The Rise Of Agentic AI In Business Transformation
The Rise Of Agentic AI In Business Transformation Follow on: The rise of Agentic AI in business transformation Imagine a world where business operations don’t just run on predefined scripts but adapt dynamically—where systems anticipate challenges, make independent decisions, and execute tasks with minimal human oversight. This is not a distant future scenario but present-day reality powered by Agentic AI, a transformative technology that is redefining enterprise automation. Unlike robotic process automation (RPA), which relies on predefined scripts and rule-based workflows, Agentic AI can autonomously analyze situations, formulate plans, and execute tasks—mimicking human-like cognitive abilities. But Agentic AI moves beyond automating repetitive tasks to enable machines to make decisions, learn from experience, and collaborate seamlessly with humans and other systems. This shift marks a turning point in how businesses integrate AI into their workflows. A recent report by Gartner revealed that 55% of organizations are either piloting or have already deployed generative AI solutions. This new wave of AI is redefining enterprise automation, improving efficiency, and scaling operations with minimal human oversight. With AI-first organizations already witnessing significant efficiency gains through autonomous AI-driven decision-making, the question is no longer whether businesses should adopt Agentic AI but how quickly they can integrate it into their transformation roadmap. A shift from rule-based execution to adaptive intelligence For all its success, RPA remains inherently limited—its reliance on hard-coded rules and structured data limits its effectiveness in complex, dynamic environments. As a result, businesses that once leaned heavily on RPA are now hitting a ceiling—their automation strategies unable to keep up with the pace of operational change. RPA is insufficient for handling complex workflows, leading to inefficiencies and increased operational costs. Agentic AI breaks through these constraints by moving beyond scripted automation. According to a report by Gartner, by 2028, 33% of enterprise software applications will include agentic AI. Instead of requiring manual intervention for every process variation, AI agents autonomously interpret context, anticipate needs, and make real-time decisions. For example, an AI agent in a financial institution can monitor market trends, predict risks, and execute trades within seconds—something traditional RPA simply cannot achieve. More importantly, Agentic AI fosters collaboration between AI agents and human teams, ensuring automation enhances human intelligence rather than replacing it. This shift from passive automation to intelligent decision-making is what makes Agentic AI a critical component of modern enterprise transformation. Key attributes that set Agentic AI apart include: Autonomous planning and execution – AI agents define goals, strategize, and execute tasks independently, eliminating the need for human intervention. Continuous learning and adaptation – These systems retain memory, analyze past interactions, and improve over time, ensuring they stay relevant as business conditions evolve. Multi-agent collaboration – Unlike RPA, which works in isolation, Agentic AI collaborates seamlessly with other AI systems and human teams, optimizing workflows dynamically. Real-time decision-making – By processing live data from multiple sources, AI agents make informed decisions instantly, responding to market shifts, operational changes, and customer needs in real time. MAGE Agentic AI: A modular approach to autonomous automation Despite its potential, many businesses struggle with the complexity of AI implementation. To bridge this gap, MAiGE Agentic AI offers a modular, enterprise-ready framework designed to replace traditional RPA with self-learning AI agents. Unlike conventional automation systems that require constant human intervention, MAiGE enables businesses to deploy intelligent AI-driven workflows that continuously evolve based on real-time data. This reduces development costs, shortens implementation timelines, and extends automation coverage beyond rule-based tasks. Key components of MAiGE that drive intelligent automation include: Agent management – Dynamically orchestrates AI-driven workflows, eliminating the rigidity of traditional RPA bots. Tool integration – Seamlessly connects AI agents with enterprise applications, APIs, and cloud ecosystems, ensuring system-wide adaptability. Memory and learning – Retains historical context, allowing AI to self-optimize without requiring repetitive reprogramming. Planning and reasoning – Enables AI agents to analyze objectives, strategize solutions, and execute multi-step tasks proactively. Security and compliance – Ensures AI operations adhere to evolving regulatory and governance standards, reducing compliance risks. Performance benchmarking – Monitors AI-driven automation efficiency in real time, continuously refining execution strategies. Reshaping enterprise automation with Agentic AI The impact of Agentic AI extends far beyond simple process automation. AI-driven agents are already transforming industries, not just by automating tasks but by enhancing strategic decision-making, optimizing workflows, and creating new opportunities for efficiency. Across industries, Agentic AI is driving real-time, intelligent business decisions at scale. Some of its most impactful applications include: Customer service – AI-driven virtual agents autonomously resolve inquiries, reducing response times and lowering support costs. AI-powered virtual assistants are moving beyond scripted responses. They analyze customer sentiment, predict intent, and resolve issues proactively—improving resolution times while enhancing customer satisfaction. Finance & banking – AI-enabled fraud detection and risk assessment models improve security and operational efficiency. AI agents dynamically assess risk, detect fraud in real time, and make split-second trading decisions, providing an edge that traditional automation could never deliver. Healthcare – Intelligent AI agents automate patient scheduling, medical record analysis, and administrative workflows. AI is streamlining everything from patient management to diagnostics, helping medical