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Using AI to thrive in the digital economy

Using AI to thrive in the digital economy

Image source: https://unsplash.com/photos/K21Dn4OVxNw 

Digital technology is involved in an increasing number of sectors in the economy, including how products and services are consumed or provided, how transactions are completed, and how activities are carried out. Over the previous two years, the workplace culture has changed dramatically, leading to digital transformation challenges.

By 2022, it is expected that more than half of the global economy would be digitally based or affected. Services and commerce are increasingly being delivered digitally by default. Indeed, the vast majority of government and banking transactions are now completed online.

With increasingly automated operations and location-independent working cultures, digital technology is also transforming how work is done within firms. By 2025, every 10,000 workers in the manufacturing sector are expected to be joined by 103 robots. Employees, offices, and other resources will not be bound by the location of future hybrid work models. Digital technologies greatly impact family life, just as they do at work. The number of intelligent devices in homes has exploded in recent years, and this trend is projected to continue.

The ability of a digital organisation to create an outstanding customer, employee, and ecosystem experiences at scale is critical to its success. Artificial Intelligence (AI) is a major priority in most enterprise-wide digital transformation projects and programmes. Many countries worldwide, including several in the Middle East, have seen the benefits of Artificial Intelligence and have committed to investing in AI technology.

Recent Developments In AI Solutions

1. AI And Vaccine Development

A new vaccine may take years, if not decades, to create. However, barely three months after the first reported cases, vaccine candidates to combat covid-19 were already starting human testing by March 2020. AI models assisted researchers in analysing massive volumes of data regarding coronavirus, which enabled vaccine development to reach new heights.

A virus’ protein has tens of thousands of subcomponents. Machine learning models can sift through this deluge of information and anticipate which subcomponents are the most immunogenic—capable of eliciting an immune response. This can help researchers develop targeted vaccines. AI in vaccine development could change the way all vaccines are developed in the future.

2. Fully Automated Driving And The Rollout Of Robotaxis

In 2020, autonomous driving technology advanced further, with top businesses testing driverless cars and launching public Robotaxi services in numerous cities. Soon, due to scalability and commercialisation, autonomous driving vehicles will not require a human safety driver.

3. Applied Natural Language Processing

The processing aspects of human language for natural language systems advanced significantly in 2020. Sentiment, intent and even visual understanding, which is the ability to express knowledge about an image through language, had improved considerably. These natural language models enable more accurate search results and more sophisticated chatbots and virtual assistants, resulting in improved user experiences and commercial value.

4. Quantum Computing

In 2020, quantum computing made significant progress, with the Jiuzhang computer achieving quantum supremacy. This is significant for AI because quantum computing (as opposed to binary-based classical computers) has the potential to supercharge AI applications. Quantum computing, for example, might be used to train a generative machine learning model across a more extensive dataset than a traditional computer can handle, improving the model’s accuracy and utility in real-world scenarios. Deep learning algorithms and other advanced technologies are becoming increasingly important in developing quantum computing research.

Technologies That Enable The Digital Economy

The digital economy is the economic activity generated by billions of daily online interactions between individuals, businesses, devices, data, and processes. Hyperconnectivity, or the increased interconnectivity of people, organisations, and machines as a result of the Internet, mobile technologies, and the Internet of things, is the backbone of the digital economy (IoT). The digital economy is taking shape, challenging long-held beliefs about how businesses are organised, how firms interact, and how consumers receive services, information, and products.

1. The Internet Of Things (IoT)

The Internet of Things (IoT) bridges the gap between the digital and physical worlds by collecting, analysing, and predicting business operations.

As sensor prices continue to fall, we are approaching a day when everything — people, businesses, gadgets, and processes – may be connected. This fusion of the physical and digital worlds places all assets in a digital domain dominated by software.

In a world of intelligent, linked gadgets, IoT solutions allow organisations to analyse data collected by sensors on physical items. This data has the potential to transform enterprises by uncovering hidden trends and insights that can help you make better decisions and respond faster. When a company can understand its physical and digital asset inventory at any given time, it can operate with more precision, paving the way for the ultimate lean corporation. Within the next two years, this will not be a nice-to-have distinction but a requirement for any digital organisation.

