Since the days of George Boole’s logical reasoning and Makoto Nishimura’s Gakutensoku, AI has significantly evolved into a powerful tool by empowering machines to think and act like human beings. Along with mobile and cloud platforms, AI is considered one of the notable technologies that ushered in the fourth industrial revolution. Its influence on businesses is significant and consequently, industries are now experiencing paradigmatic shifts in productivity and quality. This blog will talk about practical AI use cases across major industries.
According to O’Reily’s survey, in 2020, around 85% of the organizations either evaluated or used AI in their businesses. Half of the respondents were in the “mature” phase of AI adoption, whereas about one-third were in the “evaluation” phase. These numbers depict a significant growth from O’Reilly’s 2019 survey. As per that report, only 27% of the organizations were in the “mature” phase, and 54% were in the “evaluation” phase.
AI’s impact on industries like banking and finance, healthcare, automobile, telecommunication, manufacturing, defense and military, entertainment and media, education, and others has been remarkable so far. It is opening up future avenues as well, which means we are all set to witness more innovations in the coming years. But before you start implementing AI into your business, identify how AI can impact your business first. Also, you should be aware of the right approach to implementing AI in your business for the best results.
This blog has covered all the top use cases of AI spanning healthcare, marketing, retail, and more. Let’s check them out.
1. AI in Healthcare
Just compare the time taken to develop vaccines for Influenza and COVID-19. In the former case, researchers spent a decade to come up with the one after the virus surfaced, whereas, for the latter, they took less than a year to produce multiple. And it is AI that made the difference.
The vaccine is not the only field where AI is making significant progress. It has impacted many more like oncology, where Deep Learning is doing well in identifying cancer cells. Other areas like personalized medicines to smart wearables are also leveraging AI features to create better use cases.
This change is visible in the predicted growth of AI in the healthcare sector. Allied Market Research reports that AI in the healthcare market could grow up to $194.4 billion by 2030 with a CAGR of 38.1% for a forecast period of 2021 to 2030. The industry is going to benefit from the rapid percolation of Machine Learning (ML), Natural Language Processing (NLP), and Deep Learning (DL) into different healthcare subsectors. Few such use cases:
Image processing
AI can analyze the massive amount of data that healthcare organizations store in various forms like images, clinical trials, and medical claims. Using DL algorithms, doctors can fetch diagnostic results from CT, MRI, and image data in real-time and accelerate treatment procedures.
Treatment optimization
A proper treatment of patients with cardiac arrhythmia or stroke depends significantly on medical procedures initiated within the initial golden hour. Such scenarios could benefit from AI’s ability to reduce the time taken for crucial decisions, real-time analysis of medical records, irregularity identification, and outcome prediction.
Disease analysis
Patients can always benefit from a more precise and faster diagnosis of diseases. AI-powered diagnostic tools can quickly scan through family history, patient symptoms, data related to vital organs, medical reports, etc., to help doctors accurately pinpoint the underlying disease and start treatment accordingly.
2. AI in Education
Education is a sector that was a little slow in adopting technological advancements, and when the pandemic hit, things started to look vulnerable. The sudden shift to online learning due to the COVID-19 exposed many flaws in the education systems.
The pandemic years have triggered a massive global surge in technology adoption in education and transformed the teaching and learning landscape. Over 47% of learning tools will be empowered with AI in the next three years to customize the experience of various learning groups, teachers, and tutors. This growth is propelled by the growing number of educators who now rely more on virtual learning.
AI is making quality education more efficient and affordable. In the near future, it can impact the lives of billions of people across the globe. Few such impactful use cases are mentioned below:
Improving administrative activities
Grading and assessment require accuracy and fast response, which AI can deliver by automating basic administrative activities like reviewing multiple-choice questions, doing fill-in-the-blanks, and more. These can free up educators from secondary tasks and focus more on the academic excellence and personality development of students.
Developing personalized learning
Every student is different and so are their needs for the learning pace, methodology, and aspirations. AI-powered education models can help teachers to plan customized learning plans for individual students. AI-powered Edtech platforms can identify the knowledge gaps and suggest possible customized learning routes and track the progress.
3. AI in Automobile
Thanks to AI, automated driving is no longer a figment of imagination. Automobile manufacturers have made significant progress in the area, and it is fast becoming a part of the mainstream market.
The global inclination towards AI for vehicle manufacturing is set to push the global autonomous cars market size to $11.03bn by 2028 at a CAGR of 31.3% during the forecast period 2021-2028. Moreover, makers are now adopting AI-backed methods to build personalized units for a better user experience, which can further bolster growth. Some use cases are mentioned below:
Sensor data processing & path planning
Better path planning can mitigate traffic hassles by optimizing vehicle trajectory. AI’s intelligent algorithms can help drivers navigate better traffic patterns and be on time. As a step toward safe driving, vehicles with advanced obstacle detection systems can detect and identify objects ahead and around them to avoid a collision.