professionals focus on higher-value care rather than administrative burdens. Retail & supply chain – AI optimizes inventory management, demand forecasting, and logistics automation. Agentic AI is revolutionizing operations by predicting demand fluctuations, optimizing logistics, and enabling predictive maintenance, reducing downtime and improving overall efficiency. Manufacturing – Predictive maintenance powered by AI minimizes downtime, enhances production efficiency, and reduces operational costs. AI-driven predictive maintenance systems are reducing downtime and optimizing production schedules, eliminating inefficiencies that previously required manual oversight. Accelerating transformation: Why enterprises are moving beyond RPA The shift from pre-scripted automation to intelligent, adaptive AI-driven workflows is no longer optional—it is a competitive necessity. Traditional RPA requires constant updates and maintenance, whereas Agentic AI eliminates this bottleneck by continuously learning and adapting. With AI-powered decision-making, businesses can respond faster to market changes, customer demands, and operational disruptions. This real-time adaptability ensures that enterprises remain competitive in an increasingly digital economy. For enterprises considering the shift, the advantages
AI Copilots: Your Intelligent Partner In The Future Of Work
AI Copilots: Your Intelligent Partner In The Future Of Work Follow on: AI copilots—intelligent, adaptive assistants—are gaining enterprise attention. Not replacing humans, but enhancing productivity, streamlining processes, and enabling seamless human-machine collaboration. So, what’s happening in the Copilot world today? AI copilots are already transforming industries, making a tangible difference in: Software Development – GitHub Copilot suggests code snippets, helping developers write faster and with fewer errors. Customer Service – AI copilots provide instant responses, enhancing customer interactions. Healthcare – AI assists doctors with diagnoses and treatment plans, improving patient outcomes. Finance & Data Analysis – AI copilots summarize reports, extract insights, and enhance decision-making. With great power comes great responsibility. Organizations must address: Data Privacy & Security – Protecting sensitive information in AI-driven processes. Bias & Transparency – Ensuring AI is trained responsibly. Human Oversight – AI should augment decisions, not replace human expertise. We can help you accelerate your Copilot Journey—from strategy to implementation—so you can harness AI’s full potential while staying secure, ethical, and efficient. Start your journey today! Listen to our recent Podcast , where Rajeev Bhuvaneswaran, VP of Digital Transformation & Innovation Services, takes the mic and shares deeper insights on AI Copilots. SUBJECT TAGS #HTCNXT #EnterpriseAI #AICopilot #ArtificialIntelligence
AI Transformation: A Strategic Roadmap For Enterprises
AI Transformation: A Strategic Roadmap For Enterprises Follow on: Nearly half (49%) of technology leaders in PwC’s October 2024 Pulse Survey report that AI is now “fully integrated” into their core business strategies. This statistic underscores the growing importance of AI as a cornerstone of modern business transformation. However, while its potential is immense, the journey to AI integration is far from straightforward. Enterprises face challenges ranging from aligning AI initiatives with business goals to addressing ethical concerns and legacy constraints. Drawing from insights shared by Sudheer Kotagiri, Senior Vice President of Data and AI at HTCNXT, in a recent podcast, here’s a roadmap to navigating the AI revolution. AI Landscape & Emerging Trends: The Future Is Now AI is transforming industries at scale—optimizing pricing, inventory, and customer engagement in retail; improving diagnostics and personalized treatment in healthcare; and enhancing underwriting, fraud detection, and claims in insurance. Notably, Generative AI is a game-changer, reshaping everything from marketing to customer service. Powered by cloud-based large language models (LLMs), businesses are embedding AI into daily operations, automating tasks, cutting costs, and delivering hyper-personalized outcomes. Best Practices for Strategic AI Integration Here’s the catch—AI’s transformative power isn’t a plug-and-play solution. Enterprises must think strategically to harness its full potential. The key lies in aligning AI initiatives with real business objectives. Listen to the podcast as Sudheer shares a secret sauce: start small. Pilot projects, he suggests, are the perfect testing ground to minimize risks, gather insights, and pave the way for scaling AI successfully. Investing in the right talent, designating leadership roles, and fostering collaboration across departments are also critical steps. Yet, the biggest roadblock isn’t always technology—it’s failing to adopt responsible AI practices. Addressing the Challenges of AI Integration AI integration isn’t without its hurdles. Legacy systems, incompatible data formats, and security challenges like navigating compliance with regulations such as GDPR can stall progress. But here’s where the podcast gets interesting—Sudheer dives deep into overcoming these obstacles. From investing in scalable cloud infrastructure to streamlining data integration and safeguarding sensitive data against cyber threats, it’s all about proactive planning. AI adoption also demands embracing a culture of experimentation, breaking down silos, and fostering a shift in mindset at every level, with leadership playing a pivotal role in sustaining momentum and keeping innovation at the heart of the enterprise. A Wrap For Now – But the Journey Continues in the Podcast AI is the ultimate disruptor, but it takes more than ambition to harness its potential. The enterprises that get it right are those that balance strategy, innovation, and ethics. At HTCNXT, we enable businesses to do exactly that—delivering frameworks that go beyond adoption to measurable success. What’s the missing link in your AI strategy? Don’t just wonder—discover it. Tap into Sudheer Kotagiri’s exclusive insights on mastering AI integration. Listen to the podcast and connect with us to turn your AI ambitions into measurable success.
Fact vs myth: Is there scope for investment in GenAI?
