Our lives can now be distinctly divided into the era before generative AI and the exciting opportunities that followed. From the technology that makes it possible for us to talk to our smartphones to autonomous driving features in cars and personalized offers in online marketplaces, there are quite a few generative AI opportunities.
Do you remember AlphaGo’s victory over a world champion Go player in 2016? Back then, the buzz around it was huge. At the same time, it quickly faded from the public's consciousness. The same can’t be said about generative AI applications like ChatGPT, GitHub Copilot, and Stable Diffusion, right?
What we’re witnessing now is how these tools not only optimize tasks like data organization and classification but also help with creative tasks like text, music, and digital art generation. Of course, they inspire individuals and businesses alike to explore new possibilities.
In this article, Oleh Komenchuk, an ML Engineer at Uptech, and Dima Kovalenko, CEO and co-founder of Uptech, guide you through the most promising generative AI business opportunities. We’ll explore real-world use cases across various domains to prove that the genAI revolution is more than just hype – it's a transformative force for business success.
Is Generative AI Hype Worth It?
With each new development, generative AI brings a lot of excitement — and growing regulatory attention, too. The potential for this technology is vast, but businesses must stay informed and weigh the rewards against the risks in an increasingly AI-driven world.
First things first, let’s clarify what generative AI actually is.
In simple terms, generative AI is a branch of artificial intelligence that can analyze existing content – whether it’s text, images, or code – and produce new, distinct outputs based on this data. These capabilities stem from large foundational models (Large Language Models, Transformers, etc.) – prediction engines that recognize patterns in data and use them to produce something entirely new yet related.
The rapid pace of GenAI development
Now, why is the buzz around generative AI intensifying?
Since OpenAI’s release of ChatGPT in late 2022, advancements have accelerated at a breakneck pace. For instance, the launch of GPT-4, which happened just four months later, introduced major improvements over its predecessor.
Other players are moving quickly, too:
- By May 2023, Anthropic’s Claude model could process an entire novel’s worth of text – around 75,000 words – in under a minute, a substantial leap from its earlier versions.
- Google’s May 2023 launch included its own AI-powered updates – the introduction of PaLM 2 and a suite of tools under its Search Generative Experience (SGE), bolstering its Bard chatbot, among other applications.
- A few recent innovations include Gemini AI, released by Google in March 2023, and GPT-4o (the “o” for omni), released in May 2024, which set the stage for even newer models like the o1 series.
These are just a few of the genAI innovations that have completely changed the world today. To prove these words, let’s look at some numbers and stats provided by trusted sources such as Standford, Gartner, McKinsey, and others.
Key insights from the top AI Reports
Numerous AI reports are available today, each bringing a unique perspective on how generative AI is progressing across different industries. We’ve gathered insights from top players – such as Stanford’s AI Index, Gartner’s Hype Cycle, and McKinsey’s AI research – to provide a well-rounded view of AI’s current impact, potential growth areas, and transformative influence on business and society.
Insights from the 2024 AI Index Report by Standford
Standford AI Index Report is an annual report that gathers, organizes, and presents valuable insights into artificial intelligence technologies. This makes it easier to understand the developments and trends in the AI field.
Let’s put this in perspective with some insights from the 2024 AI Index Report by Stanford.
- Human-level capabilities? Yes and no. AI indeed excels in areas like image classification and English comprehension. However, it falls short in complex tasks, such as advanced mathematics and strategic planning. Well, at least for now.
- Industry’s hold on AI development. Industry players dominate the frontier of AI development, with the contribution of 51 new models in 2023 compared to academia’s 15. Industry-academia collaborations are also rising, which shows the importance of partnerships in pushing AI boundaries.
- Generative AI investment skyrockets. Investment in generative AI rose relatively high in 2023, even as overall AI funding weakened: It reached a staggering $25.2 billion. Key players like OpenAI, Anthropic, Hugging Face, and Inflection secured massive funding rounds. From this, we can tell that the market is confident in generative AI’s potential.
- Productivity gains for workers. Studies in 2023 underscore AI’s potential to boost productivity and improve work quality. Speed aside, AI can also bridge the skill gap as it offers a valuable resource for both high- and low-skilled workers. On the other hand, improper AI oversight may lead to performance dips.
- Accelerated scientific progress. In fields from algorithm efficiency (AlphaDev) to materials discovery (GNoME), AI’s influence on scientific research has become more intense: it drives advancements that would have been impossible in a purely human-driven environment.
