IDC

This year, the convergence of cloud computing and generative AI (GenAI) is creating unprecedented opportunities for innovation. For growing tech vendors and startups, leveraging these technologies is not just a competitive edge—it’s a necessity. This blog explores how GenAI is transforming cloud infrastructure, offering practical insights and strategies to help your business thrive.  The Convergence of Cloud and GenAI  Cloud computing provides scalable, on-demand resources that allow businesses to be agile and responsive. Generative AI, on the other hand, brings advanced machine learning capabilities that can turn vast amounts of data into actionable insights and automate complex tasks. The intersection of these technologies marks a paradigm shift, enabling smarter, more efficient, and highly adaptive cloud environments.  Key Benefits of Integrating GenAI with Cloud Infrastructure  Predictive Scaling and Resource Allocation  AI models can forecast workloads based on historical data, enabling dynamic resource provisioning. This means your cloud infrastructure can automatically scale up or down based on demand, ensuring optimal performance and cost-efficiency. Predictive scaling helps avoid over-provisioning and under-utilization, common pitfalls in traditional cloud management.  Predictive scaling involves using AI to analyze patterns in data usage and forecast future needs. This proactive approach ensures that resources are available when needed, without wasting money on unused capacity. For example, an e-commerce platform might see spikes in traffic during holidays. AI models can predict these spikes and adjust resources accordingly, ensuring smooth operation during peak times.  Automated Cloud Cost Optimization  Managing cloud costs is a critical challenge for startups and growing tech vendors. GenAI can analyze spending patterns and recommend cost-saving measures. AI-driven tools can help you select the most cost-effective instances, rightsizing your infrastructure, and even automate budget alerts and recommendations. This ensures that you get the most out of your cloud investment without unnecessary expenses.  Cost optimization is not just about reducing expenses but about making smart investments in cloud resources. AI can provide detailed insights into where money is being spent and identify areas where savings can be achieved. For instance, AI might suggest moving non-critical workloads to less expensive storage options or shutting down underutilized instances automatically.  Intelligent Load Balancing and Traffic Management  Efficient load balancing and traffic management are crucial for maintaining high performance and user satisfaction. AI-powered traffic prediction models can anticipate traffic spikes and direct load accordingly, optimizing resource use and minimizing latency. Additionally, smart CDN optimization and adaptive application performance management ensure that your applications run smoothly, even under varying load conditions.  AI can predict traffic patterns and dynamically adjust load balancing to ensure optimal performance. This is particularly important for applications with fluctuating traffic, such as social media platforms or online gaming services. By distributing traffic efficiently, AI helps prevent bottlenecks and ensures a seamless user experience.  Enhancing Cloud Security with GenAI  Security is a paramount concern for any tech business. GenAI enhances cloud security by providing advanced anomaly detection, adaptive security policies, and automated threat response.  Anomaly detection involves using AI to identify unusual patterns in data that could indicate a security breach. By continuously monitoring network traffic and user behavior, AI can detect and respond to threats in real time. Adaptive security policies use AI to adjust security measures based on current threats, ensuring robust protection.  Automated threat response leverages AI to triage incidents, contain threats, and even predict future vulnerabilities. This proactive approach to security helps businesses stay ahead of potential threats and maintain a strong security posture.  AI-Driven Cloud Management  GenAI streamlines cloud management through intelligent monitoring, automated incident response, and performance optimization.  Intelligent monitoring involves using AI to analyze logs and performance metrics, identifying issues before they become critical. This proactive approach minimizes downtime and ensures smooth operation. Automated incident response uses AI to classify and route incidents efficiently, often resolving common issues without human intervention.  Performance optimization is another key area where AI can make a significant impact. By continuously analyzing performance data, AI can identify bottlenecks and recommend optimizations to improve efficiency. This ensures that your cloud infrastructure runs at peak performance, delivering the best possible user experience.  Cloud-Native AI Development  Developing AI models in a cloud-native environment offers significant advantages in terms of scalability and flexibility.  Containerized environments package AI models with all their dependencies, ensuring consistency and portability. This simplifies deployment and scaling, allowing businesses to respond quickly to changing demands. Kubernetes, a popular orchestration tool, automates the deployment, scaling, and management of containerized AI services, providing robust and reliable infrastructure for AI workloads.  Serverless architectures offer another layer of efficiency. Event-driven AI processing allows code to be executed in response to specific events, optimizing resource usage and reducing costs. This pay-per-use model means businesses only pay for the computational power they consume, making it a cost-effective solution for many applications.  