Summary Bullets:

• Conversational AI is driving a major voice resurgence in customer service by improving the IP voice experience.

• Long call queues on account of human agents being thinly stretched across large call volumes have led to customer frustrations and poor CX, but modern versions of conversational AI are set to change that by taking away the frictions associated with voice.

It was believed that voice would diminish in value as digital channels started to emerge as an alternative to the voice channel. While messaging and chatbots offer cost and scale advantages, GlobalData’s research shows that voice continues to remain a vital channel for connecting with customers despite the frictions. Voice provides instant gratification and tends to be more straightforward to use as it does not involve navigating through messaging channels, websites and so and having to type messages. While the elderly demography tends to prefer voice due to greater familiarity, voice is seen to be popular even with the younger generation.

The modern versions of conversational AI are reinforcing the value of voice by taking away the frictions, leading to improved experience, at least for some use cases. Conversational AI is not new, but the older versions were highly programmed, rigid, and could only answer limited, non-interactive queries. Modern conversational AI relies on probabilistic models, making it highly interactive and capable of closely emulating human agents. This technology has given birth to virtual AI receptionists that handle calls, greet customers, carry out authentication, identify intent, and route calls. This eliminates long queues and grants customers 24/7 access. The value multiplies with real-time translation, allowing AI receptionists to speak multiple languages. This is a game-changer for diverse ethnic groups who do not speak English as a first language, especially when accessing critical local government public services where expressing needs in a native language is vital.

Driven by rapid tech advancements, conversational AI is expanding from basic receptionists into autonomous AI agents. These agents handle tasks like answering queries, booking appointments, and taking orders. To do this, they require robust technical foundations, integrating seamlessly with knowledge bases and CRMs (systems of record) to provide targeted responses. Linked to the right workflows, AI agents can check calendars to schedule medical visits or process restaurant orders. Combined with 24/7 availability and translation, this dramatically elevates the voice experience for customers. Furthermore, non-technical teams can easily build these bots in agent builder studios by simply describing the desired persona and goals, removing the need for complex coding. As 8×8 CEO Samuel Wilson noted at the company’s analyst summit this June, “The next billion voice numbers will go to AI agents.”

Despite the promise, architectural challenges remain. Latency is a primary hurdle; the traditional multi-step process (like speech-to-text) can create latency, leading to awkward pauses that disrupt conversational flow. Robust guardrails are also critical to stop AI from hallucinating false information, which carries severe financial and legal risks. Additionally, backend integrations can be clunky due to poorly documented APIs or failing connectors. Finally, the compute costs for generative (GenAI) and agentic AI remain high and can be prohibitive for many businesses. While agentic conversational AI is still in its developing stages, steady breakthroughs are addressing initial operational hurdles to gradually enhance the contact center voice experience. This ongoing progress will drive deeper adoption of the voice channel, counteracting previous assumptions that voice would decrease in value.

The quest for ever-more automation within customer contact solutions continues apace. There will always be scenarios where human-to-human conversations are the preferred and best option. But to enable companies to offer voice interaction at the scale that customers demand, AI will have to carry a significant share of the burden. GenAI- and agentic AI-powered conversational AI is fundamentally shifting the voice experience in contact centers and customer service.

The post A New Breed of Conversational AI is Leading to Voice Resurgence in Customer Service appeared first on IT Connection.

Summary Bullets:

• As rapid advances in AI application development drive demand for more processing capacity, US-based cloud providers are investing heavily in building out facilities to support these deployments.
• But not everyone is on board with expansion plans, with public criticism stalling some development efforts, forcing hyperscalers to pivot to new locations, often in more remote areas.

AI is changing the cloud landscape, creating the near-term need for a vast increase in processing power and storage space. Hyperscalers are responding with substantial facility construction plans. Just this year alone, Amazon Web Services, Google, and Microsoft Azure are pouring a total of $500 to $700 billion into extensions of their data center footprints. Given AI’s dominance in enterprise technology investment plans, this is a logical track. However, not everyone is on board with these aggressive development plans -and AI plays a role in that resistance.

Fifty-two percent of adults are more worried than excited about AI, according to results from a Pew Research Center survey of 3,488 adults fielded earlier this year. Those queried had trepidations related to AI about everything from job displacement and interference with human creativity to unreliable or even malicious output.

