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Amidst evolving technological disruptions, legacy systems risk slowing down the pace of innovation for businesses. As these are often the technical backbone of their operations, it’s critical to update these systems if companies are to remain competitive, adapt to changing market trends, and drive operational efficiency.   “The big problem with legacy modernisation has always been that it’s very hard just to understand what a system does today, particularly if it’s old or even inaccessible,” says Mike Turner, global leader of the Software and Platform Engineering service line at Cognizant. “Documentation will be out-of-date, and we could be looking at millions or even tens of millions of lines of legacy code.”  The risks generated from sluggish legacy systems are significant. Firstly, there are the high maintenance costs of managing outdated systems over time—resources that can be devoted to other growth initiatives. Inefficiencies that result from slow response times can also reduce productivity. This is alongside poor integration with modern applications, which restricts scalability and adaptability to changing business requirements.   Then there are security concerns, with Yugal Joshi, partner of IT services at Everest Group, pointing out that a robust cybersecurity posture is necessary to counter vulnerabilities of legacy systems. “As artificial intelligence scales, legacy systems will be more vulnerable to cyber threats,” he says.  Modernising for the AI era Bringing legacy modernisation back into sharp focus is the current era of artificial intelligence (AI). More than ever, this technology has presented companies with an opportunity to radically reshape the narrative around cumbersome legacy modernisation initiatives.   For instance, Turner suggested that businesses tap into large language models (LLMs) to translate code into natural language, such that it can be summarised and presented to stakeholders. “That reduces the risk level of optimisation efforts because I can actually understand what I have—what the dependencies and impacts are—so I can turn code into specifications that I can use to build a more modern system,” he adds.  And with AI gradually incorporated across myriad processes and applications, there is no better time for businesses to reimagine new technological foundations that are compatible with modern applications. Such an outlook will transform the developments in legacy modernisation.   That’s why businesses should consider how to further automate and streamline existing systems, with AI at the core of this transformation. “This is a fantastic opportunity for organisations going through this optimisation process,” says Turner. “Businesses can plan for the next five, ten, fifteen years of transformation that we’re going to see, as AI comes into the mainstream and scales across applications.”   Guiding modernisation with a clear business strategy  Before setting the wheels of AI-enabled legacy modernisation in motion, businesses need to have a comprehensive understanding of their existing system and consider their strategic objectives.   Rather than simply overhauling their legacy systems, Turner and Joshi agreed that these goals should be discussed and outlined. This can mean prioritising areas in the legacy system that are causing IT complexity and how they can be simplified to accelerate change.   Another approach is seeing which aspects of business will benefit most from the new capabilities of AI and how this can guide legacy modernisation strategies. “With AI changing the way things are done within software engineering in general, as well as in modernisation, businesses, service partners, and their technology partners will need to figure out the right path forward,” says Joshi.  Driving modernisation success is also about framing this change as a portfolio of opportunities. This includes considering whether any upgrades can be tied to key performance indicators, from driving revenue growth to reducing operational costs.  “Think about tooling and automation. Think about factories. Think about standard methods. Think about how to balance systems that are being changed and migrated together, with ongoing business operations. Think about modernisation as building a capability over time, for the long term, where initial activity can realise savings, which can then fund that capability and accelerate those initiatives over time,” Turner explains.  Improving business outcomes   Several companies that have embarked on their AI-enabled legacy modernisation journey have seen results beyond just efficiency gains. One healthcare organisation has worked with Cognizant to streamline their membership platform that was running on multiple legacy systems, which has led to fragmented data silos.   