The Generative AI Evolution: Emerging Trends and Applications Across Industries
The Generative AI Evolution: Emerging Trends and Applications Across Industries Follow on: “It’ll be unthinkable not to have intelligence integrated into every product and service. It’ll just be an expected, obvious thing.” – Sam Altman, co-founder and CEO of OpenAI. Generative AI (GenAI) has expanded the horizons of innovation and challenged us to rethink the potential of workflows, efficiency, and intelligence. Yet, its evolution is young and ongoing. The possibilities seem endless, with big players like Microsoft, OpenAI, Google, and Meta investing heavily in advancing GenAI. But how does sentient evolution impact businesses? As Altman said, it would be unthinkable not to have smarter products and services during this reinvention, especially since it could generate trillions in value. McKinsey [1] identified 63 ways generative AI could be applied across 16 business functions, potentially unlocking $2.6 trillion to $4.4 trillion in annual financial benefits. Let’s take a closer look at GenAI trends and use cases that will shape 2024 and beyond. Emerging Trends Shaping the Future of GenAI The past year has been a breakthrough for GenAI, especially with OpenAI’s ChatGPT, inviting real opportunities for the public to experiment. 2023 also saw the explosion of general-purpose AI applications, with enterprises gearing up for a cognitive shift. GenAI applications initially started by recognizing patterns in customer demands, creating personalized marketing strategies, and summarizing lengthy text documents. As improved models arrived, the usage has expanded to aid in personalizing medical treatments, streamlining insurance underwriting, and enhancing inventory and supply chain management. Notably, GenAI has made significant advancements in various fields despite being in its early stages, proving its potential. Here is a glimpse of what might come next: The popularity of multimodal models While transformer models have been the backbone of recent GPT and DALL-E AI successes, we now witness the emergence of advanced neural architectures. These sophisticated structures optimize information processing in AI systems beyond traditional models. Apple’s newly introduced MM1, a more advanced multimodal AI model, can process and generate both visual and text data. It’s also pre-trained to offer in-context predictions – allowing it to tally objects, adhere to customized formatting, identify sections of images, and execute OCR tasks. Moreover, it demonstrates practical understanding and vocabulary related to everyday items and the ability to conduct fundamental mathematical operations. Evidently, multimodal Generative AI holds immense potential for shaping the user experience across various sectors, from scientific research (think analyzing complex datasets with visual and textual components) to social sciences (enabling richer analysis of human interactions). Once realized, this can significantly impact industries. For example, in a modern insurance workplace, a multimodal approach could help improve training and development modules for customer service representatives. In this case, training modules could incorporate role-playing scenarios with branching narratives based on past customer responses, allowing trainees to practice their communication skills in a simulated environment. Similarly, in the healthcare sector, multimodal AI is poised to transform diagnosis, treatment and patient care. By merging text and visual data from EHRs, medical images, genetic profiles, and patient-reported outcomes — intuitive healthcare systems can forecast disease likelihoods, assist with interpreting medical images, and customize treatment plans. This will allow practitioners and professionals to augment the quality of care and improve outcomes in a timely order. The rise of autonomous agents 2024 will be the breakthrough year for autonomous agents. Gartner’s predictions affirms this — their report indicates that by 2028, about one-third of interactions with Generative AI services will be marked by heightened autonomy, propelled by the fusion of action models and autonomous agents. Another report [2] revealed that 96% of global executives believe ecosystems built around AI agents will be a primary growth driver for their organizations in the next three years. It all started with AutoGPT’s arrival in 2023 and has been developing since, with others like Microsoft, UiPath, and OpenAI joining the autonomous AI revolution. These GenAI applications are trained to instantaneously generate and respond to prompts for tackling complex tasks without manual intervention. Unlike traditional chatbots, which wait for the next manual instruction, autonomous agents are proactive, constantly learning and adapting. This will be a game-changer for retail, banking, healthcare, and insurance industries, where quick interactions are the key to sustained success and better outcomes. For example, OpenAI, the maker of ChatGPT, is working on a category of autonomous AI agents that manage online tasks like booking flights or crafting travel plans without relying on APIs. Currently, ChatGPT can perform agent-like functions, but access to the appropriate third-party APIs is required. This will transform how travel enterprises operate, helping them to streamline operations and speed up customer service. GenAI ventures into education While the resistance was strong at first, the educational sector is slowly opening up to the GenAI intervention — with labor shortages wreaking havoc [3] across the global sector. To reduce the burden on teachers, GenAI tools can be deployed to optimize course planning and curriculum delivery. With its potential to synthesize large volumes of data, it is also suited to develop a customized syllabus and curate a list of potential reading materials for students — while also assisting with drawing detailed lesson plans based on historical data. Additionally, GenAI can be primed to improve student outcomes. For example, predictive systems can proactively identify at-risk students who require early interventions. Educators can use this information to personalize their approach for targeted students and even help them with customized course materials to improve their performance. It is also a critical tool in today’s environment for empowering students and ensuring they’re future-ready. A leading technology company recently partnered with eight UGC-funded universities to advance the integration of AI and enable the use of Generative AI through their OpenAI service. This technology will be accessible to professors, teachers, researchers, and students across the academic, research, and operational sectors of these institutions. Through this, the universities plan to revolutionize their teaching and learning modules and ensure that students are equipped with the required AI skills for their academic and professional journeys. Emergence of personalized marketplace
Navigating beyond Generative AI: The dawn of hyper-intelligent systems
Navigating beyond Generative AI: The dawn of hyper-intelligent systems Follow on: In the past year, Generative AI (GenAI) has emerged as one of the most remarkable breakthroughs, triggering a transformative wave across the global economic and IT landscape. From redefining customer engagement and reshaping product development to inspiring innovative shifts in business models, GenAI has impacted every facet of the business. Businesses are waking up to the potential of GenAI and pushing the boundaries of Machine Learning (ML) and data processing to enhance innovation, productivity, and creativity at scale. This is the time to step up the game and drive hyper-intelligence. Hyper-intelligent systems, the next frontier of AI, go beyond data generation and manipulation and exhibit higher-order cognitive abilities such as reasoning, planning, learning, and creativity. This blog will explore the technical evolution, industry use cases, and notable examples of hyper-intelligent systems and how they will revolutionize the world in the post-generative era of AI. Innovative trends shaping the future of AI Imagine being in a world where machines can not only automate routine tasks but also perform complex and creative work with superhuman intelligence! This can be realized with hyper-intelligent systems. Hyper-intelligent systems can reshape the world by combining and integrating various next-gen technologies, such as AI, RPA, BPA, IDP, ML, and process mining. It promises a new chapter in AI evolution, characterized by advanced neural architectures, quantum machine learning, neuromorphic computing, and the ethical considerations of AI integration. Let’s dive deep into the key facets of this transformative journey. Go beyond transformers: Explore advanced neural architectures While transformer