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The agents will represent businesses and people, so the goal will be to give those proxies specific identities that show where they came from and what they’re allowed to do. Certainly, there’s a need to verify the agent’s creator, which could be a person or business, but it’s also important to determine if it’s acting on behalf of someone else and if that person is a bad actor.   There are many different possible agentic connections, and they all need verification. Like any new technology, the possibilities are as plentiful as the pitfalls. The need for KYA in digital commerce  A big part of agentic technology is capturing what the user would do and building that into the agent. A person, for instance, could create an agent for grocery shopping and set parameters for how much to spend, how often to buy household staples, what recipes to scan for ingredients and timelines for when food should be delivered.   source

Why IT? My eye-opening role The big question hit me early: Why was I there? I lacked legal or accounting expertise. The answer came when I was tasked with evaluating the target’s technical architecture — and translating risks into financial and business liabilities. As Principal Architect, this was familiar ground. I reported back to the M&A leadership, and my insights proved not just valuable but essential to negotiations. In some cases, they helped avoid multimillion-dollar pitfalls. Over several deals, my feedback shaped outcomes, from price adjustments to deal structures. This led me to develop a blueprint for technical due diligence, rooted in the Open Group Architecture Framework (TOGAF). Focused on risk and cost, it emphasized technical architecture and data architecture — spotting technical debt and data quality issues. Back then, “technical debt” was obscure outside IT, and data as a core asset was even less recognized.  source

Mastering the nuances of efficient cloud management is no longer just important—it’s urgent. CIOs face immense pressure to convert their significant cloud investments into tangible returns swiftly. With economic pressures, geopolitical concerns, and evolving regulatory landscapes, the demand for strategic, high-impact cloud adoption has never been greater.  Redefining cloud investment success  In today’s fast-evolving digital landscape, understanding the complexities of cloud ROI is crucial. Initial licensing costs are just the beginning. Overinvestment and lack of oversight can derail even the best-laid plans. Plus, the rapid integration of GenAI and data analytics adds layers of complexity. The risks of redundant capabilities and inefficiencies are real—CIOs must act immediately.  Learn advanced strategies to maximize cloud ROI To tackle these challenges and capitalize on cloud investments, here are some advanced strategies CIOs should consider:  1. Boost data strategy and architecture  Manage the complexity and cost of storing, processing, and analyzing vast amounts of information. 2. Embrace a cloud-native platform  CIOs should adopt a cloud-native approach by deploying containerization and infrastructure-as-code techniques. 3. Use financial discipline: FinOps  Implementing FinOps processes and technologies is essential for granular monitoring and optimization of cloud costs. 4. Conduct rigorous cloud management audits Thorough audits to identify and fix inefficiencies are critical. 5. Ensure security and compliance Modernize security frameworks and implement comprehensive compliance tools to protect against new threats. KPMG: Experienced in cloud optimization  Our solutions are designed for both immediate and long-term impact, including: holistic cloud modernization, expert FinOps implementation, and cutting-edge security solutions. KPMG leverages deep expertise in business, technology, and data. We help organizations swiftly unlock and maximize their cloud investments’ value.  Learn more about the details behind these strategies and see short industry examples that illustrate how they can help you get the fundamentals right for cloud ROI success. source

Let me begin the dashboard journey. Dashboards were once reporting tools, developed and designed to visualize KPIs and track specific projects or initiatives. I would say that in my organization, as well as most businesses, they played the same role. They were existing and crucial as well, but weren’t mission-critical. The agile era and digital transformation era followed, marked by increased complexity and velocity of indicators. Even the leadership would like to have live and flexible visibility into their requirements. This is where simple data presentation in visualization (such as the graphs and friendly UX in a static dashboard) isn’t enough. We need something that can work with our intelligence, something very dynamic and flexible. Leadership doesn’t just want to use it for reporting and monitoring; they want to take one step ahead with data-driven decision making, forecasting, risk and conflict detection and management, and transparency. This is where dashboards have taken many shapes, names and use cases, including operational dashboards, Scrum boards, strategic dashboards, real-time dashboards, tactical dashboards and analytical dashboards. source

Employing a staff with the right blend of skills and experience is a challenge for CIOs building AI-ready organizations. We explore some of the issues they need to resolve. source