Through modernisation, the company has redesigned the application’s user interface, better integrate their data, and eliminate these silos, resulting in a tool that can offer customised recommendations to their users. “Having all of the data in place and being able to come up with good recommendations based on that data is really compelling to users,” says Turner. “We increased the registration rate for that application by 125 percent.”  At the same time, Joshi shared that some companies have seen an increase in uptime of their operations—an outcome of a more resilient system. Another has reduced their partner onboarding process for an onboarding portal from three days to just a few hours.   From improved customer experiences to reduced operational costs, legacy modernisation can yield benefits that can help businesses thrive in the age of AI.   Find out how more about modernising your legacy systems in a landscape that’s engineered for AI with Cognizant.  source

<role> You are an elite intelligence analyst trained in the methodologies of Richards J. Heuer Jr.’s “The Psychology of Intelligence Analysis.” You specialize in structured analytical techniques, cognitive bias detection and rigorous hypothesis testing. Your expertise lies in uncovering hidden assumptions and blind spots that others might miss. </role> <objective> Conduct a rigorous, structured analysis of the provided document using analytical methods from “The Psychology of Intelligence Analysis.” Your goal is to challenge assumptions, test hypotheses and identify potential blind spots with the objectivity of an external auditor. </objective> <analytical_framework> <step_1_assumption_audit> Surface Key Assumptions • Extract all explicit assumptions stated in the document • Identify implicit assumptions that underpin the core argument • Flag assumptions that are: – Untested or unvalidated – Dependent on volatile/uncertain variables – Taken as universally true without evidence • Rate each assumption’s criticality to the document’s thesis </step_1_assumption_audit> <step_2_hypothesis_generation> Generate and Test Alternative Hypotheses • Formulate 2-3 competing hypotheses that could explain the same situation • For each hypothesis, consider: – What if the core premise is framed incorrectly? – What if key stakeholders behave differently than assumed? – What if the proposed approach addresses symptoms rather than root causes? • Compare alternatives using a simple matrix of pros/cons/evidence </step_2_hypothesis_generation> <step_3_devils_advocacy> Apply Structured Devil’s Advocacy • Construct the strongest possible case AGAINST the proposal • Identify specific failure modes and their likelihood • Consider unintended consequences and edge cases • Answer: “If this initiative fails completely, what went wrong?” • Present counterarguments as if you were a skeptical stakeholder </step_3_devils_advocacy> <step_4_scenario_analysis> Conduct What-If Analysis Execute these specific scenarios: 1. “What if our fundamental understanding of the situation is wrong?” 2. “What if implementation proves significantly harder than anticipated?” 3. “What if external factors dramatically change the landscape?” 4. One additional scenario based on the document’s specific domain </step_4_scenario_analysis> <step_5_bias_detection> Identify Cognitive Biases and Mental Models Scan for these specific biases: • Confirmation bias: Cherry-picked supporting evidence • Anchoring: Over-reliance on initial information • Availability heuristic: Overweighting recent/memorable examples • Sunk cost fallacy: Justifying based on past investment • Pattern matching: “This worked elsewhere, so it will work here” Document specific passages that exhibit these biases </step_5_bias_detection> <step_6_diagnostic_framework> Develop Diagnostic Indicators Create 3-5 SMART indicators that would: • Validate or falsify key assumptions within specific timeframes • Be observable and measurable (not subjective) • Serve as early warning signals if the proposal is off-track • Include both leading and lagging indicators Format: “If [assumption] is true, we should observe [specific indicator] by [timeframe]” </step_6_diagnostic_framework> <step_7_synthesis> Formulate Tentative Conclusions Clearly categorize findings into: • KNOWN: Backed by evidence in the document • ASSUMED: Stated or implied but unverified • UNKNOWN: Critical gaps requiring investigation • SPECULATIVE: Educated guesses based on patterns Assign confidence levels: • High confidence (80-100%): Strong evidence • Medium confidence (50-79%): Reasonable inference • Low confidence (0-49%): Significant uncertainty </step_7_synthesis> </analytical_framework> <output_format> Structure your analysis with these sections: ## 1. Assumption Inventory • Critical Assumptions (make-or-break) • Supporting Assumptions (important but not fatal) • Risk Rating for each ## 2. Alternative Hypotheses • Present 2-3 alternatives in structured format • Evidence for/against each • Overlooked possibilities ## 3. Devil’s Advocacy Brief • The case against this proposal • Failure scenarios ranked by probability/impact • Questions a skeptical reviewer would ask ## 4. What-If Scenarios • Scenario → Implications → Mitigation options • Focus on actionable insights ## 5. Cognitive Bias Report • Specific biases detected with examples • Impact on decision quality • Suggested corrections ## 6. Diagnostic Dashboard • Early warning indicators • Success metrics with thresholds • Monitoring plan ## 7. Executive Summary • Confidence assessment by component • Critical unknowns requiring resolution • Recommended next steps with priorities </output_format> <communication_style> • Write with the precision of an intelligence briefing • Use active voice and concrete examples • Avoid hedging language – be direct about uncertainties • When critiquing, focus on the idea, not the author • Balance skepticism with constructive alternatives • If you identify a weakness, suggest how to address it </communication_style> <quality_controls> • Limit each section to 3-5 key points for clarity • Support claims with specific references to the document • Distinguish between minor issues and fundamental flaws • If data is missing, explicitly state what’s needed • Resist the urge to fill gaps with speculation • Challenge your own analysis: “What am I missing?” </quality_controls> source

Hakkoda Labs took the road less traveled and built AI agents that disrupted their own business processes, effectively handing over their proprietary data migration IP — the company’s secret sauce — to any developer. The project itself was launched as a skunkworks challenge to build an AI business analyst. The data consultancy, which was acquired by IBM in April, formalized the effort in mid-2023, hiring AI engineers to help pilot the project. Dubbed a “support engineer” model, the project used OpenAI’s large language models (LLMs) to perform data migration tasks, such as source target mapping; extract, transform, load (ETL); and extract, load, and transform (ELT) — the essential work of data scientists and data engineers. Many enterprises are using AI for automation but Hakkoda’s “sophisticated and specialized” AI agents for developer-oriented use cases such as ETL and schema matching are far less common that other generative AI applications such as document summarization and content creation, according to one of the company’s founders. source

As enterprises expand across regions, so do their cloud platforms and digital ecosystems. But with the rise of AI and its unprecedented appetite for data, networks are now under more pressure. Many businesses are learning the limits of legacy architecture the hard way. In the race to meet today’s standard of high-throughput, low-latency, and round-the-clock connectivity, IT teams are struggling to stretch their global internet networks without weakening them. The challenge lies in breaking a familiar equation: the more networks expand, the weaker they become. Scaling high-performance connectivity often means managing a growing number of service providers, navigating inconsistency between service-level agreements (SLAs), and operating amidst increasingly fragmented oversight. Is flawless network deployment even possible, or are enterprises being sent on a wild digital goose chase? Can network infrastructure keep up with the AI ambitions of CIOs? As many as 75% of CIOs are actively working on AI applications, according to Foundry’s 2025 State of the CIO report, with research and implementation of AI products topping this year’s list of leadership priorities. Whether in driving revenue, boosting operational efficiency, or reimaging customer experiences, the expectation is for AI to deliver across the board. But unlocking the said potential requires more than plugging in the latest AI model. Even the most sophisticated algorithm can’t output without being fed a critical mass of data. Doing so will require infrastructure robust enough to deliver not only uninterrupted data streams, but to do so securely across borders and clouds that stretch thousands of kilometres. The hidden costs of legacy network infrastructure While sufficient for day-to-day operations, Dedicated Internet Access (DIA) and broadband internet were never designed to meet the demands of globally scaled, AI-powered enterprises. And this is costing businesses dearly. A Fivetran report revealed that 68% of organisations without proper data centralisation strategies have lost revenue due to failed or underperforming AI initiatives[1]. Indeed, IT teams are often left to navigate the entanglements between internet service providers (ISPs) in the dark, with each organisation having its unique mix of contracts, support tiers, and billing systems. Best-effort delivery models, limited network visibility, and a lack of performance guarantees characteristic of traditional internet led to the inevitable yet inexplicable latency spikes, network outages, and erratic app responsiveness that businesses have become painfully familiar with. But it is these very flaws that derail AI transformation initiatives before they even start. “Businesses often underestimate the complexity of using the public internet as a global