Capability Archives - 乐播传媒视频 /category/capability/ IT Consulting, Strategy & Outsourcing Services Company Tue, 22 Sep 2026 11:57:10 +0000 en-US hourly 1 https://wordpress.org/?v=7.1.3 /wp-content/uploads/2020/03/itc-logo.png Capability Archives - 乐播传媒视频 /category/capability/ 32 32 Data Modernization: The Missing Link Between Cloud and AI Value /blog/data-modernization-the-missing-link-between-cloud-and-ai-value/ Tue, 22 Sep 2026 11:57:10 +0000 /?p=49995 The post Data Modernization: The Missing Link Between Cloud and AI Value appeared first on 乐播传媒视频.

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For many enterprises, the cloud journey is well underway. Applications have been modernized, workloads run in scalable cloud environments, and AI is increasingly embedded into business processes. Yet a fundamental gap remains: an enterprise cannot become truly AI-driven while intelligence is trapped inside application silos.

The real constraint is increasingly not access to AI, but access to the right data, in the right context, at the right time. Enterprise data remains distributed across applications, functions, and platforms, with different definitions, fragmented lineage, and limited understanding of the business relationships behind it. Organizations can therefore deploy intelligent applications without necessarily creating enterprise intelligence across the business.

That makes data modernization a critical step in the cloud-to-AI journey. The objective is not simply to consolidate data or build another modern platform, but to create an enterprise data foundation that understands the business: connecting data across domains, enriching it with semantics and relationships, and making it trusted, governed and consumable by AI, even while the underlying architecture remains distributed.

What counts as data modernization has therefore changed. A few years ago, moving data to the cloud or building a modern data platform could itself represent modernization. In an agentic environment, the bar is higher: data also has to be engineered for how people and AI agents will understand and consume it.

AI-ready data is specific to the job

The traditional view of data quality is often too general for AI. A dataset may be complete, consistent, and suitable for reporting, yet still fall short for the particular task a model or agent is expected to perform.

argues that data cannot be made 鈥淎I-ready鈥 in the abstract. Readiness depends on the use case, the AI technique, the required confidence level, and factors such as semantics, labelling, representativeness, lineage, operational performance, and cost. Conventional high-quality data does not automatically satisfy those requirements.

For agentic AI, data readiness increasingly includes business context: what a value represents, how it relates to other business objects, which definition applies and what policies constrain an action. Data modernization is therefore expanding from moving and cleansing data to engineering meaning around it.

Context is becoming part of data engineering

One way to think about this is as a semantic or context spine across the distributed data estate.

  • 聽An ontology defines business objects and relationships;
  • a knowledge graph makes those relationships explicit;
  • a context graph assembles what is relevant to a task; and
  • a semantic layer exposes consistent terms and metrics to people, applications and agents.

This direction is increasingly visible in the market. describes context layers combining semantics, governance and runtime context; discusses ontology-driven semantic layers and virtual knowledge graphs; and uses ontology as a shared business model. The intent is similar: give AI a governed model of what enterprise data means and how it relates.

Consider a hotel. Front-office teams and revenue managers may use the same property data but need different context. The front office may need room availability, guest preferences, and maintenance exceptions; revenue management may need demand, rate performance, and occupancy. The data product should reflect the consumer and use case.

A human also brings tacit knowledge that an agent lacks. Asked which room to offer and at what price, an agent may need to connect reservations, loyalty status, inventory, rates, maintenance, and policy rules. The context spine makes those relationships explicit rather than leaving the model to infer them.

Unstructured data needs more than a place to live

Cloud-native platforms can ingest PDFs, images, audio, diagrams, and other unstructured information. But getting all that content onto a modern platform does not suddenly make it useful to AI.

A PDF used by an AI application may be separated into text, tables, images, metadata, sensitivity labels, embeddings, summaries, and quality scores. McKinsey notes that these derived objects must remain connected to the source and to one another, preserving meaning, lineage, and control throughout the pipeline. If those connections are lost, fairly basic questions become surprisingly difficult to answer: Where did this response come from? Was the right version of the document used? Can the result be reproduced and verified?

Data engineering therefore has to extend beyond ingestion. Unstructured content must be classified, secured, and linked to relevant structured records so a contract, manual or communication connects to the business entities it describes.

Trust has to travel with the context

Once business context becomes reusable across applications and agents, governance has to travel with it. Master data establishes common identities; metadata, lineage, and provenance show where information came from; and policy controls determine what each user or agent can see and do.

This also affects trust in AI outputs. notes that systems working directly over raw enterprise schemas can end up guessing how tables fit together, producing inconsistent metrics and hallucinated answers. A governed semantic layer reduces that guesswork and makes responses easier to trace. As agents begin acting on enterprise information, BCG argues that data quality, lineage and provenance become risk priorities because flawed information can trigger actions before a person intervenes.

Domain expertise turns data into business context

Technology can make enterprise data accessible; domain expertise makes it meaningful. Ontologies, graphs, and semantic layers only become valuable when they reflect how the business actually works: which relationships matter, which definitions are valid, how industry processes connect and which policies shape decisions. That is how raw data becomes AI-ready business context.

This is where 乐播传媒视频’s deep domain expertise can make a fundamental difference. Our context-hub approach is intended to encode reusable industry concepts and relationships so intelligence can move beyond individual applications and operate with a richer understanding of the enterprise.

The next test of data modernization

So, what should leaders now expect from a modern data estate? The bar is clearly moving. As AI moves deeper into enterprise workflows, organizations will need to show that the information feeding those systems remains relevant, intelligible, traceable, and appropriately protected at the moment it is used. Having the data migrated, consolidated, or available on a cloud platform will tell only part of the story.

The harder work lies in making that data usable in context. Structured and unstructured information has to come together, business meaning needs to survive across systems, common identities and definitions have to hold, and governance must continue to work as data moves through retrieval and AI consumption. Once AI begins influencing more consequential decisions and actions, gaps in any of these areas become much harder to overlook.

Cloud modernization created the digital substrate for AI. Data modernization now has to create the business understanding that lets intelligence operate across it: a distributed foundation that can be logically unified, enriched with semantics and relationships, governed end to end and shaped for people and agents.

For CIOs, that gives data modernization a more practical measure of success: how confidently can the enterprise put its data to work through AI? As we move through 2026 and into 2027, that question will become increasingly important. Scaling AI will depend as much on the quality and context of the data underneath it as on the sophistication of the model sitting above it.

