COVER STORY

The significance of artificial intelligence (AI) is being accepted across industries. The cover story examines how does AI influence financial, logistics, hospitality, education, and other sectors.

On January 23, 2020, almost immediately after CNN reported the confirmation of the first Coronavirus case in Washington, United States, Diana J. Flaherty, a Huston-based entrepreneur, wanted to cancel her entire Singapore itinerary, scheduled for the first week of February. After several hours of tiring attempts, her flight, hotel bookings, and transfers got canceled. She had called and emailed travel agents, transfer service providers, and third parties to get the job completed.

None of the chatbots, IVR interactions, or online ticketing services, which she reached out responded appropriately – the time difference between countries added more stress to the job.

Now; two years later, intelligent IVR assistants, conversational automation systems, AI-powered chatbots, and data processing tools automated not just the travel booking process but nearly 70 percent of the customer-business engagements ranging from inquiry to service delivery, from issue registration to technical support, and from itinerary design to meeting schedules, and more.

Pandemic-Ready IT Systems and Hybrid Work

Even though most of these technologies have existed back then, their implementation has been fast-tracked due to the COVID-19 pandemic. Cost, reduced workforce, travel restrictions, and several other constraints have forced enterprises to invest in technology that reduces human involvement. Besides building pandemic-ready IT systems, most enterprises have permanently shifted to hybrid work mode and most of them are functioning with less than 25 percent of their workforce from offices.

It is already proven that the abilities of AI are incredible and beyond imaginable limits especially when it collaborates with human intelligence and thought processes. While solution providers accelerate the potential of their tools with the help of AI, cybercriminals use AI systems to infiltrate into both public and private networks. Most of the cyberattacks recently reported are facilitated by AI-powered agents and applications.

Traditionally, AI remained as a classy technology stood only by commercial segments that demanded quick return on investments (ROI). But when the unrelenting rampage of COVID-19 variants impacted nearly all verticals, the idea of creating resilience with modern technologies had popped up. Since then, the healthcare, research, and drug sectors started to get benefits out of AI.

Dr. Jassim Haji, President at International Group of Artificial Intelligence articulated, “the use of AI in healthcare wasn’t adequate and much less compared with commercial verticals especially in modern societies, such as the United States, China, and Europe. Had there been the modern technologies used efficiently in hospitals, research labs, patient care units, and drug development centers earlier, we could have saved many lives that the COVID-19 pandemic claimed.” Read his exclusive interview.

Referring to the early trends, positive impacts such as enhanced customer engagement and revenue growth have been witnessed in some of the key sectors including finance, manufacturing, aviation, hospitality, and logistics.

Unlocked Revenue Growth in Finance

Finance Institutions believe in the potential of AI. Hence, they started using AI-driven solutions for reducing operating expenses and automating intensive processes. Eventually, it led to revenue growth. A survey by NVIDIA titled “State of AI in Financial Services,” also showed a substantial financial impact of AI for enterprises with 34% of those who replied agreeing that AI will increase their company’s annual revenue by at least 20%.

FinTech and investment firms use AI for the following benefits:

• Algorithmic trading
• Fraud detection
• Improved productivity
• End-to-end trading infrastructure
• Anti-money laundering (AML) and KYC
• Accurate insurance policies
• Automated call centers
• Risk assessment and management

Robots Replace Workforces in Logistic Sector

The logistic sector had a major setback due to the pandemic. The entire supply-chain process got tarnished due to worldwide lockdowns and the absence of workers – the crisis was visible even to transfer medical supplies between countries. Solutions providers considering the option of replacing workers with robots, introducing self-driving cars, automation in documentation, inventory, warehouse, and clearance processes.

In the logistic sector, AI-powered systems are calibrated for:

• Big data analysis
• Better demand prediction
• Planning and resources management
• Real-time route optimization
• Warehouse automation
• Product inspection

Dynamic Pricing Options in Hospitality Sector

Hospitality probably conducts an increased amount of customer engagements and it is higher than most other sectors. Thus, automation and AI implementation have been done focusing on offering customers a unique experience.

Eg.,

• Chatbots and virtual assistants: Commonly used to handle inquiry and complaints
• Apps and sophisticated UIs: Provide hassle-free booking and easy cancellation
• Revision of customer reviews: AI-powered applications can evaluate customer reviews that help organizations learn better customer handling
• Prediction and data analysis: Customers are offered dynamic pricing based on their engagements

The market intelligence firm IDC estimated the adoption of AI up to 70 percent by online travel agencies and 60 percent by hotels, worldwide before 2020. The prediction of SITA, an IT solutions provider to the air transport industry, was hinted about the investments in AI and machine learning (ML)-based programs by 52 percent of airlines and 45 percent of airports by 2021.

