AI's impact on Indian IT industry: How Anthropic's growth predicts automation

This article can be applied world wide. This AI influenced transformation of IT is much anticipated and changes the way IT services companies manage Human Resources. But is it really a threat,?I don’t believe so. I am an IT services company and use AI at whatever nascent state of evolution, it is. Ultimately new ways of thinking are natural to evolve and we, as IT services businesses, always are at the center of this change. The client must receive all the time and cost benefits at all times alongside a continuously consultative approach.

#Cognizant Adopts #Anthropic’s #Claude to Accelerate Enterprise #AI Adoption at Scale and Deploys Claude to Drive Internal AI Transformation - https://lnkd.in/dAhisBmk | #technews #technology #futuretech

The API call succeeded. The ticket closed automatically. The required approval never happened. Silent failures are the most dangerous kind. This scenario plays out more often than you'd think. AI agents integrate perfectly with legacy systems on a technical level. But they miss the human reasoning built into those systems over decades. The real challenge isn't connectivity. It's context. Legacy systems encode years of business logic, approval workflows, and compliance requirements. When AI agents bypass these guardrails, they create silent failures that are hard to detect and costly to fix. Here's what works: • Start with read-only mode first • Build a translation layer between AI and legacy systems • Validate business intent, not just syntax • Create human-readable audit trails • Make everything reversible The numbers tell the story. AI project failures jumped to 42% in 2025, with legacy integration being the top culprit. Success isn't about replacing old systems. It's about teaching AI to respect the wisdom embedded in them. Integration is negotiation. Context beats connectivity. Trust is the true deliverable. How are you bridging the gap between AI ambition and legacy reality? #AIIntegration #LegacySystems #EnterpriseAI 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gQj9hJbw

Taking a contrarian view to much of the breathless hype surrounding AI today, IBM CEO Arvind Krishna says that “you are going to be somewhat underwhelmed” by the impact of AI over the next two or three years. However, he says that, as with the web in the wake of the dot-com boom and bust at the turn of the century, AI’s real benefits will be realized when it becomes integral to IT infrastructure and a core component of business processes. Here’s what else IBM’s CEO had to say on AI’s impact during a Q&A session with CRN’s Jennifer Follett and Steven Burke at #XChange Best of Breed:

In our Artificial Intelligence Solution Architect (AISA), training Duc Haba has shared a Data Driven Methodology to succesfully deploy an AI Model, find below some key aspects: The 4 Phases of AI Development: - Phase 1: Discovery & Feasibility Study. The foundation! We define measurable objectives (KPIs) and address AI Legal & Ethical Considerations upfront. The goal is a clean AI Requirement Documentation. No technical debt before we even start. - Phase 2: Data Preparation & Model Selection. The engine room. This is where we focus on Data Cleaning and Transformation. We set up the AI Platform Environment and establish our Target Accuracy Benchmark. This phase is key to mitigating Garbage In, Garbage Out risks. - Phase 3: Prototype & Experimentation. Build, test, and learn. We design a functional Working Application and focus heavily on Risk Assessment (e.g., model bias) and defining the AI QA Process. We gather stakeholder and SME Feedback for continuous iteration. - Phase 4: Production Deployment & Evolution. The finish line is just the beginning. The focus shifts to Performance Monitoring (with a user feedback loop), User Training (UAT), and establishing an Evolution Strategy. Success is a Successful Deployment meeting the target accuracy. The Secret Sauce: Success isn't just about the model—it’s about clear communication, thorough documentation, and a willingness to adapt. We ensure this methodology is shared with all team members (PM, AI Scientist, Data Engineer, Stakeholders) from day one. What structured methodologies do you rely on for your AI projects? Let’s discuss! #AI #DigitalTransformation #ChangeManagement

📢 Boardrooms across Ireland: If we use #AI, who owns the output? Where does responsibility lie? Where does the data go? Deepak Chaudhari, Country Head of Tata Consultancy Services☘️Ireland states; ❝These questions aren’t obstacles – they’re the starting point for serious AI implementation, and concern about how to best use AI is warranted.❞ Enterprises are now using intelligent choice architectures to transform decision-making ❝There is nervousness, the efficiencies are wanted, but it is a challenge to secure and implement them correctly.❞ Indeed, this tension between wanting AI’s transformative potential and fearing its implications defines the current enterprise moment. Boardrooms worldwide are caught between FOMO ‒ and fear of regulatory impact, neither of which makes for sound technology strategy. However, Chaudhari said: ❝There is a clear path forward; understanding where AI can bring value!❞ #TCSIreland #TCSDublin #TCSLetterkennyGDC #TCS #OneTCS #TCSAI #ArtificialIntelligence #tataconsultancyservices #PerpetuallyAdaptiveEnterprise #BuildingonBelief