2. Digital Supply Networks

While the global middle class is predicted to triple by 2030, there is a rising demand for critical business resources, which are growing at a 1.5-fold slower rate. The solution to this disconnect is to change how businesses safely share data in real-time so that next-generation commerce applications can thrive. The digitalisation of everything is creating new intelligent digital networks, which radically alter how commerce is managed, optimised, shared, and deployed.

The Impact Of AI On Supply Chains

1. Demand Forecasting Is Improving Warehouse Supply And Demand Management

With algorithms and “constraint-based modelling,” a mathematical approach in which the consequence of each action is bound by a minimum and maximum range of constraints, machine learning is used to find patterns and influential elements in supply chain data. Warehouse managers can make significantly more informed inventory stocking decisions thanks to this data-rich modelling. This type of big data predictive analysis is changing the way warehouse managers manage inventory by delivering deep levels of knowledge that manual, human-driven procedures and self-improving forecasting loops can’t provide.

2. AI Is Optimising Routing Efficiency And Delivery Logistics

Companies that do not grasp delivery logistics well risk slipping behind in a world where almost everything can be ordered online and delivered using data. Customers today expect fast, accurate shipping and are more than willing to go elsewhere if a company fails to meet that expectation. According to McKinsey & Company, roughly 40% of customers who tried grocery delivery for the first time due to COVID-19 plan to use it indefinitely. 

AI-driven route optimisation platforms and GPS tools powered by AI, such as ORION (a company used by logistics leader UPS) create the most efficient routes from all the options. It is a task that is impossible to accomplish using traditional approaches, which are insufficient for fully analysing the myriad route options.

3. Machine Learning AI Is Improving The Health And Longevity Of Transportation Vehicles

Data from IoT devices and other information collected from in-transit supply chain vehicles can provide crucial insights into the health and longevity of the costly equipment required to keep commodities moving through supply chains. Machine learning uses historical and real-time data to provide maintenance recommendations and failure forecasts. This enables businesses to remove vehicles from the chain before performance concerns cause a backlog of delays.

Uptake, founded in Chicago, analyses data to forecast mechanical problems in a wide range of vehicles and cargo containers, including trucks, autos, railcars, combines, and planes, using AI and machine learning. To meet forecasts, the company leverages data from IoT devices, GPS data, and data directly from vehicle performance records, which can drastically cut downtime.

4. AI Insights Are Adding Efficiency And Profitability To Loading Processes

Supply chain management necessitates a lot of in-depth analysis, such as how commodities are loaded and unloaded from shipping containers. The fastest, most effective means to transport things on and off trucks, ships, and planes require both art and science. Zebra Technologies, for example, provides real-time visibility into loading processes using a combination of hardware, software, and data analytics. These findings can be used to maximise trailer space and reduce the amount of “air” transported. Zebra can also assist businesses in developing faster, less dangerous, and more efficient parcel handling methods.

Revolutionising Logistics With AI

1. Robotics

Routine operations such as delivery, transportation, storing, picking, packaging, and routing can all be done by robots. The main difference between ordinary industrial robots and AI-assisted robots is that the latter can do more complex tasks without human involvement. Smart robots may potentially evolve as a result of learning new tasks and performing increasingly complex activities. This means that this equipment can partially, or perhaps totally, replace humans in the distribution process, making it more predictable, easy to monitor, and successful. Drones, for example, can carry a specific amount of cargo and fly or move on land or water. RFID (radio-frequency identification) technologies can automatically sort, identify, and deliver things across a warehouse. As a result, robotics in logistics can help humans manage the many stages of delivery while simultaneously increasing output.

2. Autonomous Vehicles

Autonomous vehicles have the potential to enhance delivery efficiency considerably. This technology has the potential to improve predictability, dependability, and cost-effectiveness. While fully autonomous delivery cars have yet to be developed, it is only a matter of time. As technology progresses, consumers may receive their shipments without the need for human participation in the not-too-distant future. According to McKinsey research, autonomous vehicles, particularly drones, will deliver over 80% of all deliveries. This technology will improve the efficiency of the distribution process by eliminating transportation constraints and difficulties.