AI anxiety is translating into opposition to in-region data center expansion. A Gallup poll of 1,000 US adults conducted earlier this year, found that 71% of those surveyed are totally opposed to the building of new data center facilities used to support AI applications in their region. By comparison, 53% object to construction of a new nuclear energy plant – a perennially unpopular build in the US for decades.

Participants in the telephone survey cited several concerns related to the new data center expansion, primarily focused on resource consumption, cost, and quality-of-life impacts. Fifty percent said excessive resource requirements associated with these builds in areas like water and energy consumption along with secondary effects such as loss of farmland, wildlife, and deforestation are behind their resistance to facilities’ expansion in their areas. Twenty-two cited concerns about property values and increased traffic. Another 20 percent noted that new data centers might bring higher utility costs and cost of living expenses.

Localities are hearing and responding to this resistance to data center expansion. Due to regional complaints, more than $100 billion in facility buildouts was stopped or disrupted in just one quarter. Over 550 local governments have suspended new facility construction or stopped issuing new permits.

Industry observers warn that impeding expansion could have unintended harmful consequences, including hindering the establishment of effective cyber defenses against hostile adversaries and creating barriers to the development and deployment of technological innovations. But cloud providers have been adept at circumventing obstacles to expansion, finding locations in more remote areas that are more hospitable to new facility construction.

Hyperscalers and other cloud providers are targeting more rural areas in the South and Midwest, and more remote locales in states like Oklahoma, Maine, and Virginia. Nearly half of all new data center builds are in the south, with states like Texas being hot spots.

The post Public Opposition to New Data Centers Disrupts but Doesn’t Derail US Facilities’ Expansion appeared first on IT Connection.

Summary Bullets:

• After a period of ‘token maxxing,’ organizations are looking to reign in and better control inference costs.

• Instead, enterprises are now embracing ‘value-maxxing,’ which focuses on outcomes.
Last week, Google announced several enhancements to Gemini Enterprise designed to help enterprises obtain greater and faster ROI on their AI projects. The improvements address one of the biggest frustrations expressed by organizations today, namely that the benefits promised by AI are taking too long to realize. Companies are clamoring for domain specific solutions in order to speed the deployment, reduce the integration complexity, and increase the value obtained from AI projects. Additionally, business leaders are eager for better tools to help them manage AI costs. They are looking for improved visibility on token use and costs, more proactive spending controls, and more flexible payment options.

On Tuesday, August 25th, Google announced Gemini Enterprise for Financial Services and Gemini Enterprise for Legal. The industry-specific solutions include out-of-the box AI capabilities such as specialized agents that provide shortcuts for directing workflows, data connectors, and sector-optimized models. Reusable packages of instructions teach AI agents to perform specialized tasks that are customized to meet company-specific requirements; connectors link agents to internal systems and data while maintaining access controls; pre-built agents are available to deploy out of the box; and software provider partnerships facilitate industry-specific customization and integration, while avoiding vendor lock-in. Though initially rolled out for the financial services and legal industries, Google plans to offer similar solutions for other industries, including healthcare, life sciences, and professional services.

The following day, August 26th, Google revealed expanded tools for managing AI spending. It announced that Google Antigravity, its AI agent development platform, and Android Studio, for building applications, will now be included in Gemini Enterprise subscriptions. Usage across Antigravity, the platform, and the app rolls up into a single view instead of separate license and billing siloes. Furthermore, Google is providing expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise. Customers can purchase a mix of per-seat subscriptions along with a new pay-as-you-go option, to help avoid hitting token caps in the middle of a job. Companies that commit to a minimum monthly spend will receive discounts on token costs. To better control spending, Google has rolled out new guardrails that enable administrators to set limits on AI spend by project, help estimate agent runtime costs, and identify anomalies in spending. Project level guardrails can pause an agent when API call limits are reached; a FinOps agent provides spending summaries in natural language.

Google’s announcements directly address concerns many organizations have over the spend on AI inference. After a period of ‘token maxxing’ wherein greater token usage was associated with greater productivity, organizations are looking to rein in and better control inference costs. Despite declining token costs, overall consumption, and therefore spend, are skyrocketing. Thus, the industry is now embracing ‘value-maxxing,’ which focuses on outcomes. It seeks to identify and quantify results, whether they be improved performance, more insightful decisions, or greater efficiency.

