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10 Real-Life Examples Of How AI Is Used In Business

The widespread adoption of Artificial Intelligence (AI) in business has rapidly transformed it from a niche technology into a core component of modern corporate operations. AI is driving efficiency and innovation across various industries, leading to a growing demand for AI-powered solutions, including those designed for web application development. By 2024, AI has become deeply embedded in corporate strategies, as organizations seek to harness its capabilities to gain a competitive edge in an increasingly digital marketplace.

Even though there are more than 10 examples of artificial intelligence in business, many businesses still struggle to utilize it fully. Only 26% of businesses possess the necessary skills to move beyond pilot projects and achieve real benefits from AI deployments, according to a 2024 analysis by the Boston Consulting Group (BCG). This suggests that while AI adoption is expanding, many firms continue to face challenges in scaling its value.

Artificial intelligence in business is more prevalent in sectors like fintech, software, and banking, which have undergone early digital disruption. These industries have leveraged AI to enhance customer satisfaction, streamline processes, and develop new product lines. AI-powered applications, for example, have revolutionized software development by enabling rapid prototyping and customized solutions.

There are a lot of examples of AI tools in real life. The software industry is evolving due to the proliferation of AI applications, particularly those

Beyond web development, AI-driven applications are rapidly reshaping software-driven businesses. Companies like OpenAI and Anthropic have seen unprecedented user adoption of their AI tools, while startups such as Cursor and Mercor have achieved high valuations and significant revenue growth. This surge in AI adoption highlights the increasing reliance on automation and intelligent systems in white-collar jobs, further demonstrating AI's transformative impact on the software industry.

Along with 10 examples of artificial intelligence in real life, AI has significantly enhanced operational efficiency in the finance industry. For instance, JPMorgan reported that using an AI coding assistant increased software engineers' productivity by up to 20%. This development highlights AI's role in optimizing human resources and fostering innovation by allowing the bank to reallocate engineers to high-value initiatives, particularly in data and AI. You can read our article about 20 Best AI Stocks To Buy Now if you are interested in stocks that are most likely to benefit from the developments in AI.

Despite the benefits of AI in business, obstacles remain in the successful integration of AI. Scaling AI solutions beyond initial proofs of concept remains a challenge for many businesses. According to the BCG survey, 74% of businesses have yet to achieve measurable results from their AI initiatives, often due to difficulties in expanding and incorporating AI into core business operations.

To overcome these challenges, organizations are prioritizing fundamental business transformations, making strategic investments in AI capabilities, and fostering a culture that embraces technological change. The focus is on aligning AI initiatives with business goals to ensure that technology drives value creation rather than functioning as a stand-alone solution.

The integration of AI into business remains a dynamic and evolving landscape, so there are more than 10 real-life examples of how AI is used in business. While significant progress has been made, though, many enterprises have yet to realize AI's full potential. Beyond technological advancements, the future of AI adoption requires strategic alignment, cultural adaptation, and a strong commitment to delivering measurable business value.

Artificial intelligence (AI) is rapidly transforming industries, revolutionizing how businesses operate and make decisions. From automating repetitive tasks to providing deep data insights, AI-powered solutions are enhancing efficiency, reducing costs, and improving customer experiences. Companies across various sectors—finance, healthcare, retail, and beyond—are leveraging AI to streamline processes, boost productivity, and drive innovation.

This article presents 10 real-life examples of how AI is used in business, compiled from various sources referenced throughout this piece. Each example highlights a specific application of AI and its impact on industry. Whether it's predictive analytics in sales, AI-powered fraud detection in finance, or generative AI in content creation, these examples showcase the diverse ways AI is reshaping modern business operations.

From self-driving technology to customer service chatbots, AI is not only improving existing processes but also creating new opportunities for growth. By understanding how different industries are integrating AI, businesses can gain valuable insights into leveraging this technology for their own success. The following list provides a snapshot of AI's real-world influence, demonstrating its potential to transform the future of work and commerce.

Energy Sector

Predictive solutions driven by AI are enhancing resilience and guaranteeing a steady supply of electricity by anticipating and reducing grid outages brought on by severe weather or cyberattacks.  The reason why using AI in the energy sector is on our list of 10 real-life examples of how AI is used in business is because AI is also reducing the impact of fluctuation in renewable energy generation and optimizing grid operations for cost-effectiveness.

