As decades roll over decades, the world continues to experience phenomenal development across all sectors, be it energy, manufacturing, information and communication technology, healthcare, solid minerals, and infrastructure, to mention a few. These sectors are being transformed at a phenomenal pace which has made its mark in the way processes are being carried out and has led to continual increases in efficiency and productivity while we remain focused on the quest of making it better through research, collaboration, innovation, and creativity.
These developments can be attributed to so many factors, one of which stood out is technology as it has its footmarks across all walks of life. Examples include biotechnology, the Internet of Things, blockchain, and most importantly business operations which is a major driver of the economy. Among the various emerging technologies, Artificial Intelligence (AI) stands out as a key player, and in this article, we will focus on its pivotal role in enhancing operational efficiency.
What is Artificial Intelligence?
Artificial Intelligence, according to IBM (International Business Machines Corporation), is a technology that enables computers and machines to simulate human learning, comprehension, problem-solving, decision-making, creativity, and autonomy. To take this further, it allows computers, machines, or other AI-enabled devices to function with human intelligence. To achieve this, models are built with the aid of algorithms, which is a set of well-defined, step-by-step instructions designed to perform a specific task or solve a problem. These models are trained using a large quantity of historical data which enables them to recognize patterns and perform tasks like making predictions, analyzing data, optimization, and many more.
But how can we Enhance Operational Efficiency Using Artificial Intelligence?
Operations is a very critical aspect of a business such that downtime for a short time can have a significant effect on the productivity of a business. Using the oil and gas sector as an example, equipment downtime during the drilling process will affect other areas of production and this will lead to a reduction in operational efficiency and financial loss due to idle labor, equipment rental fees, and delays in the project timeline. However, the implementation of AI in business operations could help prevent certain issues and improve efficiency. The application of AI in business operations includes but not limited to:
1. Routine Task Automation: This involves the use of AI to automate repetitive tasks such as data collection and processing, customer service response, invoice processing, and so on. The advantage of this is it helps reduce human errors, save time, and free up the workforce for more important and complex tasks. However, for this implementation to be successful, the tasks have to be rule-based and repetitive.
2. Data Analysis for Decision Making: Data is an important factor to reference when making business decisions that will affect its operation. However, drawing intelligence from raw, unprocessed data is difficult. Therefore, for meaningful insight to be obtained, the data is cleaned, analyzed and most times visualized. While we have software such as Microsoft Excel, Google Sheets, Tableau, and Power BI that have been used for this purpose, they cannot analyze a large amount of data at a fast pace and this is where AI comes in. With the use of a branch of AI called Machine Learning, models can be built and trained to clean, process, analyze, and visualize the data which provides insights to guide decision making which improves efficiency and productivity.
With the use of a branch of AI called Machine Learning, models can be built and trained to clean, process, analyze, and visualize the data which provides insights to guide decision making which improves efficiency and productivity.
3. Predictive Maintenance: Artificial Intelligence can also be used to create Failure Mode and Effect Analysis (FMEA) models that effectively analyze sensory data of equipment to detect or predict potential equipment failure.
This helps the operations team to proactively schedule equipment maintenance, prevent sudden equipment failure leading to downtime, save cost, and reduce the timeline for fault diagnostics.
4. Supply Chain Optimization: Supply Chain and inventory management is a critical part of operations and with its ability to learn from historical data and analyse a large amount of data in real time, AI can be used to forecast inventory trends, predict market trends, optimize logistics, allocate resources, and optimize routines. This helps reduce operation disruption, optimize workflow, and save time and cost.
5. Quality Control: Machine Learning models, which have been trained using historical data, can help detect anomalies in product qualities. With its capability to collect and analyse data in real-time, the models can be used to automate inspections and flag defects or deviations from normal and acceptable standards. This is usually faster and more accurate than manual inspection. It also enhances decision-making making which in-turn improves operation efficiency.
6. Content Generation: Creating content—whether it’s text, images, audio, or videos—can be a time-consuming process, especially for individuals with limited experience using tools like Canva, Photoshop, or InDesign. The challenge becomes even greater when facing writer’s block or having to produce large volumes of content in a short amount of time.
