Summary Bullets:

• As telemetry arrives, Observe adds structure that reflects each customer’s business, creating a context graph, which gives an AI agent a roadmap for querying data.

• Querying a structured database is quicker and burns far fewer tokens, which helps address the AI cost concerns that are top of mind among enterprises.

The need to manage costs and recognize a positive ROI are top concerns among organizations rolling out generative and agentic AI. Even the largest enterprises are taking a more careful look at token economics as they seek to scale the use of AI tools throughout their organizations. As employees, developers and agents all gain access to generative AI and use an ever-growing number of tokens, spending is skyrocketing. During the Snowflake World Tour 2026 in New York City in October, Jeremy Burton, former CEO of Observe and currently General Manager of Snowflake’s Observability business, notes that companies let chaos reign while they experimented with AI and now need to rein in token usage. One often-overlooked lever is context. The context graph helps agents investigate operational telemetry more efficiently, which can improve answers and reduce token usage in those workflows. For this reason, the impact of Snowflake’s acquisition of Observe, which helps organizations better structure and understand how data is being used, has the potential to manage overall enterprise AI spending as well.

Snowflake’s acquisition of Observe expands its observability portfolio. As telemetry arrives, Observe adds structure that reflects each customer’s business, creating a context graph that maps semantics and relationships across logs and metrics, which gives an AI agent a roadmap for querying data. An agent could dig through raw data, but querying a structured database is quicker and burns far fewer tokens. As Observe puts it, the company does not have better AI; it has better data. Observe ingests data based on OpenTelemetry open standards and can store it in Apache Iceberg formats, providing greater flexibility and control. It uses object storage, elastic compute, and interoperable standards to meet customers’ requests for flexibility and cost sensitive solutions.

Observe complements Snowflake Cortex AI Gateway, announced in July 2026. The gateway governs how Snowflake and third-party agents access tools, models and data. It routes requests to approved models based on cost, quality, and latency; allocates spend to teams and workloads; sets limits; and records agent actions. Observe Agent Observability goes deeper, tracking which tools agents access and correlating agent activity with backend telemetry. It adds more detail directly from the agents themselves, such as connections to underlying application and infrastructure behavior. Observe stores the telemetry in Snowflake on low-cost object storage. At the scale of telemetry data, scan-based queries using Observe’s architecture are cheaper and faster than using index-based tools. Together, Cortex AI Gateway and Observe provide an end-to-end picture of an agent, from chat client to gateway to back end. Plenty of tools observe the agent itself; Observe provides deeper insight into the surrounding telemetry.

Improved context sits at the center of several trends related to how data is accessed. Software is going headless, as users prefer prompts and APIs to complicated interfaces when interacting with information. Walled gardens are crumbling as APIs, CLIs, and MCP streamline access to information and enable AI agents to drive actions. Evaluations, which interrogate how an agent reached its answer in testing and production, depend on rich telemetry. The next opportunity lies in context graphs. Enterprises that structure their data first can make more efficient use of tokens. With AI bills climbing, vendors that offer solutions that reduce token usage offer a compelling value proposition.

The post Snowflake’s Observe Helps Businesses Rein in Token Usage by Improving Context appeared first on IT Connection.

Summary Bullets:

• AI-driven conversational discovery experience boosts the relevancy of marketplaces among broader audiences.

• Network services evolve into consumable and programmable products via enterprise agents.

Agentic AI innovations have boosted hyperscaler marketplaces’ relevance, and global telecommunications giants are taking notice.

Struggling for years on trying to fit a square peg into a round hole, telcos are recognizing the online storefronts as a prime route to sales growth by leveraging agentic AI to streamline enterprise delivery of network services. Factors range from agentic AI-driven product procurement through conversational discovery of solutions, to improved productization of infrastructure services.

Advancements in open-source software (OSS) have resulted in a greater number of carrier solutions being listed on marketplaces, alongside standardized APIs (enabled through MCPs, GSMA Open Gateway, and CAMARA) for streamlined connectivity. Networks (e.g., networks-as-code) are evolving and becoming more consumable and programmable via enterprise agents, with no complex coding or integration required by enterprises. Ericsson-led network-API operator alliance, Aduna, is a good example of such activities. Nokia’s contribution of Network-as-Code APIs to Google Cloud Marketplace is another.