- Growing regulatory attention. As AI becomes more prevalent, regulatory measures have multiplied. In the US, AI-related regulations grew from just one in 2016 to 25 by 2023, with a notable 56.3% increase in the past year alone.
- Rising global awareness and concerns. Public perception of AI is evolving, too. Ipsos reports that 66% of people worldwide now believe AI will impact their lives soon, with 52% expressing concerns about AI technologies. In the US, a Pew survey found that over half of Americans feel more concerned than excited about AI – a jump from 38% in 2022.
Thoughts from Gartner's 2024 AI Hype Cycle
Now, let's move to the 2024 AI Hype Cycle by Gartner. This is an annual report that maps out where different AI technologies stand in their journey from innovation to real-world adoption. The report is worth special attention as it shows whether the hype around certain technologies is meeting reality.
Here are the key takeaways and explanations of the image above:
- Many AI technologies face reality checks. Several AI technologies, including generative AI tools, are now entering the "Trough of Disillusionment." What does this mean? They follow early excitement: Businesses are encountering real-world challenges in implementation, and expectations are adjusting.
- Vendors at a crossroads. AI providers are at a "moment of reckoning." They need to prove that their technologies can deliver real business value. Many companies are grappling with high costs and struggling to see returns on their AI investments.
- User concerns on business value. Businesses are becoming more critical of AI, with increased scrutiny of its value, costs, and accuracy. As the initial hype fades, users approach AI investments with a more cautious, results-oriented perspective.
- Hype vs. reality. While some technologies, like AI agents and large language models, have generated significant buzz, most have yet to reach reliable, practical applications. That said, as these technologies mature, some are expected to climb the "Slope of Enlightenment," where their value becomes clearer.
From all the abovementioned, we can securely state that AI still holds significant promise. However, for many technologies, the hype has yet to fully translate into practical, widespread business use. At the same time, as the AI sector develops, some AI tools are likely to bridge this gap and deliver true business value. We’ll detail what genAI technologies are already delivering or have what it takes to deliver business value.
Key highlights from McKinsey Report 2023
McKinsey’s 2023 report, The Economic Potential of Generative AI: The Next Productivity Frontier, is another source that explores how generative AI can drive productivity and economic growth. It outlines the impact generative AI could have on global industries, workforce transformation, and productivity. Here are the key highlights:
- Massive economic impact. Generative AI could contribute between $2.6 trillion and $4.4 trillion to the global economy each year across various use cases. This would increase AI’s total economic impact by up to 40%, and the figure could double when generative AI integrates into broader software applications.
- Primary business areas for value creation. About 75% of generative AI's value is found in four areas – Customer Operations, Marketing and Sales, Software Engineering, and R&D. In these areas, generative AI supports tasks like customer interactions, content creation, and code generation.
- High-impact sectors. Banking, high tech, and life sciences stand to benefit most. For instance, banking could see an additional $200 billion to $340 billion in value each year. In retail and consumer goods, the impact could range from $400 billion to $660 billion annually.
- Workforce transformation. Generative AI could automate 60-70% of tasks that consume employees' time today, particularly knowledge-based tasks that involve natural language processing (NLP). This could transform the workforce, with up to 50% of current tasks automated by 2045.
- Boosting productivity growth. Generative AI has the potential to increase labor productivity by 0.1 to 0.6% annually through 2040. Combined with other automation tools, it could add up to 3.4% to productivity growth each year. To achieve this, though, businesses must reskill workers and support job transitions.
- Long-term challenges and potential. While generative AI holds great promise, fully realizing its potential will take time. Business leaders will need to manage risks, support skill development, and adapt processes to capture the benefits of this powerful technology.
Overall, these reports show where AI stands today. Generative AI has vast potential to boost productivity, transform industries, and drive economic growth, but the success will depend on thoughtful adoption and workforce readiness.
At Uptech, we have deep experience in generative AI-powered app development. Our team can help your business understand and make the most of the power of generative AI. Through our AI consulting services, we help you make sense of the data, identify key market opportunities, and find the AI capabilities that best fit your goals.
Now, let’s move to the next question.
How Should Businesses React to the GenAI Hype?
It seems like pretty much everyone is eager to jump on the generative AI bandwagon. Otherwise, you wouldn’t be here, would you? But that doesn’t mean that you, as a business owner or decision-maker, should necessarily do the same. Generative AI holds huge potential, but it’s important to assess how it aligns with the specific goals and challenges of your business. This section provides practical steps to help you understand the opportunities and risks of generative AI, evaluate its fit for your business, and plan for secure and successful implementation.