MLOps, the practice of combining machine learning with DevOps, is essential for managing the lifecycle of AI models. Automating the deployment, monitoring, and retraining of models ensures they remain accurate and relevant over time. Version control for data, models, and code is crucial in maintaining consistency and reproducibility. Continuous integration and deployment (CI/CD) pipelines enable frequent updates and improvements, keeping AI systems at the cutting edge of performance and reliability.  Future of Cloud AI  Looking ahead, the integration of AI with emerging technologies like quantum computing and autonomous systems will further revolutionize cloud infrastructure. Companies that stay ahead of these trends will be better positioned to leverage new opportunities and maintain a competitive edge.  Quantum computing will enable the development of more sophisticated models and algorithms, accelerating research and innovation. Hybrid quantum-classical cloud architectures will become more prevalent, allowing organizations to harness quantum computing’s power for specific tasks while leveraging classical computing for others.  The next generation of cloud infrastructure will be characterized by autonomous systems powered by AI. These self-organizing and self-optimizing environments will manage resources, detect anomalies, and resolve issues without human intervention, reducing operational complexity and enhancing system reliability.  Conclusion  The convergence of GenAI and cloud computing is revolutionizing how tech vendors and startups manage their infrastructure. By embracing predictive

Ever since GenAI burst onto the scene, the CIOs on IDC’s CIO Executive Council have threaded through all gatherings and conversations.   CIOs are actively engaged in proofs-of-concept, exploring build versus buy options, and working on updated governance – standard fare for addressing emerging and new technologies.  AI and GenAI aren’t your grandfather’s emerging technology.  Everything from business models, customer behavior, employee productivity, and critical skills for the workforce is rapidly changing.  CIOs keep raising one key question:  “How does their company view the strategic role of IT in the age of AI?” Executive Council CIOs are experiencing a spectrum of IT roles in the age of AI.  Some have benefited from a consolidation of scope & responsibility under their remit.  Other CIOs are experiencing their role being relegated to “running the utility” and aspects of technology leadership dispersed amongst other “C’s” including a new Chief AI Officer, Chief Digital Officer, and Chief Technology Officer.  Regardless of the current state, Executive Council CIOs along with IDC analysts agree that the current technology and risk environment is a gift to bolster making the case to the C-Suite and Board of Directors of the strategic importance of IT.   Here are the top 3 “gifts”: CEOs expect CIOs to drive digital transformation to create new revenue streams:  IDC’s Worldwide 2024 CEO Survey found that over the next two years, CEOs are increasingly looking to the CIO as a strategic business leader of technology to enable the business growth strategy.  While the AI leadership profile is still evolving, about half of the organizations surveyed are adding AI leadership responsibilities to an existing technology or functional leader.  AI has the strategic expectation arrow pointing squarely in the direction of the CIO. The Critical Ingredient to Fuel AI is Data:  The current state of a company’s corporate and customer data is fully revealed as more and more AI use cases are embraced.   Every functional line-of-business and technology leader is experiencing the angst of disconnected, missing, poor quality and inaccessible data to constantly train AI models.  Of IDC’s identified top 10 GenAI Use Cases for Business, 50% of the top use cases are in marketing. The C-Suite has identified a recession proof investment in AI to improve the digital customer experience, supporting business growth.  Marketing is facing data and technology barriers to move at the speed of the customer that CIOs are best positioned to resolve.   The data paradox is reshaping the CIO’s strategic collaboration and relationship with key functional leaders, such as the CMO.  The gift of necessity has been delivered to the CIOs doorstep. Check out more in the report The Data Paradox Reshaping the CIO and CMO Relationship. CrowdStrike and the Scare in the Boardroom:  During a recent IDC CIO Executive Council Connect, I hosted Frank Dickson, Group Vice President, Security & Trust, with the Council for a deep dive conversation on 3 big things CIOs Must Do because of the CrowdStrike Outage (read more in CrowdStrike Update Outage Exposes Four Critical Issues:  Next Steps for CIOs). Out of the conversation came the realization that while extremely painful for many in our community, it is the perfect gift for CIOs and CISOs to demonstrate the strategic importance of dealing with technical debt, modernizing IT infrastructure and getting back to the basics of robust systems management.   CIOs have a direct correlation to negative brand and revenue impact if the company lacks a strategic mindset about and investment in technology.  Harnessing these 3 inflection points, CIO’s have the opportunity today to make a strong case about the strategic role that IT plays to enable business growth.  The following is the IDC CIO Executive Council’s Guidance to position IT as a Strategic Investment.   Speak the language of the business:  Put the words “IT” and “technology” to the side.  Focus on the business problem at hand and the investment that is required.   Start with defining what is the critical investment is to be successful and how you will measure business outcomes.  