models have been the backbone of recent GPT and DALL-E AI successes, we now witness the emergence of advanced neural architectures. These sophisticated structures optimize information processing in AI systems beyond traditional models. For instance, Capsule Networks (CapsNets) are at the forefront, offering a paradigm shift in information processing. By encoding spatial hierarchies between features, CapsNets enhance the robustness of recognition and interpretation abilities, paving the way for more nuanced AI applications. Take a quantum leap with quantum machine learning (QML) Quantum Machine Learning (QML) enhances machine learning algorithms and models using quantum computing to process information faster and perform computations using quantum superposition and entanglement. One remarkable way to leverage QML is to combine quantum algorithms with neural networks, creating hybrid models to tackle complex and large problems. Integrating quantum algorithms into neural networks has the potential to solve currently intractable problems, unlocking new possibilities and capabilities for AI. Some prominent examples include quantum support vector machines, quantum neural networks, and quantum clustering algorithms, which showcase higher efficiency and speed in solving real-world challenges. Bridge the gap to human intelligence with neuromorphic computing What if AI systems could think like humans? That’s the idea behind neuromorphic computing, where machines are built to mimic the brain’s structure and power. This could make AI systems faster, smarter, and more self-reliant. For example, Intel’s Loihi chip can spot patterns and process senses with minimal energy. Neuromorphic computing has several applications in various industries. It can be used for image and video recognition, making it helpful in surveillance, self-driving cars, and medical imaging tasks. Neuromorphic systems can also control robots and other autonomous systems, allowing them to respond more naturally and efficiently to their environment. Explore the nexus of AI and edge computing The integration of AI into everyday devices necessitates a shift towards edge computing. Edge AI, involving local data processing on devices, reduces latency and enhances privacy. This is pivotal in critical applications like autonomous vehicles and smart cities, where real-time decision-making is imperative. For instance, Edge AI can enhance the real-time processing capabilities of video games, robots, smart speakers, drones, wearable health monitoring devices, and security cameras by enabling on-device data analysis and decision-making—thus reducing latency and dependence on external servers. According to Gartner, edge computing will be a must-have for 40% of big businesses by 2025, up from 1% in 2017. This is because sending tons of raw data to the cloud is too slow and costly. Ethical AI and explainability: Pillars of hyper-intelligent systems As AI capabilities are widely adopted, so is the need for ethical frameworks and explainability. WHO, for instance, recently released AI ethics and governance guidance for large multi-modal models. The growing concern surrounding AI ethics and guidelines also stems from criticism surrounding AI models, particularly deep learning systems, which are perceived as ‘black boxes’ due to their complex and opaque decision-making processes. To tackle this, a discernible trend is emerging within the AI community, emphasizing the development of more transparent AI systems. The push for explainability ensures that decision-making processes are understandable and scrutinizable, fostering fairness and accountability. By combining ethical AI and explainability, enterprises can create AI systems that are fair, accountable, and trustworthy. These systems can unlock the benefits of hyperintelligence while avoiding the pitfalls and dangers. AI-powered synthetic biology to shape biomanufacturing and biotechnology The convergence of AI and synthetic biology opens exciting possibilities and transforms how we understand and interact with biological systems. AI can help synthetic biologists in many ways, such as designing DNA sequences, optimizing gene expression, analyzing genomic data, optimizing biological processes, and discovering new drugs. One of the most exciting applications of AI and synthetic biology is CRISPR, a technology that allows precise and efficient editing of any genome. By combining AI with CRISPR and genomic analysis, for instance, researchers can accelerate the identification of specific genetic markers, enabling more precise gene editing targeting for personalized medicine. This integration facilitates the interpretation of vast genomic datasets, allowing for a deeper understanding of individual variations—paving the way for advancements in tailored therapies and bioengineering applications. By embracing this interdisciplinary approach, LSH enterprises can empower new-age disease treatment. As these AI evolutions come into play, organizations are curious to embrace the platforms that will help them maintain the competitive edge and scale. Prominent players in this transformative space, such as Amazon Augmented AI, Google Quantum AI Lab, and Microsoft Azure AI, are harnessing these trends into their
Top Emerging Trends for AI Platforms in 2024 and Beyond
Top Emerging Trends for AI Platforms in 2024 and Beyond Follow on: “It’s hard to overstate how big of an impact AI and machine learning will have on society over the next 20 years” – Jeff Bezos. In this age and probably in the next century, artificial intelligence (AI) will be the cornerstone for futuristic enterprises seeking to make an impact. From language translation to disease detection, AI has come a long way—the global AI market is projected to touch $1,811.8 billion by 2030. [1] Businesses are increasingly investing in AI to boost productivity, scalability, and security. And at the core of this journey is AI platforms—sophisticated frameworks that help organizations adapt, learn, and automate. The landscape of AI platforms AI or artificial intelligence platforms, are integrated software solutions that provide a framework for developing, deploying, and managing various artificial intelligence applications. These platforms typically include a combination of tools, libraries, and services that facilitate the implementation of AI algorithms, machine learning (ML) models, and other cognitive computing processes. AI platforms offer a centralized environment for data processing, analytics, and the creation of intelligent applications. In practice, these platforms can benefit organizations in multiple ways – from implementing dynamic pricing models at retail stores to detecting complex diseases in healthcare, quicker fraud detection in insurance, and optimizing baggage management systems in airports – the possibilities are never-ending. As AI evolves, these platforms will continue to play a pivotal role in driving innovation across diverse industries. Spotlight on emerging AI trends and the new AI platforms In tandem with the dynamic landscape of AI platforms, it is crucial to delve into the emerging artificial intelligence trends that are poised to shape and redefine their capabilities. Here are 11 emerging trends surrounding AI that are gaining popularity: Augmented AI Augmented AI or augmented intelligence integrates artificial intelligence (AI) capabilities with human intelligence to enhance and complement human decision-making and problem-solving. This type of AI platform uses deep learning and ML along with AI to improve collaboration, expedite data processing, and enable automation and personalization. For instance, at HTCNXT, we’ve designed MAGE, our enterprise AI platform for advanced medical imaging, diagnosis and personalized product recommendations. Looking ahead, businesses will see an integration of this augmented potential with Augmented Reality (AR) and Virtual Reality (VR)—leading to a highly immersive and interactive world that brings together the digital with the physical. For instance, AI-driven AR applications could enhance the travel experience by providing real-time information about landmarks, historical sites, and points of interest for self-guided tours. Quantum AI Quantum Information Science (QIS), AI/ML, and deep learning use qubits to solve complex problems by speeding up data processing and analysis. This can help improve ML algorithms, thus making everything faster – from logistics to supply chain management. In personalized medicine, for example, quantum AI can expedite the drug discovery process by simulating molecular interactions at unprecedented rates, thereby advancing the pace of research. Edge AI With Edge AI, the deployment happens directly on local devices or edge devices, rather than relying on a centralized cloud-based system. This results in real-time visibility