You may find this blog chilling, you may equally find it eye opening, in either case it is a demonstration of how far AI (Artificial Intelligence) has come.
To stay somewhat ‘’on brand’’, I decided to interview an AI on the subject of Fixed Asset Management.
Unless you have been living under a rock, then undoubtedly you will have heard about OpenAI and its latest creation ChatGPT.
Why would we share this with you?
It is the first time an AI product has been widely available for the masses (albeit, currently as a free demo). It is also the first time an organisation such as AGS has had the ability to interact with a language model AI and demonstrate why we would be looking to implement its intelligence and subsequent capabilities within our own software.
Below is a transcript of my questions (in bold) and ChatGPT’s answers. This interview is unedited and the responses are entirely that of the Artificial Intelligence.
Q. Hello, I would like to interview you about AI and its role in Fixed Asset Management. Could I start off by asking you to introduce yourself?
A. Hello! I am Assistant, a large language model developed by OpenAI. I am trained on a wide variety of topics, including AI and its various applications, including the one of Fixed Asset Management. I would be happy to help you with your interview and answer any questions you have about the topic to the best of my abilities.
Q. In the first instance, could you explain exactly what AI is, in detail?
A. Certainly! At a high level, AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. This can include tasks such as understanding natural language, recognizing objects in images, making decisions, and problem-solving.
There are several different types of AI, but the most commonly discussed are:
These are the most basic form of AI, and they can only react to the environment based on their programming. They do not have the ability to form memories or use past experiences to inform future decisions. A classic example of a reactive machine is Deep Blue, the computer that defeated chess Grandmaster Garry Kasparov in 1997.
These AI systems have a limited memory and can use past experiences to inform current decisions. Self-driving cars are an example of AI systems with limited memory because they must be able to recall past events and use that information to navigate the road ahead.
Theory of mind:
This is a type of AI that can understand and interpret human emotions, intentions, and beliefs. This kind of AI is still in the early stages of development and is not yet commercially available.
This is the most advanced form of AI, in which the machine has a sense of self and consciousness. This type of AI does not yet exist and is still the subject of much research and debate.
There are multiple techniques and methodologies used to develop AI Systems, broadly they can be categorized in two types:
This kind of AI systems are based on a set of pre-defined rules and decision tree, also it could lack of flexibility.
These kind of AI systems are able to improve over time by learning from data, and do not require explicit programming. It could be supervised, unsupervised and Reinforcement learning
Overall, the goal of AI research is to create systems that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Q. How do you perceive AI playing a role in Fixed Asset Management?
A. AI has the potential to play a significant role in fixed asset management by automating many of the tedious and time-consuming tasks that are currently done manually. Some examples of how AI could be used in fixed asset management include:
Asset tracking and inventory management:
AI-powered systems can be used to track the location and usage of assets, making it easier to manage inventory and maintain an accurate record of assets.
Asset Maintenance and Predictive Maintenance:
AI can analyze sensor data from assets and predict when maintenance will be needed, allowing for proactive maintenance and reducing the likelihood of unexpected downtime.
Lease and contract management:
AI can assist in the management of leases and contracts, by monitoring the terms and conditions of agreements and automatically generating renewals or purchase orders.
Optimization and decision-making:
AI can be used to optimize the utilization of assets, and make decisions on when to replace or upgrade them, in order to maximize return on investment.
AI-based systems can be implemented to detect any fraudulent activities in the process of fixed asset management, this can be accomplished through Machine learning models that can detect any unusual transactions or patterns in the data.
In addition to these examples, AI can also be used to automate many of the mundane, repetitive tasks that are currently done manually, such as data entry and reporting, freeing up human resources to focus on more strategic tasks.
It is important to note that AI can be used to augment the work of human asset managers and it can improve the efficiency of processes in the fixed asset management, but it is important to consider the potential challenges as well, such as data integrity, data privacy, and security. It is also worth noting that the implementation of AI solutions in fixed asset management may require a significant investment in terms of both time and money.