3. Computer Vision

Components of a vision system can detect things, items, specific activities, and colours and perform actions using a sophisticated algorithm. Such technology could detect damage and boost productivity during the manufacturing process. For example, Amazon uses a computer vision-based AI system to dump a trailer of items in 30 minutes instead of the hours it would take otherwise.

Furthermore, computer vision-enabled devices can detect damage automatically, determine the origin and severity, and take steps to avoid future cargo-related catastrophes. Another application of computer vision is product loading and unloading. This technology not only recognises and locates objects and packages in the store, but it does so independently as well. Machine learning systems are widely used to reduce customer churn, increase supply chain quality, and improve the security of the delivery process.

4. Predictive Analysis

Any logistics company must be able to work efficiently, deliver on time, and save money on transportation. To do so, an in-depth analysis based on historical data is required to identify risk trends, execute corrective measures, and provide projections. Predictive analysis is the only way to improve logistics operations, change shipment patterns, offer on delivery, and estimate consumer behaviour. According to the MHI Annual Industry Survey for 2020, the percentage of logistics companies utilising predictive analysis increased from 17% in 2017 to 30% in 2019. It can not only increase supply chain visibility, optimise routes, and simplify tracking and planning shipments, but it can also detect unexpected events and threats. If effectively implemented, it will significantly reduce operating costs and help organisations make better decisions.

5. Big Data

Logistics, like every other industry, generates a large amount of data. Without a well-maintained data management system, handling all of this information would have been more challenging. Companies can save money and avoid late shipments and deliveries by gathering data from a variety of sources, including drivers’ applications, devices, and systems, and analysing how different aspects affect the delivery process. Using big data analytics, you may gain insight into previous delivery statistics and driver ratings, and make improvements. More than 91% of Fortune 1000 companies have been found to be investing in big data. Additionally, AI-driven data analytics allows firms to account for variables like fleet maintenance schedules, vehicle sensors, bad weather, and fuel costs. It not only gives drivers ideas for where to go and helps them travel more efficiently, but it also allows businesses to save logistical costs route by route.

Managing The Effects Of Data Gravity

As a result, demand for data centres has risen to unprecedented levels, resulting in the growing acceptance of the public cloud, spectacular growth in the hyper-scale industry, and the creation of edge networks to create the necessary tools to capitalise on the huge amounts of data being generated.

AI can prove critical in converting large data volumes into actionable insights, as well as determining which data to maintain and, perhaps most crucially, where it should be kept. The possibilities are essentially unlimited if businesses can develop a synergistic link between their data and AI technologies. However, determining the best AI technique to apply and implement is not always straightforward. Customers are beginning to consider not only how to operate their workloads, but also how to optimise and automate them as AI adoption grows. Finding the best location to run an AI task – whether it is deep learning model training or inference (which applies the trained model to a problem) is a critical issue to tackle to boost efficiency.

This telemetry data can then be used to track system availability, efficiency, and location. When combined with historical data, organisations can anticipate future performance outcomes and determine where their AI workloads should be run. “For example, if a model predicted that a massive workload will take two weeks to run on one platform, but could be completed in 48 hours using another, that information could help a data science team communicate the resources they need for a project to be successful, while also making sure they meet their deadlines.

Mitigating Financial Risks With AI

One prevalent assumption that is not discussed much is that the cost of model creation is the most important financial issue in AI/ML. We would be all set if we had the top data/AI scientists. The whole lifespan of AI may be far more complex, and include data governance and management, human supervision, and infrastructure expenditures (cloud, containers, GPUs etc.) With AI work, there is always the element of uncertainty, which means that the process will be more experimental and non-linear than with software development, and even after all the expensive development work, the result may not necessarily be favourable. Make sure your finance team understands this and that AI/ML isn’t treated like just any other technology project.

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