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
AI beyond the hype: why this is truly a transformative moment for technology
AI beyond the hype: why this is truly a transformative moment for technology Follow on: AI has been envisioned as a business multiplier for decades, but its adoption has only recently gained pace. Why? Low technology maturity levels, the lack of enabling infrastructure, and AI-led capabilities limited AI’s growth. While its potential was undeniable, its implementation was a challenge. The technology landscape, however, has changed dramatically over the past few years. Enabled with cloud scale and GPUs, complex data crunching to enable AI at scale is possible today. With rapid evolutions in hardware, newer capability areas in AI, such as generative (specifically almost human-like Large Language Capabilities), have emerged. Now, AI can accelerate the time to value. AI’s transformative potential, hence, is not only being acknowledged but also realized at scale. However, we need to keep in mind the benefits AI implementation will offer to the customers instead of looking at AI as just another piece of technology. Every AI project should be driven by user/customer centricity. The dawn of reality: validating the AI metamorphosis The year 2023 witnesses a pivotal juncture in the AI narrative, a watershed moment affirmed by the findings of the McKinsey Global Survey. At the heart of this transformation is the meteoric rise of Generative AI (Gen AI) tools. What was once an experimental pursuit is now propelling businesses to harness gen AI in their daily operations. According to the report, nearly half (48%) of medium to large organizations in the US have advanced to higher AI maturity levels of AI maturity, marking an 8% surge compared to last year’s survey findings. Among mid-to-large US organizations, 52% are currently in the experimental phase of AI implementation. Those at the mature stage are more inclined to leverage AI for future strategic gains, while the experimenting group primarily focuses on mitigating risks. However, irrespective of where these businesses stand on their AI maturity journey, nearly 40 percent of enterprises affirm their intent to amplify AI investment. At HTCNXT, we realized AI’s potential quite early, and our mission of transforming enterprises into AI-first organizations began at home. At HTCNXT, our core mission is AI, and we have been developing ready-to-deploy industry-specific solutions, working with clients to implement AI for their problem areas, and developing our own AI platform called MAGE. We have also been eating our own dog food and using our own solutions in our business services group, customer service products, and quality engineering teams. AI solutions today sit at the heart of many of our processes. And we have seen realized benefits in productivity and efficiency as we do this. For our customers, our solutions have helped a global automotive manufacturer eliminate recurring problems in procurement and streamline their workflow to save significant costs. Our solution enhanced the interactive training modules with a VR-based 360° solution for a multinational mass media and entertainment conglomerate. We also helped a large public research university in California build AI/ML-driven solutions for managing technical data. And this is just the tip of the iceberg. Riding the AI wave Building on our AI expertise, we developed our platform MAGE. Its plug-and-play solution is designed for enterprises to harness the full potential of AI, innovate with precision, and elevate their operations to new heights. Being component-driven, it can be seamlessly integrated into existing IT infrastructure to drive value sooner. Furthermore, our tested approach helps enterprises adopt AI technology through the stages of Learn, Scale, and Transform. Leveraging the immense capabilities of MAGE, we are helping enterprises test the viability of AI for their business, scale its implementation, and take the strategic call to enable enterprise-wide adoption. Our holistic approach has been instrumental in improving AI maturity levels for enterprises at a rapid pace. Setting up the ethical guardrail Large language models (LLMs) hold great potential but also bring challenges for organizations. These models can produce content that doesn’t match an organization’s needs or ethical guidelines. Without safety measures, there’s a risk of creating harmful or biased content. To use LLMs responsibly, organizations must establish guardrails to define their boundaries. There are four ways to create effective guardrails for LLMs: Develop specific LLMs from trusted sources, but this is resource-intensive and doesn’t eliminate all risks. Customize LLMs with optimization techniques aligned with industry policies. Manually verify models for vulnerabilities through red teaming, which is slow and costly. Use agent-based models