Q. An often voiced concern is around practical application of technology. While some technologies appear very interesting, they are not always very practical to use or useful day to day. How do you perceive AI will differ in this regard?
A. AI is a rapidly evolving field, and there have been significant advances in recent years that have made it more practical for use in a variety of industries, including fixed asset management. However, there are still some limitations and challenges to consider when implementing AI solutions in practice.
One of the main challenges in practical application of AI is the need for large amounts of high-quality data. In order for AI systems to make accurate predictions and decisions, they need to be trained on large amounts of data that accurately represents the problem domain. This can be a significant obstacle, especially for organizations that lack the resources to collect and maintain large datasets.
Another challenge is related to interpretability, which is the ability to understand the decision-making process of an AI system. In some cases, the decision-making process of AI can be hard to interpret and explain, which can be a problem for organizations that need to comply with regulatory requirements or demonstrate the accountability of AI systems.
Additionally, There is also a concern around security and privacy when it comes to AI. The sensitive data used to train and operate AI models, should be protected from unauthorized access, and there should be a consideration of data privacy laws and regulations.
Despite these challenges, there are many practical applications of AI that are already in use in various industries, and many more are being developed and tested. The key is to find the right use cases and tailor the solution to the specific needs and resources of the organization.
Fixed Asset management, is a field where AI can bring many benefits to companies, such as reducing human error, increasing efficiency, and optimizing the utilization of assets. But as with any technological implementation, it is important to consider the potential challenges and limitations, and to approach the implementation of AI solutions with a clear understanding of how they will be used and how they can add value to the organization.
Q. Another, often considerable and contentious barrier to using technology such as AI is cost. Could you provide some insight into the cost implications (positive or negative) that AI could bring to Fixed Asset Management?
A. The cost implications of using AI in fixed asset management can vary depending on the specific use case and the organization’s current infrastructure. In general, there are both potential costs and potential cost savings associated with implementing AI solutions.
One potential cost of using AI in fixed asset management is the cost of acquiring and implementing the technology. This can include the cost of hardware and software, as well as the cost of hiring staff with the necessary expertise to set up and maintain the system. Additionally, there is a cost of data preparation and labelling, to use the data in the training process.
Another cost to consider is the cost of maintaining and updating the system over time, which can include the cost of software upgrades, hardware replacement, and employee training.
However, there are also potential cost savings associated with using AI in fixed asset management. For example, by automating tasks that are currently done manually, AI can reduce the need for human labor and increase efficiency. Additionally, by optimizing the utilization of assets and reducing downtime, AI can help organizations to maximize their return on investment. Predictive maintenance, for example, can help to prevent costly equipment failures.
It is also worth noting that the cost of implementing AI solutions can vary widely depending on the specific technology and vendor. Some AI solutions can be relatively inexpensive and easy to set up, while others can be quite complex and costly. The cost-benefit of AI solutions should be evaluated case by case.
Overall, while there can be significant costs associated with implementing AI solutions in fixed asset management, these costs can be outweighed by the potential cost savings and other benefits. It is important for organizations to carefully evaluate the potential costs and benefits of using AI in fixed asset management and to choose a solution that is appropriate for their specific needs and budget.
Q. Finally, would you be able to provide one fact about fixed asset management that I could take away and digest at a later date?
A. Sure, here is one fact about fixed asset management that you may find interesting:
Fixed asset management is not only about keeping track of physical assets, but also about ensuring compliance with legal and regulatory requirements, as well as financial accounting standards.
The International Financial Reporting Standards (IFRS) and the Generally Accepted Accounting Principles (GAAP) both provide guidelines for accounting for fixed assets, which must be followed by organizations that are publicly traded or that want to attract investors.
This compliance aspect, makes it important to track assets, depreciation, and disposal and thus, can leverage on AI solutions to improve the accuracy and efficiency of these process.
Thank you for your time.
You’re welcome! I’m glad I could help. If you have any other questions, feel free to reach out.
I hope you found that an insightful and engaging read. While there may still be a way to go, it cannot be underestimated how important AI will become in the practical application of technology in industry.
Author and Interviewer.