Author:

Shan Duggatimatad,
Vice President, DATA

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]]> The Cloud Cost Reckoning: Why FinOps Must Move from Visibility to Accountability /blog/the-cloud-cost-reckoning-why-finops-must-move-from-visibility-to-accountability/ Wed, 09 Sep 2026 11:15:01 +0000 /?p=49887 The post The Cloud Cost Reckoning: Why FinOps Must Move from Visibility to Accountability appeared first on 乐播传媒视频.

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A monthly cloud review can now tell an enterprise a great deal: which accounts are driving spend, where utilization has increased, which resources are underused, and where rightsizing or commitments could improve the economics. The harder conversation begins once everyone has seen the dashboard. Who owns what happens next? And can the business explain what it received for the money spent?

For conventional cloud infrastructure, much of the technical machinery for cost management is already in place. FinOps practices have matured, platforms provide increasingly granular visibility, and enterprises have more ways to monitor and optimize consumption. The challenge is increasingly about turning that visibility into consistent governance and action across the organization.

AI makes that harder. GPU infrastructure, model consumption, token usage, data movement, and experimentation introduce new patterns of spend, often across multiple teams and environments. There are fewer established playbooks, and the economics can change quickly as use cases scale.

That is why the cloud cost conversation is moving beyond finding waste. The real question now is whether enterprises can build financial accountability into the way technology is designed, consumed, and operated.

AI changes the economics of cloud consumption

The scale of the change is already visible. The found that 98% of respondents now manage AI spend, up from 31% in 2024. It also points to a broader evolution in the discipline, with FinOps teams becoming involved earlier in technology decisions rather than only analysing spend after the fact.

The reason is fairly practical. AI costs do not always behave like traditional infrastructure costs.

There is no single optimization formula yet, but architectural choices matter. Retrieval-augmented approaches can reduce unnecessary use of larger models in some scenarios. Open-weight or smaller models may be more economical for certain workloads. Model selection, usage patterns, data movement, and the way an application is designed can all affect the eventual cost.

McKinsey鈥檚 research highlights how difficult that can become in practice. Its 2026 Enterprise AI FinOps survey found that 93% of qualified respondents had exceeded their AI budgets, while only 20鈥25% had mature AI FinOps capabilities. The research also found that AI expenditure is often spread across foundation-model contracts, copilots, APIs, AI-enabled software, experimentation environments, and business-unit purchases.

So the issue is not simply how much AI costs. Enterprises need to understand where the demand is coming from, what is driving it, and whether the consumption is justified by the outcome.

Cost decisions need to happen earlier

This changes when cost management needs to enter the conversation.

If a workload has already been architected, deployed, and scaled, the options available to a FinOps team are narrower. It can identify waste, recommend rightsizing, negotiate commitments, or flag unusual consumption. Some of the biggest economic decisions, however, may already have been made.

For AI workloads, choices around model selection, workload placement, retrieval architecture, context size, and the balance between proprietary and open-weight models can shape cost well before the first monthly bill arrives.

This is why FinOps is becoming more closely connected with architecture and engineering. Cost needs to be considered while workloads are being designed, alongside performance, scalability, resilience, and security. Forecasting also becomes more important because business teams need a clearer view of what happens to the economics when an AI use case moves from a pilot to wider adoption.

The goal is to make cost a design consideration rather than a correction exercise.

From cost insight to operational action

The other challenge is execution. A cost anomaly is useful only if someone can investigate it, decide what needs to change, and make that change without compromising service performance.

At 乐播传媒视频, we bring together capabilities such as SpendEffix, cloud optimization and modernization, cloud managed services, account management, ITSM, automated ticketing, and multi-cloud performance monitoring to support this operating model.

SpendEffix provides the FinOps layer for visibility and financial management, while cloud optimization and modernization help address workloads where inefficient consumption is rooted in the underlying architecture. Multi-cloud monitoring brings cost into the context of performance and utilization, while managed services, ITSM, and automation provide mechanisms for carrying an identified issue through to action.

That connection becomes important as cloud estates expand. Finding another optimization opportunity is rarely the difficult part. Making sure the right team owns it, understands its operational impact, and closes the action is where accountability begins.

Automation can help shorten that cycle further, particularly around anomaly detection, remediation, forecasting, and repetitive optimization activity. It also allows FinOps teams to spend less time chasing individual cost events and more time looking at patterns, architecture choices, and business value.

The real question is what the spend delivered

FinOps began by giving enterprises a clearer view of variable cloud consumption. That visibility remains essential, especially as AI creates another fast-growing category of technology spend. The next stage is about making that information useful in decisions.

As enterprises move through 2026 and into 2027, mature FinOps practices will increasingly need to connect consumption with ownership, architecture choices, forecasts, and business outcomes. The monthly bill will still tell leaders what they spent. The more valuable capability will be understanding what drove that spend, whether it was worth it, and who has the information and authority to act when the economics change.

Author:

Varoon Rajani
Sr. VP & SL Head 鈥 CS

The post The Cloud Cost Reckoning: Why FinOps Must Move from Visibility to Accountability appeared first on 乐播传媒视频.

]]> Securing Multi-Cloud in the Age of AI Complexity /blog/securing-multi-cloud-in-the-age-of-ai-complexity/ Fri, 24 Jul 2026 07:25:34 +0000 /?p=49752 Multi-cloud has become the operating reality for large enterprises. Business units choose SaaS platforms for speed. Technology teams use different cloud providers for performance, scale, cost, regulatory or ecosystem reasons. […]

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]]> Multi-cloud has become the operating reality for large enterprises. Business units choose SaaS platforms for speed. Technology teams use different cloud providers for performance, scale, cost, regulatory or ecosystem reasons. Legacy systems continue to support critical processes. AI is now entering this environment with a higher demand for trusted data, clean access models and consistent governance.

For CIOs, CISOs and risk leaders, the concern is not the size of the technology estate alone. The harder issue is fragmentation of control.

A cloud environment may look secure when viewed on its own. Risk builds when data, identities, applications, APIs and automation cross environments without a consistent security and governance model. AI makes that gap harder to carry because it relies on the foundations that are most difficult to manage across a distributed estate: data quality, access control, lineage, policy enforcement, observability, and response.