If you’re not doing so already, get HR plugged into all aspects of your AI agent orchestration and governance, including your strategy, COEs, and councils. Make governance a race to the top, not the bottom Potential issues related to lack of AI governance are stark. “Left unmanaged, AI agents will create chaos for IT, InfoSec, and data security teams, exposing companies to reputational, financial, and legal risks,” says Barkin. “Every unsanctioned agent deployment becomes a potential policy violation, and every ungoverned interaction poses a risk of AI behaving unpredictably, misaligned with corporate ethics or regulatory expectations. This isn’t a distant threat, it’s an operational minefield already materializing in enterprises pushing AI-first without AI-governed strategies.” This shouldn’t be simply about compliance either. Organizations should go beyond complying with regulations, such as the EU AI Act, and look to help advance the industry, not just tick a box. “Success will be measured not by how many agents you deploy, but how safely and effectively they deliver outcomes, with compliance and control built in by design, Barkin adds. source

GIAC Strategic Planning, Policy, and Leadership (GSTRT) The GIAC Strategic Planning, Policy, and Leadership (GSTRT) certification is designed to validate several areas critical to IT leaders. This includes developing and maintaining cyber security programs, business analysis, strategic planning, and management tools. The exam covers business and threat analysis, security programs and security policy, and effective leadership and communication skills. It’s designed for CISOs, information security officers, and security directors, and shows you have an understanding of effective management strategies, policy development, security program analysis, security program development, threat management, and other relevant topics for IT security leaders. Exam fee: $999 Expiration: Four years and $499 due at time of renewal. GIAC Security Leadership (GSLC) The GIAC Security leadership (GSLC) certification is aimed at security leaders who want to demonstrate they have a handle on governance and understand how to protect, detect, and respond to security threats. The exam covers how to build a security program that will meet business needs, as well as how to manage security operations and teams, and security projects for the lifecycle of the program. It can validate your knowledge in areas such as cryptography concepts for managers, incident response and business continuity, managing cloud and application security, implementing encryption and privacy measures, assessing an organization’s security awareness, and more. Exam fee: $999 Expiration: Four years and $499 due at time of renewal. Information Technology Infrastructure Library (ITIL) 4 The ITIL framework from Axelos is a cornerstone in IT service, and if your organization subscribes to the methodology, a certification will help prove your ITIL 4 expertise. It’s a great certification for IT managers because it focuses heavily on implementing effective management strategies to improve team efficiency and organizational processes. You can be certified up to the expert level, which demonstrates high competency in ITIL 4 best practices. Like the COBIT 5 certification, you’ll have to find a third-party vendor that offers an accredited program or exam. Prices will vary depending on the company or training provider you choose. Exam fee: Varies, depending on vendor. Expiration: Three years For a deeper look at the ITIL cert, see ITIL certification: Mastering IT services management.  Information Technology Management and Leadership Professional (ITMLP) The Information Technology Management and Leadership Professional (ITMLP) certification offered by the IT Management and Leadership Institute was designed to validate your skills and abilities as an IT manager. The certification includes a three-day boot camp that covers topics such as technical leadership, managing hybrid and virtual IT teams, creating innovative IT solutions, IT funding and cost management, vendor management, and client services. At the boot camp, you’ll also learn more about IT trends including digital transformation, virtual and augmented reality, big data, ML, DevOps, cybersecurity, and more. It’s best suited for CIOs, IT executives, current or future IT managers, project managers, and business analysts. Exam fee: $1,995 Expiration: Doesn’t expire PMI Agile Certified Practitioner (PMI-ACP) IT managers and leaders who rely on the Agile framework will benefit from a PMI-ACP certification. This certification demonstrates your ability to work on or lead an Agile team. It covers Scrum, Kanban, Lean, extreme programming (XP), and test-driven development (TDD). To qualify for the exam, you’ll need a secondary degree or equivalent, 24 months or two years of Agile experience within the last five years or a current PMP certification (see below), and 28 hours of training in Agile practices, frameworks and methodologies. Exam fee: $435 for members, $495 for nonmembers. Expiration: Three years For additional Agile-related certs, see our roundup of Agile certs to take your career to the next level. Project Management Professional (PMP) If you already have a strong background in project management, you might want to consider PMI’s Project Management Professional (PMP) certification. The exam demonstrates your Agile expertise and ability to utilize Agile methodologies such as Scrum, Lean, Kanban, and more. To qualify for the PMP exam, you’ll need a secondary degree or equivalent, 24 months of Agile experience within the past five years, and 28 hours of training in Agile practices. Exam fee: $425 for PMI members, $595 for nonmembers. Expiration: Three years Scaled Agile Framework (SAFe) The Scaled Agile Framework (SAFe) certification series offered by Scaled Agile is designed to validate your skills and open new doors for career advancement, with a focus on business agility, scaling Agile, and leading Agile teams. For IT leaders tasked to oversee Agile IT teams, this is a certification series that can help validate your skills in several different areas. Courses are offered on topics including implementing SAFe principles and practices, leading SAFe teams, navigating conflict, and fostering collaboration, as well as courses on Agile product management, Agile software engineering, SAFe DevOps, and many more. You can choose the courses that best align with your career goals, and get certified in those specialized areas to demonstrate your expertise.   Exam fee: Varies by course. Expiration: Varies by certification. Six Sigma There are five levels of certification within the Six Sigma methodology, starting with green belt, and from there, you can move up the hierarchy to the top level: Executive Leadership. It’s a mentorship and training program that emphasizes IT project management and leadership. Each level mentors a lower level of Six Sigma trainees, emphasizing effective leadership through change and staying agile through organizational transformation. Certifications are offered through your organization or through third-party vendors, so pricing will vary depending on available options. Exam fee: Varies by vendor Expiration: Doesn’t expire TOGAF 9 TOGAF 9 is a standard developed by The Open Group for enterprise architecture management. TOGAF 9 certification is designed to certify your knowledge in a common body of core knowledge about the methodology and framework. The framework is specifically focused on enterprise architecture and aligning IT goals with business goals. As an IT management framework, it’ll help demonstrate your capabilities with cross-departmental communication by defining business and IT goals, and eliminating process errors across the organization. Like the ITIL and COBIT 5 certifications, you’ll need to find a third-party vendor to take the exam. There are also two different exams to pass: the TOGAF 9 parts one and two, or you can opt for the combined exam that allows you