Companies like Devon Energy and BP are leveraging AI to optimize drilling operations. AI-driven models used by BP accelerate and reduce the cost of oil production by identifying potential issues and guiding drill bits with greater precision. Similarly, Devon Energy has used machine learning models to increase drilling efficiency by 15%, highlighting AI's role in modernizing traditional energy extraction methods.

Fast-Food Industry

The fast-food business, which is renowned for its quickness and speedy service, is using AI more and more to improve consumer satisfaction and operational effectiveness.  Fast food businesses are at the forefront of using cutting-edge technologies, from AI-driven order taking to predictive analytics for inventory management.  This article examines the state of artificial intelligence in fast food chains now, focuses on practical applications, and talks about potential future developments.

McDonald's is implementing AI across 43,000 of its locations to enhance customer satisfaction and operational efficiency. Innovations include AI-equipped drive-thru lanes, interconnected kitchen appliances, and predictive maintenance for equipment like ice cream machines. These advancements aim to reduce employee stress and improve service quality, reflecting AI's growing impact on the fast-food industry.

Software Development

AI-powered applications, particularly those utilizing large language models (LLMs), are transforming software development. Gen AI turns concepts into requirements while working alongside human developers.  It then creates test cases, code, and documentation after transforming those requirements into user stories, which are concise summaries of software features written from the viewpoint of the end user.  This partnership expedites the development process and enhances the end product's quality.

Companies like Cursor and Mercor have rapidly achieved high values and substantial revenue growth by offering AI-driven coding assistants. These tools boost developer productivity and streamline coding processes, demonstrating AI's influence in the tech industry. It is the reason why we list this example on our list of 10 real-life examples of how AI is used in business.

Packaging Industry

From production to packaging to distribution, artificial intelligence (AI) has shown itself to be a dependable technology to direct this industry's next evolution.  As people in this industry recognize 10 real-life examples of how AI is used in business, AI is widely used today in the packaging industry. The circular economy, sustainable consumer goods and services, and growing consumer demand for eco-friendly materials and paper-based packaging are some of the main factors pushing the packaging sector to adopt AI. Workflows and business processes can be improved in a matter of minutes rather than decades with artificial intelligence and its underlying technology, machine learning.  For instance, Aptargroup's Fabio di Memmo and Monolith CEO Richard Ahlfeld discuss the benefits of Monolith's no-code AI platform for packaging in the webinar that is available below. They also discuss how to speed up decision-making from month to minute.

Ranpak, a provider of eco-friendly packaging solutions, incorporates AI and automation to optimize warehouse efficiency. By deploying AI-driven packaging systems, the company enhances productivity while maintaining human oversight. This strategy illustrates how AI can complement human labor and lead to improved operational outcomes.

Automotive Industry

AI is transforming how we interact with cars, especially in self-driving technology so we cannot make a list of 10 real-life examples of how AI is used in business without mentioning the automotive industry. AI algorithms process data from lidar, cameras, and sensors to detect objects, navigate routes, and make driving decisions in real time. It also enhances safety features like automated brakes and adaptive cruise control. In-car AI assistants enable voice commands for climate, audio, and navigation, making driving smoother. Tesla's Autopilot is a leading AI-powered system, using neural networks, cameras, and sensors for self-parking, traffic-aware cruise control, and autonomous lane changes. These innovations improve safety, convenience, and overall driving experience.

The BMW Group utilizes AI to optimize customer interactions through the MyBMW app. By analyzing vast amounts of data daily, the app connects millions of users to their vehicles, providing real-time updates and personalized experiences. This integration showcases AI's role in enhancing consumer engagement within the automotive sector.

Retail Sector

Among the many difficulties that retailers face are the environment catastrophe, economic instability, and geopolitical upheaval.  AI provides a strategic perspective, cutting-edge data, and forecasts to help retailers quickly adjust to market fluctuations, even while traditional approaches may be losing ground.  According to Analytics Insight, 40% of retail executives currently employ intelligent automation technologies, and 80% of them anticipate their companies using them by 2025. However, stores cannot rely on artificial intelligence to fix problems on their own.  They must adopt a pragmatic strategy that concentrates on the aspects of their company where AI can make the biggest difference.