The real multi-cloud risk is control drift

Most enterprises have invested heavily in cloud security. Identity platforms, vulnerability tools, encryption standards, posture management, security monitoring and cloud-native controls are already in place. Yet many leadership teams still struggle to get fast, reliable answers to basic questions: who has access to what, where sensitive data is moving, which exposure creates the highest business risk, and whether policies are being applied consistently across environments.

We are witnessing a , where the speed of cloud adoption is outpacing the ability of security teams to maintain consistent visibility, detection and response.

In a multi-cloud estate, this becomes an operating model challenge. Each platform has its own way of managing identity, policy, logs, configurations and controls. When enterprises add more tools without connecting the control model, security teams are left correlating risk across systems that were not designed to work together.

For a CIO, the key cloud question is no longer, 鈥淒o we have controls?鈥 The better question is, 鈥淐an those controls work together when the business runs across multiple environments?鈥

AI raises the cost of weak governance

AI changes the security conversation because it brings data, access and action closer together. A model or AI-enabled workflow may draw from customer, transaction, operational, product, service or employee data, and then use that context to support decisions, trigger workflows or guide automated responses.

That puts far more pressure on the data foundation. If enterprise data is duplicated, poorly classified, inconsistently governed or difficult to trace, the issue is no longer only whether the data is secure. It is whether the enterprise can trust how that data is being used, who or what has access to it, and whether the outputs can be explained with confidence.

Agentic AI adds another layer to this challenge. has identified agentic AI as a top cybersecurity trend for 2026, citing unmanaged agents, unsecured code and compliance exposure as emerging risks. As AI systems begin to act across workflows, call tools, access systems and interact with enterprise data, governance has to extend beyond where data sits to what actions AI-enabled systems can take. The access question also changes. The identity estate now includes service accounts, API keys, workloads, automation scripts, machine identities and AI agents that can hold permissions, trigger actions, call systems and move across environments. Many are created for a specific use case and remain in place long after the original need has changed.

This is why AI-ready cloud security has to begin with an AI-ready data foundation. Applications may continue to run across different cloud and SaaS environments, but AI-relevant data needs stronger consolidation, governance and lineage, while non-human identities need clearer ownership, least-privilege access, credential lifecycle management and faster privilege revocation. Without that, enterprises carry cloud fragmentation into their AI programs, creating gaps in privacy, accuracy, auditability and decision confidence.

For leadership teams, the implication is clear: cloud security, data governance and AI governance need to be designed together. The highest-risk gaps will appear where AI needs the greatest confidence – in data access, policy enforcement, traceability and delegated action.

Security architecture needs to become more coherent

The answer is not to simplify ambition or slow down AI adoption. It is to make the control model more coherent.

Enterprises need a unified view of identity, data exposure, configuration, vulnerabilities, runtime behaviour and response across cloud environments. They need policy consistency across platforms rather than separate interpretations of the same security principle. They need vulnerability management that prioritizes exploitable paths to critical assets, not just severity scores in isolation. They also need resilience planning that assumes incidents may cross cloud, SaaS and legacy boundaries.

AI can help security teams with correlation, anomaly detection, triage and remediation. It can also be misused by attackers to identify gaps faster and at scale. That dual reality makes governance even more important. Human oversight, explainability, guardrails and clear accountability need to be built into AI-supported security workflows, especially where automated action is involved.

For CXOs, the agenda is clear. Multi-cloud security has to move from a platform-by-platform control exercise to an enterprise governance capability. The priority is to reduce fragmentation where it creates the greatest risk: identity, data governance, policy enforcement, observability, vulnerability prioritization and response.

AI will not wait for cloud estates to become simpler. Security architecture has to make them more governable.

Author:

Varoon Rajani
Sr. VP & SL Head – CS

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]]> The Real AI Bottleneck May Be Your Cloud Foundation /blog/the-real-ai-bottleneck-may-be-your-cloud-foundation/ Wed, 22 Jul 2026 05:23:14 +0000 /?p=49773 For the last decade, cloud transformation has largely been measured by migration progress. How many workloads moved? How much infrastructure shifted? Which applications were rehosted, refactored or retired? For many […]

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]]> For the last decade, cloud transformation has largely been measured by migration progress. How many workloads moved? How much infrastructure shifted? Which applications were rehosted, refactored or retired? For many enterprises, that was the right question at the time.

AI is changing the test.

As enterprises move from pilots to scaled adoption, cloud maturity is being measured less by where workloads sit and more by whether the cloud foundation can support trusted data, secure access, elastic compute, controlled spend, modern applications and resilient operations. That is where a gap is becoming visible.

The issue is not cloud adoption. Most enterprises have already made meaningful progress. The issue is that many cloud estates were built for application migration and infrastructure flexibility, while AI demands something more integrated: governed data, stronger security, cost discipline, modernized applications, and automation-led operations.

forecasts worldwide AI spending to reach $2.59 trillion in 2026, up 47% year over year, driven heavily by hyperscalers and vendors. That scale of investment will put pressure on enterprise cloud foundations that were never fully modernized for AI-grade workloads.

For CIOs, the maturity question is becoming sharper: is the cloud estate ready to carry enterprise AI, or will it amplify the debt already sitting inside the technology landscape?

Cost is becoming an AI readiness issue

Cloud cost has always been part of the CIO conversation. AI is making it harder to treat cost purely as a consumption management problem.

AI workloads introduce new cost patterns. GPU consumption, model training, inference, token usage, data movement, storage growth and experimentation cycles can all change the economics of cloud programs. In our client conversations, cloud cost control and FinOps are already among the most urgent areas of concern, especially as AI adds a new layer of consumption unpredictability.

The FinOps Foundation鈥檚 notes that FinOps has expanded beyond cloud into a proactive, technology-wide discipline, with AI now dominating the forward-looking agenda. This is an important signal. Cloud cost maturity can no longer depend only on monthly reports, budget alerts, or infrastructure optimization exercises. AI requires a more active cost model: tagging, ownership, workload-level visibility, consumption forecasting, governance policies, and automated controls built into the operating model.

For CIOs, FinOps has to move closer to architecture. The question is not simply whether cloud spend is rising. The better question is whether teams can connect spend to business value, workload design, data movement, performance needs, and modernization choices.

A lift-and-shift estate will usually cost more than expected because it carries old application assumptions into a consumption-based environment. AI makes that even more visible.

