With BP3 Global, explore the latest AI industry trends redefining business operations, customer experiences, cybersecurity, healthcare, and manufacturing.
Recent breakthroughs in the artificial intelligence (AI) industry have brought it to the forefront of the tech conversation. New user-friendly tools have made AI more accessible to non-experts and have illuminated many business opportunities. As artificial intelligence development is moving so quickly, staying on top of AI industry trends is crucial.
The business applications for AI are already vast, and growing by the day as interest grows and companies fear being left behind. While the awareness of AI options for business is increasing, several misconceptions remain. The source of a prominent misunderstanding is that AI is not one thing, but rather many things with many different applications.
Today, we’ll demonstrate how these AI applications can benefit various industries by showcasing 14 artificial intelligence trends to watch out for.
Increasing efficiency within a business is an ongoing challenge for all companies. AI presents opportunities to dramatically reduce reliance on human labor, saving time and money while reducing errors.
To streamline your business processes, you first need to find areas for improvement. Some issues may immediately stand out to management, but improvements are not always easy for humans to spot. This is particularly true when people may feel blamed for the deficiency or when changing a process may put their current job at risk.
AI and machine learning (ML) can objectively analyze historical data from your company's internal processes to identify inefficiencies and bottlenecks, regardless of where they occur. This is called process mining and is the first step in optimizing your business performance.
AI’s ability to analyze data isn’t limited to internal company information. ML algorithms can quickly understand vast datasets in a fraction of the time it would take a human. Predictive AI can then be used to inform decision-making. This is commonly used to project customer behavior, marketing trends, and operational performance.
Smaller companies that may not have access to large amounts of historical data can use generative AI to expand on a dataset. The generative AI will find patterns in the existing data and generate synthetic figures to create a more complete record. These AI statistics will improve the accuracy of the AI’s predictions.
Complex neural networks mimic the functionality of a human brain to produce superhuman analysis. This level of machine learning gives highly accurate predictions, but it can sometimes be difficult to understand its reasoning. Explainable AI (XAI) is a new trend that improves trust and confidence in AI predictions by explaining their reasoning.
Robotic process automation (RPA) is a great way to automate routine tasks that become repetitive and boring for a human. You train the system by demonstrating the task and recording each step in the process. This is great for data entry, document processing, and producing invoices.
While RPA is useful by itself, combining it with AI gives you intelligent automation (IA). This enables you to automate decision-making tasks like fraud detection, medical diagnosis, and responding to customers. These systems are highly scalable and allow for rapid growth. They also free up your staff for other tasks, optimizing your allocation of human resources.
An AI trend that has been around for a while is the creation of personalized customer experiences. Recent advancements in language models have allowed companies to automate genuinely excellent customer service.
One way to encourage customer engagement is through customized product recommendations. The AI system will record customers' preferences based on the products they search, add to their cart, and buy. This will then be cross-referenced with a database of customers with similar preferences to recommend other similar products that they bought.
Using this information, companies can also create targeted marketing campaigns. Businesses traditionally segment their customer base into like-minded groups and send out specific marketing materials that resonate with group members. The latest AI can produce marketing content that is specific to individual preferences, allowing for greater personalization of the customer experience.
The terms chatbot and virtual assistant are often used interchangeably. While both allow you to put your customer service on autopilot, today we will draw a clear distinction between them as we consider the value of both.
A chatbot is a decision tree that gives customers several options in response to a question. The next question it asks depends on the previous answer. Chatbots utilize RPA rule-based technology, which means they are great for dealing with simple requests but are limited when it comes to complex interactions.
A virtual assistant, on the other hand, incorporates natural language processing (NLP). The NLP algorithm allows the virtual assistant to understand the real meaning behind requests and use decision-making to answer. Virtual assistants can deal with a wide range of detailed customer service requests. They become particularly powerful when combined with voice recognition, allowing your customers to communicate through multiple mediums.
Both of these customer service tools reduce the cost of labor for dealing with requests and allow customers to contact your business 24/7.
Personalized user experiences are not limited to product recommendations. The growth of AI has also brought about adaptive interfaces tailored to individual user needs. These algorithms have situational awareness and will customize an application's layout and features based on the user’s preferences.
These insights and changes can often be recorded in real time, adapting to the user as they move through their customer journey. This technology improves engagement, leading to higher conversion rates.