enterprise network. Performance may be acceptable in-country, but once your data starts crossing borders or connecting to international cloud platforms, the lack of end-to-end control and predictability becomes a real operational barrier.” Hon Kit Lam, Vice President, Hybrid Connectivity Services, Tata Communications Compounding the challenges is the absence of unified global service standards, making it nearly impossible for IT teams to maintain consistent visibility, governance, or user experience across regions. What started as a cost-efficient, internet-first strategy quickly became a bottleneck, undermining the very transformation it was meant to enable. Building a smarter internet with predictive AI Modern enterprises go beyond best-effort internet by harnessing the predictive capabilities of AI to ensure end-to-end reliability around the clock. New models such as IZO™ Internet WAN by Tata Communications have begun offering programmable, high-availability internet backbones built for businesses. In a world first, IZO™ Internet WAN combines a diverse range of access types—including dedicated access, broadband, 4G/5G, and LEO satellite connectivity—across over 150 countries to deliver consistent performance, intelligent traffic routing, and seamless global coverage, even in remote or underserved regions. This empowers enterprises such as DNV, an independent expert in risk management and recognised advisor to the maritime and oil and gas industries, to address challenges such as enabling secure and cost-effective access to worldwide corporate services. “With over 15,000 employees across 325 offices and 100 countries, DNV wanted a secure and high-performance network to streamline its global operations. We addressed that through a hybrid of IZO™ Internet WAN with Global VPN links and Managed Security Services. Our global network management freed them to focus on their core business and improve their employees’ efficiency and effectiveness. Meanwhile, the high availability of our network enables them to enhance their customer experience,” Lam concluded. In the era of AI-driven enterprise, intelligent connectivity is no longer a mere IT concern but a core business differentiator. Speak with an expert to learn how IZO™ Internet WAN can benefit your business. source

That destination flexibility is another key factor for Oracle’s cloud database future, Ellison said, underscoring that the company’s multicloud strategy gives customers the opportunity to use the Oracle database in the cloud of their choice, including associated AI capabilities. According to Ellison, this flexible model is proving very popular, resulting in quarter-over-quarter revenue growth of 115%. Ellison also called out the latest version of Oracle’s database, Oracle 23ai, as being specifically tailored to the needs of AI workloads. According to Ellison, Oracle 23ai is “the only database that can make all customer data instantly available to all popular AI models while fully preserving customer privacy.” Oracle CEO Safra Catz backed up Ellison’s assertion that AI will further increase Oracle’s cloud database market share with solid growth figures: According to Catz, cloud database services grew by 31%, and revenue from the company’s Autonomous Database grew by 47%. source

Cost and budgeting for cloud Our cloud surveys show continued growth in cloud spending year over year. We ask our guests how their organizations budget for cloud services and how cost is impacting their commitment to cloud. How are the managing and controlling costs?  Cloud and AI We see the top areas for growth in cloud spending are really focused on AI, analytics, and getting more value from your data. We ask our guest experts is this is how their cloud spending is going and what are their top priorities when it comes to new or increased cloud investments.  There are a lot of reasons organizations are looking to the cloud to empower their AI strategies, including scalability, the ability to handle massive data sets, and the portfolio of AI development tools they offer. But we also hear concerns from IT leaders about what AI in the cloud will cost and about the security of data. We ask our guest experts what their strategy is when it comes to developing AI apps in the cloud or locally.  source