Companies like Trace One employ AI to automate the extraction of information in product lifecycle management. These businesses demonstrate AI's potential to enhance accuracy and efficiency in retail operations by streamlining processes and improving data analysis.

Hospitality Industry

There are a lot of reasons why this industry is on our list of 10 real-life examples of how AI is used in business. AI enhances customer experience by providing faster responses, 24/7 support, and personalized solutions. A 2024 Zendesk study of 4,500 CX executives found that 70% plan to integrate generative AI into customer interactions within two years. AI improves self-service systems, enabling users to resolve issues via FAQs, guides, and interactive tools. AI-powered analytics analyze chatbot logs, emails, and reviews to provide insights into customer sentiment. Chatbots like CoSupport and DeepConverse use machine learning and natural language processing to offer real-time assistance, reducing wait times and directing complex issues to human agents, ultimately improving efficiency and customer satisfaction.

Hilton Hotels introduced "Connie," an AI-powered robot concierge that provides guests with personalized recommendations and assistance. This innovation improves the guest experience by offering instant, tailored information, showcasing AI's application in the hospitality sector.

E-commerce

Predictive analytics and AI-powered algorithms help sales teams automate processes and better understand customer behavior. According to Salesforce, 41% of sales firms have fully integrated AI, with 83% of AI-using teams reporting revenue growth. AI streamlines sales by automating tasks like data entry, forecasting demand, and identifying leads. AI tools, such as ChatGPT, personalize outbound emails, while AI-driven platforms like Seamless and LeadIQ update lead scores in real time.

By analyzing customer interactions and past sales patterns, AI helps prioritize prospects and optimize strategies, allowing sales teams to focus on high-value activities and improve overall efficiency. Amazon employs AI to optimize pricing strategies, manage inventory, and provide personalized product recommendations. These AI-driven approaches enhance customer satisfaction and operational efficiency, emphasizing AI's significance in the e-commerce industry.

Social Media

Using AI in social media is surely one of the most popular 10 real-life examples of how AI is used in business. To optimize content creation, businesses increasingly use generative AI tools like ChatGPT, Google Gemini, and Jasper for writing, while DALL-E and Midjourney generate visuals. A 2024 Demand Spring poll shows 82% of B2B companies use AI for content generation, aiding in idea development, headline optimization, and drafting. AI enhances productivity, brainstorming, and analytics but isn't ready for direct publication.

Marketers should treat AI content as a starting point, ensuring it aligns with brand guidelines through fact-checking and proofreading. While AI streamlines content marketing, human oversight remains essential for accuracy, originality, and brand consistency. Facebook utilizes AI to curate personalized news feeds, target advertisements, and detect harmful content. These applications enhance user engagement and platform safety, illustrating AI's growing role in social media management.

Finance

AI is transforming the banking sector by boosting efficiency, preventing fraud, and improving decision-making.  Large financial data sets can be analyzed by AI algorithms to find trends and insights that people might overlook. Credit scoring algorithms that more properly evaluate risk are created using AI-powered technologies.  This gives underprivileged people access to loans.  High-frequency trading algorithms that execute deals at breakneck speed based on current market conditions are also being developed thanks to AI.

AI is transforming finance by detecting trends in big data, improving workflows, and preventing fraud. A Tipalti and CFO Dive poll found 59% of finance professionals prioritize AI training. AI enhances fraud detection by analyzing transactions in real time, alerting analysts to suspicious activity. It also automates compliance checks, reducing regulatory risks. AI-driven budgeting and forecasting analyze past performance and market trends to improve financial planning. Real-time data analysis tools, like Datarails and Planful Predict, help finance teams uncover insights, optimize risk assessment, and enhance decision-making, making AI an essential tool in the financial industry. All items on our list above show 10 real-life examples of how AI is used in business.

