Also Read: UST Launches UST Codon to Bring Governed AI Development Across Enterprise Platforms 

“As AI accelerates software development and vulnerability discovery, enterprises need a faster and more scalable approach to remediation. Lightwell represents a significant advancement in securing the open-source software supply chain by bringing AI-driven remediation and trusted software maintenance into the enterprise. Through our collaboration with IBM, LTM will help organisations strengthen their cyber resilience at scale.” Reaffirming the necessity of ecosystem-wide security standards, Sandip Patel, Managing Director at IBM India and South Asia, added: “Addressing today’s software supply chain challenges requires a collective defense. As AI accelerates vulnerability discovery, collaboration across the ecosystem becomes increasingly important. Bringing LTM’s engineering and transformation expertise to Lightwell can help enterprises mitigate risk and build more resilient software supply chains.” Ultimately, this collaboration combines LTM’s DevSecOps expertise with IBM and Red Hat’s AI infrastructure to accelerate secure, resilient digital innovation across complex global software ecosystems.

Read More: LTM Collaborates with IBM and Red Hat on Lightwell to Advance AI-Driven Open-Source Software Remediation

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As corporate leaders transition agentic AI models from isolated trials into production environments, disconnected legacy software, fragmented data pipelines, and API bottlenecks frequently stall progress. The MuleSoft AI Pod resolves these technical barriers by automating API construction, system integration, and security governance across multi-cloud software ecosystems.

“As organizations look to scale Agentforce and other AI initiatives across the enterprise, integration and data readiness are becoming essential,” said Roland Berthelot, Global Head of Salesforce Studio at Globant. “MuleSoft helps establish the connectivity foundation that makes those initiatives possible. With MuleSoft AI Pod, Globant is helping customers scale integration delivery while maintaining the governance, security and expert oversight enterprise adoption requires.”

Also Read: Joget Launches Agent Lab to Accelerate No-Code Enterprise AI Development

Flexible Pod Configurations and Benchmark Delivery Metrics

Engineered to match varied enterprise integration roadmaps, MuleSoft AI Pods combine autonomous AI agents with Globant enterprise architects. The agents handle structured technical tasks including initial API design, flow development, automated code testing, and legacy data mapping—while senior engineers validate system performance, verify business logic, and maintain compliance standards.

Organizations can deploy the solution across three structured delivery tiers:

Lean Configuration: Engineered to support approximately six API or integration flows per month.

Scaled Configuration: Configured to manage up to 12 integration flows per month for mid-tier environments.

Advanced Configuration: Built for high-density enterprise workloads, handling up to 24 integration flows per month.

Internal benchmarks and current deployment pilots demonstrate significant operational improvements, including:

80% Automation: Elimination of manual, repetitive integration steps through agentic automation.

15–25% Faster Time-to-Value: Accelerated project delivery schedules across complex software architectures.

35% Efficiency Gain: Improved productivity across system design, flow creation, and code validation.

Consumption-Based AI Delivery via Glob.AI Platform

The MuleSoft AI Pod is deployed through Glob.AI, Globant‘s unified technology platform designed to streamline access to specialized AI capabilities. Moving away from traditional hourly billing or per-seat licensing models, Glob.AI utilizes transparent, consumption-linked or output-based pricing—aligning technology expenditure directly with completed integrations.

The initiative expands upon Globant’s 21-year partnership with Salesforce, backed by more than 1,200 completed projects, a 4.7 out of 5 customer satisfaction rating, and over 27,400 global technologists. As a founding partner of the Agentforce Partner Network, Globant maintains over 500 Agentforce Certified Experts and 200+ Salesforce Data 360 specialists across 58 international delivery centers.

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The OECD makes this tension clear. Digital technologies can improve optimization, decision-making and energy efficiency, but they can also increase demand for electricity, water and other resources through data centers.

Eco-friendly software work should not stop at the CSR talk. The bigger chance is in everyday software choices. How a company builds, runs, and oversees systems matters? If teams use the cloud in a more careful way, and automate tasks with more thought, they can cut extra usage. Cleaner code and better data habits also help. This piece will cover where that change can begin. It will also show how IT leaders can treat sustainability like a normal way of working.