However, with the advent of Generative AI, this process has been revolutionized. Generative AI can quickly generate content based on simple text prompts, offering tailored results within seconds. Whether you need a response to a mail, design a logo or even audio, these AI models streamline the creative process by interpreting your input and producing ready-to-use outputs, greatly enhancing productivity and creativity. However, the result may not be perfect, and you will have to manually edit it to suit your needs. To get a result close to what you need, it is advised you apply the six prompt engineering concepts which include persona, context, task, format, exemplar, and tone, but you do not necessarily need to include all of them in one prompt.
Depending on the task, you should include at least three of which context is very important. Some of these content generation tools include; Chat GPT (text-for-text), Meta AI (text-for-text and text-for-image), Dall -E (text-for-image), co-pilot (text-for-text), Udio (text-for-audio), Venture AI ((Idea Feasibility Check), Leonard AI (text-for-image and text-for-motion video), et cetera.
Note: Google offers a free course on Generative AI on Coursera. It is an excellent resource to check out.
7. Improve Cybersecurity: We live in a time where cyber terrorists frequently disrupt business operations. Attacks like malware, ransomware, phishing, and zero-day exploits have led to massive financial losses. In 2023 alone, over $12.5 billion was lost to cybercrime, according to an FBI report. The application of AI can also serve as a valuable tool for security systems already in place for this cause. AI models can be built to automatically detect and respond to cyber threats in real-time to protect sensitive data and infrastructure before the intervention of cybersecurity experts.
8. Improve Customer Experience: Keeping customers happy is one of the priorities of a business. While lots of resources have already been deployed for this cause, it can be taken a step further using AI. The main aim of a service team is to deliver the best services possible to its clients and with the application of AI-powered tools, the quality of the services is improved, as human errors are reduced, diagnostics are faster, and lead time is reduced, all of which keeps the clients impressed and happy with the services. The business will gain customer loyalty and save costs.
Challenges and Ethical Concerns
Artificial Intelligence is a great tool but it also comes with its challenges, some of which are listed below.
- Data and Model Bias: AI primarily relies on data to train models and make decisions. However, if the dataset used for training or the model itself is biased, it can lead to skewed and inaccurate results. For instance, a biased model trained on a flawed dataset might consistently generate only positive outcomes, regardless of the actual scenario or context, thereby limiting its reliability and fairness in real-world applications. Skewed results could also lead to a system failure which affects the productivity of the business operation.
To resolve this in operations, businesses must ensure their training datasets are diverse and representative of actual operational environments. The models should also be audited to ensure that only accurate and reliable outcomes are generated.
- Security Vulnerability: AI-optimized systems are also vulnerable to cyber attacks such as data breaches and model poisoning by inputting malicious codes into the model’s algorithm thereby leading to false or manipulated outcomes. To mitigate this, robust cybersecurity protocols and regular audits should be implemented. This includes encrypting sensitive data, monitoring access controls, and using AI models with built-in security layers to prevent unauthorized tampering. Additionally, regular model validation, updates, and the use of diverse, unbiased datasets ensure that AI models are secure and less prone to exploitation while reducing operational risks.
Summary
Artificial intelligence is an excellent tool that can be used not only in business operations but also in our day-to-day activities. Its impact is highly significant and multifaced, beyond those highlighted in this article. With an ever-increasing community of contributors, more discoveries are being made, and already implemented tools are being updated which businesses can leverage to improve their operational efficiency to become more competitive in this ever-improving world.
By – Inioluwa Afolabi- Assistant Project Analyst
References:
- Cole Stryker, Eda Kavlakoglu, (16 August 2024). What Is Artificial Intelligence https://www.ibm.com/topics/artificial-intelligence
- Elysse Bell (March 22, 2024). How AI Is Used in Business. https://www.investopedia.com/how-ai-is-used-in-business-8611256
- Sam Daley, Matthew Urwin (Oct 02, 2024). 74 Artificial Intelligence Examples Shaking Up Business Across Industries. https://builtin.com/artificial-intelligence/examples-ai-in-industry
- Ramūnas Berkmanas (April 10, 2024). Beyond the Human Eye: AI Improves Inspection in Manufacturing https://www.assemblymag.com/articles/98449-beyond-the-human-eye-ai-improves-inspection-in-manufacturing
