The Lowdown On AI In Insurance
As AI revolutionizes the insurance landscape, here’s your opportunity to gain insights from industry experts firsthand as they discuss the transformative impact of AI across various facets of insurance operations. Whether you’re evaluating AI for the first time or expanding its use, this webinar is sure to illuminate, inform, and reinforce your own strategies for how and where AI can benefit your company. The discussions that matter: How AI is revolutionizing insurance operations and where its impact is expected to be greatest The readiness of insurance enterprises to embark on their AI journey and the obstacles encountered along the way The opportunities and concerns surrounding the integration of real-time, third-party data into AI systems Insights into the varying approaches to AI adoption in the insurance industry and the significance of Generative AI in roadmap planning The future landscape of AI acquisition, including the integration of AI into technology platforms versus standalone tools Don’t miss this opportunity to gain valuable insights and navigate the future of AI in insurance. Watch the on-demand webinar now! watch now watch now Fill the form to watch the webinar now Name* Email ID* Organization* Job Title* All fields marked with * are mandatory Δ Speakers: Eric Griffin, AVP of Operations and Systems Shared Services Kemper Insurance Joseline Lajara, Director of Product Management, Gen AI Guidewire Software Jeff Devin, AVP Architecture CopperPoint Insurance Shammo Ghosh, SVP, SME, Insurance HTC Global Services | HTCNXT Moderator: Dennis Winkler, Insurance Practice Leader ISG
Transforming Customer Support with AI in Contact Centers
Transforming Customer Support with AI in Contact Centers Follow on: The Road to AI Can AI do to contact centers what it did to Industry 4.0? All industry use cases and market predictions point in the direction of AI-driven contact centers — as the next strategic step for boosting agent productivity, supercharging customer experience, and increasing operational efficiency. According to the Innovation Center for Artificial Intelligence, AI chatbots helped the banking sector save approximately $8 billion in the preceding year. On the other side, fintech leader, Sebastian Siemiatkowski, co-founder and CEO of Klarna, predicted that their ChatGPT-powered AI assistant will generate an estimated $40 million in additional profit by 2024. Notably, the Call Center AI Market was worth USD 1.6 billion in 2023 and is rapidly expanding — having projected to reach USD 9.9 billion by 2032, with a CAGR of 22.7%. Let’s delve deeper into the benefits of cognitive contact centers, and how enterprises can unpack superior CX with them. The AI Advantage for Contact Centers A study on a company with 5,000 customer service agents revealed impressive results with generative AI adoption. Issue resolution rates soared by 14% per hour, while the time spent handling each issue decreased by 9%. Additionally, generative AI contributed to a 25% reduction in both agent turnover and customer requests to speak with a manager. So how can contact centers leverage AI? Conversational AI, often the first thought for contact centers, utilizes Large Language Models (LLMs), Natural Language Processing (NLP), and Machine Learning (ML) technologies — to enable customers to interact with AI-powered systems through voice and text-based channels, including: Intelligent Interactive Voice Response (IVR) can function intuitively to deliver real-time and human-like exchanges across channels. Chatbots engage in real-time conversations by interpreting customer queries to identify intent and provide satisfactory responses. Virtual Assistants or Assistants , like Siri and Alexa, converse with users to provide personalized support and consistent experiences across devices and platforms. Conversational AI solutions are gaining popularity because they can streamline customer interactions, reduce wait times for instant resolutions, and deflect simple inquiries, encouraging self-service. In times of labor crunch and high agent attrition, they can free up agents for more complex issues that require critical problem-solving. The second most popular application of AI in contact centers is data analysis. AI, specifically GenAI’s ability to analyze voluminous data, can scan through various statistics and key performance indicators (KPIs) to produce high-value insights for improving agent performance and customer satisfaction. This saves enterprises the trouble of manually analyzing data, allowing them to: Gain insights into agent performance, call resolution times, and customer sentiment. Optimize agent schedules, performance, and productivity through targeted, data-backed resource allocation and training programs. Design proactive customer service strategies by anticipating customer needs — based on past interactions across multiple touch points. 6 Benefits of AI Implementation in Contact Centers Enhanced self-service capabilities Improved agent productivity Reduced operational costs Actionable customer insights Intuitive customer engagement Lowered agent attrition Around the World: Successful Use Cases of AI in Contact Centers How can enterprises implement AI for contact centers? Let’s find out some popular use cases gaining traction across industries. Use Case #1 – AI systems for emergency response centers 911 response centers in the US are deploying AI tools to handle non-emergency calls. In a 2023 survey of 9-1–1 centers, 82% of respondents cited understaffing, with 74% reporting burnout. AI-powered triage systems can prioritize calls during high volume or non-emergencies to optimize agent efficiency. AI can also help dispatchers with real-time translations and speech processing in fast-paced scenarios. The latter not only helps with keeping call records but also flags key details such as location and the nature of the emergency — empowering responders to focus on issue resolution and not on documentation. Thus, AI deployment in high-impact sectors like healthcare emergency and disaster management can help bridge the gap between critical needs and timely responses, powering better outcomes. Use Case #2 – Self-service in insurance Insurance enterprises are leveraging AI-driven contact centers beyond the pleasures of self-service and more toward proactive support. Consider a scenario where a customer reports property damage to their home insurer. The insurance AI assistant authenticates the customer and guides them through the claim process. It also asks questions to help the customer understand the situation, such as the extent of the damage, the potential cause, and any immediate safety concerns. Throughout the process, the homeowner receives automated updates on their claim’s progress and simultaneously they can also ask the AI questions about the next steps, coverage details, or temporary accommodation options if needed. If the issue warrants complex problem-solving, like coverage disputes, the AI guides the policyholder to a live agent for better resolution. Use Case #3 – Cognitive assistants for human-like interactions Many payment solution enterprises have a global customer base that traditionally requires a massive contact center team — with representatives fluent in various languages. However, modern contact center AI solutions can deploy intelligent assistants with advanced speech-to-text and text-to-speech technologies for handling multilingual inquiries. Moreover, with the advent of generative AI, improved context recognition among AI assistants can make conversations as natural and human-like as possible. Additionally, fintech companies can also integrate a customized voice persona for the AI assistant to foster a consistent brand personality for all customers across borders. Use Case #4 – Contact center automation In travel contact centers, AI can reduce the burden on agents by automating repetitive tasks like flight rebooking after cancellations. An AI system can analyze the traveler’s preferences based on past records, identify alternative flights based on real-time availability, and eventually guide them through the rebooking process — all without human intervention. This improves their fast contact resolution (FCR) numbers, thus reducing the need for follow-up calls, which is a key performance indicator of customer delight — customer satisfaction rates can decrease by 45% when an issue is not resolved at first contact. Take the Leap with HTC MAiGE HTC MAiGE is HTCNXT’s built-to-purpose platform that empowers enterprises to build
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 HTC MAiGE, 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 HTC MAiGE 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.
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, HTC MAiGE, has an Ad-Recommender to make campaign journey planning data-driven. HTCNXT’s plug-and-play platform, HTC MAiGE, 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
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
HTC MAiGE Accelerate Your AI Journey
The pressure is on. Every enterprise needs to be an AI-first organization. Yet, building formidable AI capabilities presents its own unique set of challenges.
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, HTC MAiGE, 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, HTC MAiGE 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