Summary Bullets:

• US investment in fiber is accelerating at an all-time high as AT&T, Zayo, Verizon, and Lumen expand their networks to support AI and data-center growth.

• Building more fiber does not automatically translate into greater profits; customer demand, network utilization and investment discipline will determine the winners.

The US fiber market is in the middle of an AI gold rush, with telecom providers appearing determined that the machines never have to wait for bandwidth. AT&T’s recent partnership with Global Infrastructure Partners (GIP) and CPP Investments adds momentum to an investment race that already includes Zayo, Lumen, and Verizon. Each company is reshaping their networks to capture future demand from AI workloads and data center expansion. The enthusiasm is understandable, but with so many carriers reaching for their shovels, a more fundamental question remains: will AI demand deliver the returns the industry needs or will some investors be left digging for profit?

AT&T’s deal points to a shift in how communication service providers (CSPs) fund the next phase of their network expansion. By partnering with investment and private equity firms, AT&T can combine external funds with its existing fiber assets, accelerating deployment while sharing the financial risk. The venture’s wholesale, open-access model also widens the potential customer base to include operators seeking to support enterprise connectivity, data center interconnections, and AI workloads. The arrangement carries a trade-off, however. External funding eases pressure on AT&T’s balance sheet and speeds up the build, but AT&T must share future revenue and profit with its investment partners.

AI has now become the industry’s preferred justification for fiber spending, and every major carrier is investing in fiber networks to support the next phase of digital infrastructure growth. The expansion of hyperscale data centers and AI computing facilities is increasing the demand for those high-bandwidth connections between data centers, cloud platforms, and enterprise locations, which makes low latency and network resilience more important. Capacity alone will not guarantee success. Carriers must identify the routes and markets with the greatest potential, secure customer commitments, and maximize utilization to generate sustainable returns. AI is accelerating the investment cycle, but commercial discipline will determine which providers turn infrastructure spending into profitable growth.

As investment in US fiber networks continues to gain pace, providers must ensure that spending is backed by real customer demand rather than expectations of future growth. With AT&T, Verizon, Zayo, and Lumen all expanding their networks, competition will also grow across key markets, putting pressure on revenues and profitability. Partnerships like these can help spread investment costs and attract additional customers, but success will come down to how effectively CSPs use their networks. Routes that connect to established data center hubs may offer more immediate opportunities, while investments in emerging markets may bring greater uncertainty. Carriers must balance opportunity to capture growth against the risk of building capacity which may take years to generate adequate revenue.

AI is creating a significant opportunity for the US fiber market, but scale and timing of the future demand is less certain. As these four expand their networks, CSPs must look beyond growth and focus on profitable returns. AT&T’s partnership highlights one way to fund network expansion, while sharing the risk, however all carriers must have the customer demand to support long-term revenue growth. With competition increasing, more fiber will not automatically translate into greater profits. That leaves the question of whether success will come to those with the largest networks, or those making the smartest investment decisions.

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Summary Bullets:

• Technology vendors are investing in forward deployed engineers to adapt AI solutions to meet customer needs.

• Systems integrators are working alongside their technology partners to help enterprises accelerate their AI adoption and ensure business outcomes are met.

It is widely reported that only a small percentage of firms that have conducted AI pilots are moving them into production. Even fewer enterprises have been able to scale their AI initiatives across multiple business functions. Based on GlobalData’s observations and discussions with enterprises, there has been greater success in AI implementations among large enterprises that have engaged a systems integrator or consulting firm to work alongside their technology partners, including hyperscale cloud providers and AI model companies (e.g., Anthropic and OpenAI).

This is not surprising since generative AI (GenAI) and agentic AI are relatively new and rapidly evolving. There are unknowns for enterprises in harnessing the technology, both from the technical and commercial standpoints. Moreover, the use of AI is tied to business objectives, not an IT-led initiative. This suggests a need for a high-touch, consulting-led approach in which technology companies work closely with enterprises to turn AI into solutions that deliver against business outcomes. Systems integrators and consulting firms do not just provide the technical knowhow, but also business consulting capabilities and industry knowledge.

While AI studios and labs are useful in demonstrating the possibilities, actual deployment can be challenging for large-scale enterprises that have complex tech stacks with legacy systems, and messy workflows. Increasingly, technology vendors are embedding forward deployed engineers (FDEs) into client premises to support customer teams with their AI implementations. Technology vendors (e.g., AWS, Databricks, Google Cloud, Microsoft, and Salesforce) have FDEs that are highly skilled in their technology to help customers solve problems and develop custom solutions (e.g., sorting out data pipelines, configuring data models, and setting up guardrails and security controls). However, it is harder for software vendors and to some extent the hyperscalers to maintain large FDE workforces. They are therefore working with service provider partners to develop FDE teams to jointly work with clients.

For systems integrators, the goal is not just to help clients move AI into production: It is also to build long-term relationships with clients, and they are more focused on helping clients through their AI lifecycle. Cognizant is responding to this trend with its Frontier-certified workforce. This comprises two new job categories: Frontier Certified Engineers (FCEs) and Frontier Business Operators (FBOs). FCEs focus on reimagining work processes and identifying where AI can make the most business impact. They work with clients directly to realign business processes for an AI-enabled environment. On the other hand, FBOs take full ownership of the operational results via the realignment of business processes. They manage a workforce of humans, AI agents, and automated software to ensure tasks are assigned to the most efficient resources and expected business outcomes are achieved. The company plans to scale to 5,000 FCEs and 10,000 FBOs by Q4 2026.

Capgemini has introduced its outcome deployed engineer (ODE) model, aimed at supporting the agentification of clients and optimizing the time-to-value. Capgemini’s approach is to embrace technology partners’ FDE model, but focus on delivering the business outcomes clients are aiming for. This helps the company shift its engagement from a talent-driven model to a value/outcome-based model. The company is building its ODE by aligning to key technology partners. It started with Google Cloud; Capgemini’s ODEs work alongside Google Cloud FDEs in dedicated pods within a client’s environment to design, build, and deploy AI agents around workflows while committing to business outcomes. The company is expected to have similar arrangements with more technology partners going forward.

