Artificial intelligence in business: 6 industries now benefitting from AI
SparkCognition CEO Amir Husain calls artificial intelligence “the second coming of software.” And for good reason.
While traditional software once helped companies streamline workflows and processes to get more done faster, artificial intelligence takes it up a notch.
As Husain explains, it’s basically “a form of software that makes decisions on its own, that’s able to act even in situations not foreseen by the programmers” and has a “wider latitude of decision-making ability” than its predecessor.
Whether it’s automating labour-intensive tasks or mining troves of data to generate meaningful insights, artificial intelligence has become central to companies surviving. And thriving. Here are 6 industries now benefiting from AI:
Artificial intelligence in business
Nowadays, businesses looking for the competitive advantage must make smarter decisions, faster. Sometimes in real time. Or even in advance. And that’s where AI comes in.
Let’s take a look at some of the applications of AI in the different industries, with real-world examples.
1. AI in finance
The Business Insider claims that AI could save financial institutions globally up to $447 billion by helping to reduce costs through greater efficiency, while offering more bespoke products to customers.
From more personalised banking experiences to unbiased loan decisions, the applications of AI in finance are widespread. But one key area of AI innovation that’s changing the shape of the industry is fraud prevention.
In the first half of 2022, criminals in the UK stole over £600 million through fraud and scams, highlighting the desperate need for innovation to detect fraudulent activity faster. Thankfully, AI can sift through copious amounts of data – at the blink of an eye – to identify potentially abnormal transactions, which can then be investigated further.
Going one step further, machine learning models can also be trained to predict the likelihood of fraud, by analysing human behaviour at a granular level in the course of a transaction – allowing for preventive action to be taken.
For example: Mastercard was seeking a fraud-detection solution to reduce the number of genuine transactions being declined in order to improve its customer service.
They turned to Brighterion to build them an AI solution that would enable this and in 2017 launched their Decision Management Platform (DMP), which has since blocked $55 billion in confirmed fraud in the United States.
2. AI in marketing
Marketing has seen a transition from direct mail with order forms to insight-driven digital campaigns delivered through multiple online acquisition channels.
And while human attributes such as empathy, relatability and storytelling are still central to a strong marketing campaign, AI can help streamline and optimise campaigns for improved targeting to boost returns on investment for any customer segment.
From mapping content strategies to automating personalised ads, AI will soon become a pivotal function for successful marketing. And central to this is leveraging AI for behavioural and predictive analysis to really get inside the mind of the customer and figure out what they truly want.
Combining machine learning algorithms with big data analysis, AI can provide businesses with meaningful insights into their customers’ buying patterns, enabling them to hyper-personalise communications, while predicting future behaviours based on historical data collected.
For example: Netflix has been using machine learning to improve its User Experience by offering viewers tailored suggestions based on past browsing data.
No wonder 80 per cent of the shows people watch on Netflix are based on personal recommendations – a figure that will surely keep growing as the machine learning models improve.
3. AI in insurance
The battle for the competitive edge in insurance is waged in the realm of claims processing. The faster a company processes claims, the more likely new customers are to choose that company.
Traditionally, the manual process of assessing, adjusting and – eventually – processing a claim could take weeks, months or even years, leading to endless frustration on the customer’s part.
Insurance companies have semi-automated processes for small and uncomplicated claims for many years already. And this is just the tip of the iceberg.
Developments in AI have allowed insurance companies to expand their automation capability to more complex claims, where decision-making is key. Through robust machine learning models, AI is now able to analyse customer data, to generate an (almost) immediate response as to whether a claim is genuine.
There is still a long way to go for full automation, however, especially in the case of high-value negligence claims, which still need a human touch because of their nuanced nature.
For example: Lemonade Insurance gives users the opportunity to submit a claim without filling in any forms. A simple interaction with their AI chatbot, Maya – following a few prompts – will take see their claim processed and paid out within 3 minutes.
Create beautiful visualizations with your data.
4. AI in healthcare
The COVID-19 pandemic that rocked the world in 2020 created an urgent need for a vaccine to be developed, tested and licensed in as short a time as possible.
Traditionally developing any new drug has been a costly exercise in guesswork for pharmaceutical companies, with clinical trials sometimes dragging on for years before a drug can be licensed.
But thanks to the digitalisation of patient records, it has become easier to capture and store genomic data, health records, medical imaging and other useful information. AI platforms can mine these to create models, which can help develop drugs faster, cheaper and with a greater chance of success.
And it doesn’t end there. Pairing AI with big data can also help medical professionals assess risk and use predictive analysis to detect disease earlier, which can effectively prevent future pandemics from taking hold – saving millions of lives.
For example: BERG is a clinical-stage AI-based biotech platform that combines its revolutionary proprietary intelligence platform – Interrogative Biology – with traditional research and development to develop drugs that can fight rare diseases.
5. AI in the car industry
In 2021, gov.uk reported 128,209 casualties of all severities from road accidents, highlighting the need for improvements in road safety.
One possible solution is removing human error from driving, with the introduction of autonomous vehicles, otherwise known as self-driving cars.
And developments aren’t currently underway, with both established and small car manufacturers working towards realising a fully autonomous future.
So how does it work? Autonomous vehicles use a combination of radar, lidar and ultrasonic sensors and strategically-placed video cameras to gather raw data, which is then fed into a machine learning software to create models that can instruct a car to accelerate, brake and steer at appropriate moments.
This is then combined with hard-coded rules, obstacle avoidance algorithms, predictive analysis and object recognition to enhance on-road navigation. The result? A car that can weave in and out of traffic, stop at red lights, allow pedestrians to cross and park in empty bays – all on its own.
For example: Having started out as Google’s driverless car project, Waymo has now charted its own path to safely taking people from point A to B. And with 20 billion real and simulated miles under its belt, they’re certainly at the forefront of driverless technology.
6. AI in telecommunications
One of the biggest issues in today’s telecommunications industry is network disruptions, resulting in loss of connection and data. In fact, half of the world’s largest enterprises have recorded financial losses as a result of unexpected outages.
Communication Service Providers have now turned to AI companies to build self-optimising networks (SONs), allowing operators to automatically optimise network quality based on traffic data. Essentially, these companies teach machine learning algorithms to detect network anomalies within this data, which then allows providers to fix problems before they get to the disruption stage.
For the longer term, these companies may leverage predictive analysis to pinpoint potential outages by region and timezone and take preventive action to keep networks running smoothly.
For example: United States Telecommunications juggernaut, AT&T, has developed a machine learning software to enhance their end-to-end incident management process. They’re now able to detect network issues in real-time, allowing them to manage 15 million alarms daily.
Businesses across all sectors are prioritising AI to streamline workflows, improve productivity and increase profit. According to Statista, the total global corporate investment in AI grew from $12.75 billion in 2015 to 93.5 billion dollars in 2021 – with no signs of slowing down.
It is inevitable. AI is the future, and as the technology develops, we’ll see the rise of new startups, business applications and use cases. The employment landscape will change as some roles become redundant and new jobs are created as we pave the way for a new age of business.