Monitoring vehicle’s condition
Unwanted and sudden car breakdowns can be avoided with AI’s predictive maintenance. AI can help monitor the conditions of various car components and predict what can fail or go wrong and when.
Automating insurance claims
AI can analyze the vehicle’s data logs consisting of information related to the driver’s behavior on the road. At the same time, it can also predict the vehicle’s performance to alert the driver beforehand of possible accident scenarios, reducing the number of claims and expenditures. As for insurance companies, AI can provide trustable proof and this can significantly reduce the need for human involvement in claim processing plus it also can reduce false claims.
4. AI in Fintech
AI’s demand in helping the financial sector with fraud detection, security, operations, and more is growing exponentially. It will benefit more from the Fintech market’s possible valuation of $17.4 bn by 2027. Furthermore, its role in developing customer-centric products is vast as it controls several associated aspects like data collection and information analysis.
Here are some use cases of AI in the Fintech sector.
Fraud detection & compliance
The amount of money spent on fraud prevention is astonishing. AI can greatly reduce the cost by quickly analyzing millions of data points in seconds to spot anomalous transactional patterns. So, as soon as these suspicious activities are isolated, the system can check for any fraud. This saves both time and resources.
Data-driven client acquisition
Like any other sector, competition exists in the Fintech space too for selling their services to customers. AI-powered tools can leverage behavioral intelligence to obtain new clients by customizing offers to individuals based on their needs and pockets.
Accurate risk profiling
Credit scores are major deciding factors in lending loans. AI can provide real-time visibility of accurate credit scores associated with a borrower by analyzing the historical financial data, current liabilities, and many other risk parameters. This can help in deciding appropriate amounts, premiums, or interest rates for a loan while minimizing default risk.
5. AI in Insurance
The insurance industry is standing on the cusp of a significant digital transformation as traditional business models give way to new standards like connected insurance, usage-based premiums, and intelligent underwriting. AI is at the heart of this change, and insurers can leverage it to engage with customers, optimize products and pricing, predict possible defaults, and more.
The insurance process comprises several manual tasks, which can be automated with AI and machine learning to ensure better efficiency and performance. Some AI use cases are mentioned below:
Insurance lead scoring
AI can spot the most profitable insurance applications to optimize application reviews and focus more on lucrative customers to improve the conversion ratio. On a different note, by predicting the risk factor associated with a customer, insurance companies can mitigate risks by identifying a competitive premium.
Customize insurance policy
Manual policy customization is full of unwanted hassles. Insurance companies can make the process faster and more productive by automating it. Thus, it can make clients more independent in choosing the right policy covering what they need at the right premium.
Automatic claim processing
Claim management teams are often under pressure to determine the payout purpose, which can be streamlined with AI. It can improve the claim process by removing excessive human intervention, verifying provided proof, reporting false claims, and processing the payment.
6. AI in Marketing
The influx of good quality data can improve product marketing and increase a product’s reach to relevant consumers. Organizations must recognize and understand the needs of their customers to deliver a better experience and earn their loyalty. AI can simplify the process by identifying customer behavioral patterns and gauging the right time to engage customers.
More businesses are now adopting AI, which could eventually lead the AI in the marketing sector to a valuation of $40.09bn by 2025. To understand the AI in marketing, you can refer to one of our case studies where we helped a client manage 8 billion ad requests daily from 22 million a day with our AI solutions; read here.
Some of the top AI, marketing use cases, include:
Search engines & chatbots
Technologies like Natural Language Processing (NLP) and semantic search are gaining traction. These can provide the users with highly relevant information on the search results pages, even if the words or sentences are misspelled or from broad queries. NLP-powered chatbots can interact like a human via text and resolve questions with 24*7 coverage. Such features can reduce the workload of customer support teams, social media, and community managers.
Customized Ads
Online advertisement is a big market and more and more companies are using PPC ads to target the relevant audiences. AI can further improve audience targeting by predicting which customer segments are more likely to buy a product and then focusing the marketing budget on those segments. Further, AI can automate the A/B testing and multivariate testing to create real-time customized ads.
Product pricing
With the large volume of data available, organizations can study the demand and competition and influence prices in real-time. Similarly, AI can analyze customers’ willingness to pay for a particular product and compare a seller’s pricing model with its competitors to check where they stand. This has given significant advantages to e-commerce companies over those who are late in adopting AI.
7. AI in Agriculture
Farming has witnessed quite a few technological revolutions since the beginning of agriculture. But the recent population boom has posed some serious challenges for the sector as the demand for global food production is expected to increase 50% by 2050.
AI-powered solutions can increase output by improving crop quantity and quality and ensuring faster go-to-market. The AI market in agriculture will leverage these positives to reach a possible valuation of $2.468bn by 2026, at a CAGR of 21.52% over the forecast period 2021 to 2026.
A glance at the top use cases would reveal more.
Use of weather forecasting
Changing climatic conditions are gradually becoming a concern for farmers as it complicates identifying the correct time for sowing seeds. AI could help farmers analyze weather conditions beforehand by using weather forecasting features and planning activities accordingly.