to automate verification and governance, ensuring safe interactions. Among these, agent-based modeling stands out as the most suitable option for securing LLMs for enterprise use. It enforces technology and security rules, ensuring safe interactions with generative AI. HTCNXT’s MAGE weaves the AI magic MAGE is a testament to the relentless pursuit of technological excellence. This platform harmoniously orchestrates various technology stack components, effectively converging data, algorithms, and computing resources. Embodying the essence of AI evolution, MAGE paves the way for organizations to unleash innovation, gain invaluable insights, and make well-informed, data-driven decisions. MAGE empowers businesses to innovate across diverse domains, extending its transformative touch to four cornerstone industries: Retail: MAGE’s capabilities drive personalized customer experiences, dynamic pricing, virtual assistants, and demand forecasting. From visual search to product recommendations, retail revolutionizes through AI. Insurance: Enhancing risk assessment, claims processing, and fraud detection, MAGE redefines efficiency in insurance operations while delivering personalized customer experiences. We recently helped an insurer reimagine claims intake with an AI-based FNOL (First Notice of Loss) solution, enhancing digital experiences for claimants and staff while reducing costs. Health: With capabilities spanning medical imaging, personalized medicine, telehealth, and clinical decision support, MAGE transforms healthcare, enhancing patient care and medical research. Travel: By enabling personalized travel recommendations, dynamic pricing, and enhanced customer services through chatbots and virtual assistants, MAGE takes travel experiences to new horizons. Forging ahead There’s no denying that Gen AI’s advent heralds a new dawn, a chapter steeped in progress. As we traverse this landscape of uncharted potential, HTCNXT extends an invitation. Venture forth, chart your course through the universe full of AI’s promise, and together, we can script
Generative AI’s true opportunity in Retail
Generative AI’s true opportunity in Retail Follow on: The retail industry is rapidly adopting Machine Learning, Computer Vision AI, and smarter AI-led solutions to enhance customer experiences, drive supply chain optimization, smarter in-store operations, and more. Generative AI, however, can help them achieve more. It can significantly improve the team’s productivity, create personalized customer interactions, and accelerate code creation, testing, and debugging for the IT engineering teams. Gartner’s recent emerging tech roundup report reveals that a significant shift in generative AI usage is anticipated in various sectors. By 2025, generative AI is projected to play a pivotal role, generating 30% of marketing content, substantially augmented by human input, a substantial leap from the meager 2% in 2022. Advanced virtual assistants (VAs) are also undergoing a transformation. Over 50% of VAs are predicted to become specialized for specific industries, a considerable rise from the earlier 25% in 2022. Generative AI and foundation models will transform software products in two years, driving up productivity, augmenting customer support, and decision intelligence across diverse domains. Generative AI assistants are already being utilized to transform how customers search for products. For example, with a chat/voice-based interface, customers can request a recipe and receive product recommendations with a list of ingredients used in the recipe. This simplifies searching for ingredients while improving sales and customer satisfaction. The latest advancements in ChatGPT/Azure OpenAI, like plugins and code interpreters, open yet more opportunities for retailers to sharpen their targeting and complement the productivity of retail associates. A key impact area of Generative AI has been the evolution of retail media networks. This new and alternate revenue channel for retailers is already creating a positive impact on the bottom line for leading retailers and will remain the focal point of innovation for online, offline, and in-store targeting. The result? Targeted and personalized ads get better, costs drop, and shopping gets smoother. While retail media networks seize the spotlight, other use cases further underline AI’s importance in retail: Driving autonomous store operations: AI-driven automation reimagines store operations, allowing employees to amplify customer satisfaction. Automated restocking, predictive maintenance, and theft prevention guarantee secure and efficient store functions. Powering fulfillment AI: Swift deliveries materialize through AI-powered adaptable supply chains. AI monitors demand, stock availability, and location, facilitating optimized fulfillment for unparalleled customer delight. Enabling demand forecasting: AI-generated demand forecasts empower