and enables instant decision-making. With Edge AI, enterprises can yield multiple benefits, like how a pioneering healthcare firm has created a non-intrusive glucose monitoring device leveraging edge AI technology. Through real-time analysis of a user’s glucose levels, this device enables more effective diabetes management without the necessity for uncomfortable finger-pricking. AI-driven automation: Automated Machine Learning, or AutoML, leverages ML, NLP (Natural Language Processing), and other AI capabilities to automate processes, significantly saving time, expediting efficiency, and enhancing productivity. The MAGE platform, for instance, helps enterprises use AutoML to power faster drug discovery in healthcare and automate underwriting in insurance. AI-driven automation will see significant advancements in the coming year, especially in the wake of a more consumer-centric business ecosystem. In travel, for instance, AI-powered chatbots are becoming more popular for optimizing customer experience with instant and informed support — 87% [2] of users express a willingness to engage with AI chatbots if it saves them time and money. Sustainable AI: Sustainable AI refers to the development and deployment of artificial intelligence systems with a focus on minimizing their environmental impact and promoting long-term ecological sustainability. The goal is to create AI technologies that contribute to environmental conservation, energy efficiency, and overall ecological responsibility. Sustainable AI platforms prioritize energy efficiency in their design and operations – a rising focus for green enterprises looking to reduce their carbon footprint. Besides, sustainable AI platforms also take into account ethical data usage practices – to balance the power and potential of artificial intelligence with environmental and societal responsibility. Explainable AI (XAI): Customers will eventually want to understand AI to be able to trust it. Platforms will prioritize transparency, offering explainable AI (XAI) that allows users to comprehend the policies and the decision-making processes that enterprises have employed in their AI usage. The outcome will be value-generating interventions and risk mitigation. Industries with heavy regulations, like healthcare and insurance, will benefit immensely, wherein fraud-detection and risk-assessment processes are simplified using XAI. Small Large Language Models (SLMs): SLMs strike a unique balance between large language capabilities (like sophisticated language understanding and generation) and the efficiency and agility of smaller models—offering a more accessible and sustainable approach to AI. This simplifies research, quick training, and cost-effective deployment, to name a few. For example, query resolution can be made easier with SLM-driven multilingual chatbots in airports across continents, thus making communication accessible using instant language translation. Another way SLMs can be used is in clinical research to help researchers derive quick insights from extensive datasets, thus speeding up discoveries. Federated Learning (FL): As security concerns increase, federated learning (FL) now also involves training AI models across multiple decentralized devices or servers while keeping data localized. It offers advanced encryption, zero-knowledge proofs, secure multi-party computing, and ensures data analysis tools are privacy-aware. With healthcare and insurance being data-driven industries, deploying federated learning platforms can help safeguard critical information and enhance credibility. For example,
Unraveling the tapestry: The imperative of human valuation in guiding LLM’s decision-making
Unraveling the tapestry: The imperative of human valuation in guiding LLM’s decision-making Follow on: Introduction In the dynamic landscape of artificial intelligence, Large Language Models (LLMs) stand as formidable entities, capable of processing vast amounts of information and making decisions that impact users. However, the allure of these models also brings forth ethical considerations, especially when they are entrusted with decision-making for individuals. This blog post explores the crucial role of human evaluation in steering LLMs toward fairness, particularly in scenarios where biases may seep into the decision-making process. The biased tapestry of historical data Historical data, the bedrock upon which LLMs are trained, is not without its imperfections. As Cao et al. (2021) aptly point out, “LLMs may possess incorrect factual knowledge,” and biases ingrained in historical records can inadvertently find their way into the outputs of these language models. This becomes particularly concerning when LLMs are tasked with making decisions for individuals, as biased information can lead to discriminatory outcomes. Human evaluation within LLMs Evaluation is the process of assessing and gauging the performance, effectiveness, or quality of a system, model, or process. It plays a pivotal role in ensuring the reliability and appropriateness of outcomes in various fields. Human evaluation, specifically, refers to the assessment conducted by individuals to gauge and interpret results, often incorporating nuanced insights, ethical considerations, and a deep understanding of societal norms. In the context of artificial intelligence, human evaluation becomes essential for navigating complex decision-making scenarios and addressing biases that may elude algorithmic scrutiny. Human evolution vs. Historical biases One noteworthy aspect of human evaluation is the recognition that humans themselves have evolved over time. While historical biases persist in the data, the bias of human evaluators can affect the human evaluation result. For example, consider historical datasets that may contain biased views on gender roles. Human evaluators, informed by contemporary perspectives, can identify and rectify such biases, contributing to a more nuanced and just understanding of language. The evolution in human perspectives provides a lens through which biases can be identified and rectified. As society progresses, individuals become more attuned to inclusivity and fairness, providing a valuable counterbalance to the biases inherent in historical data. Deciphering decision-making in LLMs When LLMs are bestowed with decision-making capabilities, the stakes are high. LLMs, even with efforts to enhance safety, can generate harmful and biased responses. For instance, imagine an LLM tasked with evaluating job applications. Without vigilant human evaluation, the model might inadvertently favor certain demographics, perpetuating biases present in historical hiring data. Human evaluators, by contrast, bring cultural insights and ethical considerations to the table, ensuring that LLM decisions align with contemporary notions of fairness. Therefore, human evaluation becomes an indispensable tool in deciphering the complex web of decisions made by LLMs. Human evaluators bring a nuanced understanding of cultural contexts and societal norms, enabling them to identify and address biases that may elude algorithmic scrutiny. The ethical quandary: Can LLMs replace human evaluation? A pivotal ethical concern arises when considering the potential replacement of human evaluation with LLM evaluation. Let’s consider a hypothetical scenario: an LLM tasked with generating responses to user queries about mental health. Without human evaluators, the model might inadvertently generate responses that lack empathy or understanding. Human evaluation, rooted in ethical considerations and emotional intelligence, becomes crucial in refining LLMs to respond responsibly to sensitive topics. Chiang and Lee (2023) argue for the coexistence of both evaluation methods, recognizing the strengths and limitations of each. Human evaluation, rooted in ethical considerations and a deep understanding of societal dynamics, is deemed essential for the ultimate goal of developing NLP systems for human use. Conclusion: A harmonious collaboration The journey through the intricate terrain of artificial intelligence and Large Language Models (LLMs) underscores the paramount importance of human evaluation. As we unravel the tapestry of LLM decision-making, it becomes evident that historical biases ingrained in training data pose real ethical challenges. Human evolution, both in societal attitudes and individual perspectives, provides a dynamic lens through which biases can be identified, rectified, and crucially balanced. The hypothetical scenarios presented, from evaluating job applications to responding to queries about mental health, illuminate the potential pitfalls of relying solely on LLM evaluation. The ethical quandary of replacing human evaluation with LLM assessment is delicately examined, with Chiang and Lee (2023) advocating for a collaborative coexistence of both