Each systems integrator has or will have a similar FDE-like approach to engage customers in AI projects focusing on business outcomes. The aim is to help customers achieve faster implementation and return on investment (ROI). After having AI in production, enterprises also have to grapple with the rapid advancement of frontier models, the changing pricing mechanism, and an increasing number of use cases. Systems integrators add greater value to customers by supporting their AI lifecycle with continuous enhancements, including providing the AI technology (i.e., the partner ecosystem), the necessary tools (e.g., agentic AI builders and control plane), and the human expertise (technical, business, and industry knowledge).

The post Systems Integrators Adopting the FDE Model with a Focus on Business Outcomes appeared first on IT Connection.

Summary Bullets:

• Agentic orchestration has been dominating vendor narratives as growing complexity in the CX space is driving the need for a sophisticated coordination system.

• CX agentic orchestration remains fragmented as there are still infrastructural gaps, and trust remains a major issue for customers.

Agentic orchestration has been increasingly occupying CX vendor narratives in recent years, so it is time to take a deep dive into what orchestration is and why it is becoming relevant. Simply put, orchestration is bringing people, workflows, and technology together to achieve an outcome. Depending on how far we stretch the concept, orchestration is not new, and even a deterministic call routing can be described as orchestration. The question is, what is new today and why is orchestration increasingly dominating vendor narratives?

There are two factors bringing orchestration to the forefront: connected customer experience and agentic AI. Connected customer experience came into play with the advent of cloud platforms, making it possible to consolidate the CX IT stack and create a unified system. This enabled data to flow seamlessly between applications and systems for contextual insights before, during, and after customer engagement, reducing fragmented customer experiences. The second factor is the rise of agentic AI, which, as the name implies, involves agents that can autonomously perform tasks on behalf of humans. This autonomy stems from the ability to understand, reason, decide, and act. The projection is that agentic AI will proliferate, acting as digital assistants for workers and customers. While interactions today are still between agentic AI and humans, it is predicted that in the future, they will also take place directly between agentic AI.

What we are witnessing is the rise of a complex CX landscape with numerous actors, including AI, people, and applications, making it necessary to have a sophisticated coordination system. This is where orchestration becomes relevant and even critical. What makes orchestration compelling today is its agentic capabilities with a cognitive layer in the backend. It is probabilistic in nature and can understand, reason, decide, and act on its own, unlike the pre-programmed, deterministic models used for reaching a goal. Agentic orchestration is designed to autonomously map the most optimal customer journey depending on customer intent and stage, as well as determine the most suitable resources, insights, and tools to achieve a given goal. The challenge, however, is that the term is broadly used, and the underlying question is: How agentic are the orchestration layers today?

Agentic orchestration as it stands falls under two classifications: routing orchestration and workflow orchestration. Routing orchestration is when AI autonomously routes interactions to the most suitable agent, while workflow orchestration is when AI determines the optimal customer journey to accomplish an outcome. We see some instances where routing orchestration is deployed and some instances where workflow orchestration is deployed, but in most cases, orchestration remains fragmented across bits and pieces of specific, outcome-driven workflows.

In a true agentic era, orchestration will take place end-to-end across all use cases, but there are infrastructural gaps before such a state could be achieved: LLMs are still reasoning and decision tools and cannot really act although tool calling are used for driving actions; data requires more work, including quality improvement, aggregation, and the development of data graphs; and latency/real-time technology needs to be faster. The most critical factor, however, is trust. Today, AI autonomy and trust work in reverse order. It is a journey vendors are taking as they gradually fill the gaps, but there is still some distance to go before we see a pure state of agentic orchestration.

The post Beyond the Hype: The Reality of CX Agentic Orchestration appeared first on IT Connection.

Summary Bullets:

• New fears of AI breaches and vulnerabilities significantly threaten AI adoption

• Salesforce and rivals launch aggressive control plane portfolios as part of the new layer of the AI stack

On the heels of broad industry doomsday reports of AIs going rogue, Salesforce launched its mega Dreamforce conference in San Francisco, California (US) last week. CEO Marc Benioff used the stage to reposition the company as the industry platform to provide the oversight and governance of both Salesforce and third-party agents under a single view.

The message was apropos following multiple news events that have triggered a new wave of AI mistrust across the industry. In one news report, a high-ranking OpenAI official states there is a greater than 10% chance that AI could kill all humans in the next decade. In others, OpenAI models breached open AI platform Hugging Face, and Anthropic report similar hacks by its popular model Claude Opus as well. During keynotes, Benioff discussed the unfortunate hacks by leading AI models, while strongly urging customers to invest in long-established, proven, and well-funded AI platforms such as Salesforce.

A more open agentic ecosystem via APIs
The company’s latest AI premise is based on Salesforce AIforce, which is built on what the company calls a Headless Toolkit, for exposing all of its services via APIs, MCPs, or CLIs, making it easier for agents to work with Salesforce services. These 60-plus MCPs and 30-plus coding “skills” are a key component for giving coding agents such as Claude Code and Cursor direct access to the Salesforce platform. AIforce consolidates leading frontier models and combines them with Salesforce skills—portable instruction packages on how to complete a task and work with specific Salesforce metadata.

Salesforce’s solution for AI governance
Now that Salesforce is opening its AI ecosystem through MCPs and increased accessibility to its own platform services via APIs, the company has been formulating a strategic approach to multi-agent orchestration coupled with the promise of trusted Salesforce context to ensure security and governance around agentic actions. This is a pivotal step in helping assure its customers of Salesforce’s ability to combat AI threats and vulnerabilities, and it largely hinges on an AI control plane architectural construct initially previewed in 2025. The initiative is branded MuleSoft Agent Fabric, an agent orchestration capability based on a trust and data security layer. This approach is intended to reassure customers that their developer activities are being properly governed and secured, particularly in light of the recent spate of breaches and vulnerabilities associated with leading GenAI products by Anthropic and OpenAI.

More specifically, the company’s control plane maintains a central registry of all agents, MCP servers, and APIs, including the ability to discover agents automatically. It accomplishes this through a single point of control AI Gateway for all LLMs, MCPs, and agent calls. While the AI control plane provides customers with unified management, control, cost control, and visibility of agents, they can pick and choose from newer services available in Salesforce’s Enterprise AI Harness, including context, agency, action, governance, security, and models. GlobalData expects this portfolio of services to eventually include other core capabilities such as policy controls, guardrails, observability, etc.