In today’s world, the majority of enterprises have more than one cloud provider, with more data than ever in the cloud, particularly given the rise of work-from-anywhere and ubiquitous customer access. Research shows that over 80% of enterprises have more than one cloud provider. A multi-cloud approach combines the strengths of different cloud architectures from various providers, enabling each cloud type to focus on specialised tasks. While hybrid cloud relies on seamless interconnectivity, multi-cloud operates and manages data, processes, and workloads independently. Advantages of a multi-cloud approach Having multiple cloud providers has many benefits, including helping enterprises avoid vendor lock-in, manage risk, and enhance the resilience of their operations. It also gives enterprises the flexibility to choose the best-suited services for their specific needs to optimise performance. For global enterprises, engaging with multiple providers allows them to distribute their applications and data across different locations, ensuring better redundancy and disaster recovery capabilities. This approach not only ensures a tailored fit for each company but also enhances agility, which in turn facilitates faster solution development, reduces time to market, and cultivates an environment conducive to innovation. The agility inherent in multi-cloud enables a rapid deployment process, from the identification of business needs to the implementation of secure solutions, thereby ensuring a quicker time to market. Having the option to choose from multiple cloud providers can result in significant cost efficiencies, particularly in scenarios that demand high storage costs. The inherent redundancy in multi-cloud architecture ensures flexibility and availability during downtime by spreading the risk across global providers. Multi-cloud strategies support technology portability, offering platform-agnostic solutions hosted on the provider of choice and enabling seamless migration between providers for increased flexibility and simplified management. Challenges in integrating multiple cloud platforms         However, using multiple cloud providers presents several challenges, such as higher costs and increased complexity when it comes to integration, with each cloud platform typically having its own unique interfaces, APIs, security protocols, and management tools. Meanwhile, ensuring seamless data integration and interoperability across different cloud environments can be a significant challenge. While multi-cloud can potentially offer cost optimisation opportunities, it also leads to increased complexities in tracking and managing costs across different providers. In addition, enterprises using traditional network architecture for their cloud platforms are likely to face performance challenges, since paths are not optimised to offer the requisite performance and reliability. Meanwhile, those using internet VPNs will encounter difficulties, as internet VPNs typically provide limited site-to-site bandwidth, unpredictable performance, and high cloud egress costs. Security and performance now critical elements of multi-cloud connectivity Mission-critical applications are moving to the cloud, leading to higher demand from enterprises for multi-cloud connectivity services. For example, the number of enterprises globally using software-defined cloud interconnects (SDCI) services to connect to public cloud service providers is expected to grow 3 times from 10% in 2022 to 30% by 2027.  A Gartner report confirms this trend, estimating that enterprise spending on private connections to the cloud will reach US$3.6 billion by 2026. This makes security and performance a vital element for enterprises when it comes to multi-cloud connectivity. However, there are several challenges with traditional cloud connect services, such as complexity in managing multi-cloud connectivity, as well as a lack of traffic visibility, flexibility, and agility. Many enterprises are still undergoing their cloud journey and trying to navigate these challenges. Tata Communications has addressed this issue head-on with IZO™ Multi Cloud Connect, connecting an enterprise’s data centres, branches, public clouds, private cloud and third-party network on-demand, providing customers with greater agility to connect their users, branches, data centres, clouds and partners across the globe. It is an SDCI service built on virtual connection and virtual network functions and provides on-demand, real-time and scalable multi cloud connectivity. This removes the complexity of sourcing and managing connections and hardware. Enterprises can streamline management and improve flexibility. At the same time, the tool enables end-to-end predictable performance from branch to cloud, improving application performance and response time. It also reduces egress cost by using dedicated connections from the edge, leading to around 25% cost savings, and enables complete visibility into the network architecture for greater control and agility. IZO™ Multi Cloud Connect in action A recent example was when Tata Communications helped a leading airline migrate its applications to the public cloud. This brought about the need to transition from the existing on-premises architecture to a multi-cloud environment. The migration involved transferring workloads, including network and security components, across three different cloud platforms. It needed to ensure a high-performance connection, so users and travel agents could still access the systems. Moreover, it had to be scalable and at the same time simplify the network architecture. The airline leveraged Tata Communications IZO™ Multi Cloud Connect to migrate the applications securely and enable a seamless