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:

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

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

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

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

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

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

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

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

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

Summary Bullets:

• 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.

The post Meta Will Need to Shed its Consumer-centric Image to Gain Ground in Enterprise AI appeared first on IT Connection.

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.

The post Google is Keeping Workspace on the Cutting Edge appeared first on IT Connection.

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:

• 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:

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

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

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

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

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

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

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

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

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

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

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

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Summary Bullets:

• 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.

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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.

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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:

• 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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