How To Build Robust AI For Regulated, High-Impact Sectors

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As its use and impact grow, ensuring AI's reliability and accuracy is a top concern across industries. But in high-stakes applications like healthcare, defense and finance, a flawed or delayed decision can have especially severe consequences—ranging from exacerbated bias and regulatory noncompliance to compromised security or even loss of life. In these sectors, AI must be dependable, secure and transparent to meet critical performance and ethical standards.

To meet these demands, tech leaders must establish rigorous safeguards throughout the AI lifecycle—from data collection and model training to testing, deployment and monitoring. Below, members of Forbes Technology Council share best practices for ensuring AI reliability in high-stakes environments.

1. Establish A Disciplined RASCI Framework

Creating generative AI applications requires multidisciplinary teams to work in sync for effective, responsible deployment. A disciplined RASCI framework—managed via a collaborative platform where teams work together to transparently test, monitor and refine GenAI pipelines—is key. A strong governance framework with a clear understanding of risk appetite and thresholds guides the modelers. - Manish Gupta, Corridor Platforms

2. Ensure AI Works Hand-In-Hand With Human Experts

Someone has to be responsible if AI makes a critical mistake—say, in a patient's diagnosis or a major financial transaction. The AI model itself can't be held accountable. We must ensure someone is always looking out for the best outcome. As tech leaders, our job isn't just to build the smartest AI; it's to build AI that works hand-in-hand with human experts, making them better and safer. - Sanjeev Kumar, Broadcom

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3. Treat Time As A Strict Dimension

To preserve AI reliability, treat time as a strict dimension. LLMs can learn future patterns from Web data with predictions or leaks, introducing look-ahead bias. In finance, defense and healthcare, embedding time in training, retrieval and features ensures decisions reflect reality, not a distorted past. - Ashok Reddy, KX

4. Build A Cyber-Resilient Infrastructure

A key best practice for AI reliability in high-stakes environments is building a cyber-resilient infrastructure. AI needs constant, uninterrupted access to data. All-flash recovery, immutable snapshots, encryption and backups across core, cloud and edge keep data protected and available and AI systems operational, even during cyberattacks or outages. - Jamie Lerner, Quantum Corporation

5. Validate Data At The Source

Data validation at the source is the ultimate gatekeeper for AI reliability. In healthcare, defense or finance, ML systems are only as good as their inputs. By enforcing data governance at collection, companies can prevent corrupted signals from training models that could misdiagnose patients or trigger financial errors. This isn't optional in critical domains—it directly impacts outcomes. - Greg Brunk, MetaRouter

6. Use AI To Monitor GenAI Outputs

Using AI as a guardrail for generative AI applications can be a best practice to help ensure that the outputs comply with industry regulations, particularly in healthcare and financial services. Even if all the data used to train the AI models was compliant, generative AI can still produce outputs that are not. Having this extra level of review is key in high-stakes environments. - Vall Herard, Saifr

7. Ensure Robust Model Management

In healthcare, AI reliability demands robust model management—continuous monitoring, causal inference in production, multi-arm bandit testing and human-in-the-loop oversight. These safeguards ensure models remain accurate, adaptive and ethically aligned as clinical environments evolve, because in healthcare, reliability isn't optional—it's essential. - Taposh Roy, Kaiser Permanente

8. Align Security With Human Behavior

AI reliability is critical when there are life-or-death stakes, financial risks or national security threats. Best practices like stress testing, monitoring and bias audits help ensure safety. Aligning security with human behavior reduces exploitable flaws. In these fields, reliability is a moral and operational imperative. - Arun Shrestha, BeyondID

9. Implement Phased AI Deployment

Implement phased AI agent deployment with mandatory human oversight periods before full automation. This change management approach creates crucial safety feedback loops, allowing teams to identify subtle failure patterns before they impact critical systems. It's essential because AI errors can compound silently until they become catastrophic in healthcare, defense and finance. - Nick Damoulakis, Orases

10. Combine Document Control With Source Validation

Document control is essential to ensure that the AI chatbots used by employees in critical environments answer only based on validated, up-to-date documents. End users must be careful to always check the sources mentioned by chatbots in their responses. Companies can be held liable for errors made by AI agents using an invalid healthcare protocol, maintenance document or company policy. - Stephane Donze, AODocs