Security and privacy now sit at the center of cloud maturity

Security concerns around cloud migration have matured over time. Many enterprises are now comfortable running critical applications on cloud. AI, however, introduces a different security and privacy concern because enterprise data may be used to train, fine-tune, or power models and AI-enabled workflows.

The worry is straightforward: when data moves from enterprise-controlled environments into cloud-based AI services or model ecosystems, leaders want assurance that it remains secure, private, and governed.

It鈥檚 clear that cloud is increasingly central to enterprise AI strategy. At the same time cloud adoption has expanded the attack surface faster than traditional security models can protect it.

For CIOs and CISOs, this changes the cloud maturity conversation. Security cannot remain a layer applied after migration. It has to be embedded into landing zones, identity models, data governance, network design, policy enforcement, observability, and incident response.

This becomes even more important in multi-cloud and hybrid estates, where different platforms may have different security parameters, policies, controls, and monitoring structures. When data crosses environments, risk rises unless governance and visibility are consistent. Cloud maturity therefore has to include security posture, data protection, identity governance, and resilience across the full estate.

Data readiness is the hidden maturity gap

The most important AI gap may not be in cloud infrastructure at all. It may be in data readiness.

In many enterprises, data sits across source systems, warehouses, lakes, SaaS applications, and legacy platforms. Some of it is duplicated. Some is poorly governed. Some is not integrated with enterprise data platforms. Some lacks quality, lineage or metadata. That makes it difficult to use as reliable input for AI.

Cloud can help solve this, but only when used as a governed data foundation rather than just an infrastructure destination. Enterprises need to bring critical data stores, warehouses and governance layers into a more coherent cloud architecture so AI workloads can draw from trusted, classified, and well-managed data.

There is a practical reason for this. AI does not perform well on fragmented context. If customer, product, transaction, service and operational data are not connected, the enterprise cannot easily build correlation, dependency mapping or decision intelligence. Aggregating AI-relevant data in one place makes it easier to govern, correlate and use for AI with stronger security, privacy, and accuracy controls.

We recently helped an insurance organization migrate data to cloud and create a data layer with master data management and governance. Once that foundation was in place, the organization was able to run machine learning and AI algorithms more efficiently on the platform.

That is the deeper lesson for CIOs. AI readiness starts before the model 鈥 with data architecture, data governance and cloud design.

Modernization debt is becoming harder to carry

Many enterprises still carry applications built over 10, 15, or 20 years. These systems were often left unchanged because modernization was expensive, time-consuming and difficult to prioritize against new business initiatives. The result is technical debt that shows up as security risk, scalability limits, performance constraints, and operational fragility.

Cloud migration exposed some of this debt. AI will expose more of it.

A legacy application can be moved to cloud and still remain structurally unready for AI-era demands. If it cannot scale elastically, integrate easily, expose data securely, support automation, or operate with modern observability, it will limit the enterprise鈥檚 ability to use AI in meaningful workflows.

This is why modernization has to become part of the cloud maturity agenda. CIOs need a clear view of which applications can be retained, which can be rehosted, which need refactoring, which should be re-architected, which require database changes, and which should be retired.

The stronger point is that modernization should not always wait until after migration. In many cases, simply moving applications as-is creates higher cost, limited automation, and weak elasticity. Refactoring during migration can help applications use cloud-native capabilities more effectively and reduce the risk of carrying old inefficiencies into the cloud.

AI can also change the economics of modernization. AI-assisted modernization can significantly improve productivity in upgrading or transforming applications, turning programs that once took many months into shorter, more manageable initiatives.

Our recent engagement with a Tier-1 private sector bank in Southeast Asia shows how this can work in practice. The bank needed to modernize 21 legacy banking applications while maintaining business continuity, reducing technical debt, and improving release agility. 乐播传媒视频 helped the bank move to a more standardized, cloud-native modernization approach, creating a stronger foundation for scalability, security, and long-term cloud-first growth. The program delivered 35% faster release cycles, a 30% reduction in operational effort, and a repeatable modernization framework for future initiatives.

For CIOs, this creates a window to revisit modernization debt with a different business case. The goal is not modernization for its own sake. The goal is to build a cloud estate that can support secure, scalable, automated, and AI-ready operations.

A practical roadmap for CIOs

The cloud maturity gap cannot be closed through isolated initiatives. Security, cost, modernization, data, and operations are now tightly connected. A cost issue may be rooted in poor architecture. A security issue may be rooted in fragmented identity. A data issue may be rooted in incomplete modernization. An AI issue may be rooted in all of them.

A practical CIO roadmap can begin with five moves.

  • First, set up the landing zone correctly. This is the foundation for governance, security, identity, networking, and policy consistency.
  • Second, identify pilot applications and use them to validate the operating model before scaling migration or modernization.
  • Third, define tagging, ownership and FinOps pipelines early, so cost visibility and accountability are built into cloud adoption rather than added later.
  • Fourth, migrate and modernize with automation in mind. The goal is to reduce manual effort, strengthen consistency, and improve operational scalability.
  • Fifth, bring in monitoring, automation, and AIOps to manage cloud infrastructure and applications with greater intelligence and responsiveness.

For leadership teams, the broader shift is clear. Cloud maturity is no longer defined by migration completion. It is defined by whether the enterprise can support AI at scale without losing control of cost, security, data quality, modernization and resilience.

AI ambitions will keep growing. The cloud foundation has to catch up.

Author:

Varoon Rajani
Sr. VP & SL Head – CS

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]]> Automation vs AI Value Realization: What CIOs Must Fix First to Unlock Enterprise Value /blog/automation-vs-ai-value-realization-what-cios-must-fix-first-to-unlock-enterprise-value/ Fri, 28 Nov 2025 11:49:20 +0000 /?p=44453 The post Automation vs AI Value Realization: What CIOs Must Fix First to Unlock Enterprise Value appeared first on 乐播传媒视频.

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Executive Perspective for CIOs and Business Leaders

The AI Surge and the Scaling Gap

Artificial Intelligence has become the centerpiece of enterprise strategy. CEOs are pushing aggressively to 鈥済o AI鈥 across functions, and budgets are shifting fast. Gartner forecasted that worldwide generative AI spending will reach $644 billion in 2025, while IDC projected global AI investments will grow to over $500 billion by 2027.

Yet value realization remains significantly behind expectations. Only 14% of enterprises have managed to scale AI beyond isolated pilots. The challenge isn鈥檛 the maturity of AI technology; it鈥檚 the maturity of the enterprise environment into which AI is deployed.