Regardless of the jargon of the day, organizations are taking a more analytical and practical approach to cost, latency, and performance optimization. No longer is the fastest or most expensive model considered the best choice for all tasks; organizations are now recognizing that some workflows are served well enough by less intensive reasoning, and that the same level of accuracy is not required for all tasks. At the same time, many are considering open-source strategies, attracted to the potential of lower costs, ability to fine tune models, greater transparency, local deployment options, and the option of leveraging existing infrastructure investments. At the end of the day, the development of appropriate AI strategies relies heavily on a broader understanding of the business and its operating model; professionals that can combine this knowledge with technical AI expertise are invaluable.

The post Google’s New Tools Support ‘Value Maxxing’ to Address Organizations’ Growing Concern Over AI ROI and Tokenomics 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?

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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.

The post AI Requires a Reinvention of the Modern Data Center appeared first on IT Connection.

Summary Bullets:

• e& and Globe Telecom demonstrate how strategic partnerships are transforming international voice through stronger service quality, fraud prevention, and expanding global reach.

• The future of international voice will be defined by user experience and security through strategic partnerships, not the lowest termination rates.

Over recent years, international voice has been viewed as a legacy service, highly commoditized with declining traffic volumes and shrinking revenues. Beneath the surface, however, the market is undergoing significant transformation. Rather than competing solely on the lowest possible termination rates, international carriers are forming strategic partnerships to improve service quality, extend global coverage, and strengthen network resilience to combat the threat of fraud. The recent announcement between e& and Philippine Globe Telecom reflects the shift, demonstrating how collaboration is becoming a key competitive differentiator in the next phase of international voice. As the wholesale communications market continues to evolve, could these partnerships become the defining factor that separates market leaders from the rest?

The international voice market is no longer driven by price alone. While competitive termination rates remain important, customers increasingly prioritize service quality, security, performance and reliability. As international traffic becomes more complex, carriers must deliver high-quality voice services while addressing challenges such as fraud, route optimization, regulatory compliance, and network resilience.

Strategic partnerships have become a crucial enabler of this transformation. By combining network assets, regional expertise, and operational capabilities, carriers can expand their global reach, improve route diversity, and deliver a more resilient and secure communications experience. These collaborations also enable carriers to share best practices, strengthen fraud detection, and to respond more effectively to changing market demands. In an increasingly interconnected world, it’s harder for one operator to deliver every capability on its own. The most successful providers will be those that leverage partnerships to enhance their international voice offerings and create greater value for their customers.

The announcement from e& and Globe Telecom demonstrates how international carriers are moving beyond traditional traffic exchange agreements toward deeper collaborations. By combining e&’s global international voice expertise with Globe’s strong position in the Philippines, the partnership aims to enhance service quality, strengthen fraud protection, and deliver a reliable experience to its international carrier partners. While reinforcing e&’s presence in one of the most strategic locations in Asia. This is far from an isolated example, with other international carriers striking up agreements, including Vodafone, Orange Wholesale, and BICS also establishing similar agreements to extend network reach, improve service quality, and combat fraud. Across Southeast Asia, operators such as Indosat Ooredoo and Digicel Group have also formed strategic alliances with international carriers to strengthen their international voice capabilities.

The future of international voice will extend well beyond simply transporting calls across borders. As communications become increasingly digital, strategic partnerships will enable carriers expand into higher-value services while accelerating innovation in areas such as AI-powered routing and traffic optimization, voice fraud detection, CPaaS, rich communication services (RCS), and authentication solutions. By leveraging each other’s strengths, carriers can improve service quality and security, which will unlock new revenue streams for operators. The partnership between e& and Globe demonstrates this evolution. Rather than focusing only on voice termination, it highlights how alliances can provide the foundation for next-generation communication services. As the market continues to evolve, carriers that successfully combine global scale with strong regional partnerships will be best-positioned to differentiate their offerings, strengthen customer relationships, and achieve sustainable revenue growth in an increasingly competitive market.

The post More Than Minutes: Strategic Partnerships Are Transforming International Voice appeared first on IT Connection.

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:

• 8×8 AI Routing identifies the right expert anywhere in an organization that can resolve a customer’s inquiry, not just the contact center.

• While 8×8 AI Routing is marginally better than legacy systems it still merits a try out.

We’ve probably all found ourselves reciting this famous opening line to a classic song when trying to connect with someone in customer support: “Help! I need somebody. Help! Not just anybody. Help! You know I need someone. Help!”