Salesforce is not the only platform provider to devise an operating system, which sits underneath multiple vendors’ agents in order to ultimately establish a control plane of agent management, governance, security, policy, and guardrails. Salesforce is taking direct aim at key rival offerings, including ServiceNow AI Control Tower and IBM watsonx Orchestrate. The battle will only continue to heat up as vendors in this space continue to build out more comprehensive portfolios of governance services as part of their orchestration strategies.

The emerging architectural approach represents the most promising method yet for addressing what has quickly become a global crisis surrounding a newfound mistrust of AI technology based on a stunning lack of adequate security and guardrails attached to agent activities. AI companies have invested heavily in the GenAI market segment, and they can’t afford to neglect having proper controls in place to ensure ongoing adoption of AI solutions. The heavy emphasis on GenAI’s user interface over the past few years is no longer the conversation starter among enterprise buyers. They’ll be limiting investments in those tech providers with strong AI orchestration messaging squarely in place.

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Summary Bullets:

• New features from Zoom reinforce its recently reconstituted platform vision, extend its ongoing transformation, and support an important, contemporary trend in the team collaboration space.

• This latest batch of improvements underscores Zoom’s place among the top team collaboration and contact center vendors such as Microsoft, Cisco, and Google.

Earlier this month, Zoom held the latest installment of an ongoing series of customer webinars highlighting new platform features. In keeping with tradition at Zoom, the enhancements were wide-ranging, covering various tracts of its platform.

Here is a sample: the capabilities of ZoomMate, Zoom’s agentic AI assistant built into Zoom Workplace, have been expanded through several partner integrations. Users can incorporate AI models such as Anthropic; leverage connections with third-party apps including Dropbox, Canva, Zendesk, Box, Microsoft Teams, and others; and take advantage of an expanded set of tools and capabilities available in Claude and OpenAI. Aside from partner integrations, ZoomMate now supports more languages, an important move as Zoom attempts to grow its share-of-wallet outside the US. In addition to ZoomMate improvements, Zoom Contact Center now features a redesigned agent workspace that makes it easier to access a customer’s profile and history, handle live customer interactions, and pin commonly used business tools such as CRM for easy access.

The new features further Zoom’s vision for offering a ‘system of action,’ the heart of which involves using AI to convert siloed content into finished deliverables that enable users to move work forward with reduced effort. A key source of content is conversations held in meetings, chats, calls, and live discussions. The system of action is more complex than it sounds on the surface, involving three layers and additional components: a memory layer that stores information from meetings, chats, and the like; a federated AI layer that mixes AI models from Zoom and partners such as OpenAI; an orchestration layer – along with ZoomMate – which connects Zoom to third-party tools; and Zoom AI canvases (AI Docs, AI Sheets, and AI Slides) where finished deliverables are born.

Considered in their entirety, Zoom’s new capabilities extend a dramatic transformation that has been taking place at the company over recent years. Zoom ignited a corporate renaissance with the October 2023 introduction of Zoom AI Companion, a digital assistant that leverages GenAI to extend everyday capabilities such as generating action items and meeting summaries. In 2024, Zoom AI Companion 2.0 was introduced, which surfaces information across the Zoom platform. Zoom AI Companion 3.0, launched in November 2025, brings agentic AI capabilities that work across the Zoom platform as well as compatible third-party integrations. In June 2026, ZoomMate was introduced as an upgraded version of Zoom AI Companion 3.0. This latest round of announcements continues the focus on broadening Zoom’s platform, making it poised to further its transformation.

The new features also reflect a trend in the team collaboration space. Specifically, AI is moving from use in silos to being leveraged on a far grander scale – across vendor platforms, connecting parts of organizations, and linking organizations with external partners, suppliers, and the like. AI is increasingly serving as connective tissue, threading sections of vendors’ platforms such as meetings, chat, and calling; establishing links between those platforms and third-party applications used in various parts of the business, such as Salesforce; and integrating platforms from different vendors making things possible, such as joining a Zoom meeting with an external partner from a Cisco device.

Zoom has earned a place in the discussion of the top team collaboration and contact center vendors such as Microsoft, Cisco, and Google. This latest batch of platform improvements underscores that position. The stage has been set for Zoom to achieve further accolades.

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Summary Bullets:

• Slackforce Surfaces are front doors into data and context that enable users to grasp a deeper understanding of their business and make more informed decisions.

• Slackforce Surfaces represent the latest chapter in the renaissance of Slackbot, advancing Slack as a key part of the Salesforce organization.

Salesforce has deepened its investment in Slackbot, the AI-driven personal work agent built into Slack, with the debut of Slackforce Surfaces. The introduction will raise the utility of the Slack platform and further cement the starring role Slack has come to play at Salesforce.

Some clarification regarding terminology is in order. ‘Slackforce’ represents a vision of eliminating the need to ever log in to the Salesforce platform, manifested through a conversational, AI-powered interface to Salesforce built directly into Slack. ‘Surfaces’ are essentially front doors into data and context that reside in a shared space and take the form of objects such as a report, presentation deck, microsite, or dashboard. Surfaces are created by issuing natural language commands into Slackbot; for example, “Generate a dashboard that shows my sales pipeline health” or “I need help visualizing AI token usage relative to ROI.” Users can tailor Surfaces to reflect their preferences and the audience and purpose for which they are intended or let Slackbot determine the format. However Surfaces are built, they provide a live, interactive view of the data and context upon which they are based that gets updated automatically as those components change.

Surfaces serve a highly valuable function. They spare users the arduous task of corralling relevant resources such as conversations, files, and data residing in multiple, often far-flung repositories, performing analysis, and constructing some type of vehicle to depict the findings. However, the most genuine gift they deliver is insight. Surfaces enables users to grasp a deeper understanding of their business and make more informed decisions.

Despite its value, Surfaces draws some concerns. What controls are in place, if any, to prevent duplicating Surfaces across a team or an organization is unclear. Also murky is the question of whether measures exist to prohibit a user from accidentally editing or deleting a Surface. Even more critical is the issue of security. Slackbot can access a wide range of inputs in constructing a Surface. While Surfaces are built using existing permissions, in theory allowing a user to see only what they are already allowed to see, what if the permissions themselves are faulty? There seems to be nothing to stop, for example, a user in one department from accessing sensitive data from another department and having that data appear in a Surface if that user is mistakenly provided access to the data. Salesforce needs to clearly articulate how these concerns, and how to resolve them, are being addressed.