methods. Ultimately, human evaluation emerges as the linchpin in ensuring fair, ethical, and unbiased outcomes in LLM decision-making. It acts as a counterbalance to historical biases, providing a nuanced understanding of cultural contexts and societal norms. As we propel into an era where the tapestry woven by LLMs reflects technological prowess, HTCNXT ensures that this fabric is woven with the ethical standards and progressive ideals demanded by contemporary society. By incorporating human evaluation into the decision-making fabric of LLMs, we not only mitigate historical biases but also align our solutions with contemporary ethical standards. This collaboration between artificial intelligence and human insights is not merely advisable but imperative. Our integration of human evaluation within the HTCNXT Platform is a testament to our dedication to responsible AI. We provide users with a powerful tool that not only harnesses the capabilities of LLMs but also ensures that the solutions built on our platform comply with the highest standards of fairness and responsibility. Through this collaboration, HTCNXT empowers users to navigate the evolving landscape of artificial intelligence with confidence, knowing that their decisions align with both technological excellence and ethical considerations. References De Cao, N., Aziz, W., & Titov, I. (2021). Editing Factual Knowledge in Language Models (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2104.08164 Chiang, C.-H., & Lee, H. (2023). Can Large Language Models Be an Alternative to Human Evaluations? In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.870 AUTHOR Aviral Sharma AI Engineer SUBJECT TAGS #LLMDecisionMaking #HumanEvaluation #ArtificialIntelligence #LargeLanguageModels #HTCNXT
Decoding the future of Retail Media Networks
Decoding the future of Retail Media Networks Follow on: The ever-evolving retail landscape amid the upsurge of transformative technologies such as cloud computing, Generative AI, Big Data, and the Internet of Things (IoT) has spurred enterprises to reimagine their marketing and advertising tactics. Moreover, shifting channel preferences as consumers move online has led to an explosion in retail media networks (RMNs). This presents an exciting opportunity for retailers and brands eager to expand their reach and drive sales. RMNs allow brands to promote their products and services through purchased ad spaces owned by them in closed data loops. Since coming to the forefront in the last few years, RMNs have exploded, with the successes of Amazon, Walmart, and Target (Roundel) being touted. Today, as we count dozens of new retailers embarking on this almost every month, the provider space is probably helpful to review some operating principles of what retailers can focus on to build and control closely. The big RMN explosion: An exciting opportunity Today, Retail Media Networks (RMNs) have emerged as the fastest-growing spending area in advertising, exhibiting an accelerated growth of $55 billion in expenditures by 2024, eventually expanding to $106.12 billion by 2027. It’s anticipated that a noteworthy 1 in 8 dollars of ad spending will be directed towards RMNs this year, mirroring the proportion of digital media spending compared to traditional media spending in 2016. Unpacking the causes: Converging pressures The surge of Retail Media Networks (RMNs) can be attributed to the recent disruptions in the advertising industry, where ‘downward pressures’ have become a defining force steering the trajectory of marketing strategies. These include: Cookie depreciation across mainstream browsers: The gradual phasing out and doubling down on third-party cookies make it difficult for advertisers to track users. Platform changes limiting mobile tracking: Most devices today have specialized settings and apps to prevent advertisers from tracking users through interfaces and apps. Regulatory changes limiting tracking options: New privacy laws and guidelines could dramatically alter how advertisers and tech companies serve ads to their target consumer base. Mounting downward pressures like the above have given rise to increased acquisition costs and limited targeting options. Adding to the complexities, Google is cutting down on its ‘free’ real estate, thus reducing organic content visibility for brands. Therefore, in an ecosystem where sponsored content gets priority over screen real estate, marketers must incorporate paid tactics into every organic strategy to thrive. Moreover, major social media channels have also reached saturation due to an influx of top competitor brands. What does this indicate for RMNs that promise high-margin revenue for low cost? Thanks to its closed-loop system, retail media can be the way forward for businesses, making tracking and attribution measurement easier and resulting in effective brand messaging. Besides, advertising on e-commerce platforms is a high-margin, low-risk, and low-cost revenue option compared to other digital channels. The missing pieces of the RMN puzzle Organizations today are in favor of the commodification of AdTech platforms. However, to sustain an ascending position in expanding RMN prospects, it is essential to identify and resolve unmet capability gaps such as: Integrating AI and data services engines can be a significant advantage for RMN ad partners like CPGs. Enterprises are searching for enhanced AI-driven data collection and aggregation capabilities to help improve audience lifecycle management – where the advertiser can directly take charge of personalization. Other areas include but are not limited to dynamic omni-channel journey, product cross-selling/up-selling, and the following best action recommendations. Reporting analytics can help marketers experiment with platforms offering superior analytical capabilities to help measure various customer parameters or attributes, create data rooms for better audience development, optimize pricing and trade, and generate higher ROAS. A well-planned Retail Media Network (RMN) with these capabilities can help end users derive contextual insights by aggregating in-store, online, and trading data—enabling them to improve operations, optimize costs, augment retail journeys, and elevate ROAS. In today’s market, GenAI is reshaping frictionless and high-value customer experiences. When incorporated with RMN, GenAI can help value chain participants – firms, advertising leads, spend managers, media operations providers, and ad-tech providers to nurture a seamless retail ecosystem. Improved searchability and discoverability: GenAI can help advertisers automate processes like meta-tagging and semantic search, thereby enhancing search results with relevant information like product descriptions, context-based search query analysis, and videos. Advertisers can also use GenAI to categorize products based on size, color, or features and optimize content according to keywords and phrases that best align with their products and services. Elevated efficiency and ROI: GenAI can provide agencies with many advantages, from productivity improvements to transformative initiatives. Leveraging GenAI, strategists can analyze extensive data from diverse sources, crafting intricate customer profiles and building predictive models to forecast future consumer trends and behaviors. Asset managers can utilize GenAI to optimize trade executions through automated reporting based on outcomes and risk while cutting overall input costs. Dynamized user journeys: GenAI can empower retailers to address customers’ aspirations and pain points. For instance, democratized media buy-ins fuelled by GenAI capabilities can help sellers create captivating product listings based on large-language-based (LLM) models that use enriched enterprise data. Inventory managers can utilize GenAI for enhanced data analysis by screening sales, customer search, and purchase history to optimize brokerage and stockpiling for peak seasons to prevent stockouts. Future-proofing retail – The final block in the last mile Futuristic retail leaders need enhanced data-driven decision-making to maintain the transformative momentum sparked by the intersection of retail marketing and cutting-edge technologies. But with more and more non-pandemic brands joining the RMN circuit, there is a need for a purpose-driven roadmap complemented by design thinking, business goal alignment, and privacy-compliant approaches. For instance, our AI-powered platform, MAGE, has an Ad-Recommender to make campaign journey planning data-driven. HTCNXT’s plug-and-play platform, MAGE, interoperates with existing RMN systems without displacing them and empowers retailers with 360° visibility of their audience lifecycle management, revenue, pricing cycles, and ROAS. Our AI-based attribution service performs 16% better than attribution algorithms that are currently in use.