State of AI : Agentic AI bots as a goal-focused software fabric Welcome to my next post in the #stateofai series. No need to be stupefied by the AI tag! Conceptually, Agentic AI operates like a finite state machine moving through a set of states based on inputs and rules. It executes complex series of operations, capturing data from user(s) and from systems, data sources, conducting analysis and making decisions, based on both pre-programmed logic and AI-aided reasoning/ insights. It can handle dynamic workflows, adapt to the context (based on data) and pursue goals with efficiency. Unlike traditional automation that follows rigid sequences, the blend of finite state machinery and AI-led insights / decision-making allow it to manage branching, exception-handling and learn from feedback. The Agentic Middleware imperative The agentic AI layer in an enterprise acts as a smart fabric that seamlessly connects disparate data systems and applications. Each key business goal would probably get its own agent. For e.g., warranty/returns handling, manufacturing / test yield optimization. Seamless integration via APIs enable uniform communication and data flow across systems / platforms. By abstracting the complexities of underlying systems, the layer orchestrates and triggers actions across applications, parallelizing transactions if required. It also maintains systems of record and ensures that business logic and security requirements are met. The other benefit is that enterprises can now proactively resolve issues before they balloon into larger problems that are harder and more expensive to resolve. Enabling Scalable AI capabilities through a Service-Oriented Architecture The agentic smart interconnect fabric provides a scalable foundation to consume and orchestrate RPA/IPA capabilities and leverage AI services. Some of the more impactful AI capabilities in high-tech are - GenAI for contracts, invoices, financial reports, knowledge artefacts, etc, machine learning for root cause analysis, predictive analytics, etc, and SLMs - small language models. IT service providers and technology orchestrators are best placed to invest in such platform agnostic, domain and process aligned agentic middleware solutions that can shorten the AI value realization roadmap for their clients. What to expect next In my next post I will try and throw some light upon some further research (work in progress) into how various enterprise platforms are aligning their product roadmaps to this Agentic trend - both in terms of native capabilities and also from an integration and orchestration standpoint. These will help organizations chart out their own incremental innovation agenda. #AmbitionsRealized #Wipro #AgenticAI #AI #ArtificialIntelligence Namrata Sharma Abhay Sachdev Visweswara Pinninti Vivek Dixit Donnie Hall Giridhar Maddikonda Debolina Majumdar Mahima Sudhakaran Smita Patil Imran Kazi Solly David Yamini Gajapathy Shashaank Rao Akhilesh Mittal

AI isn’t just upgrading IT, it’s rewriting its DNA. For decades, growth in the tech industry was linear: more people, more projects, more progress. Today, AI is bending that curve - compressing time, cost, and complexity while redefining how value is created. The winners of this transformation will be those who reimagine, not just reskill. At CSM Technologies we see AI not as a replacement for human intelligence, but as its greatest multiplier. The new IT playbook begins here. #AITransformation #FutureOfWork #Innovation Mint https://lnkd.in/d7-svjMq

AI is changing the game for enterprise IT, and for CIOs like ours, Carter Busse, it’s an opportunity to lead, innovate, and make a real impact. At Workato, we’re seeing firsthand how AI agents empower IT teams to streamline operations, drive outcomes, and support employees across the organization. Carter shares why this is a pivotal moment for CIOs to step up, partner with the business, and show the value IT can deliver in the era of agentic AI. It's a must-read. Check out his perspective in CIONews here: https://lnkd.in/gqUry5Z3 #CIOs #AI #AgenticAI #AIAgents

AI is changing the game for enterprise IT, and for CIOs like ours, Carter Busse, it’s an opportunity to lead, innovate, and make a real impact. At Workato, we’re seeing firsthand how AI agents empower IT teams to streamline operations, drive outcomes, and support employees across the organization. Carter shares why this is a pivotal moment for CIOs to step up, partner with the business, and show the value IT can deliver in the era of agentic AI. It's a must-read. Check out his perspective in CIONews here: https://lnkd.in/gNm2dt-y #CIOs #AI #AgenticAI #AIAgents