“If OpenText proceeds with divesting non-core assets, the areas most likely to be affected include legacy development environments, software testing and quality assurance platforms, and IT operations management tools, particularly those inherited through past acquisitions that may no longer align with its current strategic focus,” Mazumdar said. Such divestitures could create uncertainty over product lifespans, slow innovation, or alter support models if priorities shift under new ownership, Mazumdar said. “This can impact integration stability, compliance, upgrade planning, and overall operational resilience.” Turner said the company may also face a choice over where to compete. “I think a decision must be coming as to whether OpenText wants to play in the enterprise business against the big beasts there or continue in the SMB market, which is far more of a channel play,” Turner said. “I don’t know which way it will leap, but I can’t see it continuing to straddle both worlds.” source

Generative AI (GenAI) has unlocked a new wave of productivity, from content generation to code suggestions. Gradually, with AI becoming more context-aware, goal-driven, and self-directed, we’re entering the age of agentic AI where systems don’t just assist, they act. As Agentic AI moves from pilot to production, it’s paving the way for something bigger—the emergence of the autonomous enterprise. This isn’t about replacing humans. It’s about reimagining the way businesses operate when AI becomes an active participant in the system, not just a support layer. For Indian enterprises, this shift is already underway. From streamlining workflows to re-architecting infrastructure and rethinking customer engagement models, agentic AI is no longer experimental—it’s becoming foundational. And the momentum is real: 74% of Indian enterprises are exploring agentic AI use cases [1] while 92% expect AI agents to handle complex customer interactions soon [2]. In an autonomous enterprise, systems don’t just automate; they decide, act, and evolve. The organisation becomes self-optimising. Processes adapt to changing conditions. Decisions are made in real time using distributed data. The enterprise becomes more responsive, resilient, and, ultimately, more competitive. This shift—from task automation to goal-driven orchestration—is especially relevant for Indian enterprises navigating complexity at scale. Whether it’s financial services, supply chains, or citizen services, the ability to delegate intent to intelligent agents offers exponential gains in speed, accuracy, and agility. We’re no longer just digitising workflows. We’re architecting enterprises that can run themselves, within guardrails. So, what enables this transformation? What makes autonomy operationally viable—not just aspirational? Defining the autonomous enterprise Let’s explore the key capabilities of autonomous enterprises. 1. AI-first workflows Enterprise applications are being redesigned around GenAI and autonomous agents. HR bots can now screen resumes and schedule interviews. Finance assistants generate real-time compliance reports. IT agents troubleshoot issues before tickets are even raised. This shift means business processes are not just supported by AI; they’re driven by it. 2. Autonomous CX AI is transforming customer experience (CX) beyond chatbots. With conversational AI, blockchain-based loyalty, and real-time personalisation, enterprises are delivering consistent, context-aware engagement at scale. 84% of CX leaders in India expect 80% of customer interactions to be resolved without human intervention in the coming years [3]. 3. AIOps and autonomous security Security operations are evolving from reactive monitoring to autonomous response. AI-driven SOCs (Security Operations Centers) are capable of detecting, diagnosing, and mitigating threats without manual input. By 2026, 20% of Indian enterprises are expected to migrate to autonomous SOCs [4]. 