Learn the market opportunities of GenAI
There are quite a few generative AI applications across industries, from customer service to software engineering, which we will examine in detail further on. GenAI can help automate processes, augment employee tasks, and even create new revenue streams. Where should you start, then? Below, we outline several practical ways to explore generative AI opportunities.
Align GenAI use cases with business goals
First things first, identify key areas where AI could make the biggest difference – whether it’s customer experience improvement, optimization of core business operations, or a way for you to provide better product offerings. For example, Gartner recommends linking AI projects to specific KPIs to ensure tangible outcomes.
How does it work?
Say customer retention is your priority. So, you can use generative AI to make improvements in how you communicate with customers, e.g., through personalization and automated customer support.
Set realistic expectations
Dreaming big and thinking big is cool. But keep in mind that while generative AI is powerful, it has limitations. It’s best suited for tasks where automation can improve speed and efficiency without compromising quality, e.g., marketing content generation or drafting code. Start small with clear objectives and assess the performance. Avoid large, all-encompassing projects until you have tested AI’s reliability for your use case.
Understand your industry’s AI landscape
You must look at how competitors or industry leaders use generative AI. For example, if you’re in retail, examine how companies utilize AI for things like personalized recommendations or visual search. This research can help you identify valuable AI applications and see where your business might benefit.
A recent Gartner survey showed that 38% of executives prioritize generative AI for customer experience, followed by revenue growth (26%) and cost optimization (17%). Knowing the potential impact areas can help you find out where generative AI might offer the most value for your business.
Understand the challenges of GenAI and ways to overcome them
Generative AI is not without risks, and these must be managed carefully to avoid unintended consequences. Here are some practical tips to navigate these challenges:
Manage accuracy and reliability
Generative AI sometimes produces “hallucinations” or incorrect information. To solve this issue, you may want to implement a human review process for all AI outputs, especially for customer-facing applications. Also, it’s a good practice to set guidelines for when to escalate AI-generated content to a human for accuracy checks.
Mitigate bias
AI bias occurs when machine learning algorithms produce skewed or unbalanced results due to underlying assumptions in the algorithm’s design or biases present in the training data. For example, in 2022, the Lensa AI avatar app, analyzed by MIT Technology Review, generated sexualized images of Melissa, an Asian woman, without her consent, while her male colleagues received empowering images. This incident demonstrated how AI can perpetuate gender and racial stereotypes.
Since no business wants to be in this situation, it’s important to establish policies to review AI outputs for potential biases, especially in sensitive areas like hiring or customer service. The AI model’s training data should be regularly updated to minimize bias and adjusted based on feedback.
Protect intellectual property and privacy
If you use or build generative AI tools that rely on public data, ensure compliance with intellectual property (IP) and privacy laws, such as GDPR and HIPAA, to name a few. You must implement strict data governance policies and educate staff about best practices for data entry. Consider using generative AI solutions that allow data control and customization to minimize IP exposure.
Bolster cybersecurity
Generative AI can really increase cybersecurity risks, particularly with tools that create deepfakes or simulated content. For instance, deepfake videos of politicians have been used to spread misinformation. In March 2023, AI-generated images showing the fake arrest of former President Donald Trump circulated widely online. Created using Midjourney, the images depicted Trump being seemingly detained by the NYPD. While intended as satire by Eliot Higgins, founder of Bellingcat, who shared them on Twitter (now X), some users falsely claimed the images were real.
To protect against such risks, coordinate with your cybersecurity team to monitor AI-related threats and consider additional safeguards, like multi-factor authentication, for AI-driven applications.
Implement responsible AI practices
The general recommendation is to ask critical questions upfront, such as who will oversee responsible AI use, how to manage compliance, and what the consequences of misuse are. Draft a clear policy that defines responsible AI use and establishes guidelines for employees on compliance and risk management.
Think critically: Does my business actually need GenAI?
Jumping into generative AI because it’s trendy can lead to wasted resources and unmet expectations. Here’s a practical approach to determine if generative AI is a good fit:
- Identify specific needs. Ask yourself if AI is the most efficient way to solve a particular business problem. For example, if improving customer service response times is a priority, a simpler chatbot might meet your needs without the complexity of generative AI.
At Uptech, we can help you with this assessment through our Software Product Discovery service.
This process ensures that you build the right product through idea validation, market analysis, and finding the product-market fit. We back your product with essential knowledge about your users, market, and the specific problem you aim to solve. As such, you are sure that you make a well-informed decision on whether generative AI is the right approach for your business.