The Slackforce Surfaces feature is the latest chapter in the renaissance of Slackbot. It closely follows the July 2026 Salesforce announcement that the scope of Slackbot had been extended to enable users to get work done far more effectively by serving as a unified front across the Slack and Salesforce platforms. Slackbot now acts as a conductor, a digital Arthur Fiedler overseeing an orchestra consisting of Salesforce products, enterprise data, third-party applications, and AI agents.

The new and improved Slackbot advances if not completes Slack’s emergence as a key component of the Salesforce organization having been increasingly embedded into Salesforce’s broader product fabric and now serving as the default collaboration interface for the Salesforce platform.

Although Slack has not achieved the same level of notoriety in the team collaboration space as the likes of Cisco, Microsoft, and Zoom, if Salesforce can resolve the concerns surrounding Surfaces, those rivals may soon have company.

The post Salesforce Expands the Value of Slackbot with the Introduction of Slackforce Surfaces appeared first on IT Connection.

Summary Bullets:

• Zayo will build 8,000 miles of new long-haul fiber across key AI corridors, with Nvidia becoming its anchor customer.

• Nvidia’s extends beyond GPUs and compute, with partnerships spanning the optical and networking ecosystem underpinning AI infrastructure.

Until now, the AI Infrastructure race has predominately been focused on GPUs, data centers and access to reliable power, yet beneath all three sits a less visible, but increasingly critical, layer – connectivity. As AI workloads become larger, more distributed and dependent on moving large volumes of data between locations, fiber is emerging as a fundamental component of the AI Stack. Zayo’s recent announcement to build 8,000 miles of new long-haul fiber across key AI corridors, backed by Nvidia as its anchor customer, could mark the new beginning of a new investment cycle for terrestrial networks. The bigger question is whether AI-related companies could become the anchor customer needed for the next generation of fiber investment?

The significance of the Zayo – Nvidia agreement extends well-beyond the fiber being built. Under the model, Zayo will build, own and operate the network, while Nvidia provides the demand certainty needed to underpin this investment. This represents a potentially important shift in how long-haul networks are financed and deployed. Traditionally, service providers have built new routes ahead of demand, invested significant capital and then sought customers to fill the capacity. AI could begin to reverse that model. Securing an anchor customer before construction gives the provider greater visibility over future demand, reduces investment risk, and provides greater confidence that new routes will generate returns. If this model can be replicated with other AI infrastructure providers and hyperscalers, it could unlock future builds that might struggle to secure investment.

The AI chipmaker’s influence on the AI infrastructure ecosystem is extending well beyond GPUs and compute. Its partnership with Corning to expand US optical connectivity production, for example, highlights how AI demand is cascading into the fiber and optical supply chain. Corning plans to increase its US optical connectivity manufacturing by 10 times and fiber production capacity by more than 50%, highlighting the amount of infrastructure required to support AI factories.

Over the last 12 months, Nvidia has made a series of partnerships to strengthen its position across the optical and networking ecosystem. Its partnership with Marvell covers custom AI infrastructure, while its relationship with Lumentum includes advanced optics and laser technology, capacity expansion, and research and development for AI infrastructure. It has also established a partnership with Coherent around advanced optics and optical networking. Together with the Zayo fiber agreement, these relationships point to a broader shift, with the company not only influencing the compute layer of AI infrastructure but also networks, optical components and fiber required to connect it.

This shift could create significant new opportunities for service providers and digital infrastructure providers. As AI workloads become more geographically distributed, demand for high-capacity, low-latency connectivity is likely to grow alongside demand for GPUs and data center capacity. The next phase of the AI infrastructure race may therefore be fought not only within the data centers, but between them. For the wholesale telecom market, the emergence of AI is creating new demand across the connectivity stack, from long-haul and metro fiber to dark fiber, wavelengths, and ethernet services connecting data centers, AI factories, and cloud infrastructure. This presents wholesale providers with an opportunity to monetize existing fiber assets while supporting new network investment in new high-capacity networks purpose-built to support the evolving requirements of AI.

While the Zayo – Nvidia partnership highlights how AI demand could influence not only how much fiber is deployed but also where it is built and how investment is justified. Nvidia may not be becoming a service provider, but its infrastructure requirements are increasingly shaping the connectivity ecosystem. If AI can justify an additional thousand miles of new fiber today, how much further could the network investment cycle go as AI capacity continues to scale?

The post AI Changing the Commercial Model for Fiber Build appeared first on IT Connection.

Summary Bullets:

• Zoom CX is a credible option for enterprises looking to transform their customer engagement with omni-channel and AI capabilities.

• Working with a broader partner ecosystem is pivotal for Zoom to win in CX space since this involves workflows and different business applications.

Zoom is well-known for its conferencing solution, which is used extensively in modern workplace, but it has gone well-beyond conferencing in recent years. The core business of Zoom has been the enabler of conversations within the workplace. To go beyond communications, the company sees new opportunities by expanding its role to help enterprises automate workflows during and after conversations (i.e., meetings and phone conversations). This vastly enlarges the value Zoom can deliver to enterprise customers, especially with the application of AI. For example, AI can eliminate many manual tasks such as generating documents from meeting with summaries and next steps, or updating CRM records after a discussion within the sales team.

Taking this concept a step further, conversations can also include external parties including customers and partners. One area that is of great strategic importance for Zoom is around customer experience (CX) – a rapidly transforming space as a result of enterprises striving to enhance CX to gain an upper hand against competition. This expands Zoom scope to help enterprises streamline workflows related to customer engagement, which can happen across customer services, delivery, marketing, and sales functions. The launch of Zoom Contact Center in 2022 was a major step toward building out the Zoom CX portfolio. The company has developed the product ground up, giving it the ability to meet customer demand with speed without the baggage of legacy systems and features. This is especially useful in having AI natively embedded in the platform instead of building a separate stack. Moreover, there should not be separate communications systems for the internal workplace and for the contact center. A key value proposition for Zoom is its common platform to address both scenarios and deliver the same experience.

