Retailers are increasingly adopting artificial intelligence and robotics, both in brick-and-mortar shops and in warehouses, and with a new robot workforce comes the need for new management methods.

That’s right: in the near future, HR departments won’t focus only on human employees, but also include a robot resources department to look after non-human workers.

According to research firm Gartner, robot resources could be a thing as early as 2025. In the next five years, predict Gartner analysts, at least two of the top ten global retailers will have reshuffled their HR departments to accommodate the needs of their new robot workers.

There is no need to start thinking about AI holidays and robot retirement parties. Rather, robot resources organizations will be procuring, maintaining, training, taxing, decommissioning and disposing of obsolete machinery.

With AI-powered robots being particularly suited to the retail industry, Gartner’s research predicts that 77% of retailers plan to deploy AI as early as 2021. Automation of tasks such as floor cleaning, data-collection or security could have promising results – and the very first use case identified by the research firm is warehouse picking.

Big retailers have already demonstrated the potential of scaling AI and robotics in the warehouse. Walmart, for example, recently unveiled Alphabot, a robotic fulfillment system implemented in the retailer’s 20,000 square-foot warehouse in New Hampshire, and which combines human labor and robot speed to pick 800 products per hour.

Similarly, US giant Kroger signed a deal in 2018 with UK company Ocado to build huge automated robot warehouses, in which dishwasher-sized robots coordinate in swarm-like behavior to pick orders before handing them over to human employees to pack into bags.

It is largely customer demand for both accuracy and speed that has boosted retailers’ interest in robotics. “The retail industry continues to transform through a period of unprecedented change, with customer experience as the new currency,” said Gartner’s research director Kelsie Marian.

“The adoption of new digital technologies and the ever-changing expectations of customers continues to challenge traditional retailers, forcing them to investigate new human-hybrid operational models.”

But just because robot resources are coming of age, that doesn’t mean that human resources are going anywhere. Quite the opposite, argued Marian: retail workers will have to work alongside new robotic colleagues, and the new paradigm will require careful planning.

She highlighted that choosing the right candidate – human or machine – for a given job will be critical for success. “Retail CIOs must provide ongoing maintenance and performance monitoring for effectiveness,” she said. “If not, the team may become counterproductive and lead to a bad customer experience.”

In that respect, Amazon may well be an example of success. The retail giant started working with robotics as early as 2012, when it purchased robot manufacturer Kiva Systems. Working in tandem with human employees, Kiva’s robots transport pallets of inventory from one location to another in Amazon’s warehouses. More recently, they have started scanning and boxing items to be sent to customers.

Amazon said that if installed in each of its 55 US fulfillment centers, the robots could eventually replace 1,300 employees. The news sound bad, but in parallel the company announced that it would pay workers up to $10,000 to quit their jobs and set up their own delivery business, in order to tackle retail’s infamous last-mile logistics challenge.

Since the initiative was announced last year, tens of thousands of workers have applied to Amazon’s new delivery service program.

According to Gartner’s analysts, such examples of human-robot collaboration will become mainstream in future retailers’ business models. “This means the robot will have to mesh with the human team – essentially meaning that both sides will need to learn how to collaborate to operate effectively together,” she said.

Hence the importance of rethinking HR departments. The good news is that those new robotic teams shouldn’t be requiring too much feedback forms and probation paperwork.

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Thanks to the technological advancements in the field of data analytics, the global market for neutral network software is witnessing an exponential rise in its size and revenue. Since neutral network software is highly effective in reducing the cost and operational time in a number of enterprises, its usage in business application, such as such as fraud detection and risk assessment, is increased by leaps and bounds.

The neural network software market is majorly driven by the remarkable rise in the demand for data archiving tools, used for organizing a massive amount of unorganized data created by various end users. Additionally, the high adoption rate of digital technologies and the increasing demand for predicting solutions are likely to boost this market in the near future. However, the slow digitization rate across emerging markets, dearth of technical expertise, and various other operational challenges may hinder the neural network software market’ growth over the forthcoming years.