- Assess potential ROI. Evaluate the costs, including implementation, training, and maintenance. Calculate the expected return on investment and compare it to other potential improvements to see if generative AI provides a clear advantage.
- Pilot first, scale later. Before you commit to a full-scale rollout, start with a GenAI MVP (Minimum Viable Product). A pilot project with measurable KPIs allows you to assess initial results, make adjustments, and build internal confidence in AI solutions while minimizing risks.
Uptech also offers MVP Development Services to help you validate your idea’s market potential, assess technical feasibility, and deliver a fully operational MVP.
So, are you sure that generative AI seems valuable for your business? If the answer is positive, then move forward with the following two steps.
Think of creative GenAI use cases for your business
Once you determine that generative AI is a good fit, consider the broad range of possibilities it could open up across your operations. Generative AI provides powerful tools for things like workflow optimization, better customer engagement, and bringing innovative ideas to life. Its applications are diverse and can be adapted to support goals across marketing, customer service, product development, and beyond.
To discover the best ways generative AI could work for you, gather input from team members, consider your strategic goals, and consult with experts.
Implement GenAI technology
And, of course, think your implementation strategy through. Here are the core steps we stick to for a successful generative AI implementation:
- Identification of needs and goals. First, together, we determine the specific problems generative AI will address within your business and outline measurable goals to gauge its impact.
- The choice of the right tech stack. Next, we select the technology stack that best suits your requirements. For instance, when it comes to custom solutions, we mainly opt for frameworks like TensorFlow, Diffusers, LangChain, and PyTorch. At the same time, third-party APIs are a more streamlined approach for AI integration within existing software.
- Data collection and preparation. High-quality data is half the success of the training process. That’s why we pay special attention to collecting relevant datasets and cleaning them for accuracy, consistency, and compliance with privacy regulations.
- Training and fine-tuning the model. Next, we use curated data to train the generative AI model. Through testing and iterative fine-tuning, we ensure the model aligns with your specific use cases and provides correct outputs.
- Model integration and testing. The following step is to embed the trained model into your app’s infrastructure, linking it with key software components. It’s required to test the model to validate its performance, security, and user experience.
- Monitoring and optimization. After launch, we continuously monitor the model’s performance, review metrics, and collect user feedback to guide regular updates.
A more detailed guide on integrating generative AI into an app can be found in a separate post.
At Uptech, we offer end-to-end support as a reliable AI software development partner. We guide you through each step of generative AI implementation – from assessing your needs to integrating, testing, and optimizing your AI solution.
Generative AI Opportunities: 14 Use Cases in Different Domains
In a recent McKinsey study, researchers analyzed 63 generative AI use cases, estimating that this technology could add $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on factors like the importance of different business functions and industry revenue scales.
At Uptech, we have extensive knowledge and expertise in AI development, so we’d like to showcase specific generative AI opportunities across various industries to help you see how businesses are already benefiting and find areas where you can benefit, too.
GenAI opportunities for fintech
A study by EY revealed that 77% of European financial service leaders see generative AI as a positive force for their industry. Generative AI transforms how financial services collect and analyze massive amounts of data and leads to faster, more personalized, and more secure services.
McKinsey projects that generative AI could boost productivity in the banking sector and generate up to $340 billion in additional value by enhancing core activities and improving customer experience.
Popular GenAI applications in fintech include:
- Automated loan processing
- AI-driven customer support
- Algorithmic trading
- Fraud detection
- Credit risk analysis
- Predictive analytics for market trends
Unfortunately, we can’t cover all of them, yet below are 3 bright examples of generative AI usage in banking.
Predictive analytics for investment decisions
One of our projects at Uptech, Tired Banker, is a platform that uses AI to simplify complex investment data and make it accessible and actionable. Traditionally, investment information – like earnings reports for S&P 500 companies – can be difficult to interpret.
Using GPT-4, we managed to build a platform that offers AI-powered insights, with investment data being broken down into understandable information, guiding users in decision-making.
Algorithmic trading and investment
In financial markets, where rapid, split-second decisions can mean the difference between profit and loss, generative AI-powered algorithms offer investors a way to gain an edge. These algorithms analyze vast amounts of market data, identify patterns, and execute trades with speed and precision, which improves the odds of profitable trades.
Quantitative hedge funds like Citadel Investment Group use GenAI to drive advanced trading strategies that capitalize on market inefficiencies. The company analyzes large datasets and uses sophisticated models. In this way, they produce high returns, often outperforming traditional investment approaches.
