Artificial intelligence is one of the most important creations of this century. It has proven its usefulness in every sector that has been employed. Industries from academia to Web3 are employing AI to make their operation more scalable and increase their efficiency at the same time. AI is defining the workforce for the future with the increasing complexity in sectors like accounting, production, and packaging, among others.
Massive recruitment follows this transition drive going right now and will continue to go on as more enterprises include AI in their operations, still before understanding what you need to be employed, it’s important to understand how the business is using AI and understand what part you can play in the growth.
AI and its Increasing Popularity in Business
Data has become an integral part of modern-day businesses, with companies accessing it more than ever. As reported by Forbes, the rate of data created and consumed has increased by a staggering 5000% between 2010 and 2020. With the help of emerging technologies, companies can now capture user data to help them make informed business decisions.
Also read: How to Become an AI Expert?
AI is No Longer Experimental
In recent years, many companies have embraced AI to use better the data they are collecting. According to Grand View Research, the global AI market was worth $62 billion in 2020 and is expected to have an annual growth rate of 40.2% from 2021 to 2028. The increasing adoption of AI is further evidenced by a McKinsey report which found that 56% of respondents in their 2021 survey reported adopting AI, up from 50% of respondents in 2020.
Let’s take aside the conventional business models. Many Web3 companies are also using AI models to improve the blockchain mechanism and make their services more secure and less time-consuming.
AI is Key to Achieving Growth Objectives
The Accenture report on AI found that 84% of C-suite executives think leveraging AI will help them achieve their growth objectives. These statistics demonstrate that AI is no longer an experimental technology and is now a core part of operations for many companies worldwide.
Leveraging the Power of Data and AI
With access to unprecedented amounts of data, companies are leveraging the power of AI to extract insights and drive business growth. From analyzing customer behavior to predicting market trends, AI is helping companies make better decisions faster. However, it is important to note that successful implementation of AI requires careful planning and integration into existing workflows.
Applications of AI in Business
We will now understand. What are some applications of AI that are popular in the business world, and understand why can these jobs be better done by artificial intelligence rather than a human operator?
Customer Service
One of the most popular services the business uses is the product chatbot to provide fast and efficient customer service around the clock. They use natural language processing, machine learning, and AI to understand customer requests. They can route customers to the appropriate department or representative.
For example, e-commerce websites can use chatbots to answer common questions about shipping, returns, and product availability. Banks and financial institutions can also use chatbots to help customers with simple tasks such as checking their account balances, transferring money, or paying bills. One of the main advantages of using them is that they learn, grow and become more intelligent over time, which improves customer satisfaction and reduces the workload for customer service teams.
Marketing and Advertising
The rise of AI technology has brought about a fundamental shift in how companies approach advertising and marketing. By leveraging data and machine learning algorithms, advertising departments can now create targeted campaigns that are more effective than ever before.
One of the main advantages of using AI in marketing is the ability to segment audiences and create personalized campaigns. They can identify, analyze, and group a large number of data to identify common characteristics and behaviors among different user groups. This allows companies to create targeted campaigns tailored to their audience’s interests and preferences.
In addition to audience segmentation, AI can predict how customers will respond to specific advertisements. By analyzing historical data and user behavior, AI algorithms can identify patterns and predict which types of ads are most likely to generate a response. This allows companies to optimize their advertising campaigns and ensure they reach the right audience with the right message.
Supply Chain Management
Integrating AI technology into supply chain management can offer significant benefits for companies. One of the most prominent advantages of AI-driven solutions is the ability to accurately predict delivery times, identify bottlenecks, and estimate the cost of materials and shipping.
AI algorithms can search various resources and analyze the data, such as weather forecasts, traffic patterns, and historical shipping data, to provide real-time insights into supply chain operations. By utilizing machine learning techniques, they are well-trained to look for patterns and trends that would be difficult for humans to detect. This data can be used to optimize shipping routes, manage inventory levels, and improve delivery times.
For example, logistics giant UPS uses an AI-powered platform called ORION (On-Road Integrated Optimization and Navigation) to optimize delivery routes for its drivers. The platform uses predictive analytics and machine learning algorithms to identify the most efficient routes for drivers based on factors such as traffic patterns, delivery volume, and package weight.