11. Ban Unreviewed AI-Generated Code

High-stakes development environments require a zero-tolerance policy for unreviewed code written by GenAI. The volume and complexity of code being produced by AI opens the door to accelerated technical debt and churn, making codebases hard to maintain when they're not actively managed. Also, there is emerging evidence of malicious use of GenAI to inject security weaknesses that can't be ignored. - Tariq Shaukat, Sonar

12. Ensure Rigorous Human-In-The-Loop Validation

One best practice to ensure AI reliability in high-stakes environments is implementing rigorous human-in-the-loop validation, where expert oversight is integrated into every critical decision loop. This safeguards against model drift, bias and false positives, ensuring transparency and accountability where errors can have life-altering or mission-critical consequences. - Hrushikesh Deshmukh, Fannie Mae

13. Add A Security Proxy If Using The MCP

Agentic AI systems using the model context protocol can fall prey to unintended data exposure through prompt injection or by accident, creating potentially huge liability. Adding a security proxy to the MCP that verifies access posture both in the inputs and responses provides essential guardrails to prevent such data leakage. - Atul Tulshibagwale, SGNL.Ai

14. Implement Continuous Monitoring And Real-Time Threat Detection

As an auditor in the healthcare industry, I have found that the best practice for maintaining AI reliability in high-stakes environments is to implement continuous monitoring and real-time threat detection. Even minor AI errors can have significant consequences, which makes proactive monitoring nonnegotiable for trust and safety. - Arpna Aggarwal

15. Implement Continuous Testing And Automate Rollback Triggers

Implement continuous adversarial and scenario‑based testing across the AI lifecycle with real edge cases. Simulating data poisoning, rare clinical conditions and system failures, then automating rollback triggers, ensures graceful degradation. This proactive validation is critical in healthcare, defense and finance, where AI failures can lead to life‑threatening or mission‑critical consequences. - Umesh Kumar Sharma

16. Maintain Visibility Into And Governance Over AI Usage

It's critical to maintain visibility into where and how AI is being used across the organization while enforcing strong security controls. Otherwise, AI models can be misused or misconfigured or become blind spots for attackers. Prioritizing continuous monitoring and governance of AI pipelines is key to ensuring reliability, accountability and alignment with security policies at every step. - Sivan Tehila, Onyxia Cyber

17. Test Edge Cases With Human Input

One powerful way to ensure AI reliability is having humans actively involved in testing edge cases, not just watching outputs. In high-stakes settings, it's often the rare, unexpected scenarios that cause harm. Real trust comes from designing for what might go wrong, not just what usually goes right. - Rahul Wankhede, Humana

18. Use Structured MRM Frameworks

Robustness is an AI model's ability to produce consistent results, even with varied and erroneous inputs. Having a structured model risk management framework, from data preparation through deployment and monitoring, enables assessment and mitigation of risks. This enhances reliability in high-stakes settings while minimizing bias, opacity and accountability concerns. - Sreekanth Narayan, LTIMindtree

19. Design For 'Graceful Degradation'

Implement "graceful degradation" by designing AI systems to maintain core functionality even when parts fail. In high-stakes environments, failures are inevitable. Build systems that visibly announce their limitations and maintain critical operations during partial failures. Your AI shouldn't be a Ferrari that breaks down completely, but a tank that keeps moving even after taking multiple hits. - Ishaan Agarwal, Square

20. Limit AI Use To Providing Outputs, Evidence And Insights

In domains like healthcare, defense and finance, the consequences of a wrong decision can be catastrophic. By designing systems where AI provides deterministic outputs, clear evidence and explainable insights, while human experts make the final judgments, organizations can combine the precision and speed of machines with the critical thinking, empathy and ethical reasoning of people. - Sean Merat, owl.Co

Bolstering Health Technology Assessment With The Help Of AI

Ways to address limitations while also enhancing the decision-making process.