For example, “We need $15M to ensure that we are buffered from an outage such as CrowdStrike and mitigate risks including short-term revenue loss and longer-term revenue impact due to a poor customer brand experience. “ Adopt an Investment Philosophy:  For some of IDC’s Council members, IT is still viewed as a utility.  Flip the way you speak about IT to change the paradigm.  Frame up IT spend just like an investment banker.  Tell the story based upon revenue, cash and operating income.  For example, the strategic investment in IT will generate $x in revenue.  Do not go for the pot of gold, rather define the right size of investment for the near-term and longer-term expected returns.  Breaking down your investment into bite size chunks allows you to truly prove the value of IT more immediately. Lay out the Investment Process for Business Acceleration:  Identify the steps to deliver value back to the business including how long it will take for the business to start to realize an initial return and expected timeframe for full results.  Council members highlighted the benefit of using the term “business acceleration” rather than “change management” to smooth out the typical user adoption challenges and align to language the CEO and Board of Directors understand. Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here. source

As geopolitical tensions continue to rise, the global semiconductor supply chain must add new locations outside of existing ones to meet customer requirements. In the past, semiconductor manufacturing was concentrated in the Greater China region (China and Taiwan). However, under the trend of de-risking and globalization, the demographic dividend and cost advantages in Southeast Asian (SEA) countries and India, which are also mostly members of multilateral trade agreements such as the RCEP and CPTPP, have made it the next important semiconductor development base. With its strengths in packaging and testing, Malaysia is actively expanding into semiconductor manufacturing and design. Singapore is the only country in SEA with foundry manufacturing and the most complete semiconductor supply chain. Vietnam and the Philippines have a competitive advantage in terms of cost and labor and have been in recent years actively developing their testing and packaging capabilities, among other areas. India has a large domestic market to attract investments and strong capabilities in design, innovation, and talent. The governments of these countries also have lucrative incentive programs to get the attention of semiconductor market players. Overall, SEA and India have built a solid foundation in the semiconductor supply chain and are aggressively looking to expand into high-value-added areas such as wafer fabrication and design. But, do Southeast Asia and India have the right conditions to capitalize on semiconductor market opportunities in the future? IDC believes that to develop its foundry industry, six major challenges need to be addressed in the short term: Infrastructure – At present, the primary issue for the development of the industry in SEA countries is the availability of adequate infrastructure, including reliable power supply, water resources, transportation networks, and telecommunications, all of which are critical to semiconductor fabrication. Compared to other electronics manufacturing industries, the semiconductor industry’s technical operations and manufacturing are more complex and problems such as power outages will result in huge losses. Among SEA countries, Singapore is the only one that is currently attracting fabs with its well-developed hydroelectric infrastructure and high degree of coordination in power supply. Vietnam’s power shortage has led to discussions between Samsung and power companies to cushion the impact, leading the government to emphasize that it will strengthen research spending and investments in power plants. Malaysia has also stressed the importance of infrastructure investment in its newly released National Semiconductor Strategy. Talent/Labor Force – The availability of skilled and well-trained labor has always been critical to the development of the semiconductor manufacturing industry. Fabs need to have a strong talent pool in engineering, materials science, and electronics. In foundries, semiconductor process engineers are at the center of this demand. Engineers need to be able to manage the entire process of wafer/chip manufacturing, improve processes, assess and manage risks/problems, perform testing and monitoring analysis, and introduce new processes. They also need to build analytics, provide analytical data, and help integrate requirements and material selection to establish the optimal balance between quality, yield, and cost. The knowledge and experience of engineers definitely affect the outcome of the entire manufacturing operation and obviously, the cultivation of relevant talents cannot be accomplished overnight. As talent is key to semiconductor development, Malaysia has planned to train and upgrade the expertise and capabilities of 60,000 highly skilled engineers. The Vietnamese government is expected to allocate USD1.06 billion (VND26 trillion) to implement a semiconductor talent training program for 50,000 semiconductor engineers. India’s Semiconductor Incentive Program also plans to train 85,000 engineers in the next 10 years. Customer and Supply Chain Ecosystem – Proximity to key customers, supply chains and target markets reduces transportation costs, lead times and transportation risks, and allows for faster response to customer demand and supports just-in-time production. Semiconductor supply chains require an ecosystem of raw materials and logistics to support local investment. In foundry, for example, a fab with a capacity of 30,000-40,000 wafers/month will need at least 10 nearby material suppliers, even if they are not in the vicinity of the fab. To support this supply chain, it must have a strong/efficient port or air cargo system with high throughput. An end-to-end semiconductor supply chain and a well-prepared ecosystem is important and takes time to build.  