Over the recent years, generative AI has taken the world by storm. While artificial intelligence has existed in our lives for quite some time on a more basic level, the advancements of this technology are redefining traditional approaches and aiding in complex processes.
Generative AI applications are not limited to any one particular industry. In fact, its influence can be traced across diverse fields. Such models analyze vast datasets and generate outputs based on set parameters. As a result, the flexible nature of this tech can be employed to supplement creativity and technical operations alike.
In this article, we are going to explore how gen AI is impacting healthcare, manufacturing, finance, and creative industries in 2025. Additionally, we will touch on the ethical implications of generative artificial intelligence and how to address them. Lastly, we will glimpse into the future of AI and how the mentioned industries will be affected going forward.
The Use of Generative AI in Creative Industries
When the topic of gen AI is brought up, conversations often pivot to how it affects creative industries. Organizations in sectors like film, music, gaming, marketing, etc., seem divided into two camps: artificial intelligence is either good or bad for business. Regardless, AI is certainly here to stay, which calls for a nuanced discussion.
Currently, text-to-image generation dominates the use of AI in creative sectors and will continue this trend in the upcoming years. A large portion of increasing interest is associated with the release of DALL-E by OpenAI in 2021 and Adobe Firefly in 2023. These and other tools put power into the hands of specialists to foster efficiency and boundless creativity.
Applications of generative AI in writing and videography have also been on the rise, sparking debates about the authenticity of generated content. So, let’s dive deeper into both of the arguments to understand how AI is shaping this field.
Those Who Are in Favor
The side that argues in favor of generative AI development focuses on how innovation is amplifying efficiency and accessibility. Use cases like rapid prototyping and the generation of repetitive elements accelerate the production process, leading to faster turnaround times and saved costs.
Beyond the art and design sectors, artificial intelligence can also stimulate idea generation for other creators to jumpstart production. It can also lower the entry level for professionals who yet lack the ability to craft immersive and engaging experiences.
Conversely, large-scale productions, such as the film industry, can benefit from generative AI by significantly reducing the resources needed to bring to life complex ideas as opposed to assembling them from scratch. De-aging characters, supplementing voice acting, and building otherworldly environments — all of this can be done with the proper use of AI.
Overall, the core message of this argument lies in a balanced approach to combining human creativity, insight, and experience with the generative powers of artificial intelligence. Rather than trying to fight technological advancement that is transforming industries, it’s more advantageous to embrace new opportunities and learn to blend innovation with tradition.
Those Who Are Against
Critics who oppose the infiltration of gen AI into creative business have valid concerns that also translate into action. An infamous example is the Writers Guild of America strike in 2023, which sought to limit the use of artificial intelligence in writing. Creators rightfully fear that gradually implementing AI into work processes can eventually replace human specialists, leading to vast job displacements.
While a dystopian scenario like this can be mitigated with proper regulations, copyright issues are another concern that plagues artificial intelligence use. Since generative AI algorithms are trained on existing images, videos, texts, and so on, the question becomes who owns the rights to the generated content. As of yet, the regulations on this topic are still in the gray area.
A more philosophical issue raised by the critics of gen AI in creative fields is the lack of authenticity. Essentially, the argument is that can consumers truly connect to a piece of art if they know it was created by a machine? As artificial intelligence evolves and becomes more sophisticated in replicating genuine human experiences, can it evoke the same emotion?
Both sides of the discussion bring valuable thoughts to the table, and we will explore the ethical implications of generative AI later in the article. However, the reality is certain that this technology will continue to influence the creative industry.
Applications of Generative AI in Healthcare
When it comes to health management where institutions require professionals to undergo long years of training and education, the integration of AI is not a matter of replacing specialists. Instead, the industry is laser-focused on improving best practices and advancing examination and patient care.
Hence why generative AI is on a path to becoming an integral part of medical practices, helping to improve various processes. Let’s uncover the three areas where generative AI applications are already showing immense potential and bringing benefits to organizations.
Medical Documentation
Information gathering and structuring is one of the cornerstones of medical examination and treatment plans. Luckily, there are several use cases for AI in healthcare that can streamline such routine operations.
For example, an AI program can transcribe conversations between patients and healthcare providers to capture their complaints, medical history, etc. The program can then generate concise clinical notes from the visit, painting a complete picture without losing any crucial details.
The process of entering the transcriptions and clinical notes into Electronic Health Records (EHR) can further be automated with AI-powered systems. This can substantially ease the load off of medical professionals’ shoulders to manually collect and enter data, allowing them to focus more on treatment.
