Artificial Intelligence and the Future of On-Demand Delivery

1st Aug 2019
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Artificial intelligence is transforming the way the on-demand delivery industry is expanding technologically. With significant potential to transform the ways of working across industries, it is no wonder that organizations across the globe are making efforts and investments to inculcate AI into their work processes. The on-demand delivery segment is on such segment, which although late, has realised that AI can bring a lasting impact to their fortunes. Artificial Intelligence is being talked about as a significantly potent transformational tool for this segment.  

 

The relevance of AI for on-demand delivery


Artificial Intelligence (AI) is being incorporated by different sectors because of its potential to improve the efficiency of their employees and processes. The same holds true for the on-demand delivery segment as well. 


Customised AI solutions can help on-demand delivery segment in not only saving time and labour costs but also in making its processes far more effortless, transparent, agile and efficient. When delivery providers will utilise AI, the customers will benefit from its faster and accurate order deliveries along with the better search results on delivery apps. 


Challenges faced by the segment  


The segment has its own unique challenges to face in order to build a sustainable method of on-time delivery. This is due to the high overheads such as transportation costs. It is also a complex logistical process that requires precise route planning. Artificial Intelligence can help this segment by keeping track of traffic and personnel, improving delivery time and reducing manpower costs. 


Recent Trends in AI adoption 


The future of on-demand delivery has AI written all over it. When companies like Amazon offer two-hour delivery to the customers, they need technological support like none other. With the help of AI based tools, companies can tackle the challenges that they face while servicing customers. Already, there are successful use-cases of AI technology adoption that are encouraging. 


1.Address Recognition


Customers can use different languages while writing the delivery addresses. Logistics software company Locus uses a combination of AI tools such as machine learning and NLP (natural language processing) to build patterns in address writing. This significantly reduces their delivery related snags caused by error in address recognition. Locus also uses AI-based algorithms on historical data to make future deductions on parameters such as the optimal time for delivery to improve their delivery rate.


2.Last mile delivery automation


Companies are striving to achieve logistics optimization by ironing out issues in their last-mile delivery. A robot-based delivery agent can reduce the manpower hiring and training expenses as it is cheaper to maintain a robot in the long term. 


Companies like Dorado are planning AI-powered drones that can deliver on-demand food between far off locations. Artificial Intelligence aided by machine learning is what makes this fantasy a reality. However, it also requires a good infrastructure and lower crime rate to actually implement this solution on a large and sustainable scale. 


3.Route planning and optimization


Route planning can help companies cut down costs and improve their profit margins. Machine learning can provide solutions for route optimization by working with historical data. Already, machine learning has proved its worth by optimization the supply chain processes across organizations. 


In the food delivery category, Food delivery startup Deliveroo is keeping competition at bay by using big data and machine learning to increase their delivery efficiency. It uses data to support team decisions by giving them insights for continuous product experimentation by tracking market trends. Secondly, its machine learning models are constantly re-trained to support customer recommendations. 


Finally, the data is also used to provide real-time operational monitoring across the town by connecting with riders and restaurants, reacting to problems swiftly and even predicting issues so that they can be addressed ahead of time. They have a dispatch engine named ‘Frank’ that utilises machine learning to calculate and match the riders with customer orders. Frank reads and assesses historical data to predict rider time, food preparation time etc. 


As already shown in the above examples, artificial intelligence is gaining in-roads into the on-demand food delivery model by providing real-time tracking, predicting, analysing and recommending abilities. All of which leads to better utilisation of resources, improved delivery timeline and efficient customer service – the three elements that point to a great future for the on-demand delivery segment. 


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