Cybersecurity is a constant battle to stay ahead of cybercriminals. Ensuring you know the latest AI trends gives you the edge you need to keep your data safe.
ML-based threat intelligence will gather data from various sources to understand new threats and emerging trends in hacking. This information can be used to stay ahead of modern threats by updating your security systems when necessary.
User and entity behavior analytics (UEBA) is designed to monitor insider threats from within. It monitors the software user’s behavior, looking for any suspicious activity. These kinds of threats often go undetected by traditional security solutions that focus largely on external threats.
Data protection and privacy are becoming more critical to internet users, and governments are proactively introducing legislation to protect them. AI can be used to produce more secure encryption algorithms so that if data is intercepted in transit, it cannot be understood. AI can also help with data anonymization so that companies can still gain insights from analytics while protecting the identities of individual users.
Unsupervised machine learning can learn from large unlabeled datasets. This type of deep learning can be used to understand the normal behaviors within a system and proactively identify anomalies. Federated learning allows multiple devices to train the algorithm, meaning systems can be fully protected no matter where a potential risk originates.
As anomalies are detected in real time, threats can be stopped as they appear. But this is also an ongoing process. As the system receives more information, predictive AI can be used to develop more robust risk management strategies to stop attacks before they happen.
The healthcare sector is often stretched for resources in an environment where results matter the most. This means the impact of artificial intelligence in improving processes and services is particularly important. Fortunately, the future of machine learning has several practical applications that can benefit all areas of the healthcare industry.
AI tools can help diagnose diseases by quickly analyzing many factors. Natural language processing can consider their personal information, medical history, and symptoms while cross-referencing an almost limitless store of medical journals. Healthcare providers can use these insights to make faster and more accurate diagnoses.
Medical scans and images play a crucial role in analyzing all manner of ailments. AI can be used in several ways, not only to analyze images more accurately but also to produce better datasets for analysis. The quality of analysis relies heavily on the amount of data the AI algorithm is trained on.
Generative AI tools recognize image patterns and produce synthetic images to bolster a dataset for more accurate analysis. Different AI tools can then be trained on this larger dataset to accurately spot things in images that a human may not be able to see. AI has been shown to be better at segmenting tumors in cancer patients than human radiologists, for example.
Generative AI can also fill gaps in low-quality, damaged, or corrupted images. This positively impacts healthcare providers who cannot afford the latest medical imaging hardware.
Language models can be used to provide personalized treatment plans that would previously have been time-consuming and expensive. AI can simultaneously consider multiple factors about a patient's medical history, risks, and symptoms to run simulations to find the most effective treatment method.
AI also provides the opportunity for personalized healthcare in real time. Wearable medical devices can track a patient’s vital signs, such as heartbeat, blood pressure, or insulin levels. The AI system can then benchmark these levels against safe baselines to recommend preemptive action when needed.
RPA can be used in healthcare to reduce administrative burden and enhance patient scheduling. Predictive AI can forecast busy times of the year when more staff and beds will be needed. These insights allow for more efficient allocation of resources, which is crucial in an overstretched industry.
Communication between departments and entities in healthcare can be slow. Data protection is crucial when dealing with medical records, but this shouldn’t slow communication during an urgent event. AI can create communication systems that allow for the immediate and secure transfer of confidential data between healthcare professionals.
Once a manufacturing business operates at scale, its business model focuses on efficiency and optimization to gain a competitive edge. AI can assist with this optimization in several key areas.
Because AI is so good at spotting anomalies, it can be used to perform quality control checks without the need for human intervention. It not only performs these checks faster than a human but also at a higher level of accuracy. This is particularly important for complex products or deficiencies that may not be visually apparent. Predictive AI can also anticipate machine failure, allowing your business to plan maintenance more efficiently.
Optimizing your production process requires considering a multitude of independent variables. Everything from marketing efforts and seasonality will affect product demand, while supplier activity and transportation will affect your distribution network. Predictive AI can consider all of these factors and provide accurate inventory levels and scheduling recommendations for a given forecast period, ensuring you do not miss out on market shares.
The ML and AI industries are exploding, so staying on top of the latest trends has never been more critical. The applications of AI to improve business processes are almost endless, and every business can benefit. If you are ready to see how your business operations can be improved through AI, then contact BP3 today.
Advice published by McKinsey in March 2017 on Intelligent Process Automation still holds true today. Find out how BP3 can transform your processes.
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