transition of the IT infrastructure to public cloud platforms. It addressed the challenges of private connectivity and enabled a direct connection to their global distribution systems—all while simplifying the architecture of the cloud environment. It also minimised the cost associated with public clouds and enhanced the performance in connection with critical business partners. As the world rapidly moves towards internet-based and cloud-based infrastructure with mission-critical applications hosted on multiple clouds, enterprises are increasingly using multiple cloud providers for their applications. Therefore, having a solution which makes connecting to any cloud seamless – with performance and security at the heart of it – has become mission critical for enterprises. source

In ServiceNow’s second annual AI Maturity Index, over 4,500 worldwide private and public sector leaders were surveyed and findings revealed that this year’s average maturity score actually dropped from last year, from 44 to 35 (out of 100 points). In addition, fewer than 1% of respondents scored over 50 on their 100-point AI maturity scale. But this is, in fact, good news for the industry and for AI adoption and here we explore why that is and how CIOs are moving forward.   In a previous article, 4 recs for CIOs as they implement agentic AI, I noted that despite the hype, CIOs agree there’s an approaching reset of agentic AI expectations. The ServiceNow report is further confirmation that the reset is underway, which is due to a number of factors. In implementing agentic AI, organizations began to know what they didn’t know. Unlike gen AI, which could be implemented as a standalone or bolted on somewhere, agentic AI requires far deeper integration — at least if you want to utilize it for maximum benefit. Instead of just throwing up a user interface and asking questions of a gen AI chatbot, CIOs are looking to use agentic AI to execute tasks and orchestrate workflows going deep into enterprise processes, such as CRM, supply chain, enterprise resource planning, HR, finance, and more. All this, plus the pace of tech advancement, existing silos and legacy apps, and hundreds of new agentic AI use cases, means seriously upping their game in terms of AI integration, orchestration and governance, keeping humans in the loop where needed and taking it beyond pilots to production-grade implementations. source

I think this is, this is a fantastic time to be a CIO. As as we can see that technology is becoming truly limitless, right? And I think many years ago, our organizations were spending a lot of time gathering data and very little time analyzing the data. But then the digital transformation era came in, and it slipped in, so we spent little time trying to gather data and a whole lot of time analyzing data. But that’s not enough. In today’s AI age, we are now starting to see that that time is becoming extremely a very important resource. And I think organizations want to make decisions, and they want to make they want to move faster. So we’re now trying to look at the organization, and we are now saying that we need to shorten the time to analyze through the help of AI, and we need to be more working towards making decisions for the organization, so that we can get a number of these use cases to operationalize and decision making is extremely important for us. So the AI era is all about shortening the time to analyze and increasing the time to make decisions right. And so now this is truly getting into that space, and how do we do it right? We can’t do it with the workforce that we’ve had. We can’t go as fast, and velocities are extremely important. Yeah, and this is the time we need to start looking at our ecosystem and say, our vendor partners. What do you do? There are 30,000 startups in the world. Can we partner with some of them? Can we co develop can we share IPs? They can build a revenue stream while we take our products faster to the market. Can we partner with our customers? They have the same problems that we do? Maybe we can partner and build a solution together. We can also partner with non competing peers, and say, you we have the same issues. Let’s go together. Let’s bring our workforce together. Let’s divide our work and let’s get stuff done. Yeah, I think these are the this is a time where unconventional ways are going to come into the are going to be the that’s the name of the game, in my opinion. And how do we make our our organizations more effective is how unconventional and how innovative that we do. This is a great time for transformational CIOs and not so good time for operational CIOs. Yeah, yeah. source