Telcos are filling key roles through marketplace channels, including fulfilling private wireless deals, reselling software and platform services, and offering enterprise customers valuable consulting and integration services.

This infra productization couples nicely with another trend in marketplaces: Agent mode UX innovations have appeared across marketplaces. They support conversational discovery experiences for software procurement and are significant for their role in broadening the audience of less technical buyers.

Within the last year, AWS released two AI-powered tools for its marketplace: agent mode, which is its version of a conversational discovery experience for software procurement; and enhanced search. Buyers describe to the agent a use case, including specific requirements, to gather product details and compare options. These conversational queries in marketplaces are significant to sellers for their role in expanding the audience to include less technical buyers looking to solve their companies’ digitization issues.

Google and Microsoft also released chat-style methods to discover appropriate solutions on marketplaces. Google’s AI-powered search experience moves beyond keyword searches to provide users with natural-language discovery across its services and AI agent catalog. Its approach is similar to AWS by allowing users to describe their goals or a technical problem in order to better identify appropriate products, including third-party offerings. Microsoft brands its conversational AI experience as ‘intelligent discovery’, with interactive chat-enabled evaluation and comparisons of products.

A lot is at stake in the battle of the hyperscaler marketplaces, reported to be worth tens of billions of dollars, including partner solutions. AWS now claims over 30,000 product listings, including an AI Agents and Tools category launched last July and later that year, and agent mode for AI-enhanced search for buyers. Microsoft rebuilt its marketplace last September after merging its Azure Marketplace and AppSource. The storefront now includes over 3,000 AI apps and agents. Adding AI Agent Marketplace last year, Google differentiates on a cost basis, cutting its fee from a flat 3% down to 1.5% for certain deals. The marketplace wars will heat up as the telcos increasingly play in this space.

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Summary Bullets:

• Chinese providers are catching up with global leaders in three areas: generative AI, operationalized solutions, and agentic AI, giving enterprises broader options to diversify their AI partners.

• However, gaps in go-to-market, professional services, and model development remain large – crucial areas to drive adoption and accelerate deployments.

AI demand continues to grow rapidly. GlobalData Market Opportunity Forecasts to 2030: Artificial Intelligence (published on June 30, 2026) forecasts the global AI market to increase at 48.2% five-year-CAGR to reach $1.3 trillion in 2030. This is also reflected by 88% of the 3,069 global enterprises interviewed by GlobalData, who increased their AI budget this year (source: GlobalData ICT Customer Insights Survey 2026. Asia-Pacific shows a similar trend with a 48.6% CAGR, accounting for 36.2% of the global AI market in 2026. While growth is reported across the stack from the GPU to the application layer, cloud-based enterprise platforms remain crucial to drive adoption by providing broad and flexible capabilities to develop, govern, and scale AI deployments. GlobalData’s recent updates on Cloud-Based Enterprise AI Platforms: Competitive Landscape Assessment (September 17, 2026) shows that global hyperscalers (Google and Amazon) continue to lead the market, followed closely by Microsoft and IBM. However, Chinese players including Alibaba, Baidu, Huawei, and Tencent are closing the gap. This is important especially in markets like Asia where these players already have a strong presence. This gives enterprises broader options to diversify their AI partners, but also attracts cost-sensitive customers through their cost-leadership strategy.

The Competitive Landscape Assessment report also analyzes vendors’ capabilities, competitive comparison, market assessment, market drivers, buying criteria, and vendor recommendations. It covers 10 vendors: Alibaba, Amazon, Baidu, Dataiku, DataRobot, Google, Huawei, IBM, Microsoft, and Tencent Cloud.