Fraud detection and prevention
With the surge in digital transactions, financial institutions face increasing threats from fraud and cyber-attacks. GenAI-powered fraud detection systems provide real-time transaction monitoring and identify suspicious patterns to reduce the risk of losses.
For example, PayPal uses generative AI to improve transaction security, resulting in an 11% reduction in fraud losses. The AI system analyzes transaction patterns, spots anomalies, and flags potentially fraudulent activities in milliseconds. Such an approach protects both PayPal and its customers from financial harm. It allows PayPal to monitor millions of transactions simultaneously and respond instantly to threats.
GenAI Opportunities for Healthcare
A 2023 Accenture report highlights the transformative potential of generative AI in healthcare. Nearly all healthcare providers (98%) and executives (89%) surveyed believe generative AI can improve enterprise intelligence and productivity, with an estimated 40% of working hours supported or augmented by language-based AI.
Typical use cases for generative AI in healthcare include:
- Medical chatbots
- Advanced simulations
- AI-improved patient care and clinical decision support
- Health risk prediction
- Personalized medicine and treatment plans
- Accelerated drug discovery
Personalized medication and care
Generative AI can use data from wearables and diagnostic tools to predict and manage complex health conditions.
Mayo Clinic, for instance, has developed AI algorithms that analyze electrocardiogram (ECG) data to predict various heart conditions, such as atrial fibrillation, amyloidosis, aortic stenosis, and hypertrophic cardiomyopathy (HCM). These algorithms can even estimate a patient's biological age based on traditional 12-lead ECGs and single-lead ECGs from smartwatches or portable devices.
One standout application is the use of ECG-AI to detect low ejection fraction, or a "weak heart pump." This condition often goes undiagnosed until symptoms appear, but the AI-driven algorithm identifies it early by analyzing ECG patterns. The 12-lead version of this algorithm has received FDA clearance for clinical use and is licensed to Anumana, while a single-lead version for portable devices is licensed to Eko Health.
Improved drug discovery and development
Generative AI is widely used in drug discovery as it cuts the time and cost traditionally required for R&D. For instance, Insilico Medicine has developed GENTRL, a generative AI platform that identifies potential drug compounds faster (days instead of years) and with more precision. In just 21 days, GENTRL generated six new molecules, four of which demonstrated the ability to inhibit DDR1 – a protein associated with various diseases, including fibrosis – at nanomolar concentrations.
This approach replaces manual processes like paper records and outdated communication methods and allows for rapid testing and regulatory submissions.
A separate attention must be paid to the use of ChatGPT in healthcare as it eliminates the need to build complex neural nets from scratch. Instead, it provides advanced AI capabilities to everyone in the industry.
GenAI opportunities for logistics
MIT Sloan Management Review reports that trucks in the U.S. are typically around 30% empty. The result? Wasted time, fuel, and added carbon emissions. Truck capacity optimization is inevitable, but to achieve it, companies must go beyond simple algorithms.
More and more logistics companies are looking in the direction of generative AI to optimize loading plans.
But that’s just one of many potential applications of generative AI in logistics. Other notable use cases include:
- Dynamic route optimization
- Inventory management
- Predictive maintenance
- Customer service automation
- Freight pricing and bidding
- Warehouse management
- AI automation
Loading plans optimization with Retrieval-Augmented Generation (RAG)
FedEx, in partnership with Dexterity AI, has adopted an innovative AI-driven solution to enhance trailer loading for ground deliveries. This setup uses Dexterity's DexR robots, equipped with advanced AI capabilities, to optimize loading efficiency.
The AI software behind DexR evaluates each new package in real-time: It assesses billions of packing configurations in under 500 milliseconds. This feature, known as Generative Wall Planning, enables the robot to create tightly packed, stable walls within trailers by quickly choosing the optimal arrangement for each incoming box.
The robots also use machine learning-based pack improvement to learn from each new load to enhance future performance.
AI automation of routine logistics tasks
Another prominent example of generative AI in logistics is from C.H. Robinson Worldwide, one of the largest freight brokerages globally, which is increasingly integrating AI into its operations to boost productivity.
Over recent months, C.H. Robinson has introduced AI-driven solutions for automating key logistics management tasks, such as processing emailed quote requests from shippers and enabling touchless appointment scheduling.
GenAI opportunities for e-commerce
Generative AI also empowers e-commerce businesses and helps them create a more personalized customer shopping experience. The most common AI applications here are AI-powered chatbots and virtual text and voice assistants that can easily handle routine inquiries – such as tracking orders, processing returns, and answering FAQs.