Desperately in need of assistance, eventually you get connected with either a person or an AI agent. You breathe a sigh of relief but then it turns out they are ill-equipped to handle your inquiry. You need help from somebody, but not just anybody. Well, 8×8 claims that its recently introduced ‘8×8 AI Routing’ will take that sad song and make it better.

8×8 AI Routing identifies the right expert for a given interaction anywhere in an organization, not just the contact center. The technology scans three 8×8 platforms, identifying contact center agents on 8×8 Contact Center plus subject matter experts on 8×8 Engage and back-office employees on 8×8 Work. Each interaction is analyzed in real-time across several factors such as transcripts, historical patterns, and sentiment to match the customer with the right resource immediately. 8×8 claims that legacy skills-based routing systems, in contrast to 8×8 AI Routing, are static, relying on skills inventories that are entered manually and often out of date. Furthermore, 8×8 says, those systems often route to whoever is available rather than whoever is necessarily best suited to resolve a specific request.

While 8×8 AI Routing is better than manual, legacy systems the difference is marginal. Identifying the ‘right’ resource hinges on AI conducting an accurate assessment of skills. While AI in general is powerful it is far from perfect. AI is known to produce errors, and the chance that a customer could get routed to the ‘wrong’ resource is not insignificant. While 8×8 AI Routing does allow for a human in the loop, with administrators having the opportunity to provide final sign-off on the skills assessment, such intervention introduces a manual element into the process. And although organizations can roll out first with a small pilot and grow the system as they gain comfort, that adjustment represents yet another layer of manual manipulation. Bottom line, while 8×8 AI Routing is not radically different than legacy systems, it does have the potential to improve an organization’s customer experience and thus merits a test drive.

The post 8×8 AI Routing Takes a Sad Song and Makes It Better appeared first on IT Connection.

Summary Bullets:

• AT&T, T-Mobile, and Verizon each improved in H1 2026, with distinct strengths across speed, reliability, and calls.

• Enterprises should evaluate 5G SA maturity, uplink capacity, and location-specific consistency—not just peak download speeds.

US mobile performance improved across the board in H1 2026, according to Ookla’s “State of the Mobile Union” reporting, drawing on RootMetrics testing. RootMetrics technicians ran over 3 million real-world tests from January to June 2026, driving 243,000 miles across all 50 states and 125 major metro markets to evaluate AT&T, T-Mobile and Verizon.

While each of the three carriers earned wins, the bigger story is that after more than half a decade, network investments are translating into more consistent, widely available 5G. That has direct consequences for mobile users and, especially, enterprises standardizing mobile connectivity for cloud apps, field operations, and AI-era workflows.

Key findings: shared speed leadership, different network strengths

AT&T earned five national RootScore Awards in H1 2026, including shares of awards for Overall Performance, Network Reliability, and Network Speed, and it won the US Call RootScore Award outright. The carrier also increased high-speed performance in cities, delivering median download speeds of at least 200 Mbps in 110 markets, up from 92 in H2 2025.

T-Mobile shared the national Network Speed award with AT&T and again led in 5G availability, posting 96.1%. It recorded 200+ Mbps median downloads in 123 of 125 major cities tested and took the most State Speed RootScore Awards (38)—demonstrating its strength in broad 5G reach and metro-area throughput.

Verizon earned the most awards overall across geographies, winning or sharing six national awards and leading with 321 State RootScore Awards and 756 Metro Area RootScore Awards. Verizon also won Best 5G Experience and Most Reliable 5G, and improved the number of major cities with 200+ Mbps median downloads from 104 in H2 2025 to 123 in H1 2026.

Verizon tops 5G experience, narrows gap on availability

In its 5G experience testing, RootMetrics cited strong performance for Verizon in both urban and rural testing. While it still trailed T-Mobile’s 5G availability, Verizon improved meaningfully—rising from 80.2% in H2 2025 to 89% in H1 2026. For users, that translates into fewer drops back to LTE and more predictable performance for video calls, collaboration tools, and cloud applications.

Performance gains are tied to spectrum and 5G architecture choices

In its report, Ookla researcher Mike Dano points to heavy carrier spending—such as AT&T’s spectrum deal with EchoStar, T-Mobile’s UScellular asset purchase, and Verizon’s AWS-3 auction activity—as a major driver behind improved outcomes. RootMetrics data indicates that overall mobile speeds on an aggregated nationwide basis increased from about 192 Mbps (H1 2024) to 334 Mbps (H1 2026), while the share of download tests running on 5G (SA or NSA) rose from about 84% to 93%.