4. Knowledge engines Enterprises are building internal LLMs and Retrieval-Augmented Generation (RAG) systems to create powerful knowledge engines. These copilots are trained on proprietary data and workflows, allowing users to simply “ask” for answers, decisions, or actions—democratising access to enterprise intelligence. Building blocks of the next-gen enterprise To move beyond GenAI experiments and toward truly autonomous operations, enterprises must revisit how they’re architected, not just in terms of infrastructure, but also in how data, trust, and sustainability are embedded into the core of the organisation. This evolution isn’t powered by a single breakthrough, but by the convergence of several enablers working in harmony. Cloud-to-edge fabric: Architecting for speed and context Agentic AI thrives on immediacy. Whether it’s a machine alert on a factory floor or a fraud detection system evaluating a transaction in real time, latency can be the difference between opportunity and oversight. This is driving a shift from centralised cloud-only models to a cloud-to-edge continuum—one where AI models are deployed closer to where data is generated. As India’s edge computing market grows nearly threefold by 2028, enterprises are investing in architectures that can act instantly and locally, without always relying on the cloud for direction. Unified data fabric: Turning fragmentation into fuel No AI, generative or agentic, can function without context. And context depends on unified, real-time access to high-quality data. But for many enterprises, data remains fragmented across silos: legacy systems, IoT feeds, unstructured documents, and third-party APIs. The move toward a data fabric—integrating these sources through metadata, pipelines, and governance—enables AI agents to reason across the business, not just within departmental boundaries. A well-connected data foundation is what allows AI to stop being a narrow tool and start becoming a holistic operator. Secure AI execution: Reimagining trust for autonomy As enterprises hand over more decisions to AI, trust must become dynamic. It’s not enough to secure data; what matters now is controlling how autonomous systems access, act upon, and learn from it. This is where AI-native identity and access management (AI IAM) and Zero Trust architectures come into play, defining what an AI agent is authorised to do, under what conditions, and with what auditability. These guardrails are essential, particularly as agents begin to interact with financial systems, customer data, and regulatory environments. Securing autonomy isn’t about locking it down — it’s about enabling it with control and visibility. Sustainable AI infrastructure: Scaling Without overheating Autonomous operations must also be responsible operations. As the energy demands of large models and AI workloads grow, sustainability has emerged as a strategic priority. Enterprises are turning to GreenOps practices, such as carbon-aware scheduling, edge inferencing to reduce cloud load, and deploying models optimised for efficiency, not just accuracy. By 2027, over half of Asia Pacific enterprises are expected to adopt decarbonisation frameworks for their AI infrastructure. Designing for sustainability ensures that growth in intelligence doesn’t come at the cost of environmental resilience. The strategic call for leaders This next chapter in AI isn’t about faster tools—it’s about reimagining the enterprise operating model. Leaders must ask: what happens when AI doesn’t wait for instructions but acts on intent? The organisations that win tomorrow won’t just use AI—they’ll be “built around it”. Adaptive, autonomous, and audacious by design. Click here to learn how to leverage new innovations for your organization with Tata Communications. Sources [1] PwC India Gen AI and Agentic AI Study 2024 [2] India AI – 2025 Trends Report [3] Zendesk’s 2025 Customer Experience Trends Report [4] IDC – Autonomous SOC Adoption Forecast 2026 [5] Great Learning 2024-25 Upskilling Trends Report