Key Takeaways AI enhances traditional HTA by improving accuracy and efficiency. Artificial intelligence addresses key limitations of traditional health technology assessments—such as data gaps and time-intensive processes—through automation, real-time data integration, and advanced predictive modeling, leading to faster and more informed decisions. AI supports personalized medicine and real-world evidence integration. By analyzing large datasets from EHRs, patient registries, and genomics, AI improves the evaluation of individualized treatments and helps generate robust RWE to guide value-based pricing and reimbursement decisions. Challenges remain in AI adoption for HTA. Despite its potential, AI faces limitations such as data privacy barriers, algorithm bias, interpretability issues, and the lack of regulatory standards—factors that must be addressed to ensure responsible and equitable implementation in healthcare policy.

Health technology assessment (HTA) for pharmaceuticals is a systematic evaluation of the clinical, economic, and social implications of a drug. It plays a crucial role in informing pricing, reimbursement, formulary decisions, and policy decisions. However, traditional HTA methods have several limitations, including data gaps, time-intensive processes, and challenges in evaluating personalized medicine. Artificial intelligence (AI) presents a transformative opportunity to address these challenges and enhance HTA methodologies while ensuring more informed formulary decisions and efficient allocation of healthcare resources.

In this article, we'll explore the limitations of traditional HTA; the ways AI can enhance HTA methodologies; AI applications in HTA; its integration with existing frameworks; its role in value-based pricing and real-world evidence (RWE) generation; and the limitations of AI in HTA.

Despite its importance, HTA faces several constraints. Many evaluations rely on clinical trial data, which may not reflect real-world effectiveness. Additionally, long-term safety and efficacy data are often unavailable at the time of assessment. The process itself is highly time-intensive—sometimes taking months or even years—leading to delays in patient access to innovative treatments. Traditional HTA models are also built on fixed assumptions, making it difficult to adapt to new data or individual patient variability. Furthermore, the assessment of personalized medicine—particularly gene therapies—is challenging due to small patient populations and individualized treatment effects. Economic biases often arise because cost-effectiveness thresholds may undervalue treatments for rare diseases or those with long-term benefits.

Many HTA models also do not fully incorporate real-world patient outcomes, limiting predictive accuracy. Additionally, the variability in HTA methodologies and cost-effectiveness thresholds across different countries creates inconsistencies in assessments. Another significant limitation is the lack of real-time adaptability in formulary decision-making, often leading to delays in drug accessibility and inefficient allocation of resources. Furthermore, when a comparator treatment is available, HTA often lacks head-to-head trial data, making comparative effectiveness assessments more uncertain and leading to reliance on indirect or network meta-analyses, which may introduce additional biases and uncertainties.

How AI can address HTA limitations

AI offers solutions to these limitations through advanced predictive modeling, automation, and real-time data integration. It enhances predictive modeling by simulating long-term drug outcomes using real-world data (RWD), thereby improving the accuracy of effectiveness and cost-effectiveness predictions. By automating data extraction, synthesis, and modeling, AI significantly reduces HTA timelines, allowing for faster decision-making. Machine learning models enable dynamic HTA models that continuously update based on new clinical data, RWE, and evolving healthcare costs.

It also helps boost the evaluation of personalized medicine by assessing treatment value on an individual patient basis, making HTA more relevant for precision medicine. Additionally, AI enables better utilization of RWD by analyzing large datasets from EHRs, wearables, and patient registries to improve predictive accuracy. AI-driven methodologies can refine cost-effectiveness models by incorporating indirect benefits, such as productivity gains and quality of life improvements.

With the help of AI-driven standardization, there is the potential to unify HTA methodologies across different countries, making assessments more comparable and scalable. AI also plays a pivotal role in formulary decision-making by dynamically assessing RWE, cost-effectiveness, and treatment outcomes to optimize drug inclusion in formularies, ensuring efficient resource utilization and cost-effective healthcare delivery.

AI applications in HTA

Natural language processing (NLP) for literature reviews

HTA requires systematic reviews of clinical trials, RWE, and economic models. In order to automate and enhance these tasks, AI-driven NLP can assist. Utilizing automated systematic reviews allows AI to scan thousands of articles and can extract relevant data much faster than human reviewers; it can also analyze trial reports and summarize how a new drug compares to existing therapies. Sentiment analysis techniques can process social media, patient forums, and electronic health records (EHRs) to assess real-world drug experiences, providing valuable insights into patient-centered outcomes. Additionally, NLP can detect biases in literature by identifying inconsistencies or missing data in clinical studies that may affect HTA evaluations. AI also facilitates multilingual data processing, enabling comprehensive global HTA assessments by translating and analyzing research published in different languages.