Geopolitical Stability – In the past, the semiconductor industry emphasized the division of labor among specialties, but with the tense U.S.-China relationship, customers are more concerned about the resilience of the supply chain than ever before. Today, countries are actively developing their own self-sufficient semiconductor supply chains to reduce dependence on others. With geopolitical factors interfering, the location of production and the stability of the supply chain have become important considerations.  Tax Incentives and Government Regulations – Since semiconductor is a capital-intensive industry, local government tax credits will be one of the main incentives for fab companies to consider investing in a country, which is currently one of the tactics used by SEA and India to attract foreign investors. Semiconductor Manufacturing Working Culture – Different parts of the semiconductor industry chain have different operating mechanisms and cultures. In the case of chip manufacturing, which SEA and India semiconductor manufacturers are actively looking to develop, the production line usually operates 24/7. Employees must not only be willing to work in shifts but should also possess a culture of “immediate response” when problems arise. In a high-yield, high-productivity fab, where process engineering/operation and quality are the top priorities, line management is very stringent because any small mistake can result in a huge loss (e.g., lead to wafer scrap) or a safety issue. Engineers and production line personnel need to ensure smooth operations and to be on-call even during off hours. Although SEA and India already have more manufacturing experience and talent than the U.S., where most of the talent is oriented to software, IDA/IP, and Fabless, it may still be difficult to establish the talent and cultural mindset for semiconductor manufacturing in this sub-region in the short term. Chip Design Challenges: Talent and Innovation Capabilities In addition to chip manufacturing, IC design is also an area that SEA and India

As GenAI increasingly becomes mainstream, its applications in smartphones are fast becoming a key design vector for smartphone manufacturers. IDC’s latest forecast estimates that GenAI smartphone shipments will grow 364% year-over-year in 2024, reaching 234.2 million units, growing to 912 million units in 2028 implying a compound annual growth rate (CAGR) of 78.4% for 2023-2028. Over the past year, many market participants have announced their own set of artificial intelligence (AI) tools to demonstrate smartphone UX changes. These are based on various foundational models, large and small language models (LLM and SLM) enabling generative AI (GenAI) features with on-device processing, and multimodal input and output. The OEMs have a hybrid approach to enabling AI features-device-based for localized and cloud-based for heavy computational activities. While many of these AI features are presently limited to premium smartphones, we should expect to see these trickle down the pecking order made possible by the use of cloud-based AI solutions as these devices will lack necessary hardware. However, a reduced scope and privacy/latency remain key considerations. Below are some of these features and how they are different across the OEMs. OEMs are Adding Their Unique Flavor with AI Features While the AI features fall within the same broad categories for all major platforms and devices, each OEM imprints their unique signature, along with the tools from the likes of Google and OpenAI. Almost every key OEM has announced features/tools for photo/video editing, writing/editing, translation and interpretation, summarizing, search enhancement etc., targeting the most used smartphone features. To most users, it may not matter if AI is the enabler as long as they get a better outcome. Case in point, users are more interested in the final portrait photo than in the hardware, software, or AI driving it. A big hit, according to the market participants, are Circle to Search, a Google AI feature tied to Android 14, mentioned by Samsung in their earnings in Apr 2024 as the most used feature and Eraser which OPPO claimed is used 15 times per day on average. Live translation is another extremely useful feature overcoming the barriers of language and can be used conveniently even in an offline mode. There is also increased focus on wellness, where Google, for example has Sleep and Snore detection, Samsung recently announced Sleep Apnea detection, and Apple has been talking about monitoring vitals. AI Features – An Extension of Brand Message for the OEMs While the end-use for GenAI features is driven by the same user needs, OEMs use AI features as an extension of their brand message and stand out from the others. Apple announced GenAI capabilities as “Apple Intelligence”, indicating AI is central and all under one hood on its devices, with features designed to have cross-functionality across various apps and iPad, iPhone, and Mac devices. It might not be a radically new way of how a user interacts with the iPhone, but enhancing the app functionalities and fun activities such as creating personalized memories, Genmojis and avatars. The revamped Siri with access to underlying user data (emails, messages, photos, locations, files etc.,) can be more context-aware, while sticking to its central message around privacy even as the user connects to ChatGPT. Apple’s vertically integrated approach relies largely on in-house language models and its private cloud infrastructure, while partnering with OpenAI for ChatGPT.  