Fact vs myth: Is there scope for investment in GenAI? Follow on: Today, every business discussion leads to GenAI. We are, after all, reading about newer, bolder, and bigger investments in this innovative technology. Just last year, Microsoft reportedly invested $10bn in ChatGPT developer OpenAI for a ~35% stake. Again, in April, PE investors like Andreessen Horowitz and Sequoia Capital put an additional US$300 mn into the AI research company. Other than these hyperscalers and investment companies, even smaller companies seem to be betting big on GenAI. A BCG survey reveals that global executives prioritize artificial intelligence (AI) and generative AI (GenAI) in their tech investments. While 71% plan to increase overall tech spending in 2024, a staggering 85% will specifically boost their AI and GenAI budgets. Interestingly, businesses and PEs are not the only parties looking to invest in GenAI. Many governments tend to allocate a significant portion of their annual budget to GenAI research and studies. For instance, the Icelandic government has partnered with OpenAI to preserve its native language. Saudi Arabia has also recently revealed its plan to invest more than $40 bn in GenAI. This fervor around GenAI falls perfectly in line with the Gartner Hype Cycle. We are at the steep log phase (incline) of the hype cycle supported by swift advancements of this technology. Amid all these significant investments, the concern about tangible ROI is also becoming part of the boardroom discussions. Many stakeholders are now pondering about the ROI they can expect. However, to answer their question, we need to first look at the actual implementations of GenAI at scale. Even though big investments are being made into this technology, the latest reports suggest nearly 90% of GenAI POC pilots won’t be in production soon — and some might even get scrapped before they can take off. So, it is too early to paint a clear picture of the long-term ROI. There also seem to be many considerations beyond just the financial calculations. Let’s break it down. You can’t predict what you can’t measure There’s no cookie-cutter approach to determining the return on GenAI investments. Why? Various entities are currently investing in GenAI technology, and they differ from each other in their goals, business use cases, and positions in the GenAI adoption journey. Naturally, the ROI measurement also varies for these entities. Here’s what it means: Most organizations appear to be at the very first step of the GenAI journey. They’re still weighing on different use cases to see which one fits their operational needs. Despite their willingness to invest in GenAI, these businesses don’t have a clear business case in mind. And if you look at this cohort, return on investment is not even a question for them at this point. The next group is those who have done the due diligence and created a specific business case for applying GenAI. Now, two interrelated factors are at play for these groups. First, many of these initiatives are undertaken due to the fear of missing out since many competitors are investing in GenAI. These initiatives are often CEO-led instead of being a CFO- or CIO-led one. So, there is no apparent financial decision on which to base these use cases, leading to the second factor – weak business cases. As a result, organizations in this category often stop at small-scale GenAI implementations. And since the main objective of this cohort is to stay relevant, the ROI really doesn’t matter to them. In the third category, we have organizations that have not only dipped their toes in the GenAI frenzy but also created successful POCs based on strong use cases. They have invested significantly in human resources and technology to build prototype solutions that seamlessly fit into the intended use cases. These businesses are now looking at scaling their GenAI solutions and rolling those out at an enterprise level. At their current stage, the ROI is less financial. It is measured in terms of productivity or efficiency gain. The financial ROI will become clear once they scale the GenAI solution and implement it organizationally. Finally, we have companies that have scaled their GenAI solution and put it into larger production. These businesses are in a position to see a return on their GenAI investment. But there are some challenges for them, too. The topmost is cost optimization. As businesses scale out their GenAI application, they will use more resources from the available LLM platforms, burning their bottom line. If the cost spirals out of control, it might overshadow the intended benefit. This situation is very similar to the whole cloud movement. The cloud journey had three parts. First, we had to decide which cloud to use through deep analysis and consulting. Then, we migrated to our preferred cloud services. Once the new cloud-based business model became the norm, we started looking at different strategies to optimize cost. The same applies to this whole GenAI situation. Your financial gains will become prominent as you mature in the journey, and cloud technology’s success is a testament to that. Pitfalls That said, there are some common pitfalls of GenAI implementation that many companies are losing momentum. Lack of strategic vision: Some companies might rush into GenAI without clearly understanding how it aligns with their business goals. This can lead to poorly defined projects that don’t deliver the expected value. Data silos and integration issues: If legacy systems are not well-integrated, they can create data silos that hinder the effectiveness of GenAI models relying on centralized and high-quality data. Resistance to change: Legacy company employees might resist adopting new AI-powered processes, obstructing the success of GenAI implementations. Focus on novelty over utility: Getting caught up in the GenAI hype and implementing flashy features that don’t solve real customer problems can lead to wasted resources. Overcoming these bottlenecks will require careful consideration of various factors beyond just cost and revenue. A long-term strategic approach that goes beyond traditional metrics and focuses on both short- and long-term as well
As the cookie crumbles: How to look at various strategies for personalized experiences
As the cookie crumbles: How to look at various strategies for personalized experiences Follow on: The digital marketing landscape is undergoing a transformation that is unraveling past operating principles, especially for the Retail and Consumer Packaged Goods (CPG) industries, as well as other B2C companies. Established operating pillars reliant on third-party data to advance personalized advertising efforts is no longer sustainable with privacy concerns on the rise and increasing regulatory pressure. Google’s slow yet apparent move to phase out third-party cookies in Chrome (representing approximately 65% of the global browser market), or similarly, the deprecation of mobile advertising identifiers (MAIDs), is forcing brands to rethink. Furthermore, regulations like GDPR and CCPA are setting stricter rules on how consumer data can be collected and utilized. Does this seismic shift signal the doom of personalization strategies for advertisers? We don’t think so. Instead, it presents a unique opportunity for companies to create diversified strategies that grow their first-party data and leverage AI to discover user intent in new ways. Let’s take a closer look at how. Emerging practices in a privacy-first world In providing an avenue for advertisers to deliver relevant, personalized experiences while staying compliant with privacy standards, the following four strategies show opportunity and emerge at the top: Consent-based first-party data strategy As privacy regulations become more