Gaps Getting Smaller

Chinese providers are catching up with global leaders in three areas: generative AI (GenAI), operationalized solutions, and agentic AI. In the report, Alibaba is rated Very Strong in GenAI, the same as Amazon and one level behind the leader, Google. Alibaba’s latest Qwen family lets users toggle reasoning on and off, supports over 200 languages and dialects, and includes the agent-focused Qwen3.7-Max. Meanwhile, Baidu, Huawei, and Tencent are rated Strong. Baidu’s ERNIE 5.1 and Qianfan platform combine in-house models with DeepSeek, Kimi, and GLM. Tencent’s Hunyuan 3D and video models have over 3 million Hugging Face downloads, and Huawei has open-sourced a version of Pangu as OpenPangu.

Operationalized solutions are the clearest convergence. Alibaba and Huawei are both rated Very Strong, matching Amazon and ahead of several others. Alibaba offers vertical portfolios in finance, education, transportation, and retail while Huawei supports over 400 prepackaged AI applications across 500 industrial scenarios. Rated Strong, Tencent’s Productivity Agent Suite spans more than 20 verticals. Tencent is also extending into physical AI with its Hy-Embodied models, Tairos platform, and Unitree partnership, although deployments remain early. Industry-specific solutions are becoming more important for enterprises exploring the technologies as well as when moving from PoC to production.

The gap in agentic AI is closing more slowly. Alibaba, Baidu, and Tencent are rated Strong while Huawei is Competitive. Alibaba offers AgentRun, AgentTeams, and Agent Sandbox. Alibaba also offers a Skills portal, which converts cloud capabilities into MCP-compatible formats. Tencent has ADP 4.0 which is compatible with OpenAI’s Agents SDK) while Huawei offers AgentArts and AgentSphere. This shows not only serious investment by these players, but also increasing initiatives to support open standards rather than build closed ecosystems. However, their development platforms and tooling are rather new and less proven compared to global players.

This growing momentum is underpinned by their vertical integration strategies with investment in infrastructure such as Huawei’s Ascend 950 chips, the 1,024-card Atlas 950 SuperPod, Alibaba’s Zhenwu M890, and Baidu’s Kunlun chips. This is also reflected in their financial performance. Huawei reported $7.5 billion in AI chip revenue, Alibaba claimed 11 consecutive quarters of triple-digit AI revenue growth and over 290,000 GenAI customers, and Tencent said its AI cloud revenue doubles every year.

Where Gaps Remain

The widest gap is go-to-market. Alibaba, Baidu, Huawei, and Tencent are all rated only Competitive. While they have a strong presence in China supported by various references, their global footprints are relatively small: Alibaba has 32 regions in 17 countries, Huawei 34 in 16, and Tencent 21 in 10. All four have a predominantly Asia-centric or China-centric customer base. Geopolitical headwinds are a structural constraint, particularly in North America and Western Europe.

Another key area is professional services. Rated Competitive, these players still lack professional and managed services as well as partner ecosystem, which is crucial in helping enterprises with deployments, but also lag in ethical and governance frameworks. IBM’s 21,000 data and AI consultants and Google’s ecosystem of over 220,000 GenAI-enabled partner consultants set the benchmark.

The third is model development. Chinese providers have end-to-end tooling, such as Alibaba’s PAI, Huawei’s ModelArts, and Tencent’s TI Platform, but the experience is less unified. Tencent’s tooling, for example, is fragmented across TI Platform, ADP, and TokenHub. Global players are also ahead in the governance and monitoring tools that enterprises need to move from PoC to production.

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Summary Bullets:

• While IP voice is less ubiquitous, when it matters it, it really matters

• Agent-to-agent communication adds a new paradigm to IP voice services

Calling IP voice a legacy service is a little unfair, but only a little. Innovation in this arena is still happening as can be seen by BT’s recent launch of its automated UC edge platform designed to help enterprises move numbers between UC platforms automatically. However, a recent and stark counterpoint to this is Lumen’s decision to exit the IP voice market.