Despite joint wins, each carrier is taking a distinct path

• AT&T is expanding mid-band capacity (including wider mid-band channel usage) and shows strong spectral efficiency. However, it continues to rely heavily on 5G NSA and, for some “lite data” tasks, leans on LTE more than rivals—potentially impacting responsiveness even when top-line speeds are improving.

• T-Mobile continues refining its extensive 5G SA footprint and leverages very large bandwidth in metro areas (often 200+ MHz total). It is also experimenting with additional spectrum layers in some markets and highlighting resilience efforts like satellite-based texting outside coverage, though usage in RootMetrics observations remained limited.

• Verizon is rapidly shifting toward 5G SA in metro testing and expanding C-band use while adding capacity features like more advanced carrier aggregation—including uplink improvements that matter as mobile use cases become more upload-heavy (video, telemetry, AI-driven apps). RootMetrics also noted some Voice over New Radio (VoNR)-related growing pains affecting call performance.
What it means for enterprises

For enterprise buyers, the report reinforces that “fastest” is less important than the best fit-to-use-case. Field service and logistics teams may prioritize coverage and 5G availability; headquarters and dense office environments may prioritize capacity and consistency; and organizations adopting real-time analytics, video, and AI workflows should pay closer attention to uplink performance and 5G SA maturity.

The bottom line is that US mobile networks are improving quickly, but in different ways. Enterprises should validate carrier performance where employees actually work—by region, indoor/outdoor mix, and application type—because competitive differences are increasingly about consistency of experience, architecture (SA vs. NSA), and how well each network handles the workloads businesses are transitioning to mobile.

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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.

The post Racing to AI: Tata Communications Accelerates Its Connectivity Position to Singapore appeared first on IT Connection.

Summary Bullets:

• Boomi is evolving from an iPaaS into an enterprise platform combining integration, automation, API management, data management, and AI agent governance.

• GlobalData recently attended Boomi’s World Tour London 2026, where agentic AI was discussed at length around announcements including Boomi Connect, Boomi Orchestrate, and Boomi Companion.

Although Boomi has historically been best known as an integration platform as a service, or iPaaS, the company is going to great lengths to emphasize that it has evolved into an enterprise platform which activates data and workflows for customers and combines integration, automation, API management, data management, and AI-agent governance. The Boomi platform acts as the connective and orchestration layer between an organization’s applications, data, and AI systems, but is increasingly moving towards management of AI agents to help data enhance business processes.

The company is investing significant resources in its Runtime environment, a hybrid platform that customers can install either locally or in a virtual private cloud. Integration is an area of major focus for the vendor, with plans to bring all business workloads into Runtime, including workflows, AI agents, and eventually small language models. Boomi has a partnership with Red Hat to help manage the deployment of open-weight and proprietary models.

Boomi is not tied to a particular ecosystem, in the vein of providers such as ServiceNow; rather, it offers an independent API control pane across all ecosystems. For example, it can connect Salesforce with SAP, synchronize customer records, automate an order-to-cash process, expose the process as an API, send transactions to suppliers through EDI, and now help AI agents replicate the same process.

The company is expanding not just beyond integration but across all platform services including investments in data readiness with Meta Hub, with strong traction among customers thanks to the growing popularity of data management systems. Currently in preview, the Knowledge Hub will also allow customers to bring unstructured data into the platform.

In the realm of agentic solutions, Boomi has recently announced new products including Boomi Connect, Boomi Orchestrate, and Boomi Companion:

• Boomi Connect offers the governance layer, sitting between the AI layer and the enterprise applications in the customer organization, with secure access, tool scoping, and observability, connecting and integrating MCP and governance by creating a single stack for CSOs. It establishes secure connections between AI tools (Claude, Copilot, and Gemini) and enterprise applications thanks to more than 1,000 MCP-enabled tools.

• Boomi Orchestrate brings IT, business applications and agents together to solve complex problems by creating blueprints allowing customer teams to build solutions. While agentic platforms are quickly becoming commoditized, Boomi has focused on delivering its proprietary offering in a way that meets customer requirements.