In addition to automated customer support, valuable generative AI applications in e-commerce include but are not limited to the following:
- Personalized product recommendations
- Dynamic pricing optimization
- Content generation for product listings
- Inventory management
- Fraud detection and prevention
- Enhanced search and discovery
- AI-powered design and merch creation
Personalized product recommendations
Powered by generative AI, personalized product recommendations help e-commerce platforms deliver highly targeted shopping experiences for each individual. Such recommendation systems analyze user behavior, purchase history, and browsing patterns. Based on this information, AI models can suggest products that would be interesting to this particular customer.
A notable example is Amazon, which uses AI-driven recommendation engines to personalize product suggestions for each user based on their shopping experience and items that are often bought together.
The algorithm applies collaborative filtering, which identifies patterns in shopping behaviors by comparing different users with similar tastes. When the system finds that two users share similar interests, it can suggest products to one user based on items the other user has already purchased. This approach, known as “people like you also bought,” enables Amazon to make relevant product recommendations, even for items the user hasn’t viewed.
This approach has led to a significant increase in conversion rates and customer satisfaction. A McKinsey study indicates that Amazon’s proprietary recommendation algorithm drives up to 35% of the company’s sales by suggesting complementary products to customers.
Predictive analytics for customer growth
Another notable gen AI opportunity is in the e-commerce and retail sectors, especially in customer acquisition and retention. A great example is Angler AI, a project we developed for a client to empower brands with AI-driven insights for growth. Angler AI uses predictive analytics to help businesses understand their customer base, create highly targeted audiences, and accurately measure marketing ROI.
Key features include:
- Angler Fit Score is an AI-powered tool that predicts potential customer conversions and enables brands to prioritize high-value leads.
- AI-based campaigns are the feature that allows businesses to generate precise ad campaigns tailored to specific audiences.
- AI-based analytics help validate campaign performance and optimize return on ad spend (ROAS).
Through Angler AI, we integrated data analytics and machine learning to create a robust customer growth platform for our client.
AI-powered design and merch creation
H&M Group is leveraging generative AI to redefine customization in fashion through its Creator Studio platform. This new tool, powered by the open-source model Stable Diffusion, enables users to generate custom artwork for on-demand clothing production without any design skills. Users input text prompts, and the AI tool transforms them into professional-quality visuals, which can then be applied to garments for immediate production.
GenAI opportunities for insurance
Generative AI in the insurance industry advances things like risk assessment, claims processing, and customer interactions. Using generative AI, insurers can process vast amounts of data, perform more accurate risk evaluations, and create personalized policy offerings.
Automated claims processing
In traditional claims processing, adjusters spend significant time manually reviewing accident details, searching for missing information, and summarizing declarations to populate claims systems. This process is slow, prone to human error, and often results in delays that frustrate both customers and employees.
Generative AI automates much of this workflow by acting as an “intelligent reading machine.” When a claim is filed, generative AI can:
- Instantly review accident declarations and extract critical information with high accuracy.
- Summarize details and populate the data directly into claims management systems.
- Automate first notice of loss (FNOL) tasks, speeding up data entry and customer communication.
With AI-driven claims processing, digital assistants can retrieve missing information, check data accuracy, extract documents and images from emails or SMS, and notify customers of their claim status.
Insurance fraud detection
Insurance fraud is a massive issue. According to the FBI, it costs the industry about $40 billion annually. Generative AI is here to help insurers tackle this problem, as it can spot suspicious patterns in real-time. By analyzing claims data, AI can identify red flags – like repeat claims from the same person or unusual claim values – and flag them for further review.
For example, AXA Switzerland, one of the leading global insurance companies, uses Shift’s AI-based Claims Fraud Detection system to detect and prevent fraudulent activities in real-time. This system analyzes claims data for red flags, such as odd billing patterns or claims from providers with a history of suspicious activity. By integrating AI into their fraud processes, AXA has significantly reduced fraudulent claims, saving millions annually.
This tech doesn’t just look at numbers; it can also check images or documents for inconsistencies. As such, fraud teams can focus on high-risk cases quickly.
GenAI opportunities for travel
The travel and hospitality industry also uses generative AI for such common tasks as chatbots and personalized itineraries. On top of these cases, there are quite a few opportunities to consider:
- Real-time travel updates and recommendations
- Booking assistance and trip planning
- Post-trip engagement
- Language translation and localization
- Personalized packing recommendations
- Travel risk assessment and advisory
- Intelligent travel expense management
- Immersive virtual travel experiences
AI-powered travel advisors
According to Accenture’s Consumer Pulse 2024 research, the demand for intelligent, AI-powered travel advisors is higher than ever. This research, which surveyed 19,000 consumers across 12 countries, highlights that travelers want AI assistance to make decisions smoother and reduce noise around options. The findings reveal that:
- 87% of travelers desire AI recommendations they can consistently trust.