Soil and crop health monitoring system
Specific AI-based applications can enable farmers to quickly identify nutrient deficiencies in the soil and determine steps to cure plants of pests and diseases. AI algorithms can also analyze crop health from images captured by drones.
Agricultural robotics
AI companies are now developing robots to perform various farming tasks efficiently, like plucking out weeds, sowing seeds, and harvesting a higher volume of crops than humans.
8. AI in Gaming
AI plays a pivotal role in the gaming industry. Its impact has grown multifold in recent years with the development of AR/VR and demand from industries where simulations are essential.
Gaming companies seek new technological solutions to develop better products and enrich customer experience. The sector is inspiring the massive incorporation of other AI-powered technologies in the process. The gaming market’s potential growth to a valuation of $398.95bn by 2026, at a CAGR of 11.0% during 2021-2026, will further trigger investment in AI.
It would be easier for you to understand how AI will impact if you dig deep into the breadth of use cases of AI in the gaming sector.
Voice intelligence
The use of voice has revolutionized the gaming industry. The inclusion of AI-driven voices and NLP is making games more lifelike. Assistance from such tools helps gamers focus more on the game instead of fiddling with external systems to do what they want.
Impact on illustration
AI has enabled gamers to interact with their surroundings more naturally. Illustrations in games now change with a shift in time or how gamers react. There are a lot of open-world games where AI is creating new areas with some pre-defined metrics.
Smarter mobile games
Mobile game developers are now exploring AI algorithms and machine learning technologies to make games more innovative and challenging.
9. AI in Real Estate
The real estate sector is leveraging artificial intelligence in buying, selling, and home financing. AI’s ability to collect, analyze and learn from a vast data flow makes the agents efficient, brokers strategic, and clients empowered with buying and selling processes. An example where a customer wanted to make investment decisions by leveraging AI solutions is projected here.
A report by CRE Innovation states that 49% of commercial estate executives believe in AI’s potential to curb cost and increase operational efficiencies. Such findings also firmly establish how secure AI’s future is in the sector.
Let’s look at how AI is impacting the real estate sector.
Transforming the home search procedure
The home search tools can now use the search criteria to determine users’ preferences and select properties based on those findings. AI algorithms can offer alternative listings to match the user’s expectations and place the best listings on top of the user’s search results.
Transactions streamlined
AI can take the workforce out of the data entry processes by offering customer relationship management (CRM) tools to auto-fill the data and generate automatic reports. It reduces the number of errors and the time taken for data-filling.
Property valuation
With AI’s ability to predict the future, real estate agents have started relying highly on it to assess a property and rental values. Applications with AI have an in-built automated valuation model that gathers data related to public records, transportation options, school district ratings, and crime rate statistics to offer an analysis of a particular property value.
Loans
AI and ML can impact the banking sector by simplifying the lending process in real estate. The method of lending loans involves a lot of paperwork, but, with AI the test process to read and determine the documents before transferring them to the underwriters can be made easy. However, this desktop test process does not entirely replace human work, but it is pretty fast.
10. AI in Retail
The retail industry has been experiencing rapid advancement in online shopping. But it also faced severe challenges when the pandemic hit. Due to this, global retailers are now bolstering their businesses with AI capabilities to withstand any challenges and improve customer satisfaction. Their focus is more on AI’s ability to boost customer retention and drive sales by providing high customer engagement, better personalization, and support to the sales teams.
The global AI in the retail market is expected to reach $31.18 billion in 2028, at a CAGR of 30.5% in 2021-2028.
Some examples of real-world AI applications that are changing the retail sector are presented below.
Product recommendations
AI can analyze data based on a customer’s search history in the retail sector. Retailers can then use the extracted data to send automatic personalized recommendations to prospective customers. The automation feature in product recommendation improves the conversion rates, order values and offers better customer satisfaction.
Value-added customer service
AI-enabled in-store assistants can build a strong communication channel that answers customers’ queries. Combining the physical and digital storefront is becoming essential, and phygital retail can help in accomplishing this. Merging the best components of e-commerce to create a phygital experience involves the three I’s: immersion, immediacy, & interaction that would improve the customer experience and boost revenue.
Demand prediction & management
AI-powered demand forecasting helps inventory managers strategize and manage products. AI with ML-powered methods can perform demand predictions with high accuracy by understanding the seasonal trends. If retailers adopt this, they can easily predict the ups and downs in demand and avoid stock-outs.
Pricing strategy
The pricing strategy can be called one of the best applications of AI in the retail sector. Product pricing can be simplified with AI, which was a challenging task for retailers in the past. Also, deciding the best product price to attract customers becomes easier with AI.
Final Words
AI’s percolation across sectors is massive. Startups can build a sustainable model around it and maximize their benefits. But before adopting this new tool, they should make a business case around it to understand how they can impact and what possibilities they can explore in the future. Also, its integration can help assess tech requirements and determine a better software architecture.