Fraud Detection and Prevention
AI technology can play a critical role in helping companies detect and respond to fraud threats. Fraudulent activities can cause significant financial losses, reputational damage, and legal liabilities in the financial industry. However, with machine learning algorithms, companies can identify suspicious transactions in real time, preventing financial crimes before they happen. Machine learning algorithms work by analyzing large amounts of data and identifying patterns or anomalies that may indicate fraudulent behavior. For example, an algorithm may detect a transaction significantly larger than the average for a specific account or a transaction outside normal business hours.
One example of an AI-powered fraud detection tool is IBM’s Safer Payments. This software uses machine learning to analyze transaction data and identify potential fraud risks. When a suspicious transaction is detected, the application alerts the appropriate parties, who can investigate the transaction and take appropriate action.
Similarly, PayPal uses an AI-powered fraud detection system to protect its users from fraud. The system analyzes transaction data and matches it with the indicators of fraudulent behavior. When a potential fraud risk is detected, the transaction stops, and the user is alerted.
Human Resources
The COVID-19 pandemic has fundamentally transformed how companies approach human resources management. With the increasing popularity of hybrid and remote working, HR teams have had to adapt to new ways of recruiting, managing, and engaging employees. AI technologies are increasingly important in helping HR teams stay competitive and effective in this new environment. Out of all, the major and substantial impact of AI in HR is virtual recruiting. As companies move to virtual recruitment processes, AI can help streamline the process and improve the quality of candidates.
For example, AI-powered recruitment platforms like HireVue use machine learning algorithms to analyze candidate responses and provide insights into their skills, personality, and fit for the role. AI can also help HR teams in the area of diversity and inclusion. By analyzing hiring and promotion practices data, AI algorithms can identify potential biases and provide recommendations for improving diversity and inclusion in the workplace. For example, Unilever uses an AI-powered tool called HireVue Assessments to help eliminate bias in the hiring process and promote diversity.
Financial Analysis and Management
The accounting industry is undergoing a significant transformation thanks to the implementation of AI. By automating tedious and repetitive tasks, AI is improving the accuracy and efficiency of the accounting process. AI can record data, categorize transactions, reconcile accounts, evaluate employee expense reports, and track pricing changes. These tasks that often took up significant amounts of time for accountants can now be done with minimal human intervention, allowing employees to focus on more complex and strategic tasks.
One area where AI is expected to revolutionize accounting is payroll processing. While automation effectively handles routine and predictable tasks, AI can analyze data, learn from failures, and make strategic decisions. This makes it an ideal payroll solution, requiring high accuracy and attention to detail. The cloud-based payroll software market, valued at $7.88 billion in 2018, is projected to reach $13.37 billion by 2026 as company executives look to invest in advanced AI-supported payroll systems.
Advantages of AI in Business
This section will discuss the advantages of implementing AI into your business model, from tasks humans cannot perform to what AI can do much better.
Efficiency and Productivity
AI and ML are rapidly becoming integral parts of businesses as they offer several advantages, including increased productivity and revenue. With the ability to automate workflows and optimize day-to-day tasks, AI can help employees accomplish more in less time, boosting productivity. As a result, businesses can become more efficient and focus on growing their revenue.
Furthermore, implementing AI and ML can lead to significant cost savings. By automating repetitive tasks and reducing the likelihood of errors, businesses can save time and money on manual labor and prevent costly mistakes. As a result, the demand for AI and ML is expected to continue to rise in the coming years as more and more businesses realize the benefits of these technologies.
Improved Decision Making
The possibilities of AI and machine learning go far beyond mere automation. They enable businesses to leverage vast amounts of data and extract insights that were previously impossible to access. This is especially true when it comes to decision-making.
With AI and ML algorithms, businesses can quickly and accurately analyze complex data sets, identify trends and patterns, and make more informed decisions. This can help organizations optimize operations, reduce costs, and increase revenue.
Cost Reductions
AI has the potential to revolutionize the way businesses operate by automating many operational tasks that were once time-consuming and tedious for employees. This gives business leaders more time for strategic thinking, creative problem-solving, and higher-level decision-making.