Healthcare institutions can also build generative AI applications to aid in training staff by providing initial or follow-up questions based on the patient’s responses. Thus, improving the process of extracting relevant information and structuring medical histories.
Imaging and Diagnostics
Advanced AI systems that are trained on vast amounts of medically relevant data can supplement imaging and diagnostics procedures. Such programs can generate high-resolution images from low-resolution scans like X-rays, MRI, CT, and so on.
A more impressive feat achieved by artificial intelligence in the medical sector is its ability to aid diagnostics by extracting valuable insights from imaging. Since the algorithms are trained to identify normal patterns and recognize abnormalities, the gen AI system can detect anomalies in scans. This can aid in precise early diagnostics of treatable and terminal diseases, which can turn the tide on the life expectancy and quality of life of patients.
While that may sound futuristic, the use of this technology in medical practices is already gaining traction. In fact, within the healthcare generative AI market, the diagnostics segment accounted for the highest revenue, bringing $655.4 million in 2023.
Personalized Medicine
Generative AI business applications not only address the struggles of healthcare providers but of patients as well. Tailored treatment plans can be issued to patients based on their individual concerns, medical history, prescription use, allergies, etc. In addition, follow-up monitoring and questionnaires can be sent to check up on the effectiveness of treatment.
Virtual assistants can also be employed by medical institutions to help users get pre-appointment answers to urgent questions about their health. In mild cases, like a cold or sprain, virtual assistants can provide recommendations for treatment according to the patient’s unique history.
The predictive analytics capabilities of AI can further aid in identifying potential risks for patients. Systems with large databases can assess a person’s current health and make predictions about the progression of certain illnesses based on responses to treatment and individual risk factors.
Overall, generative AI in healthcare fosters better patient outcomes, promotes individual approaches to treatment, and streamlines mundane processes. The applications of artificial intelligence in this industry will continue to cover more areas and expand its effectiveness going into 2025.
Generative AI Applications in Manufacturing
An industry such as manufacturing must be supported by intricate systems to ensure operations are efficient, compliant, and up to standards. Any deviations from established procedures can be costly, both financially and reputation-wise. With the growing prevalence of generative AI, the manufacturing sector can reach new heights and introduce automation to optimize production.
Companies that are already leveraging artificial intelligence in their workforce benefit from a substantial competitive advantage. So, let’s take a look at how this technology is impacting the manufacturing industry.
Product Design
One of the most widespread applications of generative AI in the manufacturing sector is product design, covering processes from conceptualization to prototyping. Software powered by this technology can create innovative concepts of products or components according to input. This can include parameters like size, weight, performance goals, materials, costs, and more.
Concepts generated by AI can be easily iterated and adjusted upon request without spending loads of precision time on redesigns. These not only include images but also 3D models, which substantially aids in prototyping, as well as the ability to input the designs into print. Product design generation can be applied in a multitude of industries, such as automotive, aerospace, robotics, etc.
Another added benefit is that this technology can supplement sustainable manufacturing. By including environmentally friendly parameters in product design, companies can shift focus to greener production with increased efficiency.
Production Planning
The ecosystem of manufacturing is notoriously complex, which is why common generative AI applications address production line optimization and management. A standout way to leverage this technology is to utilize its predictive analytics capabilities to forecast demands. Systems can analyze past market trends and fluctuations, customer behaviors, seasonal shifts, and more to help manufacturers anticipate demand.
That way the corporation can avoid production shortages and, conversely, overproducing at any given period, both of which can lead to financial losses. This approach also aids companies with inventory management, ensuring the factory has enough raw materials to keep in line with production schedules.
Advanced gen AI models can further enhance manufacturing by creating visual representations of the entire production lines. It can help identify any bottlenecks in the manufacturing process, providing businesses with actionable insights on how to dynamically optimize operations without risking downtimes.
Predictive Maintenance
Being able to pre-emptively identify issues with equipment and promptly address them before they can put a halt in production is something every manufacturing company strives for. Here is where you can build generative AI applications to enrich predictive maintenance for this exact purpose. Combined with computer vision for manufacturing, systems are supplied with the necessary sensors to oversee machinery and detect any early signs of wear down.
Since such models are trained to recognize how a piece of equipment operates under normal conditions, any indication of abnormalities, such as rust or irregular vibrations, can be detected right away. Staff is then alerted to these issues and can take immediate action to assign machinery for maintenance.