- 82% want AI advisors to suggest unique options they may not have discovered independently.
- 88% prefer AI agents that offer options from various brands, enabling broader choices.
- 79% wish AI could manage tasks on their behalf, such as negotiating rates, making purchases, or handling customer support.
- Over 87% of travelers value an AI assistant that is available at any time, in-store, online, or via mobile app.
An early example of this concept – Anthropic’s AI demo – shows how an AI could autonomously manage travel planning. In the demo, the AI takes control of the computer to book a sunrise viewing event in San Francisco: It finds the ideal spot, checks drive times, sets reminders, and arranges a calendar event. The AI performs the steps as a human would, mimicking clicks, cursor movements, and typing.
This kind of AI-powered travel advisor has all it takes to change the entire industry, as it can handle tasks like itinerary planning, reservations, and even customer support. This can potentially challenge traditional online travel agencies (OTAs) like Booking.com.
Virtual concierge services
In 2023, Marriott International introduced an AI-powered virtual concierge service called RENAI at Renaissance Hotels to elevate guest experiences. The generative AI technology allows Marriott to offer personalized travel recommendations and the best possible guest experience. How? The program allows travelers to discover local attractions, dining options, and unique cultural experiences tailored to their interests. It’s like “having a well-connected local who is available 24/7, all right from guests’ smartphones.”
The AI-powered virtual concierge uses insights from past preferences and feedback to make recommendations that resonate with individual guests.
GenAI opportunities for real estate
Generative AI has the potential to transform the real estate industry: McKinsey estimates it could generate up to $180 billion in value. To achieve this, real estate businesses must adapt and implement AI into their processes.
In the real estate sector, generative AI has the following applications:
- Improved customer engagement with conversational AI
- Property visualizations and virtual tours
- Informed and smarter investment decisions
- AI copiloting to assist in real estate interactions
Property visualizations and virtual tours
Generative AI tools allow potential tenants to visualize apartments tailored to their style preferences, such as midcentury modern or specific finishes like cherrywood versus walnut. This personalized visualization also collects valuable data on style preferences, which can then be fed back into AI models. This insight helps property managers predict which designs convert best and guides future decisions on furnishings and renovations.
For example, Compass – a US real estate platform, uses generative AI models to:
- Create compelling property descriptions and listing narratives, which makes listings more attractive to potential buyers and tenants.
- Offer virtual tours and immersive 3D visualizations, giving prospective buyers a realistic feel of properties before in-person visits.
Creation of architectural plans
Generative AI can help architects design purpose-driven spaces with the help of data from IoT sensors and computer vision. These technologies gather information on how spaces are used – like customer flow in stores or conference room usage in offices – alongside outcome data on productivity, sales, or customer satisfaction. AI can then overlay this behavioral data with architectural details, such as square footage and layout, to generate optimized architectural plans.
As shown in this illustration provided by McKinsey, generative AI-assisted architectural designs use sensor and computer vision data to simulate natural light placement, predict foot traffic patterns, and evaluate noise levels. This technology enables architects to visualize how these environmental factors interact within a space, enhancing design functionality and occupant comfort.
The interesting thing is that this list of generative AI opportunities is just the tip of the iceberg of what this technology brings to the table. There are far more genAI applications out there.
What’s Trending in GenAI: Insights from Uptech’s ML Engineer
As an ML engineer, I keep a close eye on the latest advancements in generative AI. And I see that the possibilities are truly impressive, with projects at Uptech showcasing just how AI can reshape industries:
- Hamlet uses AI to provide concise, accurate summaries of extensive content.
- Dyvo.AI creates stylish, personalized avatars from selfies, showing the creative side of AI.
- PLAI is a performance management platform that helps businesses execute strategies more effectively. It leverages AI to enhance strategic decision-making.
These are just a few applications of generative AI. Now, let’s look at some of the most exciting trends in terms of technology that are reshaping the field today.
Retrieval-Augmented Generation (RAG)
As mentioned in one of the use cases above, RAG is definitely gaining traction, particularly for enterprise applications that require high accuracy. RAG links generative AI with a knowledge base and, in this way, enables systems to access large data sources. As a result, there are more accurate and context-aware responses.