For example, AI-powered automation can schedule appointments, process invoices, and manage inventory. These tasks can be time-consuming and require manual effort to complete. By automating these processes with AI, businesses can free up employee time to focus on more value-added activities, such as improving customer experience, developing new products or services, and exploring new business opportunities.
Personalization of Products and Services
Nowadays, customers want businesses to show that they care about them. With AI, businesses can understand what customers want and create personalized experiences. For example, if you have a music app, AI can suggest songs based on the user’s liking. This makes the user feel like the app is tailored to their preferences, and they’re more likely to keep using it. AI can help businesses build better customer relationships and make their experience more enjoyable.
Challenges of AI in Business
Some major challenges in implementing AI in the business world have led to the demand for skilled engineers who can work on complex algorithms rather than novices.
Integration with Existing Systems
Most major corporations’ systems are outdated and cannot integrate AI if structural changes are not made. These changes can range from minor algorithm changes to major changes, like creating a separate database or power system to operate a system that can perform AI algorithms and operate on that and store the data separately, which can be later analyzed to provide the results.
Data Quality and Privacy
While companies have access to vast amounts of data, not all are usable for AI applications. Machine learning, which powers many AI applications, requires significant data to train models effectively. However, there are still many areas of business where the amount of data available may be limited, or the data may be unstructured and unlabeled, which makes it difficult to use for AI.
This challenges businesses that want to leverage AI in these areas. To address this challenge, companies may need to find new ways to collect and label data or use alternative techniques for training models, such as unsupervised learning or reinforcement learning. With further advances in AI and data labeling technology, we may see more breakthroughs in the use of AI in areas with limited data availability.
Staff Training and Skill Development
Implementing AI technology heavily relies on a skilled workforce to operate and manage it effectively. Unfortunately, there is currently a significant skill gap between the demand for AI talent and the supply of available professionals. This has resulted in some industries lacking progress in AI adoption.
For instance, many healthcare organizations have struggled to integrate AI into their operations because of the shortage of skilled workers. In 2020, only 18,000 AI professionals worked in healthcare worldwide, which is insufficient to cater to the growing need for AI-powered solutions.
Ethical Concerns
Recent incidents involving Microsoft and Amazon have highlighted the issue of bias in AI face recognition software. Both companies have suspended the sale of their software to law enforcement agencies due to concerns over ethnic, racial, and gender biases within the technology. These biases arise when the algorithm is trained on data that is not diverse enough, resulting in poor performance when dealing with people who fall outside the narrow scope of data.
These issues have sparked a larger debate around the ethics of using AI in certain applications and the need to develop more diverse data sets to avoid biased outcomes. While AI systems can address these biases in the future, these incidents warn of the dangers of implementing AI without proper consideration for the biases inherent in the data used to train the algorithms.
Best Practices for Implementing AI in Business
Start Small
To avoid taking on too much too soon, it’s recommended to start by applying AI to a small sample of data. This allows businesses to start simply, prove the value of AI, collect feedback, and expand accordingly.
For instance, healthcare organizations can select a specific type of data, such as information on certain medical specialties, and apply AI incrementally to prove value. It’s important to be selective in what the AI reads and focus on a specific problem that needs to be solved. Instead of throwing all the data at AI, giving it a specific question to answer is advisable.
Identify Key Business Problems
After establishing a basic understanding of AI, businesses should begin exploring how to integrate AI capabilities into their existing products and services. This involves identifying specific use cases where AI can solve business problems or provide value.
When working with a company, it’s essential to start with an overview of its key tech programs and challenges. Workshops with the management team can follow this to demonstrate how AI technologies such as natural language processing, image recognition, and machine learning can be integrated into their products and services.
The specific applications of AI technologies vary by industry. For example, AI can improve patient outcomes in the healthcare industry through personalized treatment plans and disease prediction. AI can help detect fraud and automate risk assessments in the financial industry. In the transportation industry, AI can optimize routes and improve logistics.
Partner with Experts
Once a business has established the necessary organizational and technological foundations, the next step is to start building and integrating AI capabilities. It’s crucial to start small and have clear project goals in mind. Additionally, it’s important to recognize what you know and doesn’t know about AI and seek outside expertise as needed
Bringing in AI consultants or experts can be invaluable during this process. They can guide the best AI technologies and strategies to achieve the desired outcomes. They can also help with project management and provide technical expertise to ensure a successful implementation.