Zoom Contact Center is an omni-channel solution with a single routing engine. It enables businesses to engage with their customers over channels such as voice, video, SMS, and chat. In 2023, the company added Zoom Virtual Agent, initially as a chatbot, and now as an agentic AI platform. To close the gap with other contact center offerings, Zoom also added Zoom Workforce Engagement Management, which includes Zoom Workforce Management and Zoom Quality Management. Moreover, it has introduced Zoom AI Expert Assist, a ready-to-deploy AI agent to provide end-to-end interaction guidance to contact center agents. Moreover, Zoom CX Insights provides a conversational intelligence layer to synthesize data across the CX portfolio to provide more accurate insights for faster decision-making. With the aim to simplify workflows, the ability to integrate with third-party applications is crucial. The company is enabling this through Zoom Marketplace, which already supports integration with a wide range of business applications (e.g., Google Workspace, Jira, Microsoft Dynamics, Salesforce, and many more).

While Zoom is a relatively new competitor in the contact center market, it has seen strong traction and winning significant deals. Oracle is the largest customer so far as the company moves its 15,000 global services agents to Zoom and integrates the tools with Oracle’s existing workflows. Zoom also leverages Oracle Cloud Infrastructure (OCI) to run Zoom CX, which can appeal to enterprises using OCI extensively. There are other customers that Zoom can reference too. National Storage, a self-storage company based in Australia with over 250 centers across Australia and New Zealand, has adopted Zoom Contact Center to connect with customers as well as its centers together. The company is now engaging customers through multiple channels (e.g., chat, email, and video); using Zoom AI to gain insights into customer interactions; and using Zoom for internal meetings and webinars. This highlights the power of having a single platform for both employee and customer engagements. With the system in place, National Storage is also able to further strengthen its CX leveraging AI, for example, using Zoom AI Expert Assist to provide cues to operators during customer interactions so that they can offer the best solutions.

The local government of Nara City (Japan), has also adopted Zoom Phone, Zoom Contact Center, and Zoom Virtual Agent, to replace its legacy PBX and contact center solution. This is part of its efforts to improve citizen services (a population of about 350,000) through its ‘’Nara Digital City Hall’’ initiative and to drive digital transformation within municipal operations. This customer highlights the importance of integration between the cloud-based PBX and the contact center to deliver a seamless experience for employees. AI is a requirement as well. Nara City is looking to use AI features such as call recording, transcription, and summarization to enhance visibility and efficiency of phone operations. It is also leveraging Zoom Virtual Agent for automated voice inquiry response that is available to citizens 24 by 7, and it is using multiple models alongside its proprietary models to meet requirements (e.g., accuracy, compliance, and cost) of different use cases.

Enterprises understand that the delivery of superior CX will involve different business functions that interact with customers along the buying journey. Data and AI will play a key role in delivering the CX magic. But implementation is anything but straightforward. While enterprises see the potential of agentic AI, there are still concerns around data privacy, AI governance, security, and cost. Having the right business culture and skills is equally important. This means that to accelerate the adoption of Zoom CX, Zoom will need to expand its go-to market ecosystem to help enterprises implement the solution with confidence and delivering business values. This is totally different from selling a contact center solution, which traditionally only involves the customer service function. CX specialists, systems integrators, and consulting firms are now playing a more central role in supporting enterprises in their CX transformation.

The post Zoom is Delivering Better CX by Combining Communications, AI, and Workflow into One Platform appeared first on IT Connection.

Summary Bullets:

• AI is drastically changing the fabric of the traditional data center, prompting fundamental changes in design and architecture.

• The biggest challenge is that AI infrastructure requires simultaneous scaling across multiple constrained layers: electricity, cooling, networking, chips, facilities, capital, and operations.

The rise of AI workloads is pushing data centers through a major architectural shift: from relatively general-purpose, virtualized compute environments toward high-density, network-intensive AI infrastructure. For example, rack density is rising sharply, because traditional data centers were not designed for the power and thermal profiles of dense AI server clusters. This means power distribution, floor loading, cable management, and thermal design are becoming central architectural considerations. Power availability has now become a core design constraint. Energy availability is starting to influence where data centers are built, with land and power constraints pushing some infrastructure development into new or remote regions.

Networking architecture is also increasingly important because AI workloads rely on fast, predictable, low-latency networking between servers, storage, models, and cloud regions. Meanwhile, storage architecture must support larger, faster data pipelines. Last but not least, modular AI infrastructure is becoming more attractive. Because demand for AI compute is growing quickly, operators are increasingly looking at modular, pre-engineered AI systems that can be added to existing data centers with less disruption. This helps bridge the gap between legacy data center environments and the need for AI-ready capacity.

It is also worth highlighting that edge and regional AI infrastructure are gaining importance with the rise of latency-sensitive AI applications. Regional inference hubs are emerging to reduce latency, improve resilience, and support data sovereignty requirements, and these hubs increase the need for reliable interconnection with centralized AI models and cloud regions.

All these trends are creating major challenges for companies scaling infrastructure to support high-density AI compute environments. The solution is no longer simply “adding more servers.” As explained above, high-density AI compute changes the whole infrastructure equation across power, cooling, networking, location, economics, and operational resilience.

Firstly, power is the primary bottleneck. Securing enough reliable electricity to support high-density GPU environments can be a major hurdle. Some data center projects in the US and Europe are being canceled because reliable grid connections are hard to find. Secondly, cooling systems must be redesigned. Many legacy facilities are ill-equipped for widespread AI deployment because they lack the infrastructure required for liquid cooling and other advanced cooling systems. New AI data centers need to be designed around advanced cooling from the start, while existing facilities may require retrofits to support AI workloads.

However, retrofitting existing facilities is expensive and disruptive. A large portion of the existing data center estate was built for general-purpose cloud, enterprise workloads, or colocation, not dense GPU clusters. Retrofitting these environments for AI often requires very costly upgrades. This is one reason neoclouds are gaining relevance: traditional cloud environments often cannot provide specialized AI compute quickly enough. Thirdly, site selection is becoming harder. AI growth is changing where data centers are built because energy availability, land constraints, latency requirements, and sustainability considerations increasingly determine site feasibility. Some infrastructure development is being pushed into unusual, sometimes remote regions. Moreover, legislative changes and increasingly, moratoriums like the one seen in New York (US), are hampering data center construction.

