If you were wondering why Artificial Intelligence (AI) has become a popular topic among digital transformation strategies across the industry spectrum, then you may be missing the boat on the tech world’s latest reason to make your business more profitable and more responsive to your customer base.
Vaughn Naidoo, Chief Digital & Technology Officer at Altron Systems Integration believes AI is the future of efficiency and many organisations are already missing the boat.
Defining AI for Business
AI in a business context involves the use of intelligent computer software with human-like capabilities to boost revenue, improve customer experience, increase productivity and efficiency, and drive business growth and transformation.
“The fact is that AI can save your IT department millions each year. It can optimise your internal business operations, free up resources to work on high-value business outcomes, and enhance function, features and performance at almost every level of your business.”
Simply put, Naidoo says AI can help you make better business decisions based on greater business insights.
Yet, AI is in a constant state of evolution: “As history will reveal every catastrophic event bring new opportunity, the global COVID-19 pandemic is no different. Emerging AI solutions has suddenly been catapulted into the spotlight.”
What are the benefits of AI?
Naidoo says right now there are six widely accepted and used benefits of AI in business, with one that many might not think is an added benefit.
These include:
1. Intelligent Process Automation:
By combining process automation, machine learning and natural language processing to routine activities it mimics and augments tasks performed by humans and over time it learns to do them better.
“This drastically improves efficiency and performance, reduce operational risk and frees the time for humans to focus on higher-value activities,” says Naidoo.
2. Inventory and Parts Optimisation:
User demand, supplier orders, and inventory levels are all improved using machine learning and deep learning systems. Demand is more accurately predicted while reinforcement learning can act on these predictions.
This improves inventory teams’ efficiency resulting in cost savings in terms of unnecessary stock to be stored and reduced waste due to stock expiration.
3. Network Management and Optimisation:
Mobile networks are becoming ever more complex, subscriber demands are increasing (reliability, coverage, bandwidth, security, etc…) and immediate action is often required.
“Deep reinforcement learning AI on a combination of traffic, network and customer data, achieves a greater balance between network economics, performance, and end-user experience.”
4. Predictive/Preventative Maintenance:
“When machinery in a production environment fails it results in revenue loss and a reduction in productivity,” says Naidoo.
Machine learning predicts the failure of machinery in a manufacturing process and/or the final product before it happens. This results in increased productivity reduced downtime and increased after-sales services.
5. Quality Control:
Quality and yield are major drivers in manufacturing.
Machine learning identifies and predicts defects and contributing factors across all processing stages of the factory. “The identification of features to address is used to improve the overall quality, reduce waste, and optimise yield.”
6. Single Identity Management:
Naidoo says organisations need to be able to uniquely identify the same entity (e.g. a person) over multiple internal and external systems to create a single view of a customer.
Machine learning augments the identification of a single entity (e.g. a person) over multiple, heterogeneous information sources and systems. This is applied towards improved service delivery, increased sales and personalisation.
7. Cybersecurity?
You might not think it, but AI is now what stands between your business and the world’s most advanced cybercriminals. According to Naidoo, the conventional security approach uses signatures or indicators of compromise to find threats.
This approach might be effective for previously encountered threats, but it does not detect newly discovered threats.
“By replacing traditional techniques with AI, you can increase detection rates up to 95%, but you’ll get a flood of false positives. The best solution would be to combine both traditional techniques and AI,” says Naidoo.
How much money should a business look at investing in an AI system?
“We often think of AI as this huge entity that’ll require a lot of resources. But when you break it down to its most simple form, something as simple as predicting when an event will occur could be achievable,” he says.
When making an investment in AI, one must take a longer-term view, start with the simplest form that will provide the highest return on investment. “Once the foundation is set, focus on the quick wins while keeping the long-term goals in mind,” concludes Naidoo.
Edited by Luis Monzon
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