The Business Case for Sustainable IT Meets FinOps and GreenOps

Sustainability becomes much easier to defend inside a business when it stops sounding like an extra expense. Enterprise technology teams already spend significant time dealing with cloud bills, unused infrastructure, oversized workloads and resources that keep running even when nobody needs them. That is where FinOps and GreenOps begin to overlap.

FinOps focuses on getting better financial control over cloud usage. GreenOps applies a similar discipline to environmental impact. Both ask a surprisingly similar question. Are we using more resources than the workload actually needs?

That question exposes a common problem in enterprise environments. Teams often provision for peak demand, keep development environments running around the clock and accumulate workloads that nobody has reviewed for months. The infrastructure may be technically available, but that does not mean it is productive.

AWS states that most of its service charges correlate with hardware usage. Its guidance also argues that reducing resource consumption can create both cost savings and sustainability benefits. That connection matters because it changes the internal conversation. Eco-friendly enterprise software initiatives are not necessarily about choosing sustainability over profitability. In many cases, they are about removing operational waste that hurts both.

The regulatory side adds another layer. As sustainability reporting becomes more structured, companies will face greater pressure to understand their digital footprint rather than simply publish broad environmental commitments. Building measurement into IT operations now gives businesses a better starting point later.

The smarter approach is therefore not to launch a separate sustainability project and hope teams adopt it. Sustainability should become part of normal decisions around infrastructure, architecture, development and procurement.

Also Read: Cloud Security Best Practices: How Enterprises Can Protect Data, Applications and Cloud Infrastructure

4 Pillars of Eco-Friendly Enterprise Software Initiatives

1. Cloud Infrastructure Optimization and Green Hosting

Cloud migration alone does not make software sustainable. Moving an inefficient workload from an on-premise server to the cloud can simply move the problem somewhere else. The real gain comes when companies use the flexibility of cloud infrastructure to match resources with actual demand.

Right-sizing is a good starting point. An application that consistently uses a small part of a large compute instance does not need that capacity sitting idle. Teams can review utilization and adjust instance sizes based on real workloads. The same principle applies to storage. Old snapshots, duplicate datasets and unused volumes can quietly increase both cost and resource consumption.

Autoscaling takes this further. Google’s 2026 sustainability guidance recommends automated and dynamic scaling to reduce energy waste from idle or over-provisioned infrastructure. Instead of keeping capacity fixed because traffic might increase, organizations can allow infrastructure to expand and contract with demand.

Region selection also deserves more attention. Electricity does not have the same carbon intensity everywhere. When workloads can move between suitable regions, teams can consider the carbon characteristics of the electricity supplying those locations alongside latency, compliance and cost.

These practices make eco-friendly enterprise software initiatives less about buying new infrastructure and more about using existing infrastructure intelligently.

2. Intelligent Automation and AI for Resource Management

Automation can act like a control system for digital resource consumption. It watches demand, identifies patterns and adjusts infrastructure before waste becomes normal.

A simple example is the development environment. Testing and staging servers often remain active after teams have gone home. They may do nothing for hours, yet they continue consuming resources. Automated schedules can shut them down during periods when they are not required and bring them back when work begins.

The same principle can apply to production workloads. AI and machine learning can help predict traffic patterns and adjust capacity before demand arrives. That is more useful than simply adding more servers and hoping the extra capacity gets used.

There is also a broader lesson here. Automation should not only be used to make software faster. It should be used to make infrastructure more responsive.

That distinction matters for eco-friendly enterprise software initiatives. A system that automatically responds to workload changes can avoid the waste created by fixed provisioning. It also reduces the amount of manual intervention required from IT teams.

However, AI itself needs discipline. Adding an AI system to every workflow does not automatically make the operation more sustainable. The model, workload, frequency of use and infrastructure behind it all matter. The objective should be targeted automation, not AI for its own sake.

3. Energy-Efficient Green Coding Practices

Software can consume more resources simply because it has been built inefficiently. Bloated data processing, unnecessary calculations, poorly designed queries and repeated server requests all add work that infrastructure must perform.