Combining low code with emerging AI technologies: Can users truly create compelling apps?
Combining low code with emerging AI technologies: Can users truly create compelling apps? Follow on: Enterprises are no strangers to disruptions, with uncertainty lurking around every corner. In this dynamic environment, adaptability and resilience aren’t just admirable qualities but essential for business survival. The recent geopolitical and economic unpredictability combined with the need to do more with fewer resources, has nudged businesses towards flexible solutions, such as low-code platforms enabling organizations to make a rapid recovery. Low-code platform development provides enterprises with the agility to design workflows without investing in large and expensive software development teams. This liberates enterprises from traditional, time-consuming software development processes. However, many low-code platforms have not lived up to the hype with a higher level of complexity and dependence on technical staff to create compelling apps. The emergence of Generative AI (Artificial Intelligence) acts as a transformative force, bridging the gap between software and ‘citizen’ developers while automating various elements of the software development life cycle. Combining low-code and AI can enable non-IT employees to launch workflows, create great user experiences, develop interactive reports, and generate enterprise applications quickly with higher levels of complexity than what was possible before. Importance of AI-led democratization of application development in enterprises Democratization, the process of making software development more accessible to a wider audience, including non-programmers, has become a necessity with growing software requirements and the need for enhanced digital experiences in composable enterprises. While low-code development provides the right environment to design applications, it can still be expensive and slow. However, integration of low-code with AI through user-friendly interfaces can enable business analysts, marketing experts, and other non-IT users – who will constitute ~80% of the user base for low-code development tools by 2026 to build innovative applications. Moreover, numerous up-and-coming AI technologies are propelling the low-code landscape. Emerging AI technologies in the low-code landscape Advanced AI technologies are reshaping application development by accelerating code generation and comprehending natural language commands. It is estimated that 70% of professional developers will use AI-powered coding tools by 2027. AI automates large sections of low-code development–a visual approach to software development with simple drag-and-drop features, wizard-based interfaces, and many other additional benefits. Benefits of combining low-code with emerging AI technologies Embracing AI in low-code development improves agility while delivering tangible business value. It helps businesses with: Increased accessibility for non-technical users: Integrated platforms reduce dependence on specialized IT skills by empowering non-technical users to participate in application development, including automated text completion, building a UI from a drawing, generating automated workflows, and self-service analytics, to name a few. Faster and more efficient development: Generative AI can auto-complete code, detect errors, and suggest fixes in real-time, significantly expediting the development process. Improved quality and functionality: AI-driven tools assist in generating high-quality code, ensuring adherence to best coding practices, and optimizing performance. With AI revolutionizing the low-code development process, generative AI stands at the forefront of this transformation, facilitating efficient application development. By harnessing machine learning algorithms, it speeds up delivery cycle time and suggests relevant code fragments that meet functional and operational requirements. Enabling developers to build complex applications even without extensive coding expertise, generative AI has showcased its phenomenal capabilities in the real world as well. Use cases of low-code and generative AI Many organizations have already ventured into the realm of AI-powered low-code application development. Here are a few notable examples: Appian’s AI Copilot: Appian has leveraged generative AI tools to express application designs with prompts while enabling humans to understand and visually refine what the AI has created. Google’s AutoML: By leveraging generative AI in low-code platforms, Google’s AutoML enables developers to create custom models tailored to their business needs. Microsoft Power Platform: This low-code platform provides the ability to quickly build applications, automate and optimize workflows, and turn data into engaging reports rapidly from user prompts. Pega Infinity ‘23: Utilizes generative AI-powered boosters to automate and simplify the development process in low-code environments, enabling teams to focus on high-priority tasks. Challenges in implementing AI-driven low-code platforms The alliance between AI and low-code looks promising and is already yielding excellent results. However, it comes with its own set of challenges: User education and training on AI: Users need to understand how to use AI tools responsibly, including AI concepts, their limitations, and how to avoid misuse. Bias and discrimination: AI systems can perpetuate biases present in trained data. It’s crucial to train AI models on diverse data and regularly audit for bias. Tool limitations and trade-offs: Users may encounter trade-offs in terms of flexibility, customization, or specific types of applications they can build. Complexity: The introduction of AI can add complexity to the development process, requiring users to understand the intricacies of AI models and their deployment. Addressing these challenges is essential to harness the full potential of AI within low-code platforms to develop future-oriented, ethical, and efficient applications. Harmonizing the future of low-code and AI The global low-code development platform is estimated to witness a growth of USD 148.5 billion by 2030. The integration of AI and low-code platform development is going to further drive this growth to produce: Conversational applications generation and BI/augmented analytics: AI-powered low-code platforms enable users to describe their requirements in natural language. Augmented BI empowers enterprises to generate valuable insights. Domain-specific low-code platforms: These platforms will offer pre-built components and templates tailored to the unique needs of different industries. Automatic codebase updates: low-code platforms will automatically update their codebase, reducing the burden of manual maintenance. Astounding real-world applications: AI-enabled low-code development spans from streamlining telemedicine application development in healthcare to advanced recommendation systems in retail and fraud detection applications in finance. Pursuing AI-led excellence in the low-code landscape AI’s remarkable capabilities in code generation and operational efficiencies play a pivotal role in delivering tailored experiences. It facilitates seamless integration between business applications, cloud services, third-party APIs, and databases, ensuring the efficient flow of data. As AI becomes more accessible to non-technical users, there will be a growing emphasis on its ethical
Boosting Speed and Efficiency: The Power of Generative AI in Transforming Software Development
Boosting Speed and Efficiency: The Power of Generative AI in Transforming Software Development Follow on: The advent of Generative AI models had a significant impact across industries – but most importantly, it accelerated the mainstream adoption of automation, thus enhancing speed and productivity. GenAI, unlike any other technology, has empowered developers to push beyond regular constraints and rethink possibilities at breakneck speed. From automating code generation to debugging, future-forward organizations are continuously reimagining the role of Generative AI in transforming software development. But the adoption of GenAI in software development is far from being unidimensional! It nudges organizations to proactively reassess software security and quality controls, address talent and productivity gaps, and even help with documentation, thus accelerating efficiency. Generative AI for enhancing speed. A GENerational revolution How does Generative AI impact software development? GenAI enables organizations to rethink their entire software development lifecycle (SDLC)–from initiating the first draft of a new code to examining codes for bugs and errors. An empirical McKinsey research[1] indicates that GenAI tools empower developers to write new codes in nearly “half the time” and perform code refactoring in about “two-thirds” the time. Thus, with the right tooling and processes, coupled with developer ingenuity, these speed gains can be transformed into productivity gains. At HTCNXT, we have witnessed the revolutionizing role of AI across the SDLC. For instance, we recently helped an automobile giant transform their production process with an intuitive AI algorithm to identify incorrect part codes. This helped our client improve process efficiencies by preventing over 80% of instances of wrong supplier codes in its first iteration. With our AI platform, MAGE, organizations can reduce manual effort and unlock the full potential of generative artificial intelligence with a comprehensive suite of tools and services. GenAI for enhancing development speed Here are three ways Generative AI can expedite software development: 1. Accelerating coding Advanced AI algorithms, such as OpenAI’s Codex and GPT-4, and Microsoft’s Copilot, adeptly generate code segments in response to natural language queries, expediting code creation and automating routine coding tasks. Furthermore, AI-driven testing tools rapidly detect issues and shortcomings within the code, allowing developers to rectify them quickly. This results in reduced development cycles and swifter go-to-market for software applications. 