stringent and third-party cookies fade into the past, first-party data emerges as the cornerstone of personalized marketing. First-party data refers to the information a brand collects directly from its customers through various touchpoints—such as website visits, app interactions, social media engagement, and customer surveys. Since this data is voluntarily shared by users, it is not only more reliable but also more privacy-compliant. The key advantage of using first-party data is that it provides high levels of identify confidence, meaning marketers can confidently deliver personalized messaging. Additionally, brands can build strong feedback loops—where customer actions continuously inform and refine targeting strategies. This is particularly useful for high-value customers who regularly interact with the brand. Aggregator walled gardens Despite the shift toward privacy-compliant strategies, aggregator-walled gardens—such as Google, Facebook, and Amazon—remain powerful players in the world of digital advertising. These platforms have vast amounts of user data that can be leveraged for precise targeting, especially when combined with tools like Google Ads Data Hub, Facebook’s Advanced Analytics, and Amazon’s Clean Rooms. Ultimately, it provides brands with access to aggregated, anonymized data that can be used to create highly refined audience segments. Moreover, walled gardens offer several advantages, including solid data practices and high match rates—meaning that the data they provide for audience segmentation is reliable. Additionally, since these platforms cover a large swath of users across multiple devices and screens, they allow companies to reach their target audiences more effectively. Google Privacy Sandbox and Apple Private Click Measurement (PCM) In response to the phasing out of third-party cookies, both Google and Apple have introduced new initiatives that allow brands to target users in a privacy-compliant manner without relying on traditional identifiers. Google’s Privacy Sandbox aims to provide an alternative to third-party cookies by focusing on aggregated data and known audience segments. This initiative allows advertisers to target groups based on broad categories, such as interests, without tracking individual user behaviors across websites. Similarly, Apple’s PCM enables privacy-preserving ad measurement, which supports click and conversion tracking without using cookies or personal identifiers. Both Google and Apple’s solutions offer the ability to maintain traditional targeting practices like retargeting, frequency capping, and even attribution analysis, albeit using proprietary models that don’t rely on direct user identification. Contextual targeting As the use of personal identifiers becomes more restricted, contextual targeting is gaining renewed attention. This strategy focuses on delivering ads based on the content of a webpage, app, or platform rather than user behavior or demographics. Contextual targeting offers the advantage of being privacy-compliant, as it doesn’t require access to personal data or cookies. It can also cover a large audience, regardless of their personal identifiers or browsing history. Additionally, as contextual targeting doesn’t depend on individual user data, it works effectively across a wide range of environments. Key recommendations and real-world examples For Retail, CPG, and B2C brands looking to future-proof their personalization strategies, the following recommendations become imperative: Develop strong first-party data strategies Building a robust first-party data strategy is now a cornerstone for brands looking to maintain personalized marketing efforts. Focus on collecting data directly from your customers through interactions on brand-owned channels and be transparent with your users about how their data will be used, ensuring consent is properly obtained. Further, implementing a Customer Data Platform (CDP) will allow you to centralize this data, creating detailed, actionable customer profiles for more precise targeting. Take, for instance, this leading women’s apparel company that was facing a challenge in targeting and personalization due to lower match rates on platforms like Facebook. However, by harnessing the power of first-party data across key strategies of audience segmentation, extended retargeting, and exclusions, the company enhanced its cross-selling and upsell opportunities to niche audience groups. Cultivate relationships with walled gardens and experiment with data clean rooms Platforms like Google, Facebook, and Amazon still offer significant reach and data insights. To take advantage of their targeting capabilities, brands should build strong relationships with these platforms and experiment with data clean rooms. These environments allow brands to use aggregated platform data in a privacy-compliant way, enabling more refined audience segmentation and attribution without exposing individual user data. For example, a major US-based media company launched a data clean room that allowed advertisers to merge their first-party data with the company’s own audience insights while protecting personally identifiable information. This platform offered key functionalities, including discovering customer overlaps for improved targeting, providing ad exposure data to prevent excessive targeting with frequency capping, and enabling cross-platform attribution to optimize campaign performance. Implement a graph-based fluid identity framework With identifiers becoming increasingly scarce, brands should focus on building a graph-based identity framework that allows for the flexible resolution of user identities across multiple touchpoints and
Data Fabric: How metadata is transforming AI-driven data pipelines
Data Fabric: How metadata is transforming AI-driven data pipelines Follow on: Digital transformation is a monument built atop strong, modern data management. In the era of expanding the ‘datasphere’ and overwhelming amounts of dark data, stitching together the right pieces of information in real-time is the competitive edge. However, traditional data warehouses can fall short of meeting this business-critical demand. They can be inefficient and difficult to scale as businesses grow and the volumes of data they generate increase. Driven by this gap, organizations increasingly shift toward decentralized systems that distribute data across multiple locations and allow for more flexible and scalable data management. Data fabric architecture enables this transition by providing a unified and flexible framework for accessing, storing, and managing data across decentralized systems, whether interconnected or disparate. As businesses expand across geographies and hybrid environments, a comprehensive and flexible data fabric architecture is the key to achieving data management goals, like seamless integration, holistic governance, high quality, and top-notch security. Built on composable enterprise principles, the data fabric architecture integrates, manages, and governs data, leveraging metadata for enhanced discovery, understanding, and quality. AI-Driven Data Pipelines: The Frame The ‘data about data’ is the cornerstone through which data fabric architecture speeds up value extraction. How? By providing context, structure, and meaning to the raw data. Metadata describes the data sources, transformation rules, and target data structures generating dynamic codes for data integration. Thereby, ‘active’ metadata can be cataloged, analyzed, and utilized to drive task recommendation, automation, and overall efficiency enhancement of the data fabric. Metadata: The Foundation By providing essential context and structure to datasets, metadata also enables categorization, classification, and indexing to facilitate faster and