Google continued with its legacy of using software to enhance smartphone capabilities. Pixel 8 Pro enabled many on-device GenAI features by running language models on the device. It has a host of features such as call management (Clear Calling, spam calls, Call Assistant); photo/video editing (Photo/Audio Eraser, Best Take); communication (Proof Read, Smart Reply, Summarize, Magic Compose) etc. For Google, it can be a blurry line between what is unique to Pixel smartphones vs the rest of the Android lineup. Google has been managing this by bringing some of the features to Pixel smartphones first before they go to the wider Android players. OPPO, in continuation of its focus on camera and photography features, has features such as AI Best Face, AI Eraser, AI Studio and AI Clear Face, while also expanding AI features to broader spectrum of AI applications for communication and productivity. OPPO also introduced Social Media Creation tools to assist in creating content specifically tailored for social media platforms. Samsung launched Galaxy AI tools on its flagship Galaxy S24 series and on Galaxy Z6 Foldables recently, and extended some of these features to its older models, focusing on communication and productivity. Live translation as well as real-time Interpreter are standout features. Galaxy AI includes Photo Assist, Instant Slow-Mo, AI summarization, Chat Assist and Magic compose writing/editing tools. Features such as dual-screen mode for Interpreter are customized for the foldable form factor.  While other Android players have also announced AI tools – Xiaomi’s AI-generated subtitles for video calls and Image Editor, Honor’s eye-tracking AI functions, Motorola’s personalization and privacy-oriented features – partnership with Google remains vital to have access to the broader set of tools integrated with the Android operating system.   Another area of differentiation is the size and number of LLMs and the training material used which impact performance and UX. Apple uses its own language models, and OpenAI for tasks that are beyond its realm. Android players use Gemini models (Pro and Nano) and multiple other different-sized models. OPPO has its own SLM that uses 7 billion parameters as well as an LLM AndesGPT in addition to using Gemini. There are partnerships with other tech companies including Qualcomm, MediaTek, and Microsoft. Further, there are differences in the execution of these features and their accessibility for third-party apps. iOS developers and apps have experience of automating tasks or working with features such as Siri Intents and Shortcuts. For example, developers and apps with SiriKit already integrated into their apps could see immediate enhancement with new Siri capabilities. Some of their AI tools such as writing tools would be easily available to developers for their third-party apps. Google also provides developers with APIs and

Blockchain technology is more frequently associated with cryptocurrencies and financial transactions. However, its usefulness extends far beyond the financial services sector. As a decentralized and immutable ledger, blockchain offers unmatched security, transparency, and efficiency across various industries. This blog post explores the value of blockchain outside of financial services in industries including retail supply chain management, healthcare, intellectual property protection, real estate, identity verification, energy, digital marketplaces, and government and public services. Supply Chain Management Supply chain management, a prime example of blockchain’s application beyond finance, has long grappled with issues like lack of transparency and inefficiencies, leading to problems such as counterfeit goods, fraud, and delayed deliveries. Blockchain steps in as a solution, offering a transparent and immutable record of every transaction and movement of goods within the supply chain. A case in point is the collaboration between Walmart and IBM, which use blockchain to trace the origin of food products. By scanning a product’s QR code, consumers can instantly access detailed information about its journey from farm to store shelf, ensuring product authenticity and quality and enabling swift response in case of contamination or recall. Healthcare In the healthcare industry, where the security and privacy of patient data are of utmost importance, blockchain is emerging as a game-changer. With patient data scattered across various systems and institutions, inconsistencies, breaches, and difficulties in data sharing have been a persistent challenge. However, blockchain is changing the game by creating a unified and secure patient record system accessible only by authorized parties. MedRec, a blockchain-based system, is a shining example of this. It ensures that patient data is secure and easily accessible to healthcare providers, enhancing coordination and patient care delivery. Moreover, patients have control over their data, deciding who can access their medical history, thereby instilling a sense of security and control in them. Intellectual Property and Copyright Protection Intellectual property (IP) protection is another area in which blockchain can substantially impact. The current IP systems are often slow and prone to ownership disputes. Blockchain provides a transparent, timestamped, and immutable record of IP rights, making it easier to prove ownership and combat piracy. Platforms like Ascribe and KodakOne use blockchain to protect the works of artists and photographers. By registering their creations on a blockchain, creators can ensure that their IP rights are securely recorded and indisputable, simplifying the enforcement of copyright and licensing agreements. Real Estate Real estate transactions are historically complex and slow, involving numerous intermediaries and paperwork. Blockchain can streamline these processes by providing a transparent and secure ledger for recording property transactions, thereby reducing fraud and increasing efficiency. Propy is a blockchain-based platform that enables the buying and selling of real estate using smart contracts. These