Artificial intelligence and its accompanying technologies, such as machine learning and deep learning, have ushered in an era of intelligent automation and human-level recognition. Long-standing fields, such as finance, healthcare, human resources (HR), and marketing have begun to feel the disruptive effect of AI.
Artificial intelligence has taken up the mantle of the most used buzzword in modern solutions, being billed as a revolutionary method of providing labor. These technologies have enabled a new level of low-cost and accurate labor for companies all over the world. In addition to this, they are also able to create new value opportunities for enterprises through analytics.
The effect of AI on the enterprise world is indisputable. From predictive analytics for business intelligence to deep learning applications for image recognition to recommendation algorithms for tailored recommendations, AI has found a variety of use cases in businesses.
However, the effect of AI is also found in other industries. In this article, we will explore the use cases and ethical consequences of using AI in finance, healthcare, HR, and marketing. We will also explore how these solutions function and take a look at the necessity for AI in today’s corporate landscape.
Table of Contents
Applications of AI in Healthcare
Applications of AI in Marketing
Why Do We Need AI?
Before looking into the applications of AI in various industries, we should look at the need for AI. Why do we need AI in today’s corporate landscape? The answer is simple. Artificial intelligence provides human-level intelligence at a much higher speed and a much lesser cost. This allows companies to automate several basic processes.
Automation can supercharge a company’s operations, as the AI continues to improve with newer data. This allows companies to deploy AI at one go and not have to worry about updating the model. It will continue to improve itself with time and necessary resources.
This will not replace human jobs, as many believe. On the contrary, AI will call for companies to upskill their workers to work with AI algorithms. With AI and humans working in conjunction with each other, it is possible for higher levels of productivity to be achieved with just one employee.
Artificial intelligence can automate many time-consuming tasks that clutter the workflow of humans. This provides information to the user in an accessible fashion while still allowing for an optimized workflow on the part of the user.
This synergized working relationship is the end goal of deploying AI in a corporate setting. When each employee has the tools to improve and optimize their workflow, the company as a whole can benefit from more productive and efficient employees.
How Is AI Used Today?
Artificial intelligence programs are nothing but computer codes. This means that they can be programmed to conduct different tasks, and new approaches can be used to ensure different algorithms. The technology itself can be adapted for different operations across industries.
Artificial intelligence, machine learning, and deep learning are three main categories of AI in use today. Artificial intelligence is the more general of the three terms and is defined as a program that exhibits human-like intelligence in some way. An example of this would be a chess-playing program. This program can accurately predict the best possible view in any scenario much faster than a human player.
Machine learning algorithms are also widely used today. ML is a subset of AI that includes algorithms that learn from new data. They are deployed once and will continually improve themselves based on the data they are fed. This allows companies to simply deploy the solution and experience improved results with time.
Deep learning is a specialization under machine learning. DL mimics the physical structure of the human brain using constructs known as neural networks. These neural networks are used for complex applications like decoding natural language and voice, analyzing images, and generating human-like content.
These different types of AI are deployed for various applications. Each one has its own advantages and disadvantages, making them suitable for specific use cases. Let’s delve deeper into the applications of AI for businesses.
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Applications of AI in Finance
AI is a good fit for finance due to the large amount of data financial institutions collect about their customers. While this is required to manage tax and reduce the number of fraudulent transactions, it also serves to keep a record of the customers’ financial transactions. This data can be given to AI to find spending patterns and harvest useful information regarding the customer.
Common Use Cases
Some common use cases of AI in finance include fraud detection, intelligent automation, and background checks. For fraud detection, AI systems can detect patterns associated with fraudulent transactions and inform banks about them before they happen. This allows for predictive maintenance that is also non-intrusive to the customer.
Paperwork is a huge part of the finance sector. Due to accountability and organizational reasons, there is a lot of paperwork required at every step of the way. AI algorithms can generate a complete profile of the customer based on a minimal document check, which can then be used across the entire database. This means that customer records can be retrieved through a simple code, thus reducing the time for background checks and processing requests or complaints.
AI Technology Used
Predictive analysis has found great use in the finance sector. Using big data, it is possible to gain insights about the customer. These insights can be used to provide more targeted recommendations to the customer or detect whether they are likely to pay back a loan or not.
Automation and indexing algorithms are also widely used in finance to manage the large amount of documentation that comes with banking. Automation is used to quickly update new information about customers, and smart indexing algorithms can provide employees with the information they need while restricting access.
Roadblocks and Challenges
Finance has reduced the ethical consequences of using AI when compared to other fields, as decisions taken by algorithms will not have a significant impact on an individual’s life. At most, this could cause an involuntary lockout of the customer’s account, which can easily be remedied by getting in touch with the bank.
One thing that has to be ensured is that the data for creating predictive algorithms to determine loan defaulters are not skewed. Biased data of any form will cause the AI to make inaccurate judgments regarding the eligibility of a customer for a loan. In general, the data must be free of bias for more accurate results.