The knock comes with an edge. The CEO wants answers—and fast. The board is asking why, after millions spent on AI, there’s little to show for it. Promises of transformative impact have turned into underwhelming pilots, stalled initiatives, or worse: angry customers and a public relations crisis. Agentic AI has captured imaginations across the C-suite. It’s expected to revolutionize everything, from customer experience and supply chains to fraud detection and forecasting. Executives don’t just want to explore AI; they expect results: new markets, faster decisions, operational savings, and a competitive edge. But here’s the uncomfortable truth: most enterprise AI initiatives are stuck. And unlike digital transformation, time is not on your side. AI’s first-mover advantages are real and fleeting. By the time an organization figures it out, it may be too late to catch up. AI’s massive promise: Can it possibly deliver? CIOs and CDOs, you know the answer. No way. No one can simply roll out agentic AI and expect it to deliver business benefits. The biggest hurdle—once again, no surprise— is the enterprise data swamplands, the massive piles of fragmented, unreliable data sitting inside the once promising data warehouses, lakehouses, and everywhere else. The sad truth is, most enterprise data is buried in the graveyard of half-completed IT projects. As McKinsey has reported, “pull the thread on these (AI) use cases, and it will lead back to your data.” In a survey, McKinsey found that 72% of large companies identified managing data as one of the top challenges preventing them from scaling AI use cases.  Data is the great enterprise tech dichotomy of our era. Data is simultaneously the most valuable asset and the lowest quality resource for most businesses. It is also a massive potential liability. Wrong data fed into AI models can have disastrous consequences.  Exhibit 1: AI can’t find the truth buried in the enterprise data mess Reltio Data trapped within individual apps and silos is a significant problem for enterprises. When information is siloed in disparate applications, it often becomes inconsistent and outdated. Different versions of the same data can exist across various apps, creating confusion and making it difficult to maintain a single source of truth. This inconsistency leads to a lack of trust in the data, undermining decision-making processes and operational efficiency. Enterprises are left grappling with unreliable information that hinders their ability to make informed, data-driven decisions, ultimately stalling their digital transformation efforts. Intelligent data is the answer. Winning companies are already using it In the AI era, not all data is created equal. The enterprises that win will be the ones that don’t just collect more data—they operationalize intelligent data. At Reltio, we define intelligent data as trusted, context-rich, continuously updated information that is mobilized in real time to drive decision-making by humans and AI alike. It’s the difference between feeding your AI agents a murky spreadsheet versus a crystal-clear 360° view of the customer, supplier, or product. Here’s what sets intelligent data apart: Trusted: Continuously governed, deduplicated, and validated so that decisions—automated or human—are based on reality, not noise. Context-rich: Includes not just static attributes but the relationships, transactions, preferences, and behaviors that define how your business actually works. Continuously updated: Always current—data in motion, not in a monthly batch. Because if your AI agent sees yesterday’s truth, it might make today’s mistake. Unifying: Breaks through silos across CRMs, ERPs, and data lakes, connecting all relevant domains and sources into a single, interoperable semantic layer. Ready for activation: Delivered where it’s needed—in milliseconds—to fuel everything from real-time personalization and supply chain pivots to automated compliance checks and agentic workflows. Without intelligent data, AI becomes an expensive science project. With it, you get a durable foundation that powers real-time operations, sharpens decision-making, and accelerates transformation. The rules of enterprise data are rapidly changing AI is becoming an uncomfortable boardroom conversation for data and IT leaders. It doesn’t have to be this way. The old rules of enterprise data—centralize it, catalog it, analyze it later—aren’t enough. AI demands faster, cleaner, more contextual data than most organizations are prepared to deliver. The pace of business doesn’t wait for a quarterly data refresh or a six-month transformation plan. And while some companies are still debating governance frameworks, industry leaders are already setting the pace. They’re building real-time data backbones to fuel fraud detection agents. They’re arming customer service copilots with live, trusted profiles. They’re replacing “reporting dashboards” with intelligent workflows that act on their own. The new playbook is here. And those who learn the rules first will shape the market. Explore the new rules of intelligent data. See how industry leaders are unifying trusted data to stay ahead in the AI era. source