contracts automatically execute and verify transactions, ensuring all parties fulfill their obligations before transferring the property title. This reduces the need for intermediaries, speeds up transactions, and lowers costs. Identity Verification Identity verification is essential for various services, yet traditional methods are often cumbersome and insecure. Blockchain provides a decentralized solution where individuals can securely control their personal information. Sovrin is an example of a decentralized identity management system. It allows individuals to create and control a digital identity that they can securely share with others. This system reduces identity theft risk and simplifies the verification process for individuals and service providers. Energy Sector Blockchain technology is making waves in the energy sector, particularly enabling peer-to-peer (P2P) energy trading. This allows consumers to buy and sell excess energy directly to each other, promoting renewable energy sources and increasing efficiency. Power Ledger is a platform that facilitates P2P energy trading using blockchain. Consumers with solar panels can sell surplus energy to neighbors, creating a decentralized and efficient energy market. This empowers consumers and contributes to the broader adoption of renewable energy. Government and Public Services Blockchain enhances transparency and efficiency in government and public services. From maintaining public records to managing public funds, blockchain ensures that records are immutable and verifiable. Estonia pioneered using blockchain for e-governance. The country uses blockchain to secure public records, including land registries and health records. This ensures data integrity, reduces fraud, and enhances the efficiency of public services. Digital Commerce and Cloud Marketplaces A blockchain-enabled marketplace enables peer-to-peer (P2P) on-platform buying and selling that allows the sale of goods without intermediaries. A traditional online marketplace is centralized, meaning a third party regulates the entire buying and selling process. Transactions go through intermediary platforms that retain some percentage of the transaction. Transactions become costly, and the intermediary becomes a powerful holder of sensitive data. All marketplace participants must trust the administrator as all the operations happen only on the platform. These factors are especially relevant in the context of cross-border transactions, in which there are no mechanisms to ensure effective legal protection of the parties. Integrating blockchain in digital commerce could potentially address these concerns by providing a decentralized and transparent system for transactions and data management. The immutability of transactions in the blockchain ensures that it is impossible to modify or lose data on participant transactions by the platform operator. Blockchain marketplace transactions are regulated by digitally signed agreements called smart contracts. These contracts can be accessed by everyone and are immutable. The terms must be approved by both parties before the transaction concludes. Reviews and ratings are the most requested information before buyers conduct transactions. Blockchain can be used to ensure users are provided with objective and transparent information about suppliers and solutions. Blockchain technology holds immense potential beyond the realm of crypto financial services. Its applications in supply chain management, healthcare, government and the public sector, intellectual property, real estate, identity verification, energy, and digital commerce prove its versatility and transformative power. By leveraging blockchain, various industries can achieve greater transparency, security, and efficiency, paving the way for innovative solutions to enduring challenges. As blockchain evolves and use cases expand, its adoption across different industries will accelerate, unlocking opportunities for growth and innovation. source

The Artificial Intelligence (AI) revolution has taken the world by storm. IDC forecasts that worldwide AI spending will exceed $512 billion by 2027, more than double its 2024 market size. While AI was picking up pace, the introduction of Generative AI (GenAI) changed the way enterprises leveraged AI. In India, AI and GenAI adoption is significantly increasing across software, services, and hardware for AI-centric systems, with AI and GenAI spending projected to reach $6 billion by 2027 with a compound annual growth rate (CAGR) of 33.7% for the period 2022-2027. The AI revolution has also accelerated digital adoption in India with 62% of Indian enterprises expecting more than 50% of revenue to come from digital models by 2026. Multiple AI Use Cases Are Emerging Across Indian Industries Industries that have been slow in terms of digital adoption have accelerated their journey to get ready for their AI journey while industries that have been digital leaders have already started evaluating and deploying relevant use cases. Governments in the Asia Pacific are the third-largest adopters of AI/GenAI, with spending expected to increase with a 5-year CAGR of 96.2% by 2027. This growth offers a chance to enhance efficiency, transparency, and citizen engagement in public services. In India, almost 50% of government organizations are planning to invest significantly in data management-related services, such as discovery, quality, data engineering, and governance in 2024. Hence, the approach is clearly shifting towards data-centric rather than model-centric. Additionally, a sizable chunk of these organizations is planning to significantly increase investments in AI and Machine Learning (ML), including GenAI. For example, citizen services in states like Haryana are leveraging Jugalbandi, a new GenAI-powered chatbot on WhatsApp, to facilitate a wide range of tasks, including