Focus on Data Quality and Ethics
As ambient intelligence continues to expand, the risks of security breaches, model drifts, unintentional bias, and unethical use are also rising. The proliferation of AI use cases and the central management of vast amounts of data create opportunities for security breaches. These breaches can compromise sensitive data and harm individuals, businesses, and society. One of the major risks associated with AI is model drift. This occurs when AI models drift away from their intended accuracy as they tune themselves with new data. To prevent model drift, it is crucial to monitor the model’s performance and update it accordingly.
It is essential to incorporate digital ethics upfront in the design and architecture of the AI system. Adding ethics as an afterthought is not comprehensive and can lead to harmful exposure. In addition, rearchitecting for ethics can be a costly and wasteful exercise. Therefore, businesses should consider ethical implications from the outset and ensure that their AI systems are designed and implemented with the highest ethical standards. To address these risks, businesses must implement robust security measures, monitor model performance, and incorporate ethical considerations into the AI system’s design and architecture.
Monitor Performance and Adapt as Needed
When conducting AI tests, it’s crucial to incorporate a complex approach, including measurement, precise tracking, and monitoring throughout the process. This is necessary to ensure that the testing is aligned with the business objectives and that any potential issues are identified early on. Continuous monitoring is key to the success of the testing process. This means regularly checking the model’s deployment and ensuring it functions correctly and efficiently. Regular checks help identify potential issues or inaccuracies and allow for improvements as needed.
It’s also essential to keep the data clean and maintain a set of raw data used throughout the test cycle. This can be used to check the accuracy of any model modifications or evaluate how it performs under different usage conditions. Monitoring the model for potential dangers and problems is also important. This includes checking for bias or any unintended consequences from using the model. It’s important to have a plan in place to address any potential problems that may arise and to allocate enough time to manage unexpected issues.
Become a Certified Artificial Intelligence Expert
Future of AI in Business
As AI technology continues to evolve, so does the potential for its use in various industries. Experts predict that “commonsense” tasks will become easier for computers to process, making robots extremely useful in everyday life.
Driverless Cars
AI is making what was once considered impossible, like driverless cars. Driverless cars are only a reality because of access to training data and fast GPUs, both key enablers. Accurate data is required to train driverless cars, and speed is key to undertaking the training. The introduction of GPUs made it all possible. This development could lead to new job opportunities for data scientists, machine learning engineers, and AI trainers in the automotive industry.
Personalized Dining Experience
AI can be used by a restaurant to decide which music to play based on the interests of the guests in attendance. Artificial intelligence could even alter the appearance of the wallpaper based on what the technology anticipates the aesthetic preferences of the crowd might be. This could lead to new job opportunities in the hospitality industry for AI developers, designers, and data analysts.
New User Interfaces
Experts predict that AI will take digital technology out of the two-dimensional, screen-imprisoned form to which people have grown accustomed. Instead, the primary user interface will become the physical environment surrounding an individual. This development could lead to new job opportunities in the design and engineering fields for AI developers and robotics engineers.
The Need for a Skilled Workforce
To ensure that AI technology functions as anticipated, we need a skilled workforce to manage it. Until businesses can upskill their workforce to meet AI-focused challenges, we will likely see some stagnation in AI adoption. This will lead to new job opportunities for AI trainers, curriculum developers, and technology instructors in the education sector.
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Fast Processes and Clean Data
Fast processes and lots of clean data are key to the success of AI. The development of GPUs has led to the use of AI in various industries. As GPUs continue to get faster, there is a high potential for applying AI software. This will lead to new job opportunities for engineers and researchers in the computer hardware industry.
Conclusion
AI is changing how we operate the business and how the responsibility should be divided between the workforce to make the operation more scalable. Major corporations like Apple, Samsung, and IBM have been working on it for a long time. The results can be seen in how influential they have become in using artificial intelligence in their business model and management.
As you upskill yourself, more opportunities will arise with the rise in the popularity of AI incoming with the modern development of AI. You will use your skills to develop new practices that can be employed in different businesses looking to adopt blockchain into their management model.