I think that this technology improves generative AI’s reliability, especially in applications that demand extensive background knowledge.
Multimodal models
Multimodal models are gaining more popularity for their ability to handle text, images, audio, and other data types all at the same time. This trend enables more versatile AI applications and agents that can respond across diverse content forms. As a user, you receive highly interactive experiences.
What’s more important, multimodal functionality broadens the scope of generative AI, as it makes it suitable for applications that require an understanding of multiple content types.
Lightweight and optimized models
Another thing that I have noticed is that smaller, faster models optimized for efficiency are on the rise. I see many developers using techniques like quantization and model pruning to reduce resource demands without compromising quality.
This shift allows generative AI models to run on mobile devices and edge systems. Basically, users can make the most out of advanced AI accessible even in low-resource environments.
Fine-tuning with personalization
Demand for fine-tuning AI models with personalized data keeps growing, too. This is especially true in specialized fields like finance and healthcare. Open-source repositories and tools are simplifying customization: Businesses can now adapt generative AI models to fit industry-specific needs.
For example, in healthcare, developing a large-scale transformer model from scratch isn’t always feasible due to cost and data limitations. Instead, hospitals and research centers can take existing generative AI models and fine-tune them for specific applications, such as AI medical image analysis.
A real-life example is Zebra Medical Vision, which uses pre-trained computer vision models fine-tuned on medical image datasets to detect various conditions like liver disease or lung abnormalities.
Integration with No-code/Low-code platforms
What I also noticed is the expansion of the integration of generative AI into No-code and Low-code platforms. What does it mean? AI tools become accessible to small and medium-sized businesses without in-house developers.
How To Get Started with GenAI and How Uptech Can Help
Having generative AI in place sounds great, right? By now, you may be considering ways to integrate genAI technologies into your business. There are three options available. Your choice will mainly depend on your resources, goals, and readiness.
Build an in-house AI department
If you are a mid-size company that sees AI as a strategic asset, the best variant for you is to establish an internal AI team. With an AI-focused R&D department, you can create customized AI solutions that evolve alongside your business.
Things to consider: This approach suits organizations with resources for a long-term AI strategy, including hiring ML engineers, data scientists, and software developers. You should also be ready for both initial and ongoing investment.
How we can help: At Uptech, we can collaborate on strategy, training, or augmenting your team with specialists who have GenAI expertise to expedite early projects.
Start with AI consultancy
If you’re a startup/small business/new to generative AI or want to explore its potential before a full-scale commitment, the right path is to consider AI consultancy. With consulting support, your team can understand the best GenAI applications for your business, identify early wins, and outline a clear AI integration roadmap.
Things to consider: Consultancy helps define goals, establish KPIs, and create a strategic approach without building a dedicated AI team.
How we can help: Uptech provides all kinds of generative AI consulting services. We can work with you to assess GenAI opportunities and define a plan that aligns with your business vision. Our experienced team ensures that your first AI projects are structured for minimal risk and maximum benefit.
Outsource GenAI expertise
If you can relate to companies that want to access GenAI benefits without creating an in-house team, the best scalable option is to outsource AI software development to a vendor who has relevant expertise.
Things to consider: When you work with an experienced GenAI partner, you receive fast deployment and high-quality solutions. At the same time, your team can focus on core business areas.
How we can help: Uptech offers end-to-end generative AI development services, from model customization to deployment and support. With proven results and a dedicated GenAI team, we provide solutions tailored to meet your unique business requirements.
Whichever option best suits your needs, Uptech stands ready to support your journey with GenAI. So, if you feel like making the most of generative AI business opportunities, contact us.
FAQs
How can generative AI be used in businesses?
Generative AI powers chatbots, customer service, product development, content creation, customization, analytics insights, coding, data analysis, and design innovation.
What jobs will generative AI replace?
Generative AI could automate roles with repetitive tasks, such as data entry, customer support, and some content creation, while also supporting, rather than fully replacing, creative and strategic jobs.
What is the future of generative AI?
Generative AI will likely become more advanced in the near future: It is expected to provide more personalized, efficient, and creative solutions across industries, with the potential for highly interactive applications in everyday life and business.
Which industry is likely to benefit the most from generative AI?
Industries like healthcare, finance, retail, entertainment, and marketing are expected to benefit the most from tapping into generative AI possibilities.
Why does generative AI hold so much promise for businesses?
Generative AI offers businesses the ability to automate tasks, personalize customer interactions, innovate product designs, and make data-driven decisions, all of which lead to greater efficiency, engagement, and growth.