Downtimes associated with equipment upkeep are costly for companies and can disrupt production lines, which makes predictive maintenance an invaluable tool. Coupled with the capabilities of generative AI, this process can be further streamlined and kept under stricter control.
Generative AI Business Applications in Finance
A delicate industry like finance, which deals with monetary management and personal user information, calls for enhanced security and risk mitigation. On top of that, the sector highly depends on outside market factors that must be regarded to make informed decisions, which will affect the financial state of institutions and clients. Generative AI takes center stage in supplementing these aspects.
The extent of applications of AI in fintech is vast, from enhancing internal processes to aiding clients on an individual level. Here are the three most prominent areas where this technology is shaping the future of financial services.
Risk Assessment
Getting the full picture is something traditional risk assessment and management systems struggle with. Most applications of generative AI in finance are aimed at analyzing a vast amount of data to come up with a holistic overview of the market and its conditions. For example, a gen AI system can take into account interest rate spikes, geopolitical crises, economic shifts, and other risk factors to determine the performance of the portfolio.
A model like this can also stress-test scenarios involving multiple market factors to identify vulnerable areas. Financial institutions or businesses can take these insights into consideration to adjust their portfolio to mitigate those risks.
In addition, traditional credit scoring evaluates a limited portion of data to assign a score. Meanwhile, systems powered by artificial intelligence assess alternative data, including transaction history, credit history, utility bills, and hidden factors that may indicate the financial state. As a result, a more comprehensive scoring can be achieved that realistically reflects the conditions.
Fraud Detection
Unlike traditional rule-based systems that flag suspicious activity based on pre-defined conditions, financial fraud detection with gen AI introduces an innovative way to combat evolving threats. Instead of relying on limited rules, artificial intelligence is trained to identify outliers in data that suggest potential fraud. This allows it to dynamically adapt to sophisticated schemes that would otherwise go unnoticed by standard programs.
Generative AI business applications can detect identity fraud by assessing user behaviors. For instance, if the login attempt showcases irregularities in the typing speed, device used, location, etc., the system can promptly flag that attempt as suspicious. Notably, this technology also demonstrates a lower rate of false-positive fraud reports.
Moreover, the predictive nature of this tech can better respond to emerging threats as it learns from precedents. Financial giants like Visa already utilize generative AI solutions to swiftly pinpoint enumeration attacks and prevent financial losses.
Personalized Finance
Most modern financial recommendation systems leverage some semblance of AI-powered algorithms. Taking it a step further, personalized financial services can now utilize generative artificial intelligence to evaluate individual factors about a user to tailor advice. This can include their financial history, personal goals, preferences, behaviors, risk tolerance, and so on.
Based on this data, programs can customize financial planning and investment to correlate these unique factors to market conditions, resulting in a hyper-personalized approach to finance management. Such services replace an often one-size-fits-all approach of institutions in favor of ensuring a user can make fully informed decisions that yield the best outcomes.
Among the slightly different generative AI applications for personalized finance also include robo-advisors and chatbots. These virtual assistants are capable of generating tailored advice according to the user’s profile and addressing their needs without the need for human intervention.
Ethical Implications of Generative AI
It’s no surprise to anyone who is curious about artificial intelligence that certain ethical implications are present in this topic and need to be addressed. Providers that deliver AI development services need to be mindful of these pitfalls and how to navigate them to ensure the ethical use of this technology.
To broaden your perspective, let’s examine the most common concerns and explore ways of handling these points.
Intellectual Property
As we mentioned at the beginning of the article, concerns about who owns a generated piece of content remain debated, especially in creative fields. Since gen AI uses existing materials to output content, Intellectual Property (IP) rights can be infringed and lead to legal disputes between original artists and those who use generated media.
Applications of generative AI in creative industries must be properly regulated through nuanced compliance terms to avoid disputes. As of now, companies that utilize artificial intelligence for generating media must ensure the content used is licensed and the original creators give their consent to use their art, whether it is writing, music, images, etc.
Privacy Concerns
Generative models are trained on vast databases that can often include personal information about users, which rightfully raises questions about privacy. Such data can lead to the creation of synthetic profiles that resemble actual users or breach the security of their data protection rights. Unsurprisingly, the fear of personal data being misused can infringe on laws like GDPR and hinder the adoption of AI.
What can development providers do to alleviate these concerns? First and foremost, data anonymization practices need to be put in place to ensure that in case any resemblance occurs, it cannot be used against an actual person. Users must also give informed consent to allow a comfortable scope of information to be employed to train the model.
