How AI Is Transforming Industries
Businesses in every industry are leaning into custom AI solutions to drive greater efficiency, reduce operational costs, and deliver better service to their customers.
Google CEO Sundar Pichai revealed in 2024 that over 25% of new code at Google is written by AI. This year, Amazon disclosed that it is building more than 1,000 generative AI applications, aimed at improving customer experiences in shopping, personal assistants, healthcare, and more.
With 78% of organizations now using AI, businesses are transforming their supply chains, customer experiences, and decision-making processes.
As organizations integrate AI into their businesses to cut costs and drive growth, leaders are now actively exploring new AI use cases to stay ahead. This article looks at real-world examples of AI use in the healthcare, logistics, finance, manufacturing, retail, and agricultural industries.
Business Transformation with AI
AI is now at the forefront of modernizing outdated business processes. Startups and small businesses are targeting specific business functions using custom AI solutions, while mid-market and large companies are modernizing legacy systems to increase efficiency.
For instance, Toyota implemented an AI platform that enabled factory workers to build and deploy machine learning models. This helped them save over 10,000 man-hours annually, substantially boosting efficiency and productivity across their manufacturing operations.
DoorDash also introduced an AI-powered feature to detect and address verbal abuse and harassment, a major concern for consumers. The system analyzes over 1,400 messages per minute in real time, flagging inappropriate content across dozens of languages, including English, French, Spanish, Portuguese, and Mandarin.
From improving the customer experience to automating quality control in manufacturing, artificial intelligence industry applications can be found in every sector.
AI in Healthcare: Saving Lives with Precision
AI-driven diagnostic tools have significantly enhanced patient outcomes in the last decade. A 2017 Stanford study showed AI could detect skin cancer with as much accuracy as dermatologists. More recently, their new 2024 study found that healthcare practitioners who used AI improved the accuracy of their diagnoses by 4.6%.
AI can also help mitigate the critical staffing issues facing the healthcare industry. Researchers at Columbia Business School found that a two-stage predictive model using real-time data can reduce staffing costs by 10–15% while maintaining the quality of care and enabling better surge planning.
Similarly, Enlighten Mobility, a leading medical device company, partnered with Taazaa to develop a HIPAA-compliant platform that accelerates early detection of conditions like cerebral palsy, metabolic disorders, and brain abnormalities in newborns. This custom AI solution enables physicians to diagnose and start treating these conditions years earlier than ever before.
AI in Logistics: Streamlining the Supply Chain
The logistics sector is a clear example of how AI is transforming industries. With industry-specific AI tools, businesses can accurately forecast demand and optimize delivery routes, making supply chains faster, smarter, and more efficient.
For instance, Amazon’s Supply Chain Optimization Technologies (SCOT) leads their inventory management, forecasting demand for more than 400 million products each day.
DHL’s AI-powered packaging and shipping solutions can save customers up to 35% in shipping costs. This custom AI solution also helps reduce unused shipment space in boxes, significantly reducing the number of truckloads needed. It saves DHL customers costs on boxing, packing material and shipping,
Transmetrics’ AI tools reduce fuel usage by one liter per 100 km through smarter data and route planning. DeepSea, too, uses AI to boost ship efficiency and reduce fuel consumption by 10%.
These use cases show the real-world benefits of AI in the logistics industry to drive digital transformation with reduced risks.
AI in Finance: Enhancing Security and Decision-Making
The finance industry has made immense strides in business transformation with AI, helping firms streamline operations, reduce risk, and improve customer experiences. From automating manual processes to enhancing fraud prevention, AI is driving smarter, faster decision-making for financial institutions.
Take JPMorgan Chase, for example. Its AI-driven Contract Intelligence platform (COiN) reviews complex commercial loan agreements in seconds, saving an estimated 360,000 hours of human labor each year.
In customer onboarding, AI is proving just as valuable. Juniper Research projects that by 2028, banks will save $900 million and eliminate 29 million hours from digital identity verification alone.
Meanwhile, AI-powered tools like robo-advisors are democratizing investment guidance, offering low-cost, personalized financial advice. On the compliance side, institutions are turning to AI to navigate complex regulations, such as anti-money laundering and KYC requirements.