• Boomi Companion enables existing AI tools to design, build, test, deploy, and diagnose Boomi integrations in natural language. It transforms prompts into integrations in any language the AI agent supports and offers AI-assisted development with Claude Code, Cursor, and more via open-source Agent Skills standard.
Boomi is also launching Boomi Prompt, although the release date is yet to be determined. Boomi Prompt will form a layer of intelligent routing that is not limited to routing the LLM but will route, sort out, and separate deterministic from probabilistic workflows so users do not waste tokens. This promises, when it comes out, to be a strong portfolio addition to help customers mindful of “tokenomics”, the management of costs driven by AI consumption.

Agent Studio, introduced last May, is an agent management platform for customers who have been undertaking integration processes for 20 years and do not want to start over with agents and replace everything. These types of enterprises are looking for slightly augmented intelligence to leverage agents for fixing things that are not working. Rather than starting over, they favor an integration process for testing applications. Customers are used to deterministic workflows and the Agent Studio offers an integration workflow in an structured form.

Boomi is focusing on the right objectives. In terms of governance, the company is in a solid position to benefit from a significant opportunity in the next two years as token budgets get out of control to help people to get back into deterministic processes and bring non-deterministic workflows back under control.

The caveat is that the portfolio is becoming very broad. Integration remains Boomi’s most mature and recognizable capability, while advanced data engineering, enterprise knowledge and agent-management products are newer expansion areas. Many customers may still look at the Boomi platform primarily as an enterprise connectivity and orchestration platform, but its advancements in agentic AI management should not be overlooked.

The post Boomi Targets Agentic AI Governance, but Orchestration Remains Its Raison d’Etre appeared first on IT Connection.

Summary Bullets:

• Fast-growing use of agentic AIs within organizations has triggered agentic orchestration/governance prioritization among platform providers

• Controversy remains over two distinct approaches to orchestration: control plane construct or orchestration frameworks

Enterprises deploying AI in 2026 are turning their attention from deployment of agentic AIs to the management of growing numbers of agents being released across organizations. Companies are struggling with how to manage the hundreds or thousands of individual agents built within their organizations–agents built by different teams, running on different platforms, with inconsistent security and governance. This is problematic, considering most organizations lack visibility into agent inventory, purpose, and authorization.

The practice of addressing agentic orchestration at the control plane platform layer is moving to the forefront of the conversation, spurred by the lack of visibility, security, and management associated with agentic sprawl. Control planes sit above the agent layer, governing, observing, and enforcing policy across agents regardless of where they originated. The advantage is in their ability to ensure identity and security enforcement and enable cross-vendor interoperability regardless of what framework they use for coordinating agents. This approach contrasts with orchestration frameworks which are features of agentic solutions that simply coordinate agents.

GlobalData’s research captures the scale of the agentic market, and therefore the urgency of the situation. The report “Market Opportunity Forecasts to 2029: Agentic AI” puts the global agentic AI market at a 50.6% CAGR for 2024–2029, reaching $45.4 billion by 2029, driven by enterprise demand for autonomous decision-making, multi-agent orchestration, and scalable cloud-native AI infrastructure. GlobalData reports that early adopters are even replacing traditional robotic process automation (RPA) with goal-driven, self-adapting agent systems — and that the shift from pilots to production-grade systems is accelerating.

Vendor Strategies

A control plane market is emerging, positioned as framework-agnostic. Leading AI and platform providers are announcing strategies and solutions to address this evolving branch of agent orchestration:

• IBM is positioning the next generation of watsonx Orchestrate as an agentic control plane for the multi-agent era. It supports IBM-native agents alongside LangGraph, Langflow, and agents built on the open A2A protocol, with consistent policy enforcement.

• Salesforce has built its orchestration strategy on MuleSoft’s Agent Fabric. This has been helped by its ability to consolidate multiple data sources into a single source following Salesforce’s Informatica acquisition last November. A trust and data security layer serves as the key component of its new Agent Fabric control plane.

• ServiceNow is featuring its AI Control Tower as the governance layer spanning every AI agent, model, and action running across the enterprise, regardless of which vendor built them. The company is repositioning from being a workflow automation vendor to an enterprise AI operating system, shored up by its recent acquisition of IT/OT security provider Armis, which leans heavily into its new AI Control Tower solution.

• Boomi’s control plane approach is addressed via the Boomi Enterprise Platform, which sits between disparate systems, agents, frontier models, and data sources. Boomi’s acquisition of Lunar.dev, AI/MCP gateway, plays heavily into its strategy as the prompt routing layer for governing MCP servers and access.