pension payments and college scholarship applications. While adopting AI/GenAI, the government also has a responsibility to govern the usage of this technology while giving the necessary impetus for innovation. Digital in healthcare has been a focal point in recent years, particularly in the post-pandemic period. The Indian healthcare sector is witnessing a surge in clinical data driven by patient-centric care management that is evolving into real-time patient data capture and analysis. Such a surge in the clinical data, along with immunity to AI investments by healthcare organizations aligns the healthcare sector increasingly towards an “AI everywhere” approach. There is already an increased focus on early detection of diseases, both communicable and non-communicable diseases. During our discussions with CIOs of multi-specialty hospitals, AI-based use cases, mainly focusing on diagnostic accuracy, speed, and workflow efficiency are popular. For example, Apollo Hospital is set to leverage the use of AI to detect TB from chest X-rays, as a means of triaging. They even scan the villages to screen TB cases. Another case is the launch of “iOncology.ai”, by AIIMS Delhi for early detection of breast and ovarian cancers, the two most prominent types of cancer in the country and percolate the solution to district hospitals. AI is also transforming every corner of the Banking, Financial Services, and Insurance (BFSI) sector, with the most significant impact on customer interactions, risk management, and operational efficiency in India’s financial landscape. JP Morgan’s AI-focused strategy, implemented six years ago, exemplifies the long-term commitment of leading financial institutions to AI integration. In a highly regulated industry like BFSI, institutions face challenges such as big data management, outdated IT infrastructure, market responsiveness, and cyber fraud risks, which function as a speed breaker for AI adoption. Despite that, we are witnessing growing importance for GenAI pilots among many BFSI institutions in India across various business functions primarily to enhance existing services. Telecom operators are aspiring to be more than just connectivity providers –  they want to be recognized as digital leaders. India is the second largest market by subscribers globally, yet it has one of the lowest average revenues per user. IDC predicts total connections in India will reach 1.5 billion in 2028 (both mobile and fixed) with a total data traffic of 468 exabytes. Though the demand is high, balancing churn and profit margins have continued to challenge telcos in India. Two areas which tie into churn and profit margins are customer experience (CX) and network operations. For CX, the augmentation of AI is at the forefront of addressing customers across various touchpoints. For example, any ambiguity in the bill will lead to customer churn, both in consumers and enterprises. Use of AI to explain and analyze bills consisting of varying bill cycles, bill splits, multiple payment modes, loyalty, and promotional offers reduces the number and duration of calls for contact center agents.  On the network operations side, AI is infused to shift from reactive to proactive network management eventually to predictive network management. As networks become more disaggregated with increased virtualization and edge deployments, the urgent need to look past manual troubleshooting has led to network automation. The closed-loop network management should span across network operation workflows and BSS systems.   India is at a pivotal moment, ready to become a global manufacturing hub as the world looks for alternatives to China. Today, China makes up about 28% of global manufacturing, while India accounts for only 3.3%, competing with countries like Vietnam and South Korea in Southeast Asia. For India to become a manufacturing hub, India must leverage technologies like AI, robotics, automation, IoT, and 3D printing. Furthermore, the manufacturing industry also faces challenges with regular supply chain disruptions. According to IDC’s April 2024 Global Supply Chain Survey, more than 30% of India’s manufacturers, retailers, and logistics companies are expecting supply chain disruptions due to rising costs, talent shortages, and regulatory compliance issues. These challenges are also accelerating the adoption of data-driven technologies and AI in the manufacturing sector. AI can help India with three very important competitive factors in the manufacturing industry – enable scale, reduce cost, and increase efficiency. AI can also enhance supply chain security by detecting any risk or fraud in the supply chain system. Furthermore, navigating regulatory compliance is becoming more manageable with AI solutions that

As we approach the end of the calendar year, sales teams are keenly focused on planning and optimization. A crucial element of this planning involves understanding the dynamics of sales leadership and how to harness technology to foster a competitive edge. Sales leaders must adapt by embracing innovative technologies like Artificial Intelligence (AI) to stay ahead. This blog explores how AI and generative AI (GenAI) are reshaping sales leadership and how forward-thinking leaders can harness its power to drive success. 1. AI-Driven Sales Forecasting: Enhancing Predictive Accuracy One of the most impactful applications of AI in sales leadership is in the realm of sales forecasting. Traditional methods often rely on historical data and human intuition, leading to inaccuracies and missed opportunities. AI, however, excels at analyzing vast amounts of data in real time, identifying patterns that humans might overlook, and predicting future trends with a high degree of accuracy. For example, AI-powered tools can track and analyze customer interactions, market conditions, and economic indicators to provide more reliable sales forecasts. These insights enable sales leaders to make informed decisions about resource allocation, target setting, and strategy adjustments, ultimately improving the overall effectiveness of their teams. 