The biggest challenge is that AI infrastructure requires simultaneous scaling across multiple constrained layers: electricity, cooling, networking, chips, facilities, capital, and operations. If any layer lags, be it grid access, power equipment, cooling, data center interconnect, GPU availability, or utilization economics, the entire AI compute environment becomes harder to scale. Scaling high-density AI compute is becoming as much an energy, real estate, cooling, and network engineering problem as it is a compute problem.

Neocloud platforms such as CoreWeave, Crusoe, and Lambda Labs are emerging to meet AI infrastructure demand with scalable alternatives tailored for AI developers and high-performance computing. Last but not least, server vendors including Cisco, Dell, HPE, and IBM are designing AI-ready servers with powerful GPUs, accelerators, and machine learning frameworks.

Vendors that can adapt to the need for faster deployment cycles in AI infrastructure environments will emerge victorious. Some are adapting by shifting from bespoke, slow infrastructure builds to pre-integrated, AI-native, modular, automated, and services-led deployment models that reduce time-to-capacity for GPU-heavy environments, while hyperscalers are packaging AI into full-stack services.

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Summary Bullets:

• The Zoom Revenue Accelerator updates combined with the pending acquisition of the Common Room platform will provide Zoom with complete coverage of the sales cycle.
• Providing support for the sales process represents a ‘new frontier’ that vendors are exploring and one that should ripen quickly.

Zoom announced general availability of three updates to its Zoom Revenue Accelerator (ZRA) feature, which helps sales teams close deals by analyzing customer interactions using AI. The updates consist of ‘Sales Roleplay,’ which provides practice simulations of customer conversations; ‘Sales Assist,’ which includes real-time deal guidance to keep reps focused as they engage the customer; and ‘Ask ZRA,’ which allows both reps and managers to perform natural language queries on conversation data post-discussion with the customer.

Taken collectively, ZRA and the trio of updates cover everything from deal prep to deal close. Once Zoom has closed its acquisition of the ‘Common Room’ buyer intelligence platform, which was announced in early July 2026, the entire sales cycle will be covered with the addition of the initial, prospecting stage.

Assembling a suite of tools that together bring deals from inception to completion parallels what Zoom has been doing recently with the Zoom Workplace platform in general – unifying information typically residing across a variety of systems to complete a task. That has involved three components. One is linking functionality within the Zoom platform – such as calling, messaging, and email – to work together more seamlessly. The second is linking the Zoom platform with other vendor platforms as well as third-party apps such as CRM, ERP, and WEM within an organization. The third is establishing those same types of links between organizations, such as a company and their suppliers, partners, and customers.

Providing support for the sales process represents a ‘new frontier’ that vendors are exploring. Vendors have gone ‘all-in’ on team collaboration capabilities for quite some time now. More recently in the last few years, the contact center has morphed into a hotspot. Today, a wealth of information can be gathered on customers and their interactions with an organization through currently available contact center capabilities. The ability of AI to mine and analyze that information has made tools that generate sales leads and manage the funnel a natural extension.

Zoom and RingCentral have taken the lead on that front. Look for this trend to ripen quickly and for all rivals to expand the volume of sales support features on their platforms.

The post Zoom Has Upped the Ante in Supporting Sales Teams appeared first on IT Connection.

Summary Bullets:

• Salesforce has integrated Slackbot more deeply into its platform, providing access purportedly to the entire Salesforce ecosystem.

• Despite positives the announcement generates concerns, the most pressing regarding security.

Salesforce has greatly extended the scope of Slackbot, the AI-driven personal work agent built into Slack, claiming it now spans the entire Salesforce platform. The change will add substantial value, keep Slack – the company – competitive with rivals, and cement the starring role Slack has come to play at Salesforce.

The new and improved Slackbot advances if not completes Slack’s emergence as a key member of the Salesforce organization. When acquired by Salesforce in 2021 Slack seemed destined to fall into a black hole, a Jonah being swallowed by the whale. Instead, it has been methodically elevated into a central gateway of the Salesforce platform. Slack has been increasingly embedded into Salesforce’s broader product fabric, positioned as the front end for Salesforce’s AI ecosystem and now evolving into the default collaboration interface for the Salesforce platform. Slack has been granted a new and better life by its parent.

In addition to accelerating its rebirth, the enhanced Slackbot benefits Slack by bringing greater value to users and keeping it neck-and-neck with rivals such as Cisco and Zoom, who are infusing their own platforms with the same type of cross-pollination.

This latest version of Slackbot enables users to get work done far more effectively by serving as a unified front across the Slack and Salesforce platforms. At the heart of the rejuvenated Slackbot lies MCP servers from Salesforce, the fuel behind the Salesforce ‘Headless 360’ initiative which seeks to harness capabilities anywhere in Salesforce and funnel them into Slack. Slackbot now acts as a conductor, overseeing an orchestra consisting of Salesforce products, enterprise data, third-party applications, and AI agents.

At a most basic level, users provide Slackbot a request through a natural language interface, and Slackbot fulfils it by pulling together relevant resources such as conversations, files, and data residing in multiple, often far-flung repositories. Users can, for example, update sales pipelines and surface next best actions, discover whether the marketing team is on track to achieve a forecast, or route a service case to the appropriate individuals. Over time, Slackbot gets to know users better, thus fulfilling their needs with greater speed and accuracy.

Despite the positives, there are some concerns associated with the announcement. The largest involves security. The security posture behind Slackbot is an open question and one with serious implications especially given the pooling and sharing of data which Slackbot facilitates; Salesforce needs to articulate clearly what types of safeguards are in place. Another concern is the lack of contact center capabilities to complement the collaboration capabilities found in Slack; a robust contact center portfolio has become critical for remaining competitive in the market. Last, despite rapidly accumulating AI-driven features on its platform and its association with Salesforce, the Slack name lacks the brand equity enjoyed by competitors. The likes of Cisco and Microsoft were well known in team collaboration well before the pandemic, and Zoom became a household name when it hit. Slack has not achieved the same notoriety.

If Salesforce can promptly address each of these issues, it could merit inclusion among the top players such as Cisco, Microsoft, and Zoom.