more accurate AI model development. Leveraging this, AI systems can rapidly identify relevant data points, understand their relationships, and extract meaningful patterns. According to MIT, this streamlined process accelerates model training, improves prediction accuracy, and ultimately enhances the overall performance of AI applications. The human-readable data-serialization language, YAML, is commonly used for configuration files and in applications where data is being stored or transmitted. Its ability to store structured data in a clear and concise format makes it ideal for various purposes. In the context of Markdown documents, YAML helps add metadata, such as titles, tags, and descriptions, enhancing their organization and searchability. Similarly, this approach can be applied to AI-driven pipelines. By using YAML to define pipeline components, parameters, and dependencies, we can create more structured, manageable, and maintainable AI workflows. This allows for easier collaboration, version control, and overall pipeline efficiency. The result is multifaceted: Automated Data Ingestion Metadata enables the system to automatically recognize new data sources, file types, and ingestion schedules without manual intervention – Metadata-driven data systems can recognize new data sources independently. For example, suppose a company suddenly starts collecting data from a new app. In that case, the system will automatically adjust, label the new data, and add it to the fabric without anyone lifting a finger. Simplified Complex Data Transformation Metadata ensures that data transformations like filtering, sorting, and standardization are consistently applied across diverse datasets by providing the context for each data point. By providing detailed descriptions and context for data, it allows users to easily locate, understand, and utilize information. This enables licensing conditions, whether data can be used externally and/or internally with organizational rules. For instance, metadata-driven pipelines in Azure Data Factory and Synapse Pipelines, and now, Microsoft Fabric, enable ingestion and transformation of data with less code, reduced maintenance, and greater scalability than writing code or pipelines for every data source. Realized Data Objectives Metadata configuration creates a consistent source of reliable information to avoid data inaccuracies, errors, and retrieval issues. It enables flexibility that bolsters scalability and automation possibilities while allowing stakeholders more time to analyze data, extract real business value, and accelerate project delivery. Metadata also decreases redundant data management processes and reduces respective costs, like storage costs. Automated Complex Workflows Metadata automates multiple aspects of the pipeline, including data quality checks, data standardization, and error-handling routines. For instance, metadata-driven automation can be used to define rules for data validation, ensuring that data adheres to specific formats, constraints, and business logic. It can also automate the process of standardizing data across different sources, ensuring consistency and compatibility. Additionally, metadata can be used to create error-handling routines, such as defining exception handling mechanisms or triggering notifications in case of errors. Metadata can also facilitate checkpointing for automatic restarts in case of failures by defining restart points and managing pipeline continuity. This ensures that the pipeline can recover from errors and resume execution from the last successful checkpoint, minimizing downtime and improving overall reliability. End-to-End Audit and Proactive Monitoring Metadata is embedded into audit trails across every stage of the data pipeline, ensuring compliance and traceability. It enables real-time monitoring of pipeline performance, providing early warnings for potential bottlenecks. Active metadata intelligently suggests recommendations and alerts — making it easier for people to make the right decisions after stopping the pipeline workflows when data quality issues are detected. Parallel Processing and Task Execution Metadata-driven frameworks allow simultaneous processing of multiple jobs, speeding up data pipelines significantly. They provide a clear understanding of data dependencies and relationships, enabling parallel processing, where multiple tasks can be executed concurrently. Optimized AI pipelines with parallel processing, in turn, reduce overall cycle times and improve resource utilization. By identifying tasks that can be executed independently, metadata-driven frameworks can distribute the workload across multiple processors or machines, leading to substantial performance gains. This improves scalability and performance by maximizing resource utilization and reducing cycle times. Ensured Reusability and Extensibility Metadata supports reusable components like data transformation utilities and quality check functions, reducing development time and promoting consistency. When comprehensive and compelling, it provides clear descriptions that accelerate usage, while outlining the formats it is available in, and suggesting potential ways it can be reused. It ensures that all data shared on data portals is discoverable, understandable, reusable, and interoperable – by both humans and technology/artificial
The impact of AI copilot on developers productivity
The impact of AI copilot on developers productivity Follow on: In software development, developers often get lost in a whirlwind of repetitive tasks, significantly impacting the time they actually spend writing code. Project teams need advanced tools with machine learning (ML), predictive analytics, and natural language processing (NLP) capabilities that can augment human skills, automate routine tasks, and empower developers to focus on their coding. Here, GenAI is gaining popularity as the potential gateway to streamlining and ensuring the time, quality, and safety invested in coding. Enter AI copilot, these intelligent assistants help developers write and edit code more efficiently, enabling them to complete tasks up to two times faster. As projects grow in complexity, the challenges in creating better code will increase with them, necessitating a shift in focus to improve productivity and accelerate the software development lifecycle (SDLC). This blog explores the impact of AI copilots in SDLC, challenges, real-world examples, and future implications. Let’s decode AI copilots Initially introduced for automated testing and basic code generation, AI copilots have evolved rapidly into sophisticated mediums that deliver higher-quality software solutions. Now, they understand complex code structures, provide real-time code suggestions, auto-complete features, and even fill in blocks of code based on context. Take, for instance, GitHub Copilot launched in 2021 in collaboration with OpenAI. It provides code suggestions based on context and allows developers to interact with an API without leaving the editor, empowering them to complete tasks 55% faster. Another example could be TabNine, powered by deep learning models, offers autocomplete suggestions for multiple programming languages. Or even AWS CodeWhisperer, an Amazon offering that enhances developer productivity by generating code recommendations. Notably, AI copilots are rapidly evolving, offering invaluable assistance and revolutionizing the SDLC. Explore the ‘why’ behind copilot assistance in SDLC Today, developers encounter a range of challenges that hamper coding and add complexity to their tasks. Inadequate documentation, frameworks, and APIs lead to slower development cycles, not to mention repetitive tasks, integration issues, and security concerns that can make the coding process