Organizations are chasing the want of AI: generative models, autonomous reasoning, synthetic data pipelines, edge accelerators, and self-learning orchestration layers; while neglecting the need for Intelligent Automation: workflow optimization, process re-engineering, rules-based automation, hybrid RPA, system interoperability, and predictable operational scaling.

The hard truth CIOs must confront is this: before enterprises can truly harness AI, they must first master Intelligent Automation.

AI Value Realization Won鈥檛 Happen on Top of Operational Chaos

Across industries, CIO roundtables and transformation assessments reveal a consistent pattern: enterprises are deploying AI into operations that aren鈥檛 ready for it.
High process variability, manual workflows, fragmented integration layers, poor data trust, and limited telemetry all undermine AI鈥檚 effectiveness. When AI is introduced into inconsistent systems and non-standardized processes, outputs become unreliable, governance becomes harder, and scaling becomes nearly impossible. AI cannot compensate for operational fragmentation. Automation can.

Why Automation Must Precede AI

Most enterprises are still far from ready for AI at scale. Deloitte鈥檚 2025 Workflow Automation Outlook highlights that 73% of enterprises have not reached mid-level automation maturity. McKinsey鈥檚 research shows that over 60% of AI鈥檚 economic potential depends on process optimization. AI may be powerful at reasoning and prediction, but it cannot deliver results without clean data, interoperable systems, stable workflows, measurable outcomes, and real-time connectivity. These foundations are created not by AI, but by Intelligent Automation.

Automation is the real force multiplier: it cuts cycle times by up to 80%, drives near-perfect accuracy, doubles, or quadruples throughput without adding staff, and reduces operating costs by as much as half. Once automation strips away friction and standardizes processes, AI can finally be layered on top to generate intelligence over predictable, measurable operations.

For CIOs, the message is clear: automation maturity is the prerequisite for AI value realization.

AI-Ready vs. AI-First: The Leadership Distinction

Many enterprises rush into an AI-first approach: pilots, use cases, and model experimentation, only to stall when scaling. The organizations that succeed are AI-ready: they invest first in the foundations.

Automated end-to-end workflows, governed data pipelines, API-driven integration, standardized processes across regions, and a clear governance model create the conditions for AI to deliver value. This readiness accelerates time to impact and minimizes risk, ensuring that when AI initiatives expand, they do so on solid ground.

The Enterprise Intelligence Stack

CIOs who succeed in scaling AI follow a layered maturity path. The journey begins with process standardization to ensure consistency across the enterprise. Intelligent automation then removes friction and drives efficiency.

Once workflows are automated, data engineering and governance provide the clean, reliable pipelines that analytics depend on. With trusted data in place, organizations can generate meaningful insights, creating the conditions for artificial intelligence to deliver transformative value.

Skipping these layers may create the illusion of progress, but it inevitably leads to operational bottlenecks when AI initiatives attempt to scale.

What CIOs Should Prioritize Over the Next 12鈥18 Months

To unlock AI鈥檚 full potential, CIOs must first focus on automation maturity, stabilizing high-variance workflows, and digitizing end-to-end processes to create a reliable operational backbone.

Modernizing integration architecture is equally critical. AI is only as effective as the systems it can 鈥渢alk to,鈥 making API-led connectivity, low-code orchestration, and event-driven design essential.

Building enterprise data trust is non-negotiable. Without quality, lineage, and governance, AI outcomes deteriorate rapidly. CIOs must also shift from chasing AI use cases to prioritizing business value cases, those tied directly to customer experience, cost, risk, or revenue.

Finally, establishing an AI operating model with cross-functional pods, federated decision-making, and transparent ROI governance helps reduce pilot fatigue and accelerates enterprise adoption.

Closing Thought: CIO Leadership Requires Sequencing, Not Speed

AI represents ambition, but Intelligent Automation represents readiness. The CIO鈥檚 role is not to slow innovation; it is to sequence it correctly.

AI is the destination. Automation is the road that gets the enterprise there safely, at scale, and with measurable value. CIOs who invest in automation-first strategies will unlock the full economic and operational potential of AI, while reducing risk, improving efficiency, and building an enterprise truly prepared for the next decade of intelligence-driven transformation.

Author:

Kishore Kamarajugadda,
VP-Enterprise Architect


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]]> How AI and Generative AI Are Revolutionizing the IT Sales Ecosystem /blog/how-ai-and-generative-ai-are-revolutionizing-the-it-sales-ecosystem/ Wed, 04 Dec 2024 09:52:18 +0000 /?p=42063 The post How AI and Generative AI Are Revolutionizing the IT Sales Ecosystem appeared first on 乐播传媒视频.

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Artificial Intelligence has ceased to be merely a familiar technological advancement of the future. Instead, AI has become the fundamental tool that enables vital changes within an organization, from the operators to the final decision-makers. More recently, sales leadership has been one of the areas gaining the most from AI advancements. As organizations continue grappling with the increasing complexity and competitiveness of the business environment, IT sales are being propelled by AI and Generative AI, which are solving problems and providing sales leaders with new technologies on how to interact with customers, run queues, and make management decisions based on metrics.

Initially, the main task of artificial intelligence was the mechanization of monotonous actions. At present, however, it has matured into providing actionable insights, forecasts, and suggestions. In a recent Salesforce report, 52% of IT sales leaders deploy AI technology for enhanced productivity, with a further 85% predicting that it will form a core part of their strategies within the next five years. Companies that apply AI to their business report an increase in conversions of 10 to 15% and a growth of 5 to 10% in the rate of client retention.

Managing complex sales pipelines and client contracts is a constant headache for IT sales leaders. Failing to remember a renewal date or a milestone shoots a company in the foot, resulting in incoming revenue loss and, in the future, an opportunity loss as well. AI-based applications like Salesforce Einstein, Microsoft Dynamics, and HubSpot bring in real-time alerts for such cases, along with reminders for renewals and milestones. They allow sales teams to pursue customers and improve chances for retention as well as upsells. A Salesforce global report showed that the participating companies that had AI integrated within the sales cycle saw their pipeline accuracy increase by 50%.

A case in point here is Schneider Electric, a global energy management and automation leader, that implemented Salesforce鈥檚 Sales Cloud and Service Cloud as part of its “One Schneider” strategy to unify its systems and create a 360-degree view of customers. The company enhanced its CRM with AI-powered CRM Analytics and Einstein Discovery, which analyze data from multiple sources, including IoT, to identify sales opportunities and predict conversion likelihood. This AI-driven approach helped Schneider Electric reduce its sales cycle time by 30%, ensuring that global sales teams focus on the most promising leads.