2. Automating repetitive tasks Documentation generation based on code comments, data preparation, and cleaning no longer requires human intervention. Automation has liberated developers to channel their expertise into tasks like architectural design and algorithm optimization, effectively catalyzing more sophisticated software development within shorter timeframes. 3. Augmenting innovation /AI-driven analytics Generative AI takes center stage when offering advanced analytical capabilities that propel software development innovation with data-driven refinement and informed decision-making. AI algorithms can meticulously study user interactions to unveil usage patterns, preferences, and pain points that enable developers to build responsive applications. For example, MAGE uses data-driven insights to deep-dive into customer challenges that better equip developers to build intent-based software. Generative AI: A reality of the present. Real-world applications and success stories The implementation of AI in software development is incredibly deep-rooted. Microsoft’s Kosmos-1, with its image and audio prompt response, proved the extent of it. Kosmos-1 researchers stated, “…unlocking multimodal input greatly widens the applications of language models to more high-value areas, such as multimodal machine learning, document intelligence, and robotics.” Get. Set. Generate. Tools and resources for GenAI implementation The speed at which AI is capable of helping industries suggests one thing: a widespread application by developers. In fact, a study by Gartner mentions, “By 2027, 70% of professional developers will use AI-powered coding tools, up from less than 10% today.” This growing popularity of GenAI coding tools expands the horizons for developers to integrate artificial intelligence with mature software development kits (SDKs) and low-code platforms to quickly and efficiently build software at scale. However, this is a double-edged sword! GenAI tools, although promising, are not sentient (yet). Hence, the onus is on the developers and organizations to craft meticulous, expository-style prompts that guide the technology to produce the desired output. A brave new world: Overcoming challenges in AI-driven development In a world that is swarming with the latest implementations of AI, GenAI is not devoid of challenges. Below are three pain points we’ve observed among entrants: Enterprises need to identify their GenAI goals and objectives and outline the expectations and outcomes. This will help them to expedite decision-making and implementation and ask the right questions–Are our developers GenAI ready? Do we have a defined usage policy? At what stage of the SDLC do we implement GenAI? Tech leaders need to meticulously craft strategies that not only address effective problem resolution but also lay the groundwork for an AI-first paradigm in both functionality and organizational culture. Nurturing and transforming the company culture is key to fostering this approach and facilitating a comprehensive digital transformation. Ethical AI is the buzzword for the season and for a good reason! For instance, even at an individual level, developers must adhere to best practices, avoiding the direct inclusion of credentials and tokens in their code to fend off security threats. Despite safeguards, there’s a risk of AI breaking security, and if security schemes are inadvertently shared with generative AI during the intake process, significant risks may arise. The future of GenAI in software development Despite the hurdles, Generative AI stands on the brink of revolutionizing software development in a manner unparalleled by any other tool or process enhancement. Current generative AI-based tools empower developers to accomplish tasks at a rate nearly twice as fast as traditional methods, and this is merely the initial phase. Anticipated to seamlessly integrate throughout the software development life cycle, the evolving technology holds the promise of not only enhancing speed but also elevating the quality of the development process. But to truly realize the GenAI potential in software development, organizations need a structured approach that does not discount human intuition and the need for workforce upskilling. At HTCNXT, we advocate for a harmonious integration of artificial intelligence with human expertise, fostering an environment where continuous learning
The transformative power of Generative AI in UX design
The transformative power of Generative AI in UX design Follow on: In the ever-evolving digital landscape, technological paradigm shifts have redefined how we interact with digital content. The internet, graphical user interfaces (GUIs), and voice-activated virtual assistants have all played their part in shaping the user experience (UX) and user interface (UI). But now, as we stand at the brink of the next transformative phase, generative AI has the potential to revolutionize UI/UX design, changing the way your customers experience the digital world. Generative AI and its potential in redefining UI/UX design From generative adversarial networks (GANs) to reinforcement learning models, GenAI algorithms hold the key to redefining the digital landscape. For instance, integrating AI into the design process can yield highly customized, efficient, and visually appealing interfaces. Many marketing professionals, therefore, believe that GenAI will redefine their roles in the next three years due to its transformative potential. Reimagining the front-end design of websites and apps To address these challenges, the insurance industry needs a transformative approach. There are several key areas where the underwriting experience can be significantly improved: AI-augmented creativity and design aesthetics The ubiquitous use of smartphones and digital networks has raised user expectations for seamless experiences. AI can guide designers’ creativity and provide insights for data-driven design decisions. Designers can use AI to process and analyze vast amounts of user data, identifying patterns, preferences, and behaviors. Designers can leverage this information to gain a deep understanding of their target audience. For example, AI can reveal which design elements or content formats are more engaging to users based on their interactions, helping designers make informed choices. Besides, AI can suggest personalized design elements based on individual user profiles and behavior. Designers can use these recommendations to create interfaces that adapt to each user, enhancing the overall user experience. Intelligent, adaptive interfaces As data-driven websites and apps become the norm, generative AI’s adaptability becomes essential. By analyzing user data, AI-powered interfaces can tailor experiences based on user preferences, habits, and engagement patterns. According to a study by McKinsey, AI-driven personalization will be top of the mind for almost 90% of business leaders. Accessibility and inclusiveness Generative AI can also enhance digital accessibility by considering factors like disabilities. Websites can adapt to provide larger text, alternative output modes (voice, vibration, or haptic feedback), and straightforward navigation systems. This inclusiveness ensures digital experiences cater to a broader audience, expanding the reach of UI/UX design. Generating natural language content Generative AI, specifically NLG, is the driving force behind chatbots and virtual assistants that converse with users in a natural, human-like manner. Traditional chatbots relied on pre-programmed responses, often leading to robotic and frustrating interactions. NLG, powered by advanced machine learning algorithms, enables these AI entities to understand context, decipher user intent, and generate responses on the fly. Creative assistance and co-creation Generative AI is transforming creativity by aiding in artistic tasks and fostering collaborative co-creation between humans and machines. It is pivotal in diverse creative domains such