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
AI’s expanding role in marketing
AI’s expanding role in marketing Follow on: With the line between human and artificial intelligence fading every passing day, the application areas of AI are expanding incrementally. Take the field of marketing, for example. Given its emphasis on the emotional connection with the audience, marketing is a field that typically requires a human touch. It may appear that AI can be little to no help to marketers. However, AI can help improve many marketing workflows. We must recognize the future potential of AI and marketing technology being tightly entwined, especially in light of current AI phenomena like intelligent conversational chatbots that have taken the globe by storm. Software and data-driven marketing techniques have become crucial in today’s market, where businesses rely on cutting-edge marketing technology stacks, or “martech stacks.” As the marketing landscape matures, companies must harness AI to go beyond their current martech capabilities. With AI, marketers can analyze vast data sets, uncover patterns and trends, and produce targeted advertisements at scale. Unlocking the potential: Intelligent engagement ecosystems The sheer volume of sporadic marketing campaigns can overwhelm and desensitize consumers. To bridge the gap and create successful campaigns, AI establishes intelligent marketing ecosystems where organizations and consumers can connect on a personal level. Personalization at scale: Customer data, such as comments, videos, images, and social media posts, can be analyzed using predictive models powered by statistical and machine learning methods. This segmentation allows marketers to fully understand customer needs and run personalized, focused marketing campaigns that foster increased client interaction. Actionable insights with predictive analytics: By conducting detailed data analysis on vast customer profiles, ML algorithms generate valuable insights for marketers. Demand forecasting is just one example, helping marketers anticipate changes in consumer preferences and enabling proactive product recommendations. Campaign optimization with automation: AI automates numerous repetitive tasks in marketing campaigns, such as email sending, social media planning, budget tracking, and data analysis. This frees marketers to focus on strategy while ML algorithms handle forecasting and conversion rate predictions. Enhanced content impact with Natural Language Processing (NLP): Marketers can leverage Natural Language Understanding (NLU) and Natural Language Generation (NLG) to analyze consumer behavior, create personalized messages, and gauge consumer sentiment from social media posts and reviews. Embracing the AI advantage in marketing AI empowers marketing professionals to optimize their campaigns with ease and effectiveness: Account-Based Marketing (ABM) benefits from AI in locating high-value accounts, promoting personalized content creation, and gaining real-time insights into campaign performance. AI has also enhanced sales, enabling executives to understand client demands better using speech-to-text technology and calculating the probability of prospects becoming long-term customers. Unlocking the AI advantage safely and effectively Adopting AI in marketing requires a well-thought-out strategy: Set business goals: Identify operational and marketing inefficiencies and assess how AI can address them to achieve specific objectives, considering budget constraints. Choose the appropriate tools: Select AI platforms and solutions that align with marketing goals, ensuring efficient and cost-effective implementation. Develop internal expertise: To make the most of AI in martech, organizations must upskill current staff or hire new talent with data science and AI knowledge. AI: En-route reinvention, not replacement AI’s role in martech is predicted to soar, with the market value estimated to surpass USD 48.8 billion by 2030, growing at a CAGR of 28.6%. However, AI is not here to replace human marketers; it complements their expertise to enable more effective work. While AI provides data-driven insights, marketers can creatively use this knowledge to craft unique and compelling campaigns that drive long-term business growth. The future holds immense promise as marketers leverage AI’s potential to achieve new heights of success. Reference 1 https://www.globenewswire.com/news-release/2022/08/08/2494086/0/en/AI-in-Marketing-Market-To-Surpass-USD-48-8-Billion-by-2030-Growing-at-a-CAGR-of-28-6-Report-by-Market-Research-Future-MRFR AUTHOR Amit Tyagi Chief Marketing Officer SUBJECT TAGS #ArtificalIntelligence #CloudComputing #HybridCloud #FinOps #DevOps #CloudExpenditure