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Applications of AI in Healthcare
Healthcare is quickly adopting AI technologies. The technology is well-suited to this field, as mining medical data has positive consequences for both, healthcare providers as well as the patients. This sector can also be disrupted heavily by the rise of the internet of things, which has provided embedded sensors and technology to track health records accurately.
Common Use Cases
Medical data can be used to enable predictive healthcare, where big data analytics enables doctors to accurately provide healthcare to patients remotely. Using a sensor embedded in the patient, doctors can monitor them remotely and provide warnings when the patient is on the brink of illness. This can also improve the quality of post-operation care offered to the patient.
AI can also be used to collect preliminary data about a patient’s symptoms with the help of chatbots. These bots can take inputs from the patient and deliver them to the doctor, allowing for a faster time to diagnose the issue. Similarly, image recognition technologies are also used to analyze scans for abnormalities, leaving the final decision to be made by the doctor. This allows hospitals to reduce the stress doctors face while taking on more patients.
According to Sanjeev Agrawal, President, LeanTaaSOpens a new window , “Machine learning in healthcare is in an early stage, but the initial results are promising in a few areas:
- Image recognition: Machine-assisted diagnostics that help radiologists and physicians interpret images are showing good results. However, since the biology of each patient is unique, each provider practices medicine in a unique manner, and each disease progresses in a unique manner, these technologies will likely need time before they can completely take over a meaningful share of the intelligent decisions that need to be made thousands of times each day in any health system.
- Prediction of case length and case volumes, cancellations, and no-shows: Helping to predict operating room expected case lengths far more accurately than using averages of past case lengths can contribute to improved scheduling. Also predicting significant dips or swings in case volumes can help with both overbooking and staffing shifts as needed.
- Prediction of the volume and mix of patients in infusion chairs and clinic exam rooms can help to optimize patient flow and “level load†the day and staff appropriately.
These are just a few examples of “predictive†and “prescriptive†analytics and not just “descriptive†and “diagnostic†analytics that are about “see a number, show a numberâ€. Data science done right can significantly enhance outcomes and increase utilization and access while transforming the patient experience.â€
When asked what companies can do to prepare themselves to work with AI, he adds, “Like with any disruption, it takes the right people, processes and tools to prepare for what will be a disruptive but gradual transformation:
- Hiring right: People who understand the power of these technologies being embedded into core clinical and operational processes, gaining trust to try new things and over time being given the authority to imbibe these tools into the workflow.
- Experimentation with small teams and scope: Small, tightly defined experiments to try different tools to gauge their effectiveness — think big, experiment small.
- Budgeting: Setting aside a budget for experimentation in bite-sized chunks where failure is ok, and some successes will more than pay for themselves.
- Overcoming organizational inertia: Leadership that encourages innovation from the inside and out, rewarding such attempts at change.
- IT and data governance: A lot of these projects require extracting the data in the EHR and other platforms. It’s crucial to make it easier to extract such data and push toward interoperability.â€
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AI Technology Used
Big data analytics is a huge part of healthcare today and will continue to take on these roles to optimize healthcare. Using predictive algorithms, doctors can accurately predict the illnesses that patients are likely to contract considering their medical history. Image recognition algorithms that utilize deep learning are used to analyze scans and images. These algorithms are trained to find abnormalities by ingesting the data from a variety of scans.
Chatbots are being adopted in the medical space quickly. These take the form of helplines or quick chat support and utilize natural language processing to understand what the patient is going through. It can understand this data by looking at the text given to it by a human. These symptoms are then delivered to the doctor, who takes the treatment forward.
Roadblocks and Challenges
There are ethical consequences of using AI in healthcare, as it directly deals with human health. AI faces fundamental problems in explainability because we don’t understand how it works. Such an AI, where the results are meant to be trusted and cannot be verified, may make wrong decisions. This can end badly for the patient in question, meaning that humans must be in charge of decision-making until AI is sufficiently advanced.
Like in case of other applications, the data fed to the AI must not be biased. If an image detection algorithm is trained on a biased dataset of scans taken from exceptional cases, it will not be accurate. The data must be clean and must serve to improve the algorithm in one way or another, but this approach has not been solidified yet. It is an important roadblock to surpass before using AI widely for healthcare.
While discussing the AI advancements to look forward to in the next five years, Sanjeev Agrawal, President, LeanTaaSOpens a new window , further adds, “The healthcare industry needs to get serious about using AI and machine learning to make operational and clinical decisions that improve patient outcomes.
- Operational: Similar to what we have seen in other industries, e.g., Uber uses AI and machine learning to ensure they have the right number of drivers in the right locations available for any given city or time of day, we should expect to see intelligent assistants that can construct appointment schedules that utilize the existing scarce resources (e.g., providers, ORs, machines, beds, chairs, etc.) much more efficiently while giving the patients more choices in selecting an appointment slot.We should also expect to see vastly improved patient flows throughout the health system, manifested by the emptiness of the waiting rooms scattered throughout. These intelligence assistants will be pervasive and yet mostly invisible; they will operate behind the scenes on the application pages used by schedulers to make appointments and will use AI and machine learning to gently and persistently guide schedulers into making better decisions in a timely manner.