Fraud detection is another area seeing widespread AI adoption. One standout example of third-party innovation is Brighterion, a Mastercard company, whose AI-powered platform helped reduce credit card delinquency rates by up to 32%.
A recent PYMNTS survey revealed that 71% of financial institutions are using AI and machine learning to fight fraud in real-time payment systems. Yet, despite growing demand, only 14% of these institutions have the internal resources to build and maintain advanced AI solutions.
Building AI tools in-house is costly, time-consuming, and resource-intensive for many organizations. Third-party AI providers can help by offering custom AI solutions that can be easily integrated into existing systems via APIs or cloud platforms.
AI in Manufacturing: Boosting Efficiency and Quality
Would you believe your Galaxy S23 or Z Flip 5 is built with the help of robots? At Samsung’s South Korea plant, automated vehicles, robotic systems, and mechanical arms handle everything from assembly and material transport to final quality checks.
As AI continues to show results in streamlining operations, enabling predictive maintenance, and fueling innovation across the manufacturing sector, organizations are now taking a more focused approach to evaluating AI and its impact.
In particular, AI is playing a key role in enhancing efficiency and accuracy in automotive manufacturing, which demands high precision. At BMW’s Spartanburg plant, for example, AI-powered robots have saved $1 million annually, making their assembly line more efficient while allowing workers to focus on higher-value tasks.
Beyond the automotive sector, companies in the aerospace and consumer goods space are also seeing tangible results. GE Aerospace has deployed an AI-enabled Blade Inspection Tool to enhance turbine blade inspections, cutting inspection times in half and improving accuracy. This tool supports predictive maintenance by spotting issues early, reducing downtime, and increasing operational efficiency.
Similarly, PepsiCo’s Frito-Lay has gained 4,000 additional hours of manufacturing capacity annually by using AI in predictive maintenance systems that reduced unplanned downtime and lowered costs at four of its plants.
Smaller manufacturers are following suit. Many mid-market firms are implementing custom AI solutions tailored to their production needs, while startups are building smart factories from the ground up using advanced development services, paving the way for business transformation with AI.
AI in Retail: Personalizing Customer Experiences
Retailers that adopted artificial intelligence and machine learning outperformed their peers in both 2023 and 2024. These companies achieved double-digit year-over-year sales growth and saw annual profits rise by approximately 8%, significantly outpacing competitors not leveraging these technologies.
In the last couple of years, the retail sector has seen a direct AI impact on traditional industries. This is because AI in retail has been largely successful in addressing challenges much like those in manufacturing.
Take Walmart, for example. It piloted a generative AI chatbot to negotiate deals with 89 suppliers of items such as shopping carts and store equipment. The chatbot was able to close deals with 64% of suppliers, achieving an average of 1.5% in cost savings and securing 35 additional days in payment terms. Most notably, 83% of suppliers reported a positive experience with the chatbot, demonstrating that automated negotiations can not only drive better financial outcomes but also maintain strong vendor relationships.
Kraft Heinz, a leading food and beverage company, uses AI systems to help build a “self-driving supply chain” that can anticipate factory line disruptions or ingredient shortages and automatically adjust operations to minimize impact. This approach enables real-time decision-making, such as rerouting inventory, adjusting pricing, or shifting fulfillment strategies, ensuring smoother operations even during unexpected disruptions.
Amazon already has smart robots handling its warehouse operations, but now it is gearing up to use generative AI to enhance same-day delivery. In addition to it’s well-known Alexa personal AI assistant, Amazon now offers Rufus, the company’s personal AI shopping assistant.
Ask Amazon’s Rufus, “What are the best dinosaur toys for a five-year-old?” and it suggests stuffed animals, figurine playsets, remote-controlled toys, fossil dig kits, and books on dinosaurs.
But there are still areas of retail where AI is still finding a footing. Fashion poses a unique challenge, especially the apparel segment. Fashion trends shift every season, and this rapid variability makes accurate forecasting far more difficult for AI.
Sean Barbour, Senior VP of Supply Chain at Macy’s, noted that predicting trends 6–12 months ahead often feels like guessing. Despite this, Macy’s relies on data science and analytics to handle the uncertainty. Their tools and algorithms focus on empowered decision-making across buying, supply chain, and operations.