as image generation, music composition, and storytelling. Generative AI’s creative contributions include: Image Generation: Generative models like GANs inspire artists and designers with visually striking artworks, sparking innovation. Music Composition: AI-driven tools assist musicians in composing harmonious melodies, expanding the musical landscape. Storytelling: Natural Language Processing models empower website and app developers to co-create captivating narratives by generating plots and characters. Real-life case study: Booking.com’s latest AI trip planner exemplifies generative AI-powered virtual assistants that interpret user queries to support every stage of their trip planning process pertaining to potential destinations and accommodation options. The trip planner can also provide travel inspiration based on the traveler’s requirements and create itineraries for a particular city, country, or region. BCG is using a combination of multiple GPT agents to automate clinical trial protocol development by combining data from OpenAI, National Institutes of Health(NIH), clinical trials.gov, clinical trial databases, and medical literature. Amazon employs generative AI algorithms to analyze customer data, generating personalized product recommendations. This data-driven decision-making enhances customer satisfaction and boosts sales, illustrating the power of AI in shaping effective strategies. Planck, one of the leading providers of risk insights and the tier-1 insurance underwriting workbench, is the first insurtech provider to use GenAI to address some of the most pressing roadblocks in commercial insurance underwriting. The challenges and future prospects of AI-driven UI/UX design Interestingly, adopting generative AI can give rise to ethical considerations involving data privacy, algorithmic biases, and the potential for misuse. The age of technology has amplified concerns about data privacy, as the collection and analysis of extensive user data to craft personalized experiences could infringe upon individuals’ privacy rights if not managed responsibly. Furthermore, biases inherent in training data could persist through generative AI algorithms, resulting in unjust or discriminatory outcomes. To counter these challenges, UX designers and businesses must take proactive steps by incorporating rigorous data protection measures. They should also prioritize transparency and fairness in the decision-making processes driven by algorithms. Conclusion: Navigating the Generative AI revolution in UI/UX design Generative AI is undeniably reshaping UI/UX design, marking the dawn of a new computing era. The implications of incorporating AI in front-end design are profound, redefining how we interact with the digital world. As generative AI blurs the lines between users, designers, and technology, embracing transformative opportunities becomes critical. Those who embrace generative AI’s potential will be at the forefront of this revolution. The fusion of human creativity and AI innovation will unlock unprecedented user experiences, driving a future where technology seamlessly enhances human interaction with the digital realm. Recognizing the complexity of building, deploying, and adopting such a transformative technology, we crafted MAGE, our AI platform. Given that the demonstrated use cases of Generative AI are merely the tip of the iceberg, a platform like MAGE can accelerate AI-led exploration of enterprise-wide solutions. As a platform, it simplifies and delivers pre-built use case solutions for retailers to rapidly adopt and drive value faster. Click here to learn how HTCNXT can help you leverage AI in
Generative AI in insurance: Is the promise of automation just hype?
Generative AI in insurance: Is the promise of automation just hype? Follow on: The insurance industry is vital to the growth of the global economy, providing financial protection and stability to individuals and businesses. Underwriters play a crucial role in assessing risks, determining policy terms, and ensuring the sustainability of the industry. However, challenges like the need for human intervention in routine processes, errors, etc., continue to keep underwriters from focusing on core strategic activities. This article explores how Generative AI can revolutionize insurance claims by optimizing underwriting processes, reducing manual work, and enhancing data analysis. Challenges in underwriting Multiple surveys have shown that underwriters spend only about a third of their time performing risk analysis on accounts. A staggering 40% of their time is consumed by administrative activities, such as data entry and manual analysis execution. This not only hampers efficiency but also contributes to rising industry loss ratios. External factors like inflation and increased climate-related events certainly play a role in this predicament. Still, it’s essential to acknowledge that underwriters themselves report the lowest levels of confidence in the quality of the underwriting process. Additionally, the sluggish pace at which new insurance products are introduced (an average of 18 months) and existing products are modified (about 6 months) further limits the industry’s adaptability to market conditions. Optimizing the underwriting experience To address these challenges, the insurance industry needs a transformative approach. There are several key areas where the underwriting experience can be significantly improved: Improving data access and analysis: Research from McKinsey reveals that AI in the insurance space can save time and costs. It can reduce claims regulation costs by 20-30% while minimizing processing costs by 50-65% and time by 50-90%. This will ultimately improve the customer service experience. Reducing manual data entry: Manual data entry from unstructured sources and forms remains a major bottleneck in underwriting processes. With AI-led automation, this process can be transformed, significantly reducing the need for manual intervention or errors. Enhancing data quality: The “Not Yet Good Enough” cycles in data quality management can be reduced by automating the quality improvement loops. This can improve data quality and delivery insights with greater accuracy. Streamlining workflows: Integrating workflows and underwriting business processes can save business a lot of time and reduce errors. It can improve overall visibility for all stakeholders, improving transparency across the value chain. Improving workbench experience: Enhancing the front-end workbench experience for underwriters and processor roles can boost overall efficiency. Leveraging Technology Solutions Addressing these issues requires a combination of technologies that can modernize and streamline the underwriting process: a. Modernization into microservices with APIs: This approach enhances the composability of applications and business flows. It also facilitates the productization of data that underwriters can innovate on, ensuring flexibility and adaptability. b. Integration with third-party solutions: Partnering with best-of-breed third-party solutions can help reduce manual processes in data validation and provide richer data for analysis. c. Induction of a generative engine: Generative AI can revolutionize document and unstructured data extraction, reducing the need for extensive training. It can also enable virtual agents for collaboration, training, policy adherence, rules testing, and contract/document generation. d. Application automation: Streamlining workflows across systems and processes can remove barriers to seamless integration. Additionally, machine learning and deep learning can be harnessed for risk scoring, automated analyses, and policy recommendations. e. Auto-Analysis of Third-Party Data: By applying machine learning and deep learning techniques to analyze third-party data according to insurer guidelines, manual analysis processes can be minimized. Benefits of Generative AI in Insurance Claims The incorporation of Generative AI into the insurance industry offers numerous advantages: Efficiency and accuracy: Generative AI reduces manual tasks, allowing underwriters to focus on risk analysis. This, in turn, enhances the accuracy of underwriting decisions. Adaptability: With a modernized and integrated system, insurers can react more swiftly to market conditions, introducing new products and modifying existing ones with greater agility. Data enhancement: Access to richer, high-quality data can lead to more informed underwriting decisions and better risk assessment. Cost savings: By automating various processes, insurers can significantly reduce operational costs and improve their bottom line. Forging the road ahead The insurance industry faces substantial challenges in terms of underwriting efficiency and the quality of risk analysis. These challenges not only impact profitability but also the industry’s ability to adapt to changing market conditions. Generative AI presents a solution that can optimize underwriting processes, reduce manual work, and improve data analysis. By embracing the transformative power