Robot Teachers and On-Demand Tutoring

AI-powered solutions influenced EdTech space for some time. Studies predict nearly all learning management tools will be enabled with AI capabilities in the coming years and that will be a complete transformation of the education sector worldwide. Plus, AI gives a completely different perspective of education not only to students and teachers but also to parents and institutions. When experts predict it as the future of education, the following are the merits of AI in education:

• Personalized learning programs
• On-demand tutoring using AI chatbots
• 24/7 learning from anywhere
• Better time management and task division for teachers

Conclusion

AI and ML are on the scope of defining the future for individuals, groups, organizations, industries, professional sectors, and governments. Robots outperform the existing workforce across the planet, in a decade or less, and there is almost nothing they can’t do in terms of labor. While there are positive and productive impacts, there could be, certainly, adverse impacts as well. Experts forecast massive job losses, unlawful cyber activities, cyberwarfare, etc. Therefore, active intervention of governments in both the implementation and the practice of technology is highly desired. Governments should deliberate schemes for employment protection and social cohesion (e.g., universal basic income (UBI). Simultaneously, states should encourage AI groups, societies, and think tanks to join forums and engage in regular conversations for value exchanges to have the future bright for everyone!

The post Collaborative Intelligence Can Make the Future Incredibly Bright appeared first on The Integrator.

Each year starts with new hopes and proceeds with explorations of fresh opportunities; some are continued from the past year(s). For The Integrator, co-publications, and JNS Media International, it is a year of bigger adventures and smarter outcomes.

As everyone knows, we are hosting MENA’s largest partnership-based initiative, named “SME Champions,” for business enterprises, manufacturers, financial service providers, and logistic solution providers, besides repeating our successful award ceremonies. By incorporating Golf tournaments, networking programs, gala awards, lead-gen activities, and publications, the year-round partnership initiative would establish the biggest winning streak for its investors.

The Integrator’s January edition analyses artificial intelligence (AI) and machine learning (ML) and how they impact major industries, including financial, healthcare, logistics, hospitality, and education. With a detailed cover story, exclusive interviews, and columns, we comprehensively attended every aspect of AI and ML in the issue. Besides the holistic approach, the study of the nitty-gritty of AI would certainly help businesses adopt modern technology appropriately.

The Integrator’s February edition is reaching the next milestone in success as it is our 100^th edition, which gives insights into cybersecurity. Join us to celebrate the moment and take advantage of getting your services, solutions, or views featured in it!

Happy reading!

Murali Margassery
Assistant Editor

The post AI Everywhere appeared first on The Integrator.

“The article was originally published on AIMultiple”

Artificial intelligence is transforming all industries and logistics is one of them. Logistics is the management of the flow of products between different locations. A global network of suppliers and customers complicates logistics operations and logistics companies contain both easy to automate tasks and complex processes that can benefit from AI/machine learning algorithms.

What does AI mean for logistics companies?

The technology offers a wide range of capabilities to logistics companies from autonomous machines to predictive analytics. According to Mckinsey research “AI adoption advances, but foundational barriers remain,” the logistics industry has adopted AI mostly for 4 business functions which are service operations, product and service development, marketing and sales, and supply chain management. These four business units cover 87% of AI adoption in logistics. Mckinsey estimates that logistics companies will generate $1.3-$2 trillion per year in economic value by adopting AI into their processes.

Source: Mckinsey

What are the applications of AI in logistics?

Planning

Logistics requires significant planning that requires coordinating suppliers, customers, and different units within the company. Machine learning solutions can facilitate planning activities as they are good at dealing with scenario analysis and numerical analytics, both of which are crucial for planning.

Demand Forecasting

AI capabilities enable organizations to use real-time data in their forecasting efforts. Therefore, AI-powered demand forecasting methods reduce error rates significantly compared to traditional forecasting methods such as ARIMA, AutoRegressive Integrated Moving Average, and exponential smoothing methods.

With improved accuracy in demand prediction,

• manufacturers can better optimize the number of dispatched vehicles to local warehouses and reduce operational costs since they improve their manpower planning
• local warehouses/ retailers can reduce the holding costs (opportunity cost of holding the item instead of investing the money elsewhere)
• customers are less likely to experience stockouts that reduce customer satisfaction

Supply Planning

Artificial intelligence help businesses analyze demand in real-time so that organizations update their supply planning parameters dynamically to optimize supply chain flow. With dynamic supply planning, businesses use fewer resources since dynamic planning minimizes waste.

Automated Warehousing

According to the 2020 MHI Annual Industry Report, only 12% of businesses are using AI technology in their warehouses, but it is expected to reach 60+% in 6 years.

Source: MHI/Deloitte

Warehouse robots are another AI technology that is invested heavily to enhance businesses’ supply chain management. The warehouse robotics market was valued at USD 2.28 billion in 2016 and is expected to grow at a CAGR of 11.8% between 2017 and 2022.