monotonous. Here, AI copilots are playing a transformative role in promoting developer productivity and creativity. According to a recent Microsoft study, early users of Microsoft Copilot didn’t want to go back to work without it—70% stated it’s productive, and 68% said it improved their work quality. Take a look at how AI copilots speed up different phases of SDLC: Development: Accelerates coding and helps developers focus on more crucial aspects of their work with AI assistance – code suggestions, autocompletion, and automated code generation. Security: Identifies potential vulnerabilities as early as possible and suggests secure coding practices before they become critical problems. For example, AWS CodeWhisperer flags common security issues such as SQL injection and cross-site scripting. Quality assurance: Maintains high code quality and reliability by identifying potential bugs and generating automated testing scripts. Skill development: With context-aware suggestions, these tools often serve as educational resources, helping developers learn new coding techniques and best practices with examples and explanations. Documentation and collaboration: Assists in generating and maintaining documentation by automating the creation of comments, README files, and API documentation. Thereby facilitating better communication within the project. Project management: Analyzes requirements and user stories to generate initial code templates and development plans, helping teams estimate timelines and allocate resources more efficiently. A few challenges and considerations Even though the benefits are massive, AI copilot integration can come with its own set of challenges. Accuracy issues: AI assistance relies heavily on training sets – poor data quality and biases can lead to inaccurate codes. Deploying such codes without review and validation can lead to bugs and errors, increasing post-release support costs. Overreliance and skill degradation: Developers may become reliant on AI copilots without applying their robotics quotient (RQ) to question the copilot’s output, decreasing their coding skills. Currently, GitHub Copilot backs 46% of developers’ code, with the code acceptance rate gone up to more than 35%. Security and privacy issues: Access to proprietary and sensitive information increases the risk of data exposure, either through unintended leaks or malicious attacks. Recently, to help with security and strengthen team expertise, Microsoft released Copilot for Security with new capabilities to protect and govern AI use. Integration complexity: Integrating AI copilot may involve restructuring code repositories and adapting to new workflows. This often requires significant modification of existing developmental environments and toolchains, leading to temporary productivity dips. Minimize such business disruptions with strategic planning and allocating resources. Goals misalignment: If the project goals are not well communicated with the AI copilot, it can lead to development bottlenecks, impede progress, delay time to market, and increase costs. Resolving such misalignments and not repeating them is critical. Ethical and regulatory concerns: Implementing transparency mechanisms and refining algorithms to mitigate bias are challenging yet crucial in safeguarding the development process. While these limitations may be intimidating, the risk associated with not adopting or experimenting with copilot is greater, as employees will turn to unauthorized AI tools, increasing risks like data leakage and AI hallucinations. Furthermore, it is critical to quantify the impact of the copilot on your projects and teams. Having said that, the improvements in deliverables and lower costs make a stronger business case for continuing to invest in AI copilot. Several companies have successfully implemented AI copilots with positive outcomes. One major tech company in IT consulting and outsourcing reduced its resources, effort, and overall deployment time by leveraging GitHub Copilot, accelerating the coding process. This significantly elevated the team’s capabilities, contributing to a more streamlined and effective project execution. Another large IT services organization with 1000+ developers enhanced its productivity by setting up an operating model with select teams and integrating copilots into its software engineering processes. This offered context-aware assistance throughout the SDLC, helping it shape a next-level product delivery. Copilot trends elevating SDLC innovations Future iterations of AI copilots are even more promising, with advancements that can redefine software development practices. Enhanced NLP models will enable AI copilots to understand the context behind complex queries and
The future of human-AI Interaction: The role of virtual avatars and VR
The future of human-AI Interaction: The role of virtual avatars and VR Follow on: Artificial Intelligence (AI) is redefining human-machine interactions — evolving from rule-based chatbots to context-aware virtual assistants. More recently, generative AI (GenAI) solutions have enabled businesses to hyper-personalize products and boost real-time virtual assistance. McKinsey has found that over 80% of customer interactions benefit from hyper-personalized decision-making support through conversational chatbots. Moreover, prioritizing customization yields significant revenue growth, with businesses seeing a 40% increase in revenue compared to those with less emphasis on individualized experiences. But is that enough for modern, AI-savvy consumers? Digital natives are gradually seeking more human-like interactions with AI. A study revealed that technical and non-technical users prioritize cognitive and social-emotional skills over technological prowess, indicating a shift towards AI interactions that are both personalized and more human-like. Virtual avatars and virtual reality (VR) are tools for humanizing AI. These emerging technologies replicate human interactions to offer natural, emotionally engaging, and immersive experiences. Our blog will delve into virtual avatars, virtual reality, the benefits of humanized AI, and their practical use cases. A closer look at virtual avatars and VR Virtual avatars are digital assistants that leverage natural language processing (NLP), computer vision (CV), and emotion AI to provide users with more humanized interactions. These avatars are estimated to generate revenue of USD 270.61 billion, growing 49.8% between 2024 and 2030. This growth is driven by the rising interest in human-like engagements, as virtual avatars understand and respond with more empathy and insight, enhancing the connection and communication between users like never before. AI avatars are more advanced than the typical chatbots at the bottom of the screen. Conversations with traditional chatbots can frustrate the user as they often provide pre-programmed responses, prompting users to seek assistance from a live agent. In contrast, digital avatars enhance the user experience by accelerating buying decisions through 3D virtual try-ons, showing customers products that match their interests at affordable prices, and so on. Virtual reality, or VR, is a digital simulation of the real world. When coupled with AI, VR transforms human engagements by creating immersive experiences, blurring the lines between physical and digital realms. Beyond transactional interactions, it fosters a sense of presence and realism, making AI interactions more natural and meaningful. For instance, machine learning in VR education analyzes user learning styles, identifies challenging topics based on user history, creates personalized study material, and provides intelligent feedback. This makes learning more engaging and effective. A study revealed that employees upskill 4X