It is equally important to ensure active scanning of new customer data. The most sophisticated tools, such as Perplexity AI and TextCortex, are now crucial tools. Unlike conventional analytical resources, Perplexity and several other resources combine offline and online information to create a picture of the target audience’s behavioral activities and tendencies at the moment. This allows sales teams to access such data whenever there is a change in clientele鈥檚 needs or interests, making interaction with such clients more helpful and more focused. This function is most effective with highly developing industries or highly dynamic clients.

Tools of Generative AI, such as ChatGPT, CoPilot, or Google Bard, have also been of great use in ensuring that communication is effective and, above all, personalized. Enterprise sales teams can use these tools to automate the generation of personalized proposals, along with any follow-up and marketing copy that will appeal to their clients in no time. This level of automation ensures that interactions with clients remain relevant and personal, with no substantial input of manual effort, thereby easing the burden of responding to the increasing demands of customers in terms of their expectations for the degree of personalization and speed. The application of such tools is seen globally in many different fields, such as industry and finance, where there is increasing pressure to provide clients with tailored services and fast feedback.

Another common challenge is the degradation of knowledge, particularly during team transitions or onboarding new hires. AI-powered tools like Gong.io and Chorus capture and analyze sales conversations, while IBM Watson organizes and stores insights from past client interactions. These platforms reduce reliance on 鈥渢ribal knowledge,鈥 aid new hires in getting up to speed quickly, and ensure that best practices are documented and shared across the team. With teams often dispersed across different regions, these tools are crucial for maintaining consistency and knowledge continuity in global sales operations.

For example, Iron Mountain, a global leader in storage and information management, used Gong to reduce new-hire ramp time by 3 months and improve sales effectiveness. By leveraging Gong鈥檚 AI-driven insights, the company provided data-backed coaching and visibility into sales calls, helping new reps quickly identify the best practices and improve their performance. As a result, 60% of new reps hit their key metrics within five months, compared to just 9% before Gong, leading to a 148% improvement in performance. Gong鈥檚 call tracking and coaching features enabled more efficient onboarding and faster ramp-up, even in a fully remote environment.

As we have seen, therefore, both AI and Generative AI are transforming the IT sales landscape as they allow leaders to more efficiently manage their pipelines, enhance their customer interactions, and protect crucial information. They enable sales leaders to tackle the challenges of modern, fast-paced, global markets effectively. They can remain in touch with customers, be aware of the latest trends and strategies, and be prepared for expansion. As AI evolves further, it has been predicted that AI will complement sales functions with increasing significance, and it is likely to become an important success factor for IT sales leaders globally.

Reference:




Author:

Harsh Agarwal,
Senior Manager – Business Development

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]]> The Super Apps are here (are you riding the wave?) /blog/the-super-apps-are-here-are-you-riding-the-wave/ Wed, 09 Oct 2024 12:12:07 +0000 /?p=41594 The post The Super Apps are here (are you riding the wave?) appeared first on 乐播传媒视频.

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Super Apps are redefining the future by offering unparalleled convenience and hyper-personalization. These apps seamlessly merge our digital and physical lives, anticipate and predict our every desire and need, and let us access a universe of services, from augmented reality shopping to virtual healthcare and everything in between. Their superpower? They make everything available with a single tap. As boundaries blur between industries, super apps are forging new ecosystems, creating a gateway to a world of limitless possibility.聽

So, what is a Super App? One way to think of them is as a collection of related services and functionalities on a smartphone targeted at specific interest groups, where everything is a single click away. Given their rising success with consumers, businesses will soon want Super Apps to improve workplace communication, collaboration, efficiencies, engagement, and loyalty. Super Apps have the potential to transform the mobile landscape and offer exciting opportunities for businesses and users (see Figure 1).

Figure 1

Businesses that have invested in digital transformation are developing internal applications with roles-based access for sales, marketing, operations, finance, and facilities. However, the application environment soon becomes crowded, unwieldy, and difficult to manage. This is illogical, several workflows鈥攕uch as login or chat鈥攁re common to every application, and even a tiny change in the process means the enterprise must make the change separately across several such applications. A single app reduces the effort to the barest minimum.

Building a Super App requires significant time, resources, and expertise in user experience design, software development, and infrastructure management. To succeed, Super Apps must offer a seamless and intuitive user experience while providing a wide range of functionalities and services (see Figure 2).

Figure 2

However, the potential benefits of a Super App make it a worthwhile endeavor for businesses looking to provide a one-stop-shop solution for their customers. A single Super App minimizes the overhead of onboarding and change management. It delivers richer user behavior data to help developers customize, rationalize (the features), and improve the Super App.聽

For enterprises that are amid their digital transformation journeys, an organization-wide Super App has become a necessity, especially because employees want frictionless interaction with colleagues and easy access to organizational processes, some of which are listed here:

  • Important organization-wide broadcasts
  • Project and product announcements and reporting
  • HR Playbooks, checklists, guides, and support
  • New employee onboarding
  • Training and Development programs聽
  • Procurement processes
  • Approved vendors
  • Access to the service delivery platforms
  • Polls, surveys, and internal competitions
  • Events
  • Customer details/ Customer visits
  • Community activities
  • Social messaging and chat
  • Video conferencing
  • Helpdesk access
  • Service anniversaries
  • Job postings/ Referrals
  • Podcasts, blogs
  • ESG targets and commitments
  • Rewards and Recognition
  • Travel requests
  • Expense claims
  • Timesheets
  • Attendance management
  • Salary disbursement
  • Leave management
  • User profile
  • Password reset

Super Apps are a revolution in the making鈥攁nd China鈥檚 WeChat, with a staggering , is showing us the way. And across the world, from and in Southeast Asia, and in India, and in other parts of the world, everyone is following.聽

A 2022 study called showed that 7 in 10 respondents were interested in a Super App, 90 percent of whom were motivated by the convenience of an integrated app. Gartner estimates that by 2027.聽

One of the leading reasons for the growth of Super Apps is app fatigue. App downloads have plateaued (), and studies forecast they will keep falling. Users will migrate to Super Apps鈥搘here all the action is brewing.