Fashion isn’t the only AI challenge for retail businesses. Some retailers have struggled to scale generative AI across their operations due to the need for significant organizational changes, including upgrading technical infrastructure and acquiring specialized talent.
AI in Agriculture: Driving Sustainability and Yield
Today, 8.2 billion people call Earth their home and that number will reach 9.8 billion by 2050. The demand for food will only grow and world leaders are looking at implementing AI in agriculture to address sustainability.
From machine learning-powered seeding accuracy and AI-enabled crop health monitoring to precise pesticide application, AI vision systems are transforming farming practices.
According to one study, these technologies can boost crop yields by 15–20%, reduce overall investment by 25–30%, and increase efficiency by 20–25%.
Similarly, Blue River’s technology enables precise herbicide application by targeting only the weeds, reducing the total amount sprayed by up to 90%.
Another example is AI-based digital system for intelligent irrigation (AIDSII). By analyzing environmental, soil, weather, and crop data, AIDSII helps farmers make more informed decisions. With real-time monitoring of critical irrigation parameters, AIDSII allows for better control and efficiency in water use. This not only reduces waste but also conserves valuable water resources.
The result is improved crop yield and quality, while supporting more sustainable and responsible farming practices. AI-powered drones are another example that that can help in precision mapping and surveying agricultural landscapes.
However, limited internet access in rural areas makes it difficult to use AI tools that rely on cloud connectivity. To overcome this, edge computing is used, which allows data to be processed locally, right where it’s collected, without needing to send everything to a distant server. This makes AI tools faster and more reliable, even in areas with poor or no internet access.
Competitive Differentiation: Gaining an Edge with AI
AI is not just about improving operational efficiency. It is now a key driver of competitive advantage for industry leaders. Startups are using AI solutions to break into markets, such as implementing AI-driven pricing models and AI chatbots to enhance customer experience.
Mid-market companies are also turning to AI to keep pace, applying tools like demand forecasting to optimize operations and stay competitive. Across industries, C-suite leaders now view AI-driven digital transformation as a pathway to leadership.
Innovators are going even further by developing industry-specific applications that push the boundaries of what’s possible and show how AI can deliver tangible, strategic impact across sectors.
Challenges and Considerations in AI Adoption
Despite growing investment, many AI initiatives struggle to deliver lasting value. Common issues include poor data quality, lack of risk controls, high implementation costs, and unclear business impact.
Poor data quality alone accounts for 60% of AI project failures. Mid-market companies also face challenges with legacy systems, often requiring specialized AI development services for integration.
Ethical concerns such as bias, data privacy, transparency and IP ownership add complexity, making strong governance essential
Overcoming these hurdles is critical to ensuring that AI initiatives move beyond experimentation and drive true digital transformation.
How to Adopt AI in Your Business
According to new research by Boston Consulting Group (BCG), sectors like fintech, software, and banking lead in AI adoption. However, realizing AI’s full value and impact remains a challenge. Despite widespread implementation across industries, only 26% of companies have built the capabilities needed to move beyond pilots and proofs of concept to actually deliver measurable business impact. That means 74% of companies struggle to achieve and scale value.
Leveraging AI for your business requires a well-planned implementation strategy. The first step is to identify high-impact use cases. These are generally areas of your business with highly manual, repetitive tasks that can be automated, freeing up employees to work on more high-value tasks.
Another step is assessing data readiness, as AI thrives on clean data. Mid-market firms bridge legacy gaps with AI development services, while startups prioritize scalable custom AI solutions.
BCG emphasizes that successful leaders follow a 10-20-70 rule. Investing 10% in algorithms, 20% in technology and data, and 70% in people and processes. This approach ensures that AI adoption is not just technical but also organizational, focusing on change management, training, and workflows to drive real impact.
As an AI implementation partner, Taazaa helps businesses harness industry-specific AI for lasting success.
Taazaa delivers custom AI solutions that drive business transformation with AI across different sectors. Our scalable, secure enterprise AI applications empower C-suite leaders, startups, and mid-market firms to grow. Contact us today!