The post Slackbot Spreads Its Wings but Questions Remain appeared first on IT Connection.

Summary Bullets:

• Enterprises are deploying AI agents leveraging Google Cloud’s solutions and achieving positive business outcomes.
• Google Cloud offers the full AI stack, and its sovereign cloud and cyber solutions are especially crucial for enterprise customers.

AI agents are no longer an idea. They are now being deployed by enterprises to improve internal workplace productivity and external customer experience. At Google Cloud Summit Sydney (held on June 25, 2026), more examples of agentic AI in operations were presented, moving from deterministic AI chatbots to more autonomous systems. Bunnings, a home improvement, gardening, and hardware products retailer in Australia, upgraded its Buddy AI chatbot that helped customers with product search to an AI agent that takes customers’ descriptions of their projects and fills the shopping carts with the products that they need. Bunnings indicated an uplift of conversion rates and basket sizes when customers engage with Buddy. Similarly, Woolworths supermarket has an agentic AI powered Olive assistant that is able to build shopping baskets from recipe photos and assist with proactive meal planning. These two examples demonstrate how AI agents trained with proprietary knowledge (e.g., Bunnings’s DIY catalog and Woolworths’ recipe catalog) can deliver greater customer outcomes.

Enterprises deploying AI will appreciate the importance of data. To benefit from AI, it is necessary for enterprises to tap into corporate data to impart knowledge to AI agents. Google Cloud has the advantage in this area since enterprises have been adopting its products such as BigQuery to manage their data more effectively. Moreover, the company has other associated products such as Google Maps, Google Search, and Google Workspace that customers can leverage to enhance their AI capabilities. Transurban, an Australian road operations company and toll road operator, works with Google Cloud to transform its interaction with customers. While customer relationships are mainly transactional, Transurban now leverages Google Cloud’s solutions such as Gemini Enterprise, BigQuery, and Google Maps to power its Linkt app with the “Linkt AI” assistant, which proactively suggests optimal travel routes and toll options, dynamically adjusts schedules for prevailing weather, delivers timely account balance notifications, and offers discounted hotel and attraction bookings for upcoming road trips.

Data is the most valuable asset for enterprises particularly in the age of AI. Many companies across jurisdictions are increasingly concerned about security and sovereignty. Google Cloud offers a set of options for enterprises to meet their data and AI sovereignty requirements. It addresses not just the issue of data residency but also operational sovereignty and software sovereignty. Firstly, Google Cloud Data Boundary helps customers to meet data residency requirements through a set of controls, e.g., regions where data is stored, compliance programs, and external customer or partner managed encryption keys. This option allows enterprises to enjoy the benefits of hosting data in the public cloud for operational flexibility and high availability. Google Cloud is also offering support services with personnel meeting specific geographical locations as well as monitoring capabilities with real-time alerts when organization policy changes violate the defined compliance posture.

For customers that have a more stringent requirement on operational sovereignty, Google Cloud Dedicated addresses the need by enabling solutions to be operated by an independent local partner. The solution is hosted in a standalone, local instance of Google Cloud. The local partner maintains exclusive control over security-critical systems, identity management, authentication, etc. as well as controls over communication between Google and the Google Cloud Dedicated environment. For example, S3NS (a joint venture between Thales and Google Cloud that is headquartered in Paris, France) offers PREMI3NS services built on Google Cloud Dedicated for customers in Europe, now generally available in France. S3NS has achieved SecNumCloud 3.2 qualification from the French National Agency for the Security of Information Systems (ANSSI). Google Cloud Dedicated is also available in Germany (in preview).

For clients with the most stringent sovereignty requirements, Google Distributed Cloud (GDC) air-gapped allows complete isolation, without connectivity to an external network. The solution gives customers the flexibility to use general purpose compute and GPUs, and leverage open-source software. Google Cloud has also made its Gemini available in this air-gapped option, giving customers generative AI capabilities including automation, content generation, discovery and summarization. The GDC air-gapped solution is now deployed by many government agencies including those in Australia and Singapore within the Asia-Pacific region.

Besides sovereignty, Google Cloud has been bolstering its capability to offer stronger cyber defense. This includes the acquisition of Mandiant to add threat intelligence and incident response capabilities as well as Wiz for multi-cloud security defense. At the Google Cloud Summit, the company together with Wiz demonstrated how agentic AI can help to improve protection at scale and speed. Wiz is offering three AI agents with distinct roles: the Red Agent helps to uncover vulnerabilities and validate exploitable risks across web applications and APIs; the Blue Agent is the threat investigator that gathers evidence across cloud telemetry, runtime signals, and identity context to assess the severity of a threat and allow threats to be resolved more proactively; and the Green Agent is the investigation and remediation engine, identifying the root cause of a risk and the safest and most effective resolution. Wiz is known to provide security for cloud-native applications across major cloud environments including AWS, Microsoft Azure, Google Cloud, and Oracle. Following the completed acquisition on March 11, 2026, Wiz joins Google Cloud but operates independently to maintain its brand and key value proposition.

Google Cloud offers the full AI stack including applications and agents, AI models, data platforms, and infrastructure. It has also demonstrated strong momentum through broad customer references. However, the ability to drive AI adoption ultimately lies with its partner ecosystem and its willingness to support third-party products (including AI models) and help customers operate within a multi-cloud environment. Consulting partners such as Accenture and Mantel Group were featured at the Google Cloud event, and these partners play a crucial role in helping enterprises develop their business strategy around AI and implement solutions addressing data, security, governance, and other technology challenges.

The post Google Cloud Summit Sydney: Putting Agentic AI into Action appeared first on IT Connection.

Summary Bullets:

• Australia’s digital infrastructure race is moving onshore, with long-haul fiber becoming as important as international subsea connectivity.
• Vocus and Telstra are expanding their terrestrial network to meet growing AI, cloud, and hyperscaler demand across key intercity corridors.

Over the last decade, Australia’s digital infrastructure strategy has largely centered on subsea cable investment, from new builds to strategic consortium partnerships. These new international systems have expanded capacity, improved network resilience, and strengthened Australia’s connectivity to the world. However, as artificial intelligence (AI) reshapes data traffic patterns, investment is shifting from beneath the ocean to fiber corridors underpinning Australia’s AI future.

