In a digital world, safeguarding networked physical assets – from cargo containers to cash registers – and the proprietary insights they can offer is critical.
By leveraging artificial intelligence (AI) to extract insights from these networked physical assets, businesses can gain a deeper understanding of their operations. AI also turns safeguarding assets into a strategic function that can help extend the lifespan of equipment, reduce waste, and close opportunities for fraud.
AI enables companies to detect important signals amid the constant data noise.
Additionally, AI enables companies to detect important signals amid the constant data noise by analyzing at scale without the need for human intervention. AI informs businesses about their asset security throughout their ecosystem, rather than looking at each asset in isolation. Providing this holistic view, AI supports:
- Better response to anomalies
- Better decision-making
- Better business outcomes
What is Artificial Intelligence?
AI uses computing to replicate human problem-solving and decision-making. The solution does this by learning from real-world outcomes autonomously. This improves the system so it can more accurately:
- Predict threats
- Anticipate situations that typically cause problems
- Spot opportunities to save money
Over time, this model becomes more accurate. This process is similar to the way people use life experiences to build judgement and intuition.
An AI system can spot patterns and anomalies in a stream of fast-moving data in a way that humans cannot. For example, an AI tool can analyze images of a crowd and, in real time, zero in on a person whose behavior pattern may suggest a security threat.
How Do Companies Leverage AI Today?
Global spending on AI is expected to double by 2024 to $110 billion, according to an International Data Corp (IDC) forecast. Companies that implement AI reporting reap the benefits in terms of higher revenue, lower expenses, and increased profitability.
On average, companies that use AI for decision support and business optimization outperform their peers twofold on financial measures, including 6.3 percentage points of direct revenue gains, according to research by IBM.
Companies that use AI for decision support and business optimization outperform their peers twofold on financial measures.
AI adoption is most advanced in the technology and telecommunications sectors, meaning that most industries have significant underexploited potential. Moving forward with AI requires access to data—such as that produced by connected devices on the Internet of Things—and expertise in modeling.
AI Use Case: Manufacturing
Currently, the top applications for AI in manufacturing are product design, demand forecasting, and smart robotics. But manufacturers also have opportunities to safeguard assets more effectively with AI, while also enhancing profits and improving quality.
For example, AI solutions can take historical readings from sensors on assembly lines, analyze them, and predict when parts will fail. This may include information on temperature, vibration, and leak detection. This insight allows asset managers to act pre-emptively and prevent breakdowns, emergency outages, and excessive wear. This protects the value of costly capital equipment and helps keeps production running smoothly. A U.S. government research survey discovered predictive maintenance could reduce costs more than 40 percent over a reactive maintenance program and have a tenfold return on investment.
AI Use Case: Logistics
In logistics, operators can use artificial intelligence to make better decisions in real time, reduce incremental costs, and reap savings. A popular AI application is connected containers. These are cargo containers fitted with networked devices and sensors. These collect and share information such as the containers’ location, internal temperature, expected travel and arrival times, and whether their doors remain secure.
Owners and shippers can know remotely, and nearly in real-time, where the containers are and if they experience an unexpected event. A container that moves outside its planned location or has its doors opened at an unusual time triggers a notification. If a refrigerated container is not maintaining its temperature, crews can take fast action to prevent product spoilage.
Information from connected containers is a strategic building block in smart supply chains. Operators that have visibility into their door-to-door processes can quickly use this intelligence to become more efficient. For example, they can predict how long cargo will remain in port and reduce cargo rehandling. Analysis of aggregated data from smart containers can over time help reduce shipping costs and lead time.
AI Use Case: Retail
Retail is one of the verticals projected to spend the most on AI, according to the IDC forecast. In e-commerce, the top uses are chatbots and recommendation engines. But adoption in physical retail is still in its early stages.
Despite declines among brick-and-mortar retailers, 86 percent of U.S. retail sales still occur in physical stores, according to 2021 U.S. Census data. AI can aid these retailers with applications such as personalization of customer experience, dynamic pricing, and inventory optimization.
Retailers can secure assets by employing AI for predictive loss prevention at checkout.
Moreover, retailers can secure assets by employing AI for predictive loss prevention at checkout. Using point-of-sale video analytics, AI solutions can flag:
- Label switching, when a criminal swaps a price tag for one of a cheaper product;
- Return fraud, when an individual returns a stolen or used item or one purchased with a fraudulent credit card; and
- Employee theft, when the worker gives merchandise to an accomplice by failing to scan, overriding a price, or committing gift card fraud.
Shrinkage costs retailers about 1.4 percent of annual revenue, according to the National Retail Federation in the United States. AI can reduce that by about 50 percent, according to industry research reports.