Analytical software, data mining and archiving software, and optimization software are the key products available in this market. Currently, the demand for analytical software is higher than other neutral network software. However, the data mining and archiving software is expected to witness a high-paced demand growth over the next few years, thanks to the rising need for the classification and clustering of unorganized data. The significant areas where neural network software find application is financial operations, trading, business analytics, and product maintenance.

Global Neural Network Software Market: Overview

Large-scale digitization and seamless connectivity of a vast variety of electronic end-points and sensors are two important aspects common to all enterprises that call themselves technologically advanced and digitally competent. To be able to make use of the vast volumes of data generated from interactions between the connected entities and apply it for the benefit of the business, effective analytical, predictive tools are required. Artificial neural networks, the computational devices, which could be either an algorithm or an actual hardware, are modeled after the operations and structure of neural network of living beings.

Owing to their ability to learn from the inputs provided, much as their biological counterparts, artificial neural networks are considered to be the future of data analytics. A neural network software simulates an artificial neural network algorithm for use in a computer system and is used to apply the concepts of artificial neural networks to input data.

This report on the global neural network software market presents a detailed overview of the present growth dynamics of the market and its key segments. The report includes several forward-looking quantitative and qualitative projections about aspects such as market valuation, overall sales, demand and supply statistics in key regional markets, and overall future growth prospects. The neural network software market report also presents a detailed overview of the factors expected to have a notable impact on the overall development of the market in the next few years, including growth drivers, challenges, regulatory aspects across key regional markets, opportunities, and level of competition.

Global Neural Network Software Market: Geographical Dynamics

For the study, the global market for neural network software has been segmented in terms of geography into regions such as North America, Europe, Asia Pacific, and Middle East and Africa. Of these, North America is presently the leading market in terms of revenue contribution to the global market as well as technological advancements in the field of neural network. The region leads owing to the presence of a large number of technology companies excelling in the field of neural networks, large number of enterprises with highly digitized and technologically advanced ecosystems who could be potential buyers of neural network software.

In the next few years, however, regions such as Asia Pacific and Middle East and Africa are expected to emerge as the ones with the most promising growth prospects. Rising investment in smart cities, focus on digitization of processes and operations across industrial, commercial, and public sectors, and an increasing number of enterprises adopting technological implementation would foster the growth prospects of the neural network software market in these regions.

Global Neural Network Software Market: Competitive Landscape

Some of the world’s leading tech giants such as Google Inc., Microsoft Corporation, IBM, Intel Corporation, Qualcomm Technologies Inc., and Oracle are investing vast capital and human resources towards the development of neural networks that most closely resemble and work like the highly complex biological neural network. The market is also witnessing the entry of a large number of small- and medium-sized companies, which are helping the market gain strength through innovative neural network software solutions and systems for a vast range of applications.

Other than the technology companies mentioned above, some more of the neural network software market’s most notable vendors are GMDH, Llc, Neural Technologies Limited, Afiniti, SAP SE, Ward Systems Group, Inc., Alyuda Research, Llc., Slagkryssaren Ab, Starmind International Ag, Neuralware, Slagkryssaren AB, Swiftkey, and Starmind International AG.

About TMR Research:

TMR Research is a premier provider of customized market research and consulting services to business entities keen on succeeding in today’s supercharged economic climate. Armed with an experienced, dedicated, and dynamic team of analysts, we are redefining the way our clients’ conduct business by providing them with authoritative and trusted research studies in tune with the latest methodologies and market trends.

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An initiative that will use digital technologies such as artificial intelligence has been launched to empower community health workers, promising to help save the lives of at least six million children and women in ten countries by 2030.
 
The Rockefeller Foundation initiative will be piloted in Uganda and India for its first phase that runs from this year to 2022. It will be expanded to eight other countries by 2030 in regions with a high need or high incidence of maternal mortality and which can sustain the use of digital tools such as mobile phones and the internet.
 
*Following the Ugandan pilot in Africa, the initiative  launched last month (25 September) will potentially expand across countries in East and Southern Africa.
 