Yet controversy over how to govern the fast-growing agentic AI market segment remains. Some rival platform providers are taking a different tact and keeping agentic orchestration within the confines of their own platforms and product ecosystems. They are not positioned as supporting cross-vendor governance layers in the same way as competitors:

• Microsoft has been reshaping Copilot Studio from an agent-building tool into an agent governance layer. It describes the new governance features as having centralized policy enforcement, agent lifecycle oversight, and cross-ecosystem governance spanning Microsoft 365 and partner-built agents. However, Microsoft’s architecture is embedded in and distributed across its popular platforms, including Power Platform and Azure, versus a discrete, specific control plane layer that sits above disparate agents.

• Oracle OCI’s strategy for management and governance also currently bypasses a control plane architectural model and remains within the confines of its own ecosystem. OCI Enterprise AI embeds agentic orchestration natively as a feature across the Oracle technology layers rather than positioning a discrete governance layer above them. Enterprise AI’s three integration layers are: Enterprise AI Models, Enterprise AI Agents, and Enterprise AI Governance.

Summary
The concept of a control plane architecture construct is still being defined by the market. Vendors operating in the agentic orchestration space have varying opinions and product strategies. Pioneering activities and offerings suggest this type of AI operating system will quickly become the fundamental layer for agentic AI. Operational guardrails are critical for bringing to production environments that are built around ambitious agentic AI projects.

It is worth noting that players in this market segment have generally adopted or endorsed MCP and A2A as the underlying interoperability layer, serving as the common protocol layer, while the control planes above it remain proprietary and competitive. Therefore, much of the agentic AI battle will be won or lost according to who controls the management, orchestration, and governance of disparate agents across enterprise environments.

For more on this topic and other cloud trends including escalating cloud costs, please see Cloud Watch Q2 2026: Reassessing On-Demand Economics in the Era of Escalating Cloud Costs

The post Lack of AI Agent Oversight Brings Dueling Approaches appeared first on IT Connection.

Summary Bullets:

• The EU’s MSS 2 GHz proposal strengthens regulation, security, and competition, but implementation through 2027–2029 will be phased, not immediate.
• Small spectrum block sizes favor IoT, messaging, emergency services; large-bandwidth applications face bottlenecks.

When the European Commission unveiled its plan late last month to reassign the 2 GHz mobile satellite service (MSS) spectrum at EU-level, it initiated more than a regulatory proposal. With the dust now settled on the announcement, it is clear that this represents a geopolitical and commercial realignment. Signals in the market suggest the framework is firming up around core principles: sovereignty, security, restricted eligibility, spectrum caps, and wholesale access. But beneath those pillars lies a battlefield of interests that will define not just who wins licenses, but which services Europe values the most – IoT or in-flight broadband, messaging or full-fledged device-to-device (D2D) connectivity.

Implementation is no longer a reactive guesswork exercise; it is becoming a long game. While the proposal’s two-year incumbency extension mitigates the cliff edge in May 2027 (when existing licenses expire), it also reveals the commission’s understanding: that legislative processes, spectrum allocation, compliance criteria, and defining “EU provider” are unlikely to be resolved by that deadline. Toward end-2027 and into 2028, observers should expect spectrum awards, provisional licenses, and a cascade of certification, rollouts, and enforcement activities stretching well into 2029. This also coincides with ITU’s WRC-27 conference, in which key spectrum allocations – including MSS – and updates to radio regulations are expected to be finalized and will offer clarity on the international trajectory of spectrum allocations for MSS.

Commercial implications are now emerging with greater clarity. The planned 30 MHz paired allocation in 5 + 5 MHz blocks is modest; sufficient for low-bandwidth IoT, messaging, and emergency services, yet wholly inadequate for high-throughput, low-latency use cases such as in-flight broadband or widespread D2D applications. Telcos and satellite operators should be preparing for trade-offs: either focus on niche verticals where limited spectrum suffices, or scale via partnerships, capacity leasing, or wholesale models. The missing “IoT reservation” is not a bug; rather, it is a structural risk: IoT may get parceled out only where extra capacity exists, and only if stakeholders ensure selection criteria explicitly to protect it. D2D has won primacy as per the initial tone of the Commission in its press release.