Workforce Considerations
While artificial intelligence offers an unparalleled level of automation and analysis of large sets of data for businesses, it also raises red flags among workers worried about job displacement. We have discussed the consequences of these worries in the creative sector, but generative AI applications in industries like manufacturing influence workforce concerns, too.
It’s unlikely for artificial intelligence to completely overtake jobs, as employees would still be needed to manage and oversee AI-powered processes. However, specialists would need to be retrained for such positions, which can cause skill gaps and a shortage of talent that must be addressed by employers who seek to adopt gen AI into business operations.
Bias and Discrimination
In an ideal world, there would be no prejudice, but the reality is that models are trained on historical data, which can include biases toward certain types or groups of people. If this issue is not addressed during development, the system can inadvertently perpetuate harmful stereotypes and lead to inadequate use of gen AI.
This is especially prevalent in industries like healthcare and finance, where biological differences or socioeconomic statuses of individuals can impede the model’s ability to generate unbiased outputs. The remedy to this implication lies in diversifying the datasets used to train the algorithm and continuously auditing it to eliminate discrimination.
Overreliance on AI
When it comes to generative AI business applications, companies can make the mistake of viewing algorithms as a source of unquestionable truth. In reality, even advanced models can make misjudgments. On top of that, the black-box dilemma introduces issues of output interpretability, where users are not exactly sure why the system came to a certain conclusion.
AI technology can open new opportunities for businesses to thrive, but it’s important not to lose touch with human insight. Unique experiences of people can bring nuance to the table that artificial intelligence otherwise wouldn’t. A balanced combination of the algorithmic prowess of gen AI with human expertise is the best course of action for organizations in any field.
The Future of Generative AI Applications Across Industries
From our overview in the article, you can see that generative AI is impacting various industries in different ways. One thing is for certain: artificial intelligence is here to stay. In 2025, the worldwide market size of gen AI is projected to reach $62.72 billion and is expected to grow significantly over the upcoming years.
Predicting an industry-wide future for this technology is challenging, but we can look at the current trends that populate in-demand sectors and assess the trajectory of the developments. After all, the flexibility of AI allows it to adapt to the distinct needs of each sector and deliver solutions to long-standing problems.
A trend that is clear to see right now is that gen AI is significantly transforming industries like healthcare and manufacturing. In fact, these two sectors held the largest market share in 2023. This is due to the ability of artificial intelligence to aid in clinical research and large-scale automation respectively.
So, what can we expect in the future for the industries that we discussed today?
- Creative industries. Corporations are likely to tread carefully with implementing AI into sectors like gaming, film, music, and more. A larger focus will be put on adaptable experiences that allow media to be personalized to the consumer’s individual preferences, such as VR and advertisement. Balanced collaboration of creators and gen AI tech will be the focal point, enabling more inventive storytelling.
- Healthcare. Drug discovery and development will be extensively supplemented by AI. Through algorithmic calculations, drug interactions and their effects on treatment can be simulated, leading to accelerated innovations in the field. Medical imaging and diagnostics will continue to evolve to enhance preventive care and treatment, reducing the need for invasive procedures.
- Manufacturing. Companies are likely to build generative AI applications that will support robotics in production lines, which lead to increased automation and precision. Product design will be further optimized to account for sustainable practices and more cost-effective production. Integrations with technologies like computer vision will continue perfecting predictive maintenance and workplace safety, including adherence to compliance.
- Finance. Fraud detection systems will be able to catch more sophisticated schemes and decrease the number of false-positive and false-negative flags. Algorithmic trading is going to advance, enabling trades to adapt to real-time fluctuations in the market. Personalized financial services will continue evolving and become more accessible to underrepresented demographics, democratizing finance across the board.
Overall, the future impact of generative AI on these industries will reshape traditional approaches and streamline business operations. This can lead to better outcomes for both institutions and consumers.
Final Thoughts
From our overview, you can see that the impact of gen AI is not so black-and-white in different fields. While creative industries may still struggle to balance the extent of artificial intelligence adoption, industries like healthcare, manufacturing, and finance are eagerly seeking generative AI business applications.
Companies also need to be mindful of navigating several complex challenges of introducing this technology into the workforce. However, with the right approach and skilled development partner by your side, implementing AI systems won’t pose an issue. After all, we can clearly see that the future of gen AI is bright and offers numerous advantages both for internal and external operations.
If you would like to build your own generative AI model and enhance your business, be sure to contact us. Our team at Tensorway will walk you through the next steps and deliver a project tailored to your needs.
