization.” “Why must use AI and robots today?

Alibaba claims that AI algorithms are helping to drive internal and customer service operations including smart product and search recommendations: Alibaba’s software tracks customer browsing and interactions with the website to offer product recommendations.

“What AI is going to do is accelerate the pivot from simple clustering around inventory, to combining intelligence about individuals, behaviors, trends, and context…We recently acquired Expertmaker – a company that has created an advanced AI platform enabling optimization and automation.

Devin Wenig, eBay President and CEO, May 2016 AI is emerging in the eCommerce segment of the retail industry and is being applied across multiple areas including processing customer service inquires, product packaging and delivery, and internal operations.

While a certain degree of job losses may be inevitable, research findings published in a 2017 report by the Progressive Policy Institute suggest that from 2007 to 2016 eCommerce produced 355,000 jobs while 51,000 jobs were lost in the general retail sector.

Emerj’s research on the retail industry at-large has revealed an important trend that is also applicable in the eCommerce sector: Applications that have the highest likelihood of broader retail adoption are those that have a direct, hard-line return on investment.

Artificial Intelligence in Retail – 10 Present and Future Use Cases

Artificial intelligence in retail is being applied in new ways across the entire product and service cycle—from assembly to post-sale customer service interactions, but retail players need answers to important questions: Which AI applications are playing a role in automation or augmentation of the retail process?

While many AI applications have yielded increased ROI—this case study of AI in retail marketing segmentation is one example—others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers.

Additionally, the AI creation spent time at hip apparel store the Ave, where the retailer experienced a 98% increase in customer interactions, a 20% increase in foot traffic and a 300% increase in revenue.

New England-based Boch Automotive also employed Conversica software, which it attributed to a an average 60-sale increase per month at one Toyota dealership.  It’s no longer a secret that IBM’s Watson is providing a slew of order management and customer engagement capabilities to eCommerce retailers. In 2016, launched Gifts When You Need (GWYN), which the company calls an AI gift concierge.

1-800-Flowers Chris McCann spoke to Digiday, noting that within two months, 70% of online orders were completed through GWYN.  Above is an example of the North Face’s conversational interface, which prompts users with a series of questions related to their purchase. It’s safe to say that similar systems like the one above could be built with simple if-then rules, and no machine learning whatsoever.

General Electric’s (GE) Brilliant Manufacturing software, in part inspired by GE’s relationships with client manufacturing companies over the past two decades, was designed to make the entire manufacturing process—from design to distribution and services—more efficient and hence save big costs over time.

An operational supervisor sitting behind a computer can now identify in real-time a floor-based problem that arises in workflow, rather than spending time making time-consuming walkthroughs of entire manufacturing facilities.

ability to adjust to real-time environmental conditions and adjust motion can (according to Fanuc) result in up to 15 percent cycle-time improvements in spot welding.  Once the robot’s learning process is complete (about 18 cycles later), the sensor is removed and the trained robots are then able to complete a task autonomously.

Tesla has employed about 600 Fanuc robots at its factory in Fremont, and according to Bloomberg Businessweek put in a significant order for more robots back in September 2016 in an effort to speed manufacturing efforts for the next slated delivery of its Model 3 in July 2017.

In July 2016, Amazon announced its partnership with the UK government in making small parcel delivery via drones a reality.  The company is working with aviation agencies around the world to figure out how to implement its technology within the regulations set forth by said agencies.

Similar to Domino’s DRU concept, it seems possible that autonomous delivery of goods and food by air could be rolled out at scale within the next decade.  Amazon’s touted brick-and-mortar locations, known as Amazon Go, employ check-out-free technology that allow customers to shop and leave  Customers use the Amazon Go app to check in, but thereafter the entire shopping experience is designed to be automated.

Fraud and payment security are a massive area of AI investment, and there are plenty of fraud / security companies worth looking at. Sift Science is one of many companies applying machine learning to detecting user and payment fraud –

 Our retail executive guests using AI expect that the relatively stodgy committees in these large companies is likely to be extremely critical, safe, and bottom-line focused (for more insights from machine learning industry executives, visit our AI podcast interviews channel).