Definitions will matter, especially in the case of the term “EU Provider,” which is morphing into a fulcrum for competition. The 2 GHz is currently occupied by two US firms: Viasat for European inflight connectivity, and EchoStar for IoT and mobile connectivity. Post-May 2027, such entities will need to operate through European-owned subsidiaries or JVs. AST SpaceMobile has already localized itself via Satellite Connect Europe and is expected to qualify. But expectation of strict control over decision-making, governance, and spectrum transfer will push non-EU-based providers – SpaceX (which owns EchoStar spectrum), Viasat, and Amazon Leo – to engineer complex JV structures or seek regulatory exemption. In the case of the latter, GlobalData expects both litigation and political pressure from incumbents based in the US, particularly via the US government or trade bodies, and especially within the incumbent Trump administration window. Retaliation against European satcos is also a possibility, though it is unlikely that the bloc will collectively budge.

For telcos, the strategic moment is now. Whether to bid directly, partner with incumbents, or secure wholesale access depends on scale, access to capital, regulatory risk appetite, and technical readiness. Device certification, security compliance, antenna deployment, and supply chain capacity remain gating factors. By late-2028, the winners will be those who have not only secured licenses or partnerships, but who align with the EU’s overriding narrative: resilience, sovereignty, and competition.

The post EU Proposal for MSS Spectrum Seeks to Balance Bloc’s Commercial and Sovereignty Aspirations appeared first on IT Connection.

Summary Bullets:

• Cycles between advanced AI model rollouts are significantly shortened among leaders in this space

• Developers are gaining access to agentic-injected integrated development environments (IDEs); while knowledge workers gain access to agentic AI assistants.

The second quarter marks a momentous period in the industry’s ongoing AI efforts. Platform leaders shipped next-generation agentic runtimes including autonomous and other advanced capabilities, all while managing a more compressed cycle of new AI models, which are rolling out in a matter of weeks versus months.

A few notable announcements highlight this structural shift in how enterprise AI is built, deployed, and presented to enterprises.

Microsoft’s long-awaited private review of its first in-house reasoning model, MAI-Thinking-1, an enterprise-grade medium-weight model that promises to shake up the industry in a number of ways. Microsoft is going up against the industry’s strongest models based on the strength of its mathematical and scientific reasoning abilities, for improved training loops, citing numerous Microsoft-backed engineering benchmark tests. It is taking on Claude Sonnet 4.6 and Opus 4.6 by claiming lower token costs and smaller inference footprint. For the first time since the beginning of its relationship with OpenAI, Microsoft is able to break into the enterprise space with its own AI model, on par with leading rivals. Microsoft’s win will inevitably be at the expense of OpenAI.

Expanding its AI portfolio further was the June release of Microsoft Copilot Studio – Computer Use, revamping AI assistants to perform further up the agentic AI stack. The release supports the use of computer-use agents directly in Copilot Studio, helping bypass integrations with APIs in order to develop workflow automations.

To keep pace with top rivals Google and Anthropic, OpenAI announced its biggest model release yet, GPT-5.5, emphasizing its strengths in agentic coding, scientific research, and the ability to automate tasks associated with knowledge work. As the industry’s early GenAI leader, OpenAI has been challenged to maintain its innovative prowess.

OpenAI’s newest advancements are mere weeks following its last GPT release, demonstrating the staggering breakneck pace AI model providers are compelled to maintain to keep up in this highly competitive segment. OpenAI is hoping to win back the loyalty of professional coders who have moved to Anthropic Claude in droves for its accuracy in coding. OpenAI has been most popular among consumers, while competitors, including Google and Anthropic, have gained more traction in the enterprise space.

AWS’s latest AI announcements demonstrate a deliberate pivot towards agentic AI amidst an increasingly competitive landscape. Under mounting competitive pressure, Amazon is investing heavily in tools that span developer and non-developer audiences.

The newly announced Amazon Quick agentic AI assistant is a revamp of the GenAI assistant Q Business platform, providing knowledge-based workers with insights while also being able to act and automate repetitive workflows. Quick connects internal data across AWS services, third-party platforms, and on-premises systems. Other key announcements were Kiro agentic IDE built on Code OSS and powered by Claude models, via Amazon Bedrock; and Bedrock AgentCore, serverless runtime, and AgentCore Harness, which let developers build and run production-grade AI agents quickly without needing to code custom orchestration loops.

The post AI Wars Intensify via Major LLM/Agentic Releases appeared first on IT Connection.