Lumen’s decision is based on multiple reasons. The provider is focusing on what it sees as its core value proposition, which is its network services capability. Further, after a period of selling-off much of its global infrastructure, the acquisition of Alkira has broadened the provider’s horizons again and it now intends to be a provider of global AI-networking solutions. Lumen felt that IP voice was not something it could offer as a global proposition and so decided to exit the market. While Lumen may have had broader strategic reasons for exiting, there is no doubt that if Lumen had seen IP voice as a high value, high growth opportunity then it would have acted differently.

Lumen is somewhat of an outlier, however, and many providers are, if not bullish, then at least moderately optimistic about the future of voice services. For example, BT International has made international IP voice one of its core service offerings and will continue to invest in it. BT reports that IP voice has been one of its biggest success stories in winning new logos. AT&T is also doubling down on voice, albeit with a focus that, while still possessing a global aspect, is America-centric.

What is powering this optimism when many enterprises have already taken IP voice services away from many of their workers in favor of mobile voice and video conferencing solutions? One part of the answer lies in customer contact services. Contact centers are viewed by many providers as a key growth market and voice is a key part of this. It is easy to assume that the growing use of AI is eroding the voice component of contact centers, but this is not necessarily the case. AI chatbots have automated many simple processes such as order tracking, password resets, and similar. However, extending AI into more complex and regulated environments is less easy. The token cost of agentic AI also makes the cost benefits less certain.

As AI continues to improve the cost comparison will change, but there is also the customer preference aspect that should be taken into account. Many customers continue to prefer talking to a human rather than engaging with AI. This is true among the younger generations as much as older generations. There is also the fact that where humans most want to speak to another human is in the most important interactions – when there has been a problem, or selling high value services or situations where customer retention and revenue generation are at stake. While there may be fewer calls into contact centers, when they happen, they matter – and their importance is creating greater interest in high quality voice services.

The emerging factor keeping service providers interested in IP voice is also centered around AI. In particular, it is calls that will be generated by agentic AI agents – either calling humans, or interacting with other agentic agents. As businesses and individuals begin to deploy more agentic AI agents they will need to communicate and it is not yet clear how this will happen. Some of it will be via APIs and MCPs, but not all systems will be connected, and agent-to-human-scenarios will always require either voice or text.

So, while AI is a clear threat to IP voice growth, it is also the potential savior of this oft-maligned but still hugely important technology.

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Summary Bullets:

• Meta will need to shed its consumer-centric image to gain traction in the business market, even if it only targets smaller organizations.

• Convincing organizations that they can trust Meta to secure personal information and corporate intellectual property will take time and effort.

On Monday September 28, 2026, Meta announced plans to enter what is already a crowded enterprise AI market. It will develop Meta Enterprise Platform, a collection of tools and services that will help business customers implement their own AI strategies. Few details were shared other than the platform will incorporate Muse agent, Meta Business Agent, Muse API, Muse Code, and other solutions.

In addition to plans for an enterprise AI platform, Meta revealed it has developed a version of its Muse personal assistant for enterprises. Muse for Small Business, announced one day after Meta Enterprise Platform, expands on the consumer version of the Muse AI agent announced earlier this month. It can access a variety of enterprise applications, including Asana, Box, Canva, Dropbox, Figma, Granola, HighLevel, Intuit QuickBooks, Klaviyo, Lovable, Notion, Shopify, Slack, Stripe, Zoom, and Facebook and Instagram business accounts. A limited amount of Muse usage is available for free; customers that want to consume it at higher volumes can purchase subscription plans based on token usage.

Meta will need to shed its consumer-centric image to gain traction in the business market, even if it only targets smaller organizations. Bringing onboard a strong enterprise focused leader, Chirantan Desai, as the ‘Chief Enterprise Platform Officer’ is a good first step. The industry veteran has extensive experience gained at MongoDB, Cloudflare, and ServiceNow, which are well respected enterprise-focused brands. However, he will be fighting an uphill battle as he seeks to position Meta as a leading provider of enterprise-grade solutions. The Meta (and Facebook) brands are strongly associated with consumer services and devices, further bolstered by the recent buzz around its AI glasses and Meta Quest googles, which though intriguing, have yet to see serious application by the business community.