Deep learning for survival analysis

Survival analysis is critical in HTA for predicting long-term drug benefits, particularly in oncology, chronic diseases, and rare conditions. AI enhances survival analysis by better handling time-dependent data through advanced techniques such as recurrent neural networks and long short-term memory networks. These models can accurately model changes in patient survival probabilities over time. AI improves patient outcome predictions by combining clinical trial data with RWE, including EHR and genomics. Adaptive models refine predictions as new therapies enter the market, reducing uncertainty in long-term cost-effectiveness. Additionally, deep learning techniques enable personalized risk prediction by assessing individual patient factors such as genetics, biomarkers, and lifestyle to determine who will benefit most from a treatment.

AI integration with HTA frameworks

HTA frameworks vary by country, but AI can enhance decision-making for agencies such as NICE (UK), ICER (US), and EUnetHTA (EU). In the UK, AI-driven cost-effectiveness models can refine quality-adjusted life year (QALY) estimates by integrating NHS patient data. NLP can automate literature reviews, reducing assessment delays, while AI-powered evidence synthesis improves predictions of long-term treatment effects. In the US, AI enhances budget impact modeling by integrating RWD from claims databases and EHRs. Deep learning refines long-term drug benefit projections for chronic and rare diseases, and AI can dynamically adjust pricing models based on real-world cost-effectiveness data.

In the EU, AI aids in standardizing cost-effectiveness analyses across different health systems. NLP synthesizes data from multiple EU clinical trials to accelerate drug approvals, and AI facilitates real-time monitoring of drug performance across EU nations. Additionally, AI-driven formulary decisions in these regions help balance cost constraints while ensuring that the most effective treatments remain accessible to patients.

AI in value-based pricing and RWE generation

AI has the potential to transform value-based pricing (VBP) by leveraging machine learning for dynamic pricing models that adjust drug prices based on real-world patient outcomes. It can even support predictive risk-sharing agreements by identifying patient subgroups most likely to benefit, thus refining risk-sharing contracts. NLP can analyze global HTA reports and generate customized VBP recommendations tailored to different healthcare systems. AI enables personalized value assessments by segmenting patient populations and assigning individualized value scores to drugs, ensuring fair pricing models that align with patient needs and treatment efficacy.

AI in real-world evidence generation

AI plays a significant role in RWE generation by automating data extraction from EHRs and insurance claims to provide robust insights into drug performance. Deep learning enhances predictive analytics by detecting early safety signals and unexpected adverse effects from RWD, thereby improving drug safety assessments.

AI can also facilitate real-world treatment comparisons by analyzing patient cohorts to determine which drug or therapy works best under practical conditions. Along with that, AI-powered real-time monitoring methods continuously update drug effectiveness data, allowing for dynamic reimbursement adjustments and ensuring optimal treatment access for patients.

AI in HTA limitations

Despite its advantages, AI in HTA has several limitations, including that AI models require large, high-quality datasets, which are often fragmented or inaccessible due to privacy regulations. Additionally, AI-driven predictions can be biased if trained on non-representative or incomplete datasets. The black-box nature of some AI algorithms makes it difficult for HTA stakeholders to interpret results, reducing trust in AI-driven assessments. Regulatory bodies have yet to establish standardized frameworks for AI integration in HTA, leading to variability in adoption. It's possible that AI may not fully capture qualitative aspects of patient experiences, ethical considerations, and social value, which are crucial in HTA decision-making.

Overall, AI is revolutionizing HTA by enhancing predictive accuracy, accelerating decision-making, and improving the integration of RWE. However, addressing AI's limitations, including data accessibility, interpretability, and ethical concerns, will be essential for its widespread adoption. As AI continues to evolve, it has the potential to transform formulary decisions and healthcare policy, making HTA more responsive, equitable, and efficient.

About the Author

Thani Jambulingam, PhD, is a professor of food, pharma, and healthcare business at Saint Joseph's University's Erivan K. Haub School of Business.

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