Green software engineering starts by treating resource efficiency as a development concern. Developers can refactor inefficient legacy code, remove unnecessary processing and reduce data that applications move or store. Efficient algorithms and better architecture can often achieve more than simply adding additional computing power.

The growth of AI makes this issue even harder to ignore. Microsoft reported in June 2026 that a typical query to some of the largest and most capable LLMs used between 0.16 and 0.60 Wh of electricity, depending on factors such as query length, model and data-center specifications.

That does not mean companies should stop using AI. It means they should stop treating compute as an invisible resource.

Model selection matters. So does prompt design, request frequency and the amount of data send into a system. Smaller models may be enough for routine tasks, while more capable models can be reserved for complex work. The same thinking applies to traditional software.

Programming language choice can influence efficiency, but it should not become a simplistic battle between languages. Rust, C++, Python or Ruby can all support different workloads effectively. The bigger question is whether the software performs the required work with unnecessary computation.

That is the real purpose of eco-friendly enterprise software initiatives at the code level. Build software that does its job without making the infrastructure work harder than necessary.

4. Sustainable UX and Data Minimization

Sustainability is also a front-end problem.

Every image loaded, video streamed, API call made and piece of data transferred creates work somewhere in the system. A poorly optimized interface can therefore create unnecessary demand far beyond the user’s screen.

Image and video optimization is one obvious area. Applications do not need to deliver the largest possible file to every user. Efficient formats, compression and adaptive loading can reduce the amount of data transferred without damaging the experience.

Caching can help too. If the same information is requested repeatedly, serving it from an appropriate cache can reduce unnecessary trips back to the server. API design deserves similar attention. Combining unnecessary calls, reducing repeated requests and returning only the data an application actually needs can make systems more efficient.

Dark mode can have benefits in some device contexts, but it should not be treated as the headline sustainability tactic. Data minimization and efficient delivery have a much clearer connection to resource use.

This is where eco-friendly enterprise software initiatives become part of product design rather than just infrastructure management. A sustainable digital product should make efficiency part of the user experience without making the user think about it.

How to Implement a Sustainable Software Strategy?

The hardest part of sustainability is rarely understanding the idea. It is creating a system that makes the idea measurable and repeatable.

The first step is to audit current usage. IT leaders need visibility into compute, storage, data movement and idle resources before deciding what to change. Open-source tools such as Cloud Carbon Footprint can help teams begin measuring cloud-related emissions instead of relying on assumptions.

Commercial cloud platforms are moving in the same direction. AWS launched its Sustainability console on March 31, 2026. The service provides estimated carbon emissions using market-based and location-based methods, with information available by AWS Region, service and emissions scopes 1, 2 and 3. It also supports customizable reporting and API or SDK access.

The second step is to track Software Carbon Intensity, or SCI. The important shift is to stop measuring only infrastructure and start considering the software workload itself. Teams can establish a baseline, identify the biggest contributors and set improvement targets.

The third step is cultural. Sustainability checks should enter the DevOps workflow rather than remain inside an annual ESG report. CI/CD pipelines can include checks for major changes in resource consumption. Architecture reviews can consider efficiency alongside security, performance and cost. Engineering teams can also review idle resources as part of routine operational hygiene.

This is where eco-friendly enterprise software initiatives become sustainable themselves. The goal is not a one-time cleanup exercise. It is a repeatable operating model where teams continuously question whether a workload needs the resources it consumes.

Future Trends Shaping Green IT

The next phase of sustainable digital operations will involve more than cloud optimization.

Digital Product Passports are emerging as a way to make information about physical products, materials and lifecycle characteristics more accessible. That could strengthen the connection between software systems and broader sustainability requirements.

Serverless and event-driven architectures will also remain important. Their appeal is straightforward. Instead of keeping compute resources running continuously, applications can use capacity when events or workloads actually require it.

For eco-friendly enterprise software initiatives, this points toward a bigger shift. Sustainability will increasingly become an architecture decision made at the start of a product’s lifecycle, rather than a reporting exercise added at the end.