faster when trained through VR and are 3.75 times more emotionally connected to the content. AI integration further optimizes user interaction when merged with avatars and VR technology. However, understanding the historical timeline of AI’s evolution towards humanized interactions is crucial for comprehending its relationship with users. Charting the history of AI interactions Historically, humans and computers existed as separate entities, with technology serving as a tool under human direction. This relationship has undergone a dramatic transformation in recent years. As AI becomes increasingly incorporated into our daily lives, we’re witnessing the rise of more sophisticated AI systems capable of recognizing emotional cues like tone of voice and facial expressions. This development is paving the way for more nuanced AI interactions. The automotive industry exemplifies this shift. Modern AI systems in vehicles not only enable advanced driver assistance features but also monitor driver alertness, suggesting breaks when needed. They personalize in-car entertainment, adjust climate settings, and enhance the overall driving experience. In healthcare, virtual reality (VR) is revolutionizing patient care. By creating immersive 3D models, VR helps patients better understand their conditions and upcoming procedures. This approach not only increases patient awareness but also provides reassurance, potentially leading to improved outcomes. The perks of AI-infused avatars and VRs Digital avatars and VR technology are revolutionizing diverse industries and enhancing employee and customer experiences. AI interactions are now unparalleled by boosting efficiency, providing data-driven decision support, and addressing user queries naturally. Additional benefits include: Trust and verification: GenAI models can generate falsified answers that seem correct, known as AI hallucinations. XAI can explain the rationale behind GenAI systems’ content, accelerating the process of rectifying nonsensical content, strengthening users’ faith in GenAI applications, and building GenAI systems grounded in reality. Enhancing mental health As an additional treatment, therapists can utilize VR in cognitive behavioral therapy to tactically expose patients to anxiety triggers, phobias, and post-traumatic stress disorder (PTSD) in a controlled environment. This approach can lead to better performance in real-life situations. Elevating design and modeling process VR technology empowers manufacturing companies to recreate real-world scenarios and test prototypes, resulting in better quality, resource savings, and reduced costs and time. Navigating the challenges Despite the many benefits of avatars and VRs, there are several hurdles. These include: Mastering human-like characters in virtual avatars Striking the right balance between human expressions, movements, and gestures is a highly intricate process that demands cutting-edge motion capture systems. Rising ethical and security concerns The potential for technology misuse has increased. Therefore, developers must create avatars and VR systems that adhere to safety standards and privacy guidelines, ensuring the protection of users from potential risks. Minimizing algorithmic bias Avatars and VRs might produce biased outcomes due to biases present in training data. Moreover, AI models might be working on black box models, requiring more transparency to enable users to comprehend the rationale behind AI decisions. Facilitating impersonal experiences Hyper-automation has the potential to lead to impersonal experiences. Therefore, organizations must find a delicate balance between leveraging AI for humanization and preserving genuine human interactions. An excessive reliance on AI may compromise the overall quality of customer experiences. To address these challenges effectively, developers must prioritize edge computing, enhance user interfaces, employ diverse training data, and anonymize data using decentralized technologies like blockchain. A glimpse into the future of humanized interactions Humanized AI interactions present infinite opportunities with VRs and avatars, which cater to individual needs and foster deeper connections between brands and customers, surpassing usual transactions. The human-centered AI
Unleashing the potential of XAI: Maximizing GenAI benefits and reducing risks
Unleashing the potential of XAI: Maximizing GenAI benefits and reducing risks Follow on: AI has permeated every part of our lives, evolving from recognizing patterns to achieving human efficiency. Treading deeper into the AI landscape, generative AI (GenAI) has become the new normal, reshaping every industry. Research estimates that GenAI could contribute between $2.6 trillion and $4.4 trillion to the economy annually while accelerating the impact of all artificial intelligence by 15% to 40%. In the next three years, businesses that shy away from GenAI and AI could be perceived as outdated. Despite the opportunities, there’s still apprehension surrounding the associated risks of GenAI adoption. Unlike AI, GenAI typically leverages any available data types or formats to generate results. This might be useful but also very unreliable. A recent survey indicates that 20% of global business and cyber leaders cited data leaks and exposure of personally identifiable information through GenAI as a top concern. How can we mitigate the AI trust issue? By ensuring transparency across the AI process, eliminating bias, and clarifying how AI reached its decision — through explainable AI (XAI). GenAI’s meteoric rise As highlighted earlier, GenAI, while still in the nascent stage, has carved out a niche for itself. Continuously evolving, it has broadened the range of utility from building customized models to generating creative ideas, engaging in context-aware conversations, and producing images and videos based on text commands. Its impact extends beyond any single industry. For instance, in insurance, GenAI can help underwrite policies by analyzing vast amounts of structured and unstructured data to identify patterns and predict risk. In retail, GenAI is used to make personalized product recommendations — by analyzing customers’ past purchases, preferred brands, and even social media activity. At the same time, retailers can use this information for more effective and targeted customer retention. Additionally, businesses have witnessed increased efficiency, automation of trivial tasks, minimization of labor costs, and overall transformation in their interactions with GenAI. To illustrate better, a North American life insurance provider digitized its core systems with GenAI, improving the response time and delivering exceptional customer experience. Hence, companies increasingly rely on GenAI to make vital decisions, pushing for better ways to convey how deep learning and neural networks work. When users, regulators, and stakeholders understand the ‘why’ behind GenAI’s insights, it fosters a sense of trust. This is achieved through innovative tools and processes that simplify the explainability of predictive insights. Significance of XAI between user trust and GenAI XAI assists human users in understanding the reasons and purpose of GenAI, encouraging responsible usage. As businesses shift from black-box processes to transparent white-box models, they deepen trust between GenAI models and users while fostering continued innovation. This highlights the importance of bridging the gap between GenAI workings and its users through XAI in several areas: Trust and verification: GenAI models can generate falsified answers that seem correct, known as AI hallucinations. XAI can explain the rationale behind GenAI systems’ content, accelerating the process of rectifying nonsensical content, strengthening users’ faith in GenAI applications, and building GenAI systems grounded in reality. Security and ethics: XAI is elemental in securing GenAI systems against manipulative attacks and ensuring they adhere to ethical standards. If GenAI commits a mistake, it can attract the public, media, and regulators’ attention. Legal and risk teams can then use explanations from the technical team to ensure GenAI follows the law and company rules. Interactivity and user control: Creating user-friendly XAI is indispensable yet a huge challenge with GenAI models operating as black boxes. Advanced XAI techniques enhance user interactivity by enabling users to question the rationale behind AI-generated content, request a different answer by fine-tuning the command, and receive tailored clarifications based on their explainability needs. Modernizing XAI with breakthrough technologies