Author:

Dileep Kumar,
Practice Head 鈥 Mobility

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]]> Enterprise Architecture Reimagined: Exploring Its Facets in the Ai Epoch /blog/enterprise-architecture-reimagined-exploring-its-facets-in-the-ai-epoch/ Thu, 16 May 2024 12:26:55 +0000 /?p=41263 The post Enterprise Architecture Reimagined: Exploring Its Facets in the Ai Epoch appeared first on 乐播传媒视频.

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Enterprise Architecture (EA) as a practice, as a role, as a thought process has come a long way in the last three and half decades since its inception. Various standards, frameworks, industries, and governments joined hands and climbed the ship of EA from time to time and reached destination (goal) of their stipulated time.

EA role has both evolved and involved over the period and at this time of the AI era (other terms in the AI Epoch), multiple people have coined or termed different new terminology of EA as per their own perspective. The origin of these terminologies over the period has the basic thought that 鈥EA is in crisis.鈥 These all-coined terminologies are nothing but different viewpoints of EA.

EA was never and will never be in crisis. Organizations and industries crisis from the past was failure of not implementing EA. Such as the banking crisis (post which BIAN was set up) or the real estate crisis of the 2000s or the recent COVID-19 crisis. Each time one or multiple industries gets affected and there was a change in gear within organization(s) to understand EA as per their own understanding by following EA standards and frameworks specific to their industry. Over time, the standards, and frameworks pertinent to these specific industries and Enterprise Architecture have undergone a process of evolution and enhancement.

For a span of ten years, I have maintained a catalogue of EA Reference Architecture. It is important to note that the alterations to the site links are minimal and the list is not comprehensive, extending beyond the specified industry verticals or domains. While there are multiple such catalogues accessible in the market, this one is my preferred choice as it facilitates effective tracking.


In 2024 itself, one of the 鈥 with context to obviously EA in crisis. People contested it across the news and social media with few in agreement and few in disagreement. My viewpoint is from disagreement with the strong contest is EA was and will never be specific to any stack be it business or technology stack. Then why do you need a full stack? EA was always full stack; it is not about any of the cards from the stack.

Below is the snapshot from one of EA frameworks TOGAF 10, which describes that EA is more than 70% about business and the remaining 30% would be part of data, application, and technology.

 

Issue or Crisis has always been in understanding EA. 鈥淭here is always an ambiguity in understanding EA because EA is an area of the field with everything ambiguous.鈥 Explained nicely by Dr. Pallab Saha, The Open Group CEO at one of the events. EA was never in crisis; it was always people or organizations who never realized the value of an EA until there was a crisis knock at the door. A few examples of such a mindset are in late 2020 and now ; both have not emphasized EA much. EA in such large banks has always been more of a department to complete the compliance checklist.

Now the reason this happened in Apr鈥2024 itself, is this AI Epoch. AI was always there in the background, in our studies during graduation but now it is reality in front of us. As mentioned earlier in this article, all EA frameworks and standards across industries are evolving to cater to AI (not just Gen AI). Last year 2023, even Gartner reflected to all CXOs, and leaders with similar guidance on the dilemma of EA during AI times.

The success of any EA depends on multiple factors, but the mantra is simply 鈥淪cale Fast, so that can鈥檛 be undone,鈥 again from Dr. Pallab Saha. This EA mantra allowed multiple EA designs to become frameworks and standards. Consider the example of DoDAF which is EA Framework for the US Department of Defense, MoDAF which is EA Framework for the UK Ministry of Defense, or IndEA which is for the Indian Government.

Another reason in the AI era, everyone is worried about the 鈥淓A in crisis鈥 kind of stuff is because AI is influential, and EA is noisy for 90% of organizations across the world from all diverse types of industries. The best example to explain EA being noisy would be COVID-19 crisis. Various industries disrupted by COVID-19, especially healthcare, hospitality, tourism, manufacturing, supply chain, and travel, among others. Again, the reason was not proper EA utilization whether internal or outsourced. Even research firms like Gartner, IDC, and Forrester started emphasizing EA with titles such as 鈥淓A – the dawn of new era,鈥 and 鈥淓A – must for transformation.鈥 As it again has gone backseat for few organizations, but all the organizations started realizing that EA is must to achieve their business vision with a timeline of 3-5 years as the target.

The authentic use of AI in EA is when AI operates as an auxiliary or support system, rather than serving as a substitute. AI tools have facilitated the acceleration of tasks within EA. However, the comprehension and expertise of Enterprise Architects remain essential for validating data points and perspectives in alignment with the business vision. Facilitating a collaborative environment between AI and EA governance could provide organizations with a competitive advantage, enhancing their alignment with their business vision and expediting the execution of EA tasks.

Multiple EAMs (Enterprise Architecture Management) or EA Tools are already incorporating AI in their core. One such tool is LeanIX which enables AI utilization within its EAM (Enterprise Architecture Management) tool. Even The Open Group has started work towards 鈥溾

Now coming to EA facets (multiple names), multiple terminologies coined over the period. I learned during one of the EA webinars, which is a fact, that there are more than eight (8) million architects鈥 titles in the market. If we narrow this down to EA, the count will be more than two (2) million. Organizations as per their understanding of EA and their convenience as per their businesses, have defined, coined, or termed these architectural roles. In this cycle, even EA frameworks like TOGAF have jumped and coined another term such as Digital Architect.

Another reason to have so many terminologies for an EA is because of the mindset 鈥淎rchitecture is about taking control of an organization.鈥 Architecture is not about taking control of an organization. It is about giving control to organizations. You still need the organization. The clue is in the name, organization.

From my viewpoint, there are only four facets to any EA framework as mentioned in Figure 3 earlier and they are business, data, application, and technology. To contest my thought process, there is already a trend in the market that allows Architecture-as-a-service (AaaS) such as (again open source). These AaaS tool(s) are specific to an application component of EA.

There are multiple Enterprise Architects who share similar thought processes and agreements, and they play a significant role in supporting the EA practice worldwide. One such individual is Sam Holcman, whose focus is streamlining the Business Architecture component of Enterprise Architecture. Similarly, courtesy to George Hohpe, Martin Fowler, Neal Ford, and Mark Richards who keep inspiring new architects to move towards EA. The very definition of EA is now enabled as syllabus at universities, especially in India apart from other countries which would enable EA facets taught to our educational mindset even before we start working as an individual.