Conclusion

The uncomfortable truth is that many businesses do not have a sustainability problem first. They have a resource-management problem that sustainability is forcing them to confront.

Unused compute, oversized infrastructure, unnecessary data movement and inefficient workloads are not only environmental concerns. They are signs that digital operations are not being managed tightly enough.

That makes eco-friendly enterprise software initiatives far more practical than they may sound. Companies do not need to redesign every system overnight. They can begin by auditing cloud usage, identifying idle capacity, measuring software impact and making efficiency part of engineering decisions.

The strongest sustainability strategy is therefore not the one with the biggest promise. It is the one that quietly removes waste from everyday operations while making the business faster, leaner and easier to manage. Start with the cloud bill. The environmental footprint may be hiding in the same place.

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Also Read: NTT DATA Acquires Netgain to Build Sweden’s Largest Pure-Play ServiceNow Entity

Highlighting the strategic value of governed, agentic architectures, Krishna Sudheendra, CEO, UST, stated: “UST Codon is one more state-of-the-art solution aligned to this vision, designed to help our customers meet critical business needs in a cost-effective manner. UST Codon’s AI-led, platform-agnostic and agentic-driven framework, with governance features built-in, creates a safe workflow experience for our customers.” Underscoring the importance of financial predictability in AI engineering, Kailash Attal, Chief Solutions Officer, UST, added: “UST Codon unleashes the true value of AI by translating business intent into production-ready solutions across SaaS and ERP platforms. What truly sets UST Codon apart is its pre-consumption pricing model, bringing predictability to the economics of AI-first engineering. We are proud to launch UST Codon as a practical step toward making AI more valuable, transparent and accountable in the enterprise software ecosystem.” Ultimately, this platform launch establishes a unified, risk-managed framework for organizations seeking to scale AI-driven application development across major enterprise environments.

Read More: UST Codon Brings Governed AI Development to Five Leading Enterprise Platforms

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Many organizations recognize clear opportunities where AI automation could eliminate administrative friction, yet leadership teams frequently assume that building functional software agents demands specialized engineering talent. Joget Agent Lab dismantles this technical barrier by publicly demonstrating how non-technical business leaders, process analysts, and IT professionals can construct agentic workflows using visual, no-code interfaces.

“We hear the same thing from businesses across every industry: they know exactly where AI could save them time, but building an agent still feels like something you need a developer for,” said Raveesh Dewan, President and CEO of Joget. “Agent Lab takes that assumption apart in public. We build the agent, we show our work, and by the end of the webinar you know how to do it yourself on our platform.”

Also Read: Zaelab Partners with Anthropic to Drive Enterprise AI Deployment

Program Framework: How Joget Agent Lab Operates

Selected agent builds will be unveiled during interactive, step-by-step educational webinars. Joget engineers will demonstrate the exact prompts, tool integrations, and logic flows used during construction, providing participants with a clear blueprint to replicate the builds independently.

The initiative follows a streamlined, three-step participation model:

Use Case Submission: Organizations outline a specific operational bottleneck and describe how an autonomous AI agent could resolve the issue.

Prototype Engineering: Joget’s product team reviews entries, selects high-impact use cases, and builds functional agent prototypes.

Live Technical Walkthrough: Participants attend a live masterclass to observe the completed agent in action and learn how to deploy similar builds.

Powered by Joget AI Agent Builder and AI Composer

Using Joget AI Agent Builder, agents created through the program have features like a visual, non-code software that gives both technical and non-technical staff the chance to design, test and govern AI agents with human-in-the-loop approval mechanism built in.

Companies wanting to expand into developing an array of applications will find this combined ability with that of the conversational tool Joget AI Composer very interesting. Joget AI Composer can create and edit forms, data lists, workflows, and user interfaces on the fly, as the user is interacting with them. The output of each component remains entirely editable in Joget’s visual design environment.

In respect of keeping data confidentiality and security at the highest level and making sure that governance of data is carried out in the best way, participants must provide their own Large Language Model (LLM) API keys. With this bring-your-own-key (BYOK) model, it is possible for companies that participate in the program to select any vendor that they would like, ensure total confidentiality of their data and meet their compliance standards.

