Several Asset and wealth management firms are seeking to harness the potential of AI solutions to improve their investment decisions, and making use of their historical data. In fact, 13.5% of the AI vendors in sectors like banking offer solutions for wealth and asset management.
Asset management for digital assets or distributed industrial assets are applications where humongous data about the assets (like the historical performance of a specific fund or the maintenance data) is already being recorded, making them ready for automation through Information Technology.
According to a report by the Financial Stability Board, true AI and machine learning firms have handles assets of over $10 billion till 2017. That number is expected to proliferate in the next few years.
In this article, we shall explore the current scenario of artificial intelligence applications in asset management.
Types of asset management:
Physical Asset Management
- Industrial Predictive Asset Management and Monitoring
Digital Asset Management
- Portfolio management
- Investment Advisory Consumer Applications
While Artificial Intelligence tools haven’t yet arrived in the marketplace, businesses can start now to evaluate their power and understand what’s necessary to put them to work. Companies also need to know how the technology works to solve specific asset management challenges.
What is IT Asset Management?
IT Asset Management can be defined as a set of business processes that involves IT assets across the business units within the organization. It also includes the inventory, financial, and other factors like risk management responsibilities to manage the overall life cycle of the assets, including strategic decision making using information technology.
Here is how Artificial Intelligence can help in the transformation of IT asset management:
Anticipating hardware retirement
Businesses today can use information technology and machine learning (ML) applications to plan the retirement of vital hardware assets accurately.
Instead of working on projections based on averages, ML methods can predict the hardware retirement dates. And several IT asset and configuration management tools hold the data required to train the ML models.
Here’s how it works with Machine learning and Artificial Intelligence:
- ML algorithms work on the technical characteristics of your retired assets and combine them logically.
- Then, the algorithm analyzes how each asset’s maintenance and associated downtime costs alter over time and when they were retired.
- Doing so helps your business uncover common behavior patterns in each asset group.
- These patterns later help you to build a prediction model that identifies pre-retirement asset behavior.
Calculating asset demand
Due to the unpredictable nature of demand, managing asset inventory through old methods doesn’t always work in enterprise-class organizations. To find a solution to a problem more systematically, IT managers can take the help of machine learning and AI techniques to make monthly demand predictions for each asset in the business model.
To achieve this, machine-learning models must be trained on two types of data that are responsible for demanding to forecast:
- Data generated from HR processes
- Asset retirement data.
On the HR side, data includes information on job openings, new candidates, promotions, demotions, and terminations. With the ability to predict asset demand, business owners can reduce downtime costs by enhancing asset provision, thus helping in improving the service quality.
Along with predicting asset demand, Artificial Intelligence and Machine learning tools can handle asset procurement with equal efficiency by also including cost savings. For hardware assets, ML tools organize brought assets by class, model category, and model.
The model looks at each group’s purchase-order details to find the orders that had the most favorable price and delivery costs. Then they crawl for dependencies in the data to learn which specific variables such as discounts from vendors, order size, or shipment locations accounted much.
Once the model gets to the bottom of this, they recommend the ideal purchasing options for each asset group. They can also combine multiple purchase orders or recommend changing vendors when they find a much attractive opportunity in new data.
Ultimately, AI and ML tools reduce hardware purchasing costs by allowing business owners to make a smarter and more transparent decision in the buying process.
As demands and workloads for IT asset management teams increase, one thing seems obvious: humans find it hard to do this job efficiently enough on their own. IT teams can harness the power of machine learning and artificial intelligence to manage the complexity arising in IT asset management.