The mantra that I have learned over the period, especially as an EA, is to be useful and trusted to be an influencer as an EA. To achieve or execute this mantra, the focus should be on people.

References:

  • The Software Architect Elevator 鈥 George Hohpe
  • Fundamentals of Software Architecture 鈥 Neal Ford
  • Whole EA 鈥 Tom Graves
  • TOGAF
  • DoDAF
  • BACOE
  • Gartner

Author:

Neeraj Gautam,
Enterprise Architect at 乐播传媒视频

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]]> Why Organizations Using Devops Tools Are Getting Ahead of the Rest /blog/why-organizations-using-devops-tools-are-getting-ahead-of-the-rest/ Mon, 13 May 2024 12:37:04 +0000 /?p=41244 The post Why Organizations Using Devops Tools Are Getting Ahead of the Rest appeared first on 乐播传媒视频.

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The benefits of DevOps are widely known and simple to understand. The practice brings together people, processes, engineering, operations, and technology to deliver products faster, better, and more confidently to meet business goals. However, some organizations do this better than others. Separating the leaders from the laggards is the use of tools. The teams that use DevOps tools achieve outstanding results. Our experience shows that using DevOps tools can reduce lead times to deployment by 45 percent, reduce failure rates by 50 percent, and bring down support cases by 70 percent.

With such a promising upside, the DevOps market is naturally set to balloon. suggest that it will grow from an estimated $8.66 billion in 2022 to $47.81 billion by 2030, growing at a CAGR of 23.80 percent over the forecasted period.

Life before DevOps: Chaotic

DevOps, through the adoption of agile and lean practices, has introduced a sea change in the science and art of product development. Life before DevOps was chaotic for project managers. Miscommunication between developers and the operations team would result in haphazard development, delayed deployment, the need to constantly monitor application maintenance and performance, a lack of continuous integration, and spiraling operational costs. A simple code issue would set the project manager off to see if the data team had changed values or if the developer had a reason to change a column name and to see what the tester had to say. It was a never-ending chain of debugging鈥攚ith the primary outcome being employee burnout.

Life after DevOps: Under control

This has changed. The chaos can now be brought under control. Using DevOps tools and processes, program managers can now quickly identify which code broke functionality, who wrote the code, and the time it will take to fix.

In Azure DevOps, user stories are task descriptions that are treated as work items. A developer is assigned a task and must transform the user story into a functionality. To do this successfully, the developer needs, say, a database table and an API. This information is captured in the work item, along with the relevant test cases that get updated in a tool. When the developer commits the code, it is linked to this work item.

Improving the value of DevOps

Every organization that embraces DevOps believes it has evolved as a business. In reality, these organizations, without realizing it, are living with sub-optimal results from their DevOps initiatives. The situation is comparable to that of generative AI. Every organization claims to be using generative AI. However, the value of generative AI is directly linked to the data it is trained on. If the data is inadequate, the model cannot be effective. It is the same with user stories. If the project manager does not record detailed user stories, outcomes will be sub-optimal. DevOps tools are critical to avoid this prospect.

DevOps tools allow user stories to be captured in detail. Typically, the tool will capture the features a user story needs to deliver (see Figure 1), the acceptance criteria and standards (usability, design, performance, security, etc.) that will be considered to judge if the user.

Story has been successfully executed as a feature, the difficulty level in completing the task, the developer or the team to which the task has been farmed out, dependencies/impediments, associated feature requests, time spent to deliver a task, task status, etc. These tools provide a platform to record discussions and conversations before the work item is committed as code. The complete end-to-story helps the program manager stay on top of what is happening without human dependencies. Organizations that are not using these tools cannot claim to have embraced DevOps. These organizations have only implemented an Agile process with standup meetings and scrum calls. The project manager鈥檚 role should be to ensure the tools and systems capture the details.

Once the details are in the system, a dashboard can provide a deep dive (see Figure 1): How many stories and work items do we have? How many commits have been done for a particular code snippet? What is the build health? What is the quality of the code before it is put into its environment? How many builds have been successful? Is the developer doing unit testing before pushing the code?

The barriers to benefits

Several DevOps tools are available to capture development tasks (see Figure 2). These include Azure DevOps, Bitbucket, Gitlab, GitHub, Selenium, etc., which help inject transparency into development. The challenge for the project owners and managers is to ensure the team is ready to move away from traditional development models and embrace transparency. Without a mindset change and a cultural change, the tools cannot be ineffective.

Part of the reluctance to change the mindset is rooted in a misconception. Many believe the tools will eliminate the need for humans in the process. This is not true. The tools merely show the efficiency of the process that the team is following. The tools tell us how clean the product is when it goes to market, what kind of observability we have, the time a task is likely to take, etc. Tracking this information is a huge and complicated job. The tools simplify it; they automate nothing.

Organizations need to realize鈥攁nd eventually, they will鈥攖hat they have been using DevOps for 15 years but have not simplified it. This is because they are resistant to change. The organizations that close the culture gap and change will move ahead. The rest will play catch-up.

How to trigger cultural change with Agile Methodology

The challenge for organizations is to address the gap quickly. Here is one way to do it: Most project managers have two or three projects to manage simultaneously. The Project Management Office (PMO) should make sure there are daily calls for each project, and the manager is forced to provide a status update. Usually, the manager is a layman, not a developer. To them, the PMO should ask, 鈥淭he developer did something. Did you understand what was done?鈥 The obvious answer is, 鈥淣o!鈥 But this is where the change can be triggered. The PMO must ask the manager to read the details from the DevOps tool and get on top of the delivery. The point is to challenge managers with simple questions, 鈥淗ow much time will the developer take for the next build?鈥 or 鈥淲ho do you think is the right resource for the task?鈥 or 鈥淲hat are the dependencies for a task?鈥 The answers to these questions can be generated by the tool. Many of these tools have pre-defined queries, making them simple to use.

It is easy to see that organizations that claim to have embraced DevOps may not be extracting as much from the practice as possible. Their ROI is low. For example, they may not be able to unlock the ability to experiment and innovate, deliver superior product quality, release code quickly, dramatically improve time to resolution, address unplanned work with agility, reduce the cost of production, and reduce developer burnout. To all this, there is one simple answer: Create Agile culture that uses DevOps tools like a pro.

Author:

Swetha Yalamanchili,
Head of DevOps

The post Why Organizations Using Devops Tools Are Getting Ahead of the Rest appeared first on 乐播传媒视频.

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