For example, the retail giant Amazon has acquired Kiva Systems in 2012 and changed its name to Amazon Robotics in 2015.  Today, Amazon has 200,000 robots working in their warehouses. In 26 of Amazon’s 175 fulfillment centers, robots helping humans for picking, sorting, transporting, and stowing packages.

Damage Detection/Visual Inspection

Damaged products can lead to unsatisfied customers and churn. Computer vision technology enables businesses to identify damages. Businesses can determine the damage depth, the type of damage, and take action to reduce further damage.

Predictive Maintenance

Predictive maintenance is predicting potential machine failures in the factory by analyzing real-time data collected from IoT sensors in machines. Machine learning-powered analytics tools enhance predictive analytics and identify patterns in sensor data so that technicians can take action before the failure occurs.

Autonomous Things

Autonomous things are devices that work without human interaction with the help of AI. Autonomous things include self-driving vehicles, drones, and robotics. We should expect to see more autonomous devices in the logistics industry due to the industry’s suitability for AI.

Self-Driving Vehicles

Self-driving cars have the potential to transform logistics by decreasing heavy dependence on human drivers. Technologies such as platooning support drivers’ health and safety while reducing carbon emission and fuel usage of vehicles. Tesla, Google, and Mercedes Benz are investing heavily in the concept of autonomous vehicles, it is only a matter of time before autonomous trucks are seen on roads around the world. However, according to BCG estimations, only around 10 % of light trucks will drive autonomously by 2030.

Delivery Drones

For the logistics of products, delivery drones are useful machines when businesses deliver products to places where ground transfer is not possible, safe, reliable, or sustainable. Especially in the healthcare industry where pharmaceutical products have a short shelf life span, delivery drones can help businesses reduce wastage costs and prevent investments for costly storage facilities.

Analytics

Dynamic Pricing

Dynamic pricing is real-time pricing where the price of a product responds to changes in demand, supply, competition price, subsidiary product prices. Pricing software mostly uses machine learning algorithms to analyze customers’ historical data in real-team so that it can respond to demand fluctuations faster with adjusting prices.

Route optimization/Freight management

AI models help businesses to analyze existing routing, track route optimization. Route optimization uses shortest path algorithms in graph analytics discipline to identify the most efficient route for logistics trucks.

Therefore, the business will be able to reduce shipping costs and speed up the shipping process. For example, Valerann‘s Smart Road System is an AI web-based traffic management platform that delivers information about road conditions to autonomous vehicles and users.

Back Office

Every business unit has back-office tasks and logistics are no different. For example, there are numerous logistics-related forms like a bill of lading from which structured data needs to be manually extracted. Most businesses do this manually.

Automating Manual Office Tasks

Hyperautomation, also referred to as intelligent business process automation, means using a combination of AI, robotic process automation (RPA), process mining, and other technologies to automate processes in an end-to-end manner. With these technologies, businesses can automate several back-office tasks such as

1. Scheduling and tracking: AI systems can schedule transportation, organize pipelines for cargo, assign and manage various employees to particular stations, and track packages in the warehouse.
2. Report generation: Logistics companies can use RPA tools to auto-generate regular reports that are required to inform managers and ensure everyone in the company is aligned. RPA solutions can easily auto-generate reports, analyze their contents and based on the contents, email them to relevant stakeholders.
3. Invoice/bill of lading/rate sheet processing: These documents help communication between the buyers, suppliers, and logistics service providers. Document automation technologies can be used to increase the efficiency of processing these documents by automating data input, error reconciliation, and document processing.
4. Email processing: Based on contents in auto-generated reports, RPA bots can analyze the content and sends emails to relevant stakeholders.

For more RPA and hyperautomation use cases for businesses’ back-office tasks, feel free to read our articles:

• 60+ RPA applications
• 10+ Hyperautomation applications
• Supply chain automation

Customer Service Chatbot

Customer service plays an important role in logistics companies since customers will contact companies for any issue they experience in delivery. Customer service chatbots are capable of handling low-to-medium call center tasks such as:

• requesting a delivery
• amending an order
• tracking shipment
• responding to a FAQ

Chatbots are also valuable tech to analyze customer experience, chatbot analytics metrics enable businesses to understand their customers better so that they can enhance the customer journey they deliver.

Cem Dilmegani, Founder, AIMultiple

Cem is a high-tech industry analyst and he served as a tech consultant at McKinsey and as a tech entrepreneur at Hypatos, the document hyperautomation company. AIMultiple.com provides 1M enterprises with transparent, data-driven insights on enterprise technology.

The post Use Cases, Applications, and Examples of AI in Logistics appeared first on The Integrator.