Imagine getting more done with less effort, freeing up time and resources for innovation. This desire for efficiency has been one of the market drivers driving the growth of Artificial Intelligence (AI) as a technology capable of not only transforming but also improving business practices. That’s the power of Artificial Intelligence in today’s business landscape.
The idea of AI is to enable computers to perform tasks that would otherwise need human intelligence. This is achieved by employing complex algorithms to analyse task-specific data. In the process, computers learn the rules needed to execute the task and apply those rules in real-world circumstances.
AI in Action
Traditionally, store assistants rely on their knowledge to recommend products. With AI, vast amounts of catalogue data can be analysed to suggest items that are similar to what a customer might be interested in. This is a great example of how AI works, and the concept has existed for decades but has been difficult to execute. However, advances in computing and AI research over the past 10 years have resulted in real-world applications such as virtual assistants, AI-based medical imaging, self-driving cars.
A business’s operations can be considered as a unified set of various workflows involving multiple processes. Most processes follow rules, and if AI algorithms can be trained to learn the rules in high-volume or labour-intensive processes, they can be used to minimise the amount of time or human resources required, resulting in operational efficiencies.
Use Cases: How Industries are Reshaping their Operations with AI
Logistics
The transfer of goods from supplier to consumer is a major component of the logistics industry. Delays not only lower customer satisfaction but also increase costs. By feeding previous supply and demand data to AI algorithms, they can learn to predict demand and optimise stock levels. This will eliminate stockouts, lower carrying costs, and ensure timely delivery of goods.
Logistics businesses such as Amazon and FedEx are already employing AI to improve supply chain efficiency and resilience. A recent survey found that 46% of large corporations in the United States and Europe are introducing digital applications to boost supply chain resilience. Some of the opportunities for digitisation is due to quantity of manual and repetitive work required, which is making attracting and retaining top talent increasingly difficult. However, using AI to automate these processes will not only increase operational efficiency but also reduce employee turnover.
Other AI use cases include route planning, which uses real-time weather and traffic data to optimise delivery routes, and smart warehouses which utilise autonomous robots to lift heavy packages.
Energy
Whether in established energy industries like Oil and Gas or growing renewable energy markets, operational efficiency is critical for reducing downtime, managing costs and maximising productivity. AI can analyse sensor data from assets such as power plants or wind turbines to predict equipment failures and proactively schedule maintenance. This will not only minimise downtime but extend the lifespan of the asset.
Businesses that specialise in renewable energy installations would benefit from implementing AI to manage assets, optimise performance, and maximise returns on investment. As an example, companies such as GE, are currently employing AI to solve wind turbine maintenance and operational challenges. Their application of AI resulted in not just lower maintenance costs, but also a 10% reduction in wind turbine installation costs by analysing installation logistics data.
Another use case which is becoming popular with energy trading companies is the use of AI-powered predictive analytics to forecast net-imbalance volume (the total energy imbalance between supply and demand) by analysing market prices and weather conditions.
Green energy
Automotive
The manufacturing process is an important part of the business model for automotive companies, consequently optimising production lines is key for profitability and growth in a highly competitive industry. By training AI algorithms with market demand, historical production data, and other relevant data points, production schedules can be dynamically adjusted. This enables real-time updates to production workflows based on changes in machine availability or workforce capacity.
With such AI solutions in place, production operations within the automotive companies can become lean and efficient without losing the agility required to adapt to fluctuations in customer demand. This type of hybrid approach will become increasingly essential, as a recent analysis predicts that the overall number of cars in use in Europe will peak in 2025 and then fall. Other industries that use AI in their manufacturing processes experience huge increases in operational efficiency. One example is Lenovo, the world’s biggest PC manufacturer, which has increased manufacturing volumes by 19% and capacity by 24%.
Other AI applications include defect detection of components in the manufacturing line to enable for timely reactions and predictive maintenance of equipment in production lines. By recognising possible problems early on, production efficiency can be increased.
Automotive
Challenges & Resolutions
Data quality and quantity
Challenge: One of the barriers to implementing AI in business operations is a lack of data or poor data quality. For AI to work effectively, the quality and quantity of data input into the algorithm must be relatively high. Consider the data as the information required for the algorithms to learn. If the data quality or quantity is low, the training process will produce poor results. This could occur if certain operational procedures use outdated systems that do not capture data adequately, resulting in data inconsistencies, or if data must be manually entered into systems, which can lead to human errors.
Resolution: While there are technological solutions to address this issue, it is important to recognise that the data quality of certain processes would require further improvement before AI can be used to its full potential.
Data Silos
Challenge: In most industrial AI use cases, data must be captured from different sources. For example, to be able to optimise a production line, AI might need access to sensor data, data from an ERP system and other relevant data sources. In a lot of organisations, this data is scattered across multiple systems. This presents data inconsistencies such as different names for the same equipment and integration challenges with AI algorithms.
Resolution: Combining data from different sources into one centralised data model is the best approach to resolving this problem. Fortunately, there are many data integration platforms which can be used to do this.
Ethical Risks
Challenge: The application of AI in business operations presents a few ethical considerations, including data security and privacy. Regarding data security and privacy, the data utilised in deploying AI systems must be handled responsibly. Governments have recognised this and are working on regulating the development of AI. For example, the European Union (EU) recently established legislation aimed at addressing the ethical risks AI poses in society. These regulations will have huge impact on how AI solutions are built, and it is important for businesses to be adequately prepared.
Job displacement is another possible barrier to the use of AI, notably in the industrial sector. As more manual and repetitive operations become automated, the demand for human employment could decrease.
Resolution: Creating a framework to better data governance and identify ethical risks is critical for responsible development of AI systems. While giving training opportunities for workers to upskill would help maximise productivity with AI while also retaining staff.
The Future is Now
In conclusion, AI presents tremendous opportunities for businesses to drive down costs and optimise processes. Embracing it as a digital enabler will foster growth and help organisations stay competitive in today’s fast-paced market environment. As the technology evolves, the opportunities to create operational efficiencies will only increase, reshaping the way business operations are conducted.
Contact us at digitial@scskeu.com for our new AI Regulatory Advisory & AI Governance Consultancy Service. Our expert team is here to guide you through the complexities of AI regulation and ensure your company’s compliance.
Author: Chidi Akurunwa, AI Consultant