retailers to outpace competition. These insights recalibrate prices and fortify supply chain resilience, empowering businesses with a competitive edge. Improving customer engagement – Chatbots and copilot assistants can power natural language conversations in customer support and eCommerce, simplifying shopping for customers. Refining product merchandising – Generative AI can help create persuasive descriptions and utilize unstructured data from customer interactions to generate novel product improvement ideas, thereby improving product development and refinement. Enhancing commerce – Generative AI can elevate the personalization game with specific situations and need-based product recommendations. It can generate SEO-centric product copies for websites and social media posts supplemented with generated imagery. Improving associate’s experience – Generative AI can act as a trainer and copilot for the store associates providing micro training, enabling help with routine tasks, and empowering them with natural language queries for specific information. This can lead to improved associate morale and enhanced overall productivity. Powering IT teams – Generative AI can help IT teams generate code and resolve issues faster, perform tests using LLM-generated test cases, and fix technical debt with code analysis. Empowering data teams – The retail data teams can leverage Generative AI analytics features allowing users to query for insights using natural language. This capability can empower the teams with improved insights and accurate decision-support tools. Driving supply chain resilience – Generative AI can ensure the amalgamation of data from various sources to interpret and visualize the multi-source data inputs into charts and graphs. Real-time visibility can fast-track decision-making, improve supply chain robustness, and enhance operational resilience. Real-world application of GenAI: Industry titans like Nestle and Unilever are at the forefront of this transformation through Generative AI, reshaping ad campaigns and product marketing. Collaborations among these companies are yielding innovation and cost-effectiveness, bridging creativity and efficiency in campaigns that resonate globally. Through the adoption of ChatGPT 4.0 and DALL-E 2, Nestle is harmonizing AI-generated ideas with human creativity, rewriting the norms of marketing excellence. Retailers, however, must recognize that there are shortcomings in Generative AI models. They need to ensure a proper governance model to prevent any spread of misinformation due to hallucinations, inadvertent sharing of confidential information, or privacy breaches. With the technological advancements continuing, retailers can expect more novel use cases for Generative AI. It is crucial for them to start preparing and building the foundations for an AI-first enterprise. Generative AI will undoubtedly play a leading role in revolutionizing retail experiences, and the time to create such experiences starts now. Click here to learn how HTCNXT can help you leverage Gen AI in your retail operations with our ready-to-use AI platform MAGE. Sources: A Paradigm Shift in Advertising AUTHOR Adnan Saulat Senior Vice President, Consumer Services SUBJECT TAGS #ArtificalIntelligence #GenerativeAI #AIinRetail #RetailInnovation #RetailTechnology #CustomerExperience
Pave the way for an AI-Powered enterprise
Pave the way for an AI-Powered enterprise Follow on: Machine learning and deep learning are crucial technology components to build robust foundations for AI implementation. The complexity of machine learning algorithms is increasing due to the abundance of available data, resulting in improved efficiency and precision for AI systems. Furthermore, there have been remarkable advancements in Conversational AI, leveraging Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP). These breakthroughs enable machines to comprehend and interact using human language, powering applications like chatbots and virtual assistants. This blog illustrates the seamless synergy between AI and other cutting-edge technologies such as quantum computing, cybersecurity, the metaverse, and data mesh, opening the doors to infinite new possibilities. The AI landscape with modern technologies AI with quantum computing The intersection of AI and quantum computing holds remarkable promise in enhancing the velocity and precision of quantum algorithms while unlocking newer avenues for simulation, optimization, and data analysis. Some of the ways in which AI can augment quantum computing include: Training neural networks and further improving optimization methods. Allowing seamless searches through large datasets, rapidly enhancing pattern recognition capabilities. Improving algorithms beyond the scope of conventional computers, such as parallel sophisticated automation that involve data wrangling. Enabling AI to manage enormous collections of images