of Generative AI, the insurance sector can enhance its competitiveness and better serve its customers. The future of insurance claims lies in harnessing the capabilities of AI to streamline underwriting, ensuring the sustainability and success of the industry for years to come In the quest to transform the insurance industry, it’s imperative to look at innovative solutions, and HTCNXT has emerged as a pioneering force in this landscape. At HTCNXT, we are committed to revolutionizing the underwriting experience by harnessing a potent combination of AI technologies, including generative engines and deep learning models. These cutting-edge technologies serve as the foundation for a dynamic and adaptable solution that can address the challenges outlined earlier. Our AI platform, MAGE, places a strong emphasis on modularity and plug-and-play capabilities. We understand the importance of seamlessly integrating our solutions with existing investments, whether it’s widely used platforms like Guidewire or Duckcreek, or bespoke systems unique to your organization. The headless architecture of MAGE ensures that the transition to a more efficient and agile underwriting process is both smooth and cost-effective, safeguarding your investments in current systems while boosting operational excellence. Furthermore, we recognize the vital role that data plays in the underwriting process. MAGE helps you integrate 3rd party data sources and, more importantly, enhance the analysis of this data to cater to specific underwriting scenarios. This data enrichment capability empowers underwriters with deeper insights and richer information, ultimately improving their ability to make well-informed decisions. The result is a more agile and responsive underwriting process, capable of adapting
Combining Generative AI with Automation and APIs: realizing AI at scale
Combining Generative AI with Automation and APIs: realizing AI at scale Follow on: Generative AI models and similar architectures are known for their impressive and versatile features. These models have revolutionized natural language understanding and generation. The Generative capabilities like text generation, translations, summarization, and keyword/metadata extraction are applicable in various industries in the areas of marketing automation, customer relation management applications, ERP, Promotion, and Loyalty applications and are seamlessly integrated into the MAGE platform through scalable and highly available microservice containers with API driven approach. Reusable components like data synthesizers, semantic search, and metadata extractions are integrated with automation processes that are event-driven/batch schedulers through configuration or metadata-driven approaches, which effectively can be used for any of the Model training processes like building recommendation/prediction/classifier engines, etc. How MAGE platform Generative AI APIs solve Industry use cases. MAGE platform Generative AI APIs are driven with a template-based approach that standardizes and simplifies usability and development efforts and maintains consistency across system. Based on different scenarios, the MAGE platform Generative AI APIs are integrated with Prompt Engineering frameworks that create prompt requests and send them to the Generative AI models, integrated with semantic search flows, fine-tuning models, and integration with other system and enterprise APIs based on the requests. A few of them are listed below and used in various industry use cases. APIs for the customer journey in a transactional-based system Scenario for order management system Generative AI can power chatbots or virtual travel assistants that engage with users in natural language. These AI-powered assistants driven with API based integrations can handle a wide range of tasks, including ordering products, providing product recommendations, answering questions, and assisting with pricing details. API for content personalization: Scenario for Marketing and Automation The content that has to be personalized targeting users based on dynamic profiling/segmentation can leverage MAGE Platform Generative AI API, which personalizes the content based on user profiles and targets to generate personalized email campaigns. The content that has to be personalized targeting for channel-friendly can leverage MAGE platform Generative AI APIs for channel-friendly content creation that personalizes the content based on the targeted marketing channel. Scenario for Recommendation Engines Various recommendation engines can integrate with the MAGE platform Generative API for providing personalized recommendation content to the targeted users and channels for product promotions. API for metadata extraction/summarization: Scenario for Insurance Industries Extract relevant information from claim forms, such as claimant details, incident dates, descriptions of events, and supporting documents. Summarize claim documents to give claims adjusters a quick overview of the claim, helping them make faster and more informed decisions. Generate summaries of applicant data, making it easier for underwriters to assess risk and determine policy eligibility. Scenario for Retail Industries Create inventory summaries to provide an overview of stock levels, restocking needs, and product categories. Summarize competitor data to identify pricing trends, product assortments, and market positioning. Summarize customer sentiments and reviews to gain insights into product satisfaction and identify areas for improvement. API for audio-text-iMAGE conversions Scenarios for healthcare industries: Digital scribe agent that captures a doctor’s conversation with the patient, transcribes the audio to text, translates to the required language, creates the summary from the transcribed text Scenarios for Insurance industries: Claims agent that can receive audio files from insures, transcribes to text, extracts the metadata, and determine the severity of the loss Property iMAGE interpretations converted to the text used in dynamic underwriting use cases. Generative API’s scalability and automation With APIs built in through the MAGE platform for all the Generative AI capabilities, the platform provides seamless integrations to external systems/partners. These APIs have the capability to integrate with the workflow management that caters to end-to-end business capabilities. MAGE platform user interface built with micro-frontends are integrated with scalable approach through backend APIs. Given the core components for the MAGE platform for Data ingestion, Auto EDA, data transformation, and scalable AI model components integrations, the Generative AI APIs can be seamlessly integrated with integration services that have the capability to build rule engines, workflows, etc. For example, a retail industry looking to build a robust Recommendation system can leverage the MAGE platform, integrating with Data ingestion, EDA, and transformation process that connects to different data sources related to products, customers, inventory data, etc, and use Generative AI APIs of MAGE platform that can synthesize the required data for model training, build the model and deploy the model for recommendations. As required, the MAGE platform Generative API can integrate with other deep learning/ML-based algorithms for data synthetization, classification, and enrichment, making more model accuracy. The APIs are integrated with the MAGE MLOps platform for continuous builds and deployments, monitor and check for any model drifts, and trigger the hyperparameter tunings or model fine-tunings if there are any drifts. The APIs are built with a Microservice framework with core features like service discovery, resiliency through circuit breaker, and load balancing, ensuring the services are highly auto-scalable and highly available, ensuring zero downtime. Generative API Security and Data security The Generative APIs are integrated with enterprise security leveraging OAuth2/OpenID connect. The APIs for synthetic data generator adheres to data privacy, compliances, and reduced model bias. Automation MAGE platform automation is spread over different areas, from Data Engineering(ETL) to Industry Use cases. ETL process MAGE Platform ETL process integrated with Language Model (LLM) assistants powered by NLP, allowing business users to query domain-specific entities and fetch data effortlessly. ETL framework enables seamless connections to multiple data sources, facilitating easy data ingestion, transformations, and visualizations. Auto EDA framework The MAGE Platform, Auto EDA framework, leverages NLP to provide insights into data completeness, quality, and summary. Users can visualize correlations, helping them identify and resolve data issues swiftly. The framework is integrated with MAGE platform APIs seamlessly from any channel. Web Scraping The MAGE platform has built automation scripts to scrape large amounts of text or data from websites, social media, or other online sources. This data can be used as training material for generative AI models. Document Parsers