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With more companies getting access to sophisticated generative models, competition advantage is no longer purely determined by which entity has first access but rather who effectively embeds the technology and implements it well. Deploying AI on a worldwide scale is a challenge that necessitates not only a solid technical basis but also good cross-system integration practices, solid policy, and a change programme for the team. The collaboration between Anthropic and Zaelab So gives companies the tool that embeds AI functionality directly into a company’s core business functions, technical workflows, and the operational workforce through the integration of Anthropic Claude models with Zaelab’s 10-year of success in delivering enterprise solutions to clients of different kinds and sizes.

“Five years from now, we won’t be asking which companies adopted AI,” said Evan Klein, CEO of Zaelab. “We’ll be asking which companies fundamentally reinvented how they operate because of it. That transformation won’t come from a chatbot or a proof of concept. It’ll come from integrating AI into the core systems, workflows, and decisions that run the business. That’s the opportunity we’re unlocking with Anthropic.”

Also Read: Haystack Launches First Intranet MCP Server for AI Integration

Accelerating Production AI via Forward Deployed Pods

Zaelab executes its enterprise delivery model through specialized Forward Deployed Pods—agile, cross-functional squads comprising senior AI engineers, enterprise architects, and delivery strategists who collaborate directly alongside client teams. This agile delivery methodology allows organizations to design, construct, and launch production-grade AI applications in weeks rather than months.

Through this execution model, Zaelab helps enterprises deploy AI across three core functional areas:

Autonomous Agentic Workflows: Embeds intelligent agents into high-friction business operations to automate routine tasks and enhance employee productivity.

Technology Application Consolidation: Simplifies fragmented software architectures by consolidating capabilities into unified AI-driven platforms, lowering long-term maintenance overhead.

Enterprise AI Enablement & Governance: Establishes robust data security guardrails and change management protocols to ensure organizations can scale AI securely.

Expanding the ServiceNow AI Ecosystem

Anthropic’s focus on enterprise-grade safety aligns directly with Zaelab’s delivery model. The alliance also strengthens Zaelab’s role within the broader ServiceNow ecosystem. Following a strategic investment from ServiceNow Ecosystem Ventures and a multi-year partnership between ServiceNow and Anthropic, Zaelab is positioned to bring AI-native Configure, Price, Quote (CPQ) and Customer Relationship Management (CRM) solutions to market on the ServiceNow AI Platform.

By combining leading AI technologies with hands-on delivery expertise, Zaelab continues to expand its enterprise AI ecosystem, providing global organizations with the specialized capabilities required to solve complex operational challenges.

The post Zaelab Partners with Anthropic to Drive Enterprise AI Deployment appeared first on ITDigest.

By establishing an open connection layer, Haystack enables enterprises to ground third-party AI models in verified, permission-aware company records. Rather than relying on outdated wiki exports or unverified public web data, staff members can query enterprise knowledge bases within their preferred AI workflows while maintaining absolute data governance.

Unifying External AI Tools with Internal Corporate Knowledge

The initial release focuses on read-level intelligence retrieval, delivering instant, verified answers to staff queries while embedding direct source links back to Haystack intranet pages. Future iterations will introduce full write support, enabling intranet administrators and content authors to draft, update, and manage company-wide communications using AI assistants.

Also Read: Deloitte Scales Production-Ready AI Solutions to Accelerate Enterprise Software Delivery

The release arrives amid rapid corporate AI adoption. Data from Ramp’s Business AI Index indicates that more than half of U.S. enterprises now subscribe to commercial AI applications, driving the need for unified internal knowledge access.

“Employees aren’t going to stop asking AI for help. The question is whether the answers come from your company’s single source of truth or from somewhere else,” said Cameron Lindsay, founder and CEO of Haystack. “Now the AI your team already works in draws on the same official, permission-aware knowledge your intranet protects. And soon, the people responsible for that knowledge will use AI to build it.”

Core Operational Capabilities and Enterprise Security

The Haystack MCP Server equips organizations with several strategic functional advantages:

Answers Grounded in Workflows: Claude reviews the verified knowledge pages, posts, organizational structure, calendar events, documents, and glossary items, with citation links to source content within Haystack.