and unstructured data. Solving explainable AI challenges that require enormous permutations and combinations to identify the best paths. Enhancing reinforcement learning to achieve quicker and more satisfying results. Using AI algorithms to analyze the performance of various quantum circuits. These help in spotting trends and providing the best outcomes with efficient circuits. Rapidly identifying patterns within large datasets and enabling the removal of noise data. AI with Metaverse AI has the potential to create captivating and immersive virtual environments in the metaverse. Here are a few examples of suitable applications for implementing AI: Analyzing user preferences and actions to enhance and adapt virtual settings. Implementing natural language processing for seamless interaction between individuals and virtual entities. Employing AI algorithms to generate content and manage data within virtual ecosystems. Aiding integration with wearables, voice commands, gestures, and smart eyewear. Utilizing 3D engines, geospatial mapping technologies, virtual reality (VR), and augmented reality (AR). AI in cybersecurity AI plays a crucial role in cybersecurity, threat detection, and prevention. It boosts responsiveness and speed in identifying complex cyber-attacks, leading to automated defenses and providing improved protection for sensitive data. Here are some AI applications in cybersecurity: Detecting email spam with Perceptron. Making use of Support Vector Machines to detect spam. Making use of Naive Bayes to detect spam. Detecting phishing using logistic regression and decision trees. Leveraging NLP strategies for spam detection. Spotting network irregularities, such as the Botnet death chain. Using Hidden Markov Models (HMMs) to detect metamorphic malware. Using deep learning methods for enhanced malware detection, such as Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). In conclusion Several leaders perceive AI as a readily deployable technology, anticipating immediate benefits. Consequently, substantial investments are directed toward bolstering data infrastructure, AI software tools, data expertise, and model development. While these steps are necessary, it is equally vital to synchronize the organization’s culture, structure, and operational methods to facilitate widespread AI integration. In the second part of the blog, we will further delve into other technologies, such as blockchain, data mesh, and data engineering. You can read the second part of the blog here. AUTHOR Sudheer Kotagiri Global Head of Architecture and AI Platforms SUBJECT TAGS #ArtificalIntelligence #CloudComputing #HybridCloud #FinOps #DevOps #CloudExpenditure
AI and Blockchain: A match made in tech heaven
AI and Blockchain: A match made in tech heaven Follow on: Can machines think? It’s a question that has ignited curiosity, contemplation, and even trepidation among tech enthusiasts and skeptics alike. As we stand on the brink of an AI-powered era, the concept of artificial intelligence has transcended mere sci-fi musings to become an integral part of our daily lives. But beyond the realm of philosophical inquiry lies a pragmatic and exciting reality: AI is revolutionizing businesses worldwide. Welcome to our blog, where we unlock the secrets of making AI work for your business, tapping into the minds of machines to transform your operations and leapfrog your competition. AI and Blockchain Blockchain technology has shown immense potential, but when combined with AI, its capabilities can skyrocket. Let’s explore how AI can be a game-changer for businesses operating within the blockchain sphere: Ensuring privacy and security: AI can carefully examine the information stored in blockchain technology while prioritizing privacy and security measures. Forecasting and suggestions: AI can offer valuable forecasts and suggestions for environments reliant on blockchain technology, helping businesses make informed decisions. Fraud detection: Identifying instances of fraud within decentralized systems becomes more efficient with AI’s analytical capabilities. Data analysis and protection: AI models can produce projected data analyses and ensure secure data exchange within the blockchain network. Optimizing supply chain: By anticipating demand and optimizing delivery teams within the blockchain network, AI improves efficiency in supply chain operations. Personalized recommendations: Through decentralized identity systems, AI can provide customized product recommendations while ensuring individuals retain control over their data. Identity authentication: AI can evaluate biometric or identity data to authenticate the integrity of systems within the blockchain network, enhancing security. Large language models in Blockchain: Powering smart contracts and more Large Language Models (LLMs) have found their place within the world of blockchain, revolutionizing various aspects: Smart