The launch of ChatGPT in late 2022, and the subsequent discussion on artificial intelligence (AI) and, to a lesser degree, machine learning (ML) engendered something inherent in humans: fear. There were apprehensions about AI becoming super intelligent and taking over the human race—with some even calling for a moratorium on AI experiments, or shutting the whole thing down entirely.
It is unlikely, though, that AI will become overlords and human minions—at least not yet. But AI and ML are changing life and work in other significant, although less dramatic, ways.
AI can do it: Areas of application
AI has impacted almost all areas of life and industry. From medical science and healthcare to manufacturing, and from agriculture to automotive, AI has found applications. This stands true for the end-user as well. Virtual voice assistants use AI to understand natural languages; movie and music recommendations use it to suggest the most relevant; and our phones use facial recognition to allow or deny access. These are but a few mundane uses of AI.
The greatest potential for AI is complementing analytical techniques such as regression and classification techniques, where neural networks and ML can boost performance or generate additional insights and applications. AI has widely been used for problems that involve classification, estimation, and clustering.
Identifying historical trends and recognizing patterns is another forte of AI. It can quickly analyze large amounts of data and provide insights, in a fraction of the time it takes a human to do the same. Data science stands to benefit hugely from this capability of AI. Gartner posited that 40% of data science tasks could be automated. It was an underestimation.
The applicability of AI and its use cases vary by industry. Availability of relevant data, suitability of AI techniques, and the utility of algorithmic solutions for a particular case are some of the factors that affect AI penetration. Proper annotation of training data and advancement in computational capability are helping address these.
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Top five use cases of AI and ML
AI and ML have hundreds of use cases across various sectors. Any list that attempts to narrow them to a certain number will always be contentious, and will vary much depending on who curates them. So, here we have assembled five use cases that we think are noteworthy—in no particular order of significance.
Marketing and sales is the one area that stands to benefit massively from AI. The functions align well with the capabilities of AI, particularly the generative and analytical kinds. It is no surprise therefore that marketing and sales is the most widely used function for which organizations use AI.
Artificial intelligence is used for creating personalized marketing content, summarizing text documents, segmenting customers, and analyzing sentiments, among other things. Personalizing promotions using customer data, with AI, is shown to increase sales.
Product recommendation is another effective use of AI. Targeting individual customers with personalized suggestions using AI can lead to a substantial increase in sales conversion. Companies like Amazon and Netflix are using this technique to personalize recommendations, to great effect.
AI is also used to optimize product promotions using SKU-performance data, and for tracking inventories.
2. Customer service management
Customers prioritize good experience over price. Indeed, 96% of consumers say that good customer service plays a crucial role in brand loyalty. Real-time service and personalized experience are two parameters of good customer service, and customers are entitled to them. Two-thirds of millennials expect them; they also prefer chat to any other mode of communication.
This is good news. Thanks to AI, companies can now offer uninterrupted, real-time customer support with artificially intelligent chatbots. These AI-enabled chatbots can answer frequently asked questions and help resolve common issues. They can also engage with customers and guide them through tasks and purchases.
AI also makes it possible to provide multilingual support cost-effectively. Using natural language processing capabilities, AI-powered systems can detect a customer’s language and provide support in their native language. They can also assess customers’ sentiments and respond accordingly, rerouting to human operators where necessary.
3. Supply chain management
AI and ML have a crucial role to play in streamlining and optimizing supply chain management. Nearly half of supply chain executives consider AI to be their greatest area of investment in digital operations. Investment in and adoption of AI for supply chain management, consequently, has shot up.
AI can help with various functions in this domain, such as optimizing inventory, demand forecasting, supplier management, optimizing logistic networks, and product planning. It helps analyze massive amounts of supply chain data, and thereby identify trends and make accurate predictions about future concerns.
By feeding machine learning algorithms with operational data, insights can be uncovered that help predict supply chain disruptions, recommend alternative actions for unplanned events, and alert human personnel of critical issues.
4. Predictive maintenance
Downtime and maintenance of equipment are costly. Unplanned downtime costs $647 billion annually, according to the International Society of Automation—or about $13 trillion in production value. Thankfully, AI and ML can help minimize this cost and raise production output.
AI can be used to detect anomalies, identify patterns and trends in equipment behavior, and identify potential risks and faults. It can analyze sensor data to detect equipment failure before it happens and suggest optimal timing and methods for repairs, thereby reducing downtime.
Thus, the advantages of utilizing AI for predictive maintenance are many. It can cut costs by 30% and equipment downtime by 45%, and help raise production by 25%, according to the US Department of Energy. It also enhances the reliability and availability of equipment and reduces delays and disruptions.
5. Fraud detection
Frauds are a growing risk to businesses, with fraudsters using ever more sophisticated methods. According to a survey by PwC, 51% of organizations experienced fraud in the two years prior 2022, the highest ever.
AI and ML models can help organizations stay one step ahead of fraudsters, anticipate their moves, and take preventive action. Machine learning models analyze large amounts of data and identify patterns and anomalies indicative of fraudulent activities and flag them as they happen in real time. These systems learn as they go along and become more accurate and effective over time.
But AI is a double-edged tool. Fraudsters use them, too. Beating fraudsters is a whack-a-mole game. Powerful AI models, trained with large volumes of data that are well-annotated, that can detect most—if not all—fraudulent and anomalous activities are key. The most effective way to beat an opponent is to be simply better.
Like it or not, AI and ML are becoming an integral part of our lives. And their applications and uses are only just starting. Decisions, solely made by AI or made with it, already have substantial impacts and implications. But AI systems, though not malevolent, are not entirely fair or sound, and should not be seen as such. They ought only to complement, not supplement, humans.
AI must not eliminate human judgment or reduce human oversight but should be used to assist and enhance them. It must not be allowed to perpetuate real-world biases but must be used to reduce them. And it must not be left entirely on its own. AI is not completely trustworthy; there is often a blind spot in understanding how it makes decisions or comes to a certain conclusion. So, use and deploy AI wherever and however possible, but always have a human in control.