The Precision Public Health project, backed by US$100 million funding from the Rockefeller Foundation and partners, to prevent and treat diseases aims to use data for creating effective interventions to address the health needs of populations, especially mothers.

For instance, linking pregnant women to health workers and bringing health facilities closer to where people reside to increase the number of people delivering in hospitals or assisted by a doctor or nurse.
 
Manisha Bhinge, associate director of the Rockefeller Foundation’s Health Initiative, says: “Our biggest aim is to end mortality due to preventable diseases such as *malaria, diarrhea, pneumonia in young children, infections disease outbreaks and ensure access to critical primary healthcare services. We know that community-based interventions are critical.”
 
Bhinge tells SciDev.Net that empowering communities to easily access services is vital to ensuring accessible, affordable and high quality healthcare.
 
“We want to ensure that community workers bring the right information to the right people at the right time,” explains Bhinge.
 
Interventions such as early detection of possible disease outbreaks, she adds, will ensure that key health crises such as cholera outbreaks are mitigated before they outstrip available resources as was the case with the Ebola virus in the Democratic Republic of Congo.
 
The Rockefeller Foundation will partner with organizations such as the WHO, UNICEF, and governments to deliver the project. It comes as a WHO report published last month shows that in 2017 about 295,000 women died from pregnancy and childbirth, with 94 per cent of the deaths occurring in low-resource regions.
 
According to the Rockefeller Foundation, developing countries are largely missing out in data science and this could widen inequalities in health outcomes relative to developed nations.
 
But under the initiative, data analytics will be used to predict problems such as where there are sanitation issues that could lead to cholera and diarrhea.
 
“We shall therefore navigate, and get tools in place to respond in time,” Bhinge says.
 
Jane Aceng, Uganda’s minister of health, tells SciDev.Net that leveraging data at community levels will help improve healthcare delivery in the country.

“Data can help us see who is in greatest need and hold ourselves accountable for meeting those needs,” explains Aceng. “We are looking forward to working with global partners, engaging technology companies, and translating innovations into lives saved and improved.”

Freddie Ssengooba, an associate professor of health economics and health systems management at the Makerere University in Uganda, cautions partners implementing the initiative to focus more on people rather than health systems.

“Health systems are important but can make an impact [only] if it brings health interventions to the people,” Ssengooba says, adding that data must be sent “back to the society where action is needed to offer working solutions to health challenges facing people”

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Few people will dispute that organizations have more data than ever at their disposal. But actually gaining meaningful insights from that data—and transforming these insights into action—is easier said than done. The bigger challenge, however, is how to effectively navigate through this massive amount of data in search for the right data needed to provide the necessary insights to successfully run the organization.

CIO defines digital transformation as “the acceleration of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritized way.” But more than just acceleration, digital transformation is about the need for businesses to outpace digital disruption and stay competitive in a rapidly evolving business environment.

By observing patterns in data and creating software that can regularly and reliably turn that data into actionable insights, data science can give your company an advantage that competitors can’t beat.

Modern enterprise technologies generate vast amounts of data, which can be challenging and time-consuming to analyze. By building data science models that are accessible, meaningful, and actionable, however, new opportunities can be identified quickly and speed up decision-making.

To do that, data science models should be:

Consumable—You shouldn’t need a PhD to benefit from data science. Having access to easily consumable, real-time insights and visualizations of complex sets of data can unlock new opportunities and revenue streams, help improve customer relationships, and help improve your bottom line.

Adaptable—Your data science models should be self-learning and highly automated, so users can get the most from them. Not only must your models learn and evolve, but your models and data must also be accessible through your existing enterprise platforms, so everyone can easily get to them.

Transparent—Your users must be able to drill down to understand the data that are driving the recommendation. When users understand the recommendation as well as the reasons for that recommendation, their experience is more meaningful.

There is no doubt that data science needs to be a fundamental component of any digital transformation effort and has incredible potential for businesses of all types to drastically improve operational efficiency across the board. A world of possibility awaits organizations that can crack the data science code.

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