Enterprise innovation is accelerating at a dizzying pace, from AI-powered applications and real-time data platforms to immersive customer experiences. Solutions like Microsoft Copilot are transforming productivity. Platforms-as-a-Service, such as Lattice, are reimagining how teams collaborate and grow. But beneath this digital renaissance lies the hard truth: none of it works without a network from edge to cloud that can keep up. Today’s workplace is fluid and mobile-first, shaped by hybrid teams, BYOD culture, and high-density device environments. It’s a recipe for massive bandwidth pressure, erratic coverage, latency issues, and security vulnerabilities—quietly undermining employee productivity and business growth. And that burden is growing. More connected devices. More high-bandwidth usage, such as HD video streaming. More AI apps crunching for real-time data processing and insights. And more expectations for all of it to work together, seamlessly, all the way from campus to the applications. “Without a robust, responsive campus-to-cloud network foundation, even the most advanced digital solutions can fail to deliver their full impact.” Jamsheed Sukhadwala   Associate Vice President of Core & Next Gen Connectivity Services at Tata Communications The mounting pressure on addressing today’s network demands Many organisations are still running on infrastructure built for a different era: networks designed when digital demands were simpler, slower, and far less dynamic, now stretched to their limits. And this challenge isn’t just technical, it’s strategic. IT teams are expected to deliver higher performance with fewer resources. Maintaining and optimising complex networks requires specialised skills and time-consuming efforts. Then there’s the cost. Businesses face a financial dilemma: invest heavily upfront in CAPEX to modernise the network or transition to an OPEX model. All while digital transformation marches on. This is where ambitions stall. Businesses want to modernise, but technical complexity, cost uncertainty, and limited internal bandwidth stand in the way. What they need is a network that’s not just high performing, but comprehensive yet manageable, simple yet flexible, to meet evolving business needs. What a future-ready campus network looks like A future-ready network doesn’t just keep pace with business; it anticipates it. It adapts to change, scales with demand, and delivers consistent, secure connectivity no matter how or where work happens. It’s built not just for uptime, but for agility, intelligence, and growth. And it begins with purposeful design and smart execution. Start with seamless transformation, not disruption Transitioning from legacy systems or fragmented setups shouldn’t feel like a gamble. Tata Communications’ walk-in takeover approach ensures a risk-mitigated migration, starting with a thorough site assessment, followed by a right-fit design tailored to your evolving environment. Prioritise operational simplicity and reliability Networks must perform under pressure, every day in every workplace. Tata Communications embeds intelligent incident management, periodic audits, and real-time monitoring to ensure high availability and performance. This means minimal downtime and faster resolutions, so your teams can stay focused on what matters. Shift to scalable, cost-efficient models Rather than locking into costly CAPEX cycles, forward-thinking enterprises are embracing flexible, as-a-service models. Tata Communications’ managed Wi-Fi is delivered on a predictable, OPEX-based subscription, allowing you to scale resources up or down as needed while reducing technical debt. Turn connectivity into a source of innovation Today’s networks can do more than just connect; they can inform and inspire. With location-aware analytics and behavioural insights built into the fabric of its managed services, Tata Communications helps enterprises understand how people engage and interact within a space—enabling smarter business decisions. Through its Managed Wi-Fi & LAN  services, Tata Communications empowers businesses to build exactly this kind of agile, intelligent campus network, as a service. “The modern enterprise network isn’t just about connectivity, it’s about intelligence. Businesses need a network fabric that is intelligent, secure, and delivered as a service. One that removes the burden of complexity, scales with demand, and empowers IT teams to focus on innovation, not infrastructure.”, Jamsheed added. Brewing seamless connectivity at Starbucks India When Starbucks India needed to scale its Wi-Fi network across stores to deliver consistent customer and employee experiences, it partnered with Tata Communications. Through centralised monitoring, seamless deployment, and fully managed, as-a-service delivery, Tata Communications helped Starbucks India build a robust network foundation—one that enhances operational agility while elevating the in-store experience. In a world where innovation moves fast, your network should be your enabler and catalyst, not your bottleneck. Tata Communications’ Managed Wi-Fi & LAN solution is built for modern enterprises that demand more from their networks: more speed, more simplicity, more intelligence, and more value from every connection. Speak with an expert to learn how Tata Communications’ Managed Wi-Fi & LAN solution can benefit your business. source