Vocus’s recent announcement to build the country’s first ducted long-haul fiber route between Sydney and Melbourne is the latest example of this transition. Under its newly launched Australian Digital Infrastructure Platform (ADIP), the carrier will invest approximately AUD500 million ($346 million) constructing a new intercity fiber corridor capable of accommodating up to 6,912 fiber cores (3,456 fiber pairs), with services expected to commence in 2029. Vocus expects AI workloads to drive the majority of long-haul fiber demand by the end of the decade, as the Sydney-Melbourne corridor continues to emerge as one of the country’s busiest digital highways.

While the scale of the investment is significant, the real innovation lies beneath the fiber itself. Rather than deploying a conventional cable route, Vocus is constructing dedicated fiber ducts that enable additional fiber to be installed as demand grows without incurring repeated construction charges. The approach future-proofs the corridor, enabling capacity to be added quickly and more cost-effectively while providing greater resilience and protection against cable cuts. As AI workloads continue to surge, infrastructure that has been designed for continual expansion is likely to become just as valuable as the fiber it carries.

Vocus is not the only carrier in Australia preparing Australia’s terrestrial networks for the AI era. Telstra has already committed AUD1.6 billion ($1.1 billion) with the Aura Network, formerly known as the Intercity Fiber Network, creating a new national backbone designed to support enterprises, governments, hyperscalers, and cloud providers. The carrier has already completed its 357km Sydney-Caberra route, along with its 1,095km Sydney-Melbourne coast route. Future phases will extend the network to 14,000 kilometers linking Adelaide, Perth, and Brisbane by end-2027.

Combined, Vocus’s Australian Digital Infrastructure Platform and Telstra’s Aura Network highlight a broader shift in Australia’s digital infrastructure strategy. Both operators are investing years ahead of demand, recognizing that AI training, inference, and distributed cloud applications are fundamentally changing traffic flows across domestic networks. As east-west traffic between data centers is growing faster than traditional enterprise connectivity, long-haul terrestrial fiber is becoming just as important as international subsea capacity.

The investment also marks a shift in competitive dynamics. Historically, carriers differentiated through network reach, technology, and pricing. Increasingly, competitive advantage will depend on who has the most scalable, resilient, and AI-ready infrastructure capable of supporting rapidly growing cloud and AI workloads. For hyperscalers and enterprises investing in cloud regions and sovereign AI capabilities, access to high-capacity intercity fiber may become just as important as proximity to the data center itself.

AI is redefining Australia’s connectivity priorities. While subsea cables remain essential for international connectivity, the next phase of investment is moving onshore. The race to build Australia’s digital backbone is no longer confined to the ocean floor – it is increasingly being fought across fiber corridors that will power the country’s AI future.

The post Beyond Subsea: Why Australia’s Next Fiber Race Is on Land appeared first on IT Connection.

Summary Bullets:

• Tata Communications is strengthening its global network to create an AI-ready digital corridor linking India with Singapore.
• As AI workloads grow, the India to Singapore route is becoming one of Asia’s most strategically important connectivity corridors.

Artificial Intelligence (AI) is reshaping one of Asia’s busiest digital corridors. As AI workloads, cloud adoption and investment by hyperscalers accelerates across India and Southeast Asia, demand for high-speed, low-latency connectivity is rising just as rapidly. Tata Communications’s latest investment in new subsea cable infrastructure between India and Singapore is more than another cable announcement; it reflects a broader strategy to build an AI-ready digital corridor linking India’s emerging data center hubs with the Southeast Asia’s largest cloud ecosystem. With these investments, Tata Communications seems to be quietly assembling one of the region’s most comprehensive AI connectivity platforms.

This announcement reinforces the company’s commitment to expanding the Tata Global Network (TGN) through two complementary investments. The first is the I-2SEA consortium, where Tata Communications joins Lightstorm, Microsoft, and Singtel to deploy a purpose-built subsea system connecting India, Malaysia, and Singapore, with NEC serving as the system supplier. The cable will link Hyderabad and Chennai (India) to Singapore and Malaysia, with landing stations in Machilipatnam and South Chennai (India) providing geographically diverse routes that avoid congested maritime corridors. Strategically, Machilipatnam offers one of the shortest paths between Singapore and Hyderabad’s rapidly growing hyperscale and AI data center clusters. Once onshore, the system will integrate with Tata Communications domestic fiber network, extending connectivity to more than 100 data centers across India. The cable is expected to be ready-for-service by end-2029.

Alongside I-2SEA, Tata Communications will add 20 Tbps of capacity to the MIST Cable System, between Mumbai and Singapore. These investments build on the company’s broader AI strategy. In July 2025, it partnered with Amazon Web Services (AWS) to deploy a high-capacity terrestrial fiber network interconnecting the hyperscaler’s infrastructure across Mumbai, Hyderabad, and Chennai. It has also launched the Tata Communications Izo DC Dynamic Connectivity platform, enabling enterprises to transfer large volumes of data and move in real time between data centers and multiple cloud environments.

Together, these investments highlight the importance of Mumbai and Chennai as AI and cloud infrastructure hubs. Chennai has become one of India’s leading data center markets, benefiting from its strategic coastal location linking it to critical subsea cable landing stations that provide low-latency access to global markets. At the other end of the route, Singapore remains Southeast Asia’s cloud and interconnection hub, making the route between the two countries attractive for enterprises, hyperscalers, and cloud providers.

Rather than simply adding international capacity, Tata Communications is positioning its network for the next phase of AI-driven infrastructure demand. As AI training, inference and cloud workloads generate more data center to data center traffic, network diversity is becoming as important as capacity. Today, Chennai and Singapore are connected by only one other direct system – the 24-year-old Bharti Airtel i2i Cable Network (i2icn) – highlighting why new geographically diverse infrastructure could become a critical competitive advantage in Asia’s emerging AI economy.

By strengthening one of Asia’s most strategically important digital corridors, Tata Communications is not just adding capacity; it is positioning itself to be at the forefront of the pack to capitalize on the increased demand for AI-driven connectivity. The question now is whether competitors will follow suit and accelerate their own investments or risk falling behind as the India-Singapore corridor emerges as one of the region’s most critical AI routes.

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