2. Optimizing Sales Processes with AI: Streamlining for Efficiency Sales processes often involve repetitive tasks that can drain time and energy from sales teams. AI offers a solution by automating many of these routine activities, allowing sales professionals to focus on more strategic and high-value tasks. From lead scoring to pipeline management, AI tools can optimize various aspects of the sales process, making it more efficient and effective. For instance, AI-driven CRM systems can automatically update records, manage communications, and prioritize leads based on their conversion likelihood. This not only saves time but also ensures that sales teams are focusing their efforts on the most promising opportunities. Additionally, AI can automate follow-up emails and scheduling, further streamlining the sales cycle and reducing the burden on sales staff. 3. Personalizing Customer Interactions: Enhancing Engagement Personalization can make a significant difference in customer engagement and satisfaction in the B2B space, where relationships and trust are paramount. AI empowers sales leaders to deliver highly personalized experiences by analyzing customer data and predicting their needs and preferences. AI-powered chatbots, for example, can engage with prospects in real time, providing tailored responses based on their browsing history and previous interactions. Similarly, AI-driven recommendation engines can suggest relevant products or services, helping sales teams to provide more targeted solutions to their clients. This level of personalization not only improves customer satisfaction but also increases the likelihood of closing deals. 4. Training and Developing Sales Teams: Leveraging AI for Growth AI’s benefits extend beyond sales processes and customer interactions; it also plays a crucial role in training and developing sales teams. AI-driven tools can assess individual and team performance, identify skill gaps, and create personalized learning paths that address specific needs. For instance, AI-powered sales coaching platforms can provide real-time feedback on sales calls, highlighting areas for improvement and offering suggestions for enhancing performance. This kind of targeted coaching helps sales professionals develop the skills they need to succeed in an increasingly complex sales environment. 5. Enhancing Customer Segmentation and Targeting Effective sales leadership hinges on the ability to accurately segment and target customers. AI excels in this area by analyzing customer data and identifying the most promising leads. By leveraging tools like CRM systems integrated with AI, sales leaders can segment their customer base based on spending behavior, budget capacity, and specific needs. This allows for more personalized engagement strategies, which can lead to higher conversion rates and stronger customer relationships. The insights gained from AI can also be used to build detailed buyer profiles, incorporating demographic, behavioral, firmographic, and technographic data. This enables sales teams to tailor their approaches and align their offerings with the unique needs and preferences of their target audience. 6. Optimizing Sales Strategies with AI Insights AI’s ability to process and analyze data in real-time provides sales leaders with actionable insights that can be used to optimize sales strategies. For example, understanding tech spending patterns and trends—such as the prioritization of digital transformation, cybersecurity, cloud services, and data analytics—allows sales teams to align their product positioning and messaging with current market demands. AI can also help sales leaders assess competitive standings by benchmarking against competitors. By analyzing competitors’ market positions, product features, pricing strategies, and customer feedback, AI provides a clear view of where your offerings stand in the market. This competitive intelligence is crucial for refining sales strategies and identifying areas for differentiation. 7. Driving Revenue Growth through AI-Driven Contract Management Maximizing revenue growth often hinges on effective contract management. AI can play a vital role by analyzing existing contracts to identify opportunities for upselling, cross-selling, and ensuring customer retention. By interrogating contracts for value and renewal insights, AI helps sales teams engage with customers proactively, addressing their evolving needs and increasing the likelihood of contract renewals. Sales leaders can use AI to track contract durations, renewal dates, and historical renewal rates, enabling timely and informed discussions with customers. This proactive approach not only strengthens customer relationships but also drives long-term revenue growth. Challenges and Considerations: Navigating the AI Landscape While the advantages of AI are clear, integrating AI into sales leadership is not without challenges. Sales leaders must consider data privacy issues, the need for continuous upskilling, and the importance of maintaining a human touch in customer interactions. To navigate these challenges, it’s essential to establish clear data governance policies and invest in training programs that help sales teams understand and leverage AI tools effectively. Additionally, while AI can automate many tasks, it’s important to remember that personal relationships remain central to sales success. Balancing automation with human interaction is key to maintaining trust and rapport with clients. Future Trends in AI for Sales Leadership: Staying Ahead of the Curve Looking ahead, the role of AI in sales leadership is only set to grow. Future trends may include even more advanced AI-driven analytics,