Furthermore, privacy concerns abound regarding Meta/Facebook. The company built a business model based upon the sharing of information, which is the antithesis of a corporate approach to data management. Convincing organizations of all sizes that Meta has the pedigree and experience to offer secure solutions to which they can entrust the personal information of customers and employees, as well as corporate intellectual property, will take time and effort.

Additionally, Meta is seeking to enter a very crowded market. All major software vendors, such as Oracle, ServiceNow, Microsoft, and Salesforce offer platforms on which their customers can create AI agents. And most claim to integrate with outside data sources. The large hyperscale cloud providers are already major players in the enterprise AI platform space, with Google and AWS positioned as leaders in GlobalData’s latest Cloud-based Enterprise AI Platform Competitive Landscape Assessment report.

Out-of-the-box solutions that can be customized are only a fraction of what is required for a successful enterprise AI strategy. Leaders in the market for enterprise AI platforms need to demonstrate they have the tools to implement responsible AI, including guardrails and harnesses for agentic AI. They will also need to offer observability and orchestration across agents and language models, as well as integration with predictive AI. And as implementations become more complex, the ability to offer professional services, whether in-house or in partnership with consultants and systems integrators, will be critical to helping customers deploy a strategy that focuses on value and business outcomes.

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Summary Bullets:

• Three voice-activated features coming to Google Workspace reflect a broader trend of AI becoming incorporated into everyday workflows.

• The features also mirror how vendors are opening their team collaboration platforms to support the multi-platform environment in which their users operate.

Google recently announced three voice-activated AI features coming soon to Google Workspace. ‘Gmail Live’ lets users ask conversational questions to retrieve information from their Gmail inbox such as “What’s my flight’s gate number?”. Users can talk to ‘Keep Live’ to capture ideas that cross their mind and turn them into organized, actionable notes. With ‘Docs Live’ users can engage in real-time conversation to create documents on the fly. Although Workspace users cannot avail themselves of the features quite yet, ‘Gmail Live’ and ‘Keep Live’ are available to Google AI subscribers on the Plus, Pro, and Ultra plans while ‘Docs Live’ is reserved for Pro and Ultra subscribers.

Google, like all vendors in the team collaboration space, is integrating AI agents and assistants into everyday workflows. In theory this allows users to remain head-down on their work and avoid shifting gears to access AI in a separate space. The new features are intended to fulfil that vision, and they do to a degree but there is a cleaner approach.

Rather than users invoking a given feature, in the course of conducting their work users could simply describe what they would like to accomplish and AI, working in the background, would decide which feature should be leveraged to complete that goal. If the user is best served with information living in Gmail, then AI would tap Gmail Live. If a set of well-organized notes would do the trick, then AI would leverage Keep Live. If the user’s task could be best represented with a concisely written document, then AI would engage Docs Live. Regardless of the route AI chooses, everything would happen behind the scenes, transparent to the user.

In addition to reflecting how vendors are merging AI and the flow of work, the features mirror a second trend in the team collaboration space.

Rivals have acknowledged the need to open their platforms to support the multi-platform environment in which their users operate. Specifically, AI is moving from use in silos to being leveraged on a far grander scale – across vendor platforms, joining 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 CRM; and integrating platforms from different vendors making things possible such as attending a meeting on one platform with an external partner that uses a competing platform.

The expanding reach of AI comes with a heavy price. Compliance, confidentiality, and security issues, among others, multiply when platforms are intra- and inter-connected with data flowing across boundaries and AI agents manipulating and disseminating that data. Vendors have made assurances regarding the integrity of their platforms, but there remains a sense that these issues have not been adequately addressed. Vendors need to take a much deeper look.

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Summary Bullets:

• Nokia’s Cognitive Operations launch is less about another edge platform and more about staking a claim in physical AI, bringing intelligence and connectivity to real operational environments.
• But “physical AI” is already getting crowded and cloud-led, with Nokia’s platform story at risk of being dismissed as repackaging connectivity plus analytics unless it leads to visible, scaled deployments.