contracts powered by NLP: Natural Language Processing (NLP) is employed to enable easier implementation and execution of Smart Contract policies. Decentralized forums: LLMs are harnessed to generate content on decentralized forums, fostering diverse discussions and interactions. User-controlled digital identity solutions: By incorporating LLMs into Blockchain technology, we can establish secure, private, and user-controlled digital identity solutions allowing natural language interactions. AI-powered customer support: Decentralized applications can benefit from AI-powered customer support, ensuring smoother user experiences. AI and Data Mesh: Boosting efficiency and safety AI is crucial in improving speed, accuracy, and security in data processing and analysis in data mesh networks. Here’s how AI enhances data mesh applications: End-to-end data mesh solutions: AI is seamlessly integrated across different stages of data mesh, from exploration and manipulation to governance and quality validation. Specialized models for proficiency: Trained AI models specialize in specific domains, elevating data processing capabilities. Generative AI for governance: AI techniques generate data governance policies and security controls, streamlining data management. Enhanced data tagging and classification: AI supervises data usage patterns, tags personally identifiable information (PII), and classifies it accurately. Security threat detection: AI identifies and responds to security threats, detecting malicious activities within the network. NLP-powered query services: Query-based services driven by natural language processing simplify interactions with data. Computer vision applications: From automation factories to retail and commerce, AI’s computer vision techniques, such as object recognition, open up new possibilities. Welcome to our blog, where we unlock the secrets of making AI work for your business, tapping into the minds of machines to transform your operations and leapfrog your competition. AI and Blockchain Blockchain technology has shown immense potential, but when combined with AI, its capabilities can skyrocket. Let’s explore how AI can be a game-changer for businesses operating within the blockchain sphere: Ensuring privacy and security: AI can carefully examine the information stored in blockchain technology while prioritizing privacy and security measures. Forecasting and suggestions: AI can offer valuable forecasts and suggestions for environments reliant on blockchain technology, helping businesses make informed decisions. Fraud detection: Identifying instances of fraud within decentralized systems becomes more efficient with AI’s analytical capabilities. Data analysis and protection: AI models can produce projected data analyses and ensure secure data exchange within the blockchain network. Optimizing supply chain: By anticipating demand and optimizing delivery teams within the blockchain network, AI improves efficiency in supply chain operations. Personalized recommendations: Through decentralized identity systems, AI can provide customized product recommendations while ensuring individuals retain control over their data. Identity authentication: AI can evaluate biometric or identity data to authenticate the integrity of systems within the blockchain network, enhancing security. Leveraging Large Language Models (LLM) for Data Engineering LLM plays a vital role in data engineering, bringing efficiency and collaboration to the forefront: Quick prototyping: LLMs assist in creating code snippets, examples, and mockups for rapid prototyping during the design and simulation phases. Code quality enhancement: During the construction phase, LLMs enhance code quality. Teamwork facilitation: Predefined workflows and collaborative features promote teamwork among data engineering teams. Information databases and guidance: LLMs act as valuable informational databases, offering guidance, answering queries, and facilitating efficient instruction and training. The verdict: AI as the catalyst for progress Embracing AI brings numerous improvements across various technologies, including blockchain, data mesh, cybersecurity, quantum computing, and the metaverse. These improvements encompass reduced manual intervention, enhanced operational effectiveness, predictive outcomes, improved security protocols, and the creation of valuable content and code templates. With AI, the possibilities are limitless, and creative problem-solving thrives. Finally, the merging of AI and various technologies marks a transformative era, unlocking the true potential of innovation and progress. As businesses embrace AI-driven solutions, they can look forward to a future of efficiency, security, and endless opportunities. AUTHOR Sudheer Kotagiri Global Head of Architecture and AI Platforms SUBJECT TAGS #ArtificalIntelligence #CloudComputing #HybridCloud #FinOps #DevOps #CloudExpenditure