Authorization with Permissions: Authenticates each request according to the access permissions of the specific employee, thereby ensuring that no restricted file or confidential document gets exposed.

Cross-Platform Data Analysis: Integrates Haystack with other platforms enabled by MCP—including Salesforce, HubSpot, and GitHub—to enable decision-makers to turn their sales or engineering data into company news in minutes.

Headless Intranet Management: Enables content managers to create, format, and publish their intranet content through AI conversations, thereby automatically generating their pages on both the desktop and mobile interfaces.

Maintaining strict enterprise compliance standards, Haystack carries forward its SOC 2 Type 1 & 2, ISO 27001, and HIPAA compliance certifications. The MCP server enforces individual user authentication on every session, provides administrators with granular access controls, and ensures zero customer data is utilized to train public AI models.

Following the initial launch with Anthropic’s Claude, Haystack plans to deploy additional MCP connectors for ChatGPT, Slack, and Moveworks, preserving flexibility across multi-vendor AI environments.

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With global companies rapidly embracing the use of AI code assistants, many enterprise IT leadership teams are faced with substantial operational challenges. Connecting the pilot phase of implementation to production involves overcoming challenges such as disparate developer toolchains, governance issues, and security concerns. Deloitte’s updated solution suite resolves these bottlenecks by pairing advanced AI engineering frameworks with robust enterprise controls.

Unifying Security and Speed Across the Engineering Lifecycle

By integrating specialized AI models into existing developer environments, Deloitte enables engineering teams to boost productivity without sacrificing system stability or data privacy.

Also Read: Hexaware Partners with Factory to Scale Agent-Native Enterprise Software Development 

The expanded AI software engineering suite delivers several critical operational advantages:

End-to-End SDLC Automation: Automatically writes code, refactors existing programs, and creates unit tests so that feature delivery happens much more quickly.

Embedded AI Security Guardrails: Checks code for security issues, non-compliance problems, IP leaks and other vulnerabilities continuously and automatically so that no such flaws make the software to production.

Developer Workflows Synchronization: Keeps everything in sync through developers’ favorite IDEs, CI/CD pipelines, and enterprise code managers, so no one has to fight the tools.

Scalable and Compliant Governance Architecture: Sets up a central source of control, keeps track of AI model usage, audits the code provenance and ensures compliance of the code development at a team and organization level regardless of the distance separating them.

Accelerating Enterprise Transformation

Deloitte’s production-ready AI solutions are backed by its global network of technology strategists, cloud architects, and cybersecurity specialists. By combining domain-specific software engineering practices with enterprise-grade AI infrastructure, Deloitte assists clients in modernizing legacy technology stacks and achieving measurable returns on their digital transformation investments.

The expanded AI software engineering capabilities are available immediately to Deloitte enterprise clients globally. Technology officers, engineering leads, and software architects can review platform capabilities or request an operational consultation by visiting the official Deloitte digital portal.

The post Deloitte Scales Production-Ready AI Solutions to Accelerate Enterprise Software Delivery appeared first on ITDigest.

Also Read: LTM and Anthropic Form Alliance to Drive Enterprise Claude Adoption

“Our expanded partnership with Oracle is designed to make it easier for organizations to use Gemini in the applications and agentic workflows they rely on to automate workflows, accelerate decisions, and drive outcomes.” Reaffirming the importance of choice in enterprise AI deployment, “To achieve the best business outcomes, organizations need the flexibility to choose the AI model best suited to each problem,” said Chris Leone, executive vice president, applications development, Oracle. “By bringing Gemini to Oracle AI Agent Studio for Fusion Applications, we are giving customers and partners greater choice as they build and extend agents and agentic applications that reason through complex, real-world business challenges. Oracle Fusion Applications then turn that reasoning into action through governed workflows, approvals, and transactions.” By embedding Gemini models where they offer optimal price-performance, Oracle and Google Cloud enable enterprise clients to securely execute mission-critical tasks and scale operational AI innovation.

Read More: Oracle to Make Gemini Models Available to Thousands of Enterprise Applications Customers

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