Transforming Customer Support with AI in Contact Centers
Transforming Customer Support with AI in Contact Centers Follow on: The Road to AI Can AI do to contact centers what it did to Industry 4.0? All industry use cases and market predictions point in the direction of AI-driven contact centers — as the next strategic step for boosting agent productivity, supercharging customer experience, and increasing operational efficiency. According to the Innovation Center for Artificial Intelligence, AI chatbots helped the banking sector save approximately $8 billion in the preceding year. On the other side, fintech leader, Sebastian Siemiatkowski, co-founder and CEO of Klarna, predicted that their ChatGPT-powered AI assistant will generate an estimated $40 million in additional profit by 2024. Notably, the Call Center AI Market was worth USD 1.6 billion in 2023 and is rapidly expanding — having projected to reach USD 9.9 billion by 2032, with a CAGR of 22.7%. Let’s delve deeper into the benefits of cognitive contact centers, and how enterprises can unpack superior CX with them. The AI Advantage for Contact Centers A study on a company with 5,000 customer service agents revealed impressive results with generative AI adoption. Issue resolution rates soared by 14% per hour, while the time spent handling each issue decreased by 9%. Additionally, generative AI contributed to a 25% reduction in both agent turnover and customer requests to speak with a manager. So how can contact centers leverage AI? Conversational AI, often the first thought for contact centers, utilizes Large Language Models (LLMs), Natural Language Processing (NLP), and Machine Learning (ML) technologies — to enable customers to interact with AI-powered systems through voice and text-based channels, including: Intelligent Interactive Voice Response (IVR) can function intuitively to deliver real-time and human-like exchanges across channels. Chatbots engage in real-time conversations by interpreting customer queries to identify intent and provide satisfactory responses. Virtual Assistants or Assistants , like Siri and Alexa, converse with users to provide personalized support and consistent experiences across devices and platforms. Conversational AI solutions are gaining popularity because they can streamline customer interactions, reduce wait times for instant resolutions, and deflect simple inquiries, encouraging self-service. In times of labor crunch and high agent attrition, they can free up agents for more complex issues that require critical problem-solving. The second most popular application of AI in contact centers is data analysis. AI, specifically GenAI’s ability to analyze voluminous data, can scan through various statistics and key performance indicators (KPIs) to produce high-value insights for improving agent performance and customer satisfaction. This saves enterprises the trouble of manually analyzing data, allowing them to: Gain insights into agent performance, call resolution times, and customer sentiment. Optimize agent schedules, performance, and productivity through targeted, data-backed resource allocation and training programs. Design proactive customer service strategies by anticipating customer needs — based on past interactions across multiple touch points. 6 Benefits of AI Implementation in Contact Centers Enhanced self-service capabilities Improved agent productivity Reduced operational costs Actionable customer insights Intuitive customer engagement Lowered agent attrition Around the World: Successful Use Cases of AI in Contact Centers How can enterprises implement AI for contact centers? Let’s find out some popular use cases gaining traction across industries. Use Case #1 – AI systems for emergency response centers 911 response centers in the US are deploying AI tools to handle non-emergency calls. In a 2023 survey of 9-1–1 centers, 82% of respondents cited understaffing, with 74% reporting burnout. AI-powered triage systems can prioritize calls during high volume or non-emergencies to optimize agent efficiency. AI can also help dispatchers with real-time translations and speech processing in fast-paced scenarios. The latter not only helps with keeping call records but also flags key details such as location and the nature of the emergency — empowering responders to focus on issue resolution and not on documentation. Thus, AI deployment in high-impact sectors like healthcare emergency and disaster management can help bridge the gap between critical needs and timely responses, powering better outcomes. Use Case #2 – Self-service in insurance Insurance enterprises are leveraging AI-driven contact centers beyond the pleasures of self-service and more toward proactive support. Consider a scenario where a customer reports property damage to their home insurer. The insurance AI assistant authenticates the customer and guides them through the claim process. It also asks questions to help the customer understand the situation, such as the extent of the damage, the potential cause, and any immediate safety concerns. Throughout the process, the homeowner receives automated updates on their claim’s progress and simultaneously they can also ask the AI questions about the next steps, coverage details, or temporary accommodation options if needed. If the issue warrants complex problem-solving, like coverage disputes, the AI guides the policyholder to a live agent for better resolution. Use Case #3 – Cognitive assistants for human-like interactions Many payment solution enterprises have a global customer base that traditionally requires a massive contact center team — with representatives fluent in various languages. However, modern contact center AI solutions can deploy intelligent assistants with advanced speech-to-text and text-to-speech technologies for handling multilingual inquiries. Moreover, with the advent of generative AI, improved context recognition among AI assistants can make conversations as natural and human-like as possible. Additionally, fintech companies can also integrate a customized voice persona for the AI assistant to foster a consistent brand personality for all customers across borders. Use Case #4 – Contact center automation In travel contact centers, AI can reduce the burden on agents by automating repetitive tasks like flight rebooking after cancellations. An AI system can analyze the traveler’s preferences based on past records, identify alternative flights based on real-time availability, and eventually guide them through the rebooking process — all without human intervention. This improves their fast contact resolution (FCR) numbers, thus reducing the need for follow-up calls, which is a key performance indicator of customer delight — customer satisfaction rates can decrease by 45% when an issue is not resolved at first contact. Take the Leap with MAGE MAGE is HTCNXT’s built-to-purpose platform that empowers enterprises to build their AI