Nokia didn’t use the term “Physical AI” in its announcement, but its new Cognitive Operations (CO) platform reads like a carefully constructed argument for why it belongs in that conversation. Physical AI is not just AI applied to industrial data; it is AI that perceives, decides, and supports action in the physical world, often in real time, often with incomplete connectivity, and often where failure carries operational, financial, or human consequences. This is the exact scope that Nokia is targeting with CO, initially for mining, construction, public safety, and defense. Specific examples of platform capabilities include vehicle-as-a-node, local inferencing (GPU/TPU), video/sensor analytics, safety interventions (driver fatigue/collision), and independent continuity (where operations continue despite disconnection).

The product story is straightforward and strategically on point: unify mission-critical communications, edge computing, and operational AI into a single platform that can be deployed in the field. From the point of view of its telecom heritage, the significance is that Nokia is moving up the stack without abandoning its core advantage in networks. CO is built around the idea that operational intelligence is only as reliable as the connectivity and assurance beneath it. The Cognitive Edge Node (CEN) extends this logic to vehicles and remote sites, combining multi-access connectivity (5G/LTE, Wi-Fi, satellite-ready links, LoRa, and mesh integration, where software picks the best connectivity for what is needed at the time) with local processing of data including video analytics, and AI acceleration. In effect, Nokia is selling the ability to keep operations connected and intelligent even in a harsh environment and when the cloud is unreachable.

The differentiation is not about the benefits of any particular wireless technology, rather it’s more about economics and risk. Many industrial organizations have assembled digital operations tools as a patchwork of separate asset tracking, network monitoring, video, device management, and incident coordination systems—usually from different vendors with separate interfaces and support contracts. Nokia’s promises offers a “common operational picture” instead: one operational view that fuses observability of digital twins, video analytics, predictive maintenance, and network/RF intelligence. If that promise holds true in real deployments, it will reduce downtime, speed up incident response, and limit the cost associated with a more fragmented approach.

A key element of Nokia’s “right to play” is credibility in the messy space between IT and the field. CO emphasizes deterministic behavior, SLA-driven device and traffic management, and the ability to operate when connectivity is degraded or intermittent. That’s a subtle but important argument against cloud-first industrial AI approaches that assume there will be stable backhaul connectivity and centralized processing. In mines, disaster scenes, and other contested or constrained environments, latency and reliability aren’t just “nice to have”. They are what decides whether a warning arrives in time, whether video analytics are usable, and whether communications get through when networks are congested.

But for Nokia, the “platform” narrative will only become credible when customers can point to repeatable deployments at scale. Cloud hyperscalers and industrial incumbents will argue they already deliver the AI layer, and they’ll try to make connectivity a feature, not the foundation. If Nokia can’t show rapid, production rollouts—beyond early reference pilots—its CO “platform” risks being perceived as a packaged bundle of edge hardware and dashboards rather than a new operational standard. Nokia’s decision to offer CO both on-prem (including air-gapped scenarios) and via Microsoft Azure Marketplace helps to counter that perception by giving a choice, but execution will matter when it comes to deployment timelines, integration effort, and partner-led solution breadth.

Nokia’s go-to-market packaging is notable. CO was launched with three vertical applications and a compelling concept for emergency services: “Vehicle as a Node,” where each equipped response vehicle becomes a self-organizing communications and intelligence node at the incident scene. This concept helps translate abstract edge/AI capabilities into recognizable buyer outcomes, which is how new product categories can win budget. (The other vertical applications are Cognitive Operations for Mining and Cognitive Operations for Defense.)

Physical AI will be crowded—hyperscalers, industrial automation giants, robotics firms, and private wireless vendors all want a piece of it. Nokia’s bid is to own a chunk of the operational edge layer where connectivity, compute, and assurance converge. Without explicity saying so, it’s asserting: if physical AI is going to work in the real world, it needs a network-native, field-hardened foundation. And that Nokia plans to be that foundation.

The post Without Saying So Directly, Nokia Asserts its Right to Play in Physical AI appeared first on IT Connection.

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.

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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.

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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.

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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

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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.

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