These assistants will also be prescient; they will anticipate delays and cancellations and will automatically reroute patients and providers and issue quiet, unobtrusive push notifications to the affected parties, while giving them an opportunity to override. Patient flow will resemble a crowded freeway of autonomous vehicles with fully networked communications — moving quickly and confidently, adapting as needed without colliding into each other.
- Clinical: AI and machine intelligence are well on their way to solving really complex clinical problems such as early cancer detection and unlocking new ways to cure diseases like diabetes.
The emergence of machine learning and artificial intelligence; the democratization of predictive analytics from the ivory towers of academic institutions to organizations everywhere; the growth of massively scalable, secure cloud infrastructure; and the ubiquity of smartphones and mobile apps are the tools by which hospitals will transform themselves into hospitals of the future.â€
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Applications of AI in HR
AI helps HR by optimizing processes and bringing intelligent automation to the table. AI is not only giving HR professionals better ways to connect with employees but also assisting companies to hire better talent and retain them.
Common Use Cases
The role of an HR generally involves similar tasks being repeated constantly. This takes up a lot of the professional’s time, which can be better spent doing more significant work. Intelligent automation takes the load away from HR professionals when it comes to repetitive tasks, following up and keeping tabs on many employees with little or no intervention from the professionals themselves.
AI also guides corporate decisions when it comes to ensuring job satisfaction of employees working at the company. With the help of recommendation algorithms, companies can explore what employees want and work towards fulfilling their needs. Job screening and analysis solutions also take the onus of talent acquisition off HR professionals, thereby allowing them to see whether a prospective employee is a good fit for the company and designation.
AI Technology Used
Intelligent automation is a fairly basic AI algorithm to implement. These rule-based programs can perform tasks, such as emailing prospective employees, checking up on existing employees, and keep up the morale of the workforce. Recommendation engines can analyze behavioral data of the employees, offering more in-depth insights into the emotional state of the workers.
Deep learning algorithms can also help in the screening process for hiring new employees. Through a psychometric test and a basic NLP chatbot, HR professionals can reduce the time required to shortlist candidates.
Roadblocks and Challenges
Human resources, by its nature, make use of the human connection between the HR professional and the workforce. At the same time, HR tasks also involve a lot of processes which can be optimized using intelligent automation. This means that AI will mostly be delegated to the support role, assisting HR professionals with data regarding the employees.
AI that can interpret human emotions is also being researched, with this possibility paving the way for a predictive style of HR management. By parsing text and other communication between the employees, an AI program can accurately determine how employees feel. This increases the effectiveness of campaigns undertaken by the management.
Applications of AI in Marketing
Marketing as a field has seen somewhat of a rekindling with the rise of the internet. With the power of targeted advertising, companies are finding new ways to get customers on board with their products. Big data analytics and data collection have made marketing a suitable vertical to adopt AI.
Common Use Cases
Marketing is already being disrupted by AI, with the most recent advancement being AI-generated content. AI-created content is especially effective for marketing copies, as they simply pick from a group of common terms that are used for such promotions. This allows marketing to effectively create several campaigns involving different AI-based content strategies.
AI technology in marketing also serves to increase the effectiveness of targeted marketing campaigns. Advanced technology has helped the AI solutions market to improve significantly, especially when it comes to personalized recommendations.
AI Technology Used
Recommendation engines have helped companies like Amazon take over the e-commerce market. These neural network algorithms ingest data about the user’s shopping history and use it to recommend similar products, thereby increasing the likelihood of a sale.
Predictive and big data analytics also allows companies to derive insights into customer conversion metrics. This can be used to improve the visibility of customer sales funnel, thus allowing companies to improve their operations and maximize the conversions to sales.
Roadblocks and Challenges
Owing to the fact that these AI algorithms must be trained using user data, they have given rise to a predatory attitude regarding data collection today. Companies that offer targeted advertising services, such as Google and Facebook, have come under legal fire due to the way they harvest and handle user data.
This is one of the biggest factors companies should overcome to ensure wider adoption of AI in the marketing sphere. Growing data privacy and regulation concerns may cause setbacks if today’s unethical data collection practices continue.
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Closing Thoughts for Techies
It is clear that AI and its accompanying technologies are fundamentally changing the way labor is perceived in the enterprise sector. In situations where large numbers of low-skilled workers were previously required, a single AI solution with a human supervisor will provide the same results today. This not only results in cost savings for the company but also helps the working population to upskill and keep up with AI.
The rise of cloud computing has vastly lowered the barrier for entry in the AI sphere. Moreover, many cloud service providers offer services that allow solutions to be deployed in a pinch. They also provide tailored AI solutions for companies, thereby allowing them to simply use AI in a plug-and-play manner and integrate it into their operations. This has made AI indispensable in the enterprise sector.
Due to these factors, artificial intelligence seems to be headed for widespread adoption across various industries. These applications are also becoming more diverse due to the adaptable nature of the technology. Advancements in the science behind AI are also bringing new solutions to the market, thus adding to the disruptive potential of artificial intelligence.
What are your thoughts about the future of AI in various industries? Comment below or let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!
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