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:

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

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

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

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

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

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

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

The post The Great US Fiber Gold Rush: AI is the Excuse, but Who Will Actually Make Money? appeared first on IT Connection.

Summary Bullets:

• AI is driving up voice traffic while also making voice more vulnerable, so voice security needs urgent attention.

• The building blocks are in place, but solutions are fragmented. Voice security needs to be integrated on the carrier network level and backed by strong verification at the business.

Voice security is of paramount importance today. Advances in AI are affecting voice in two ways. First, AI is improving the voice experience as it becomes more interactive, which is expected to increase overall voice traffic. Second, AI can be used to bypass security barriers, significantly raising the threat level. The threat landscape varies depending on the call type, recipients, and network routing (such as international borders); this blog focuses on voice security within customer engagement (particularly contact centers), predominantly in the US.

Voice security in customer engagement is a multi-layered issue that must be viewed from the perspective of both incoming and outgoing calls:

• For incoming calls, the objective is to protect business systems from threats like telephony denial of service (TDoS), i.e., when business operations are flooded with fake calls to disrupt service or demand a ransom, or scammers posing as customers to access sensitive data.

• For outgoing calls, the goal is to ensure customers do not ignore crucial alerts, such as notifications of fraudulent bank activity or delivery confirmations.

The initial safety net for incoming calls must start on the carrier level, where threats like TDoS can be identified and mitigated at the network stage. A carrier helps determine a call’s legitimacy using Secure Telephony Identity Revisited / Signature-based Handling of Asserted information using toKENs (STIR/SHAKEN), a mandatory US government framework acting as a digital notary. The originating carrier attaches a digitally signed certification to each call:

• Full Attestation (Grade A): The gold standard; the carrier knows the caller and confirms they legitimately own the number.

• Partial Attestation (Grade B): The carrier knows the caller but cannot verify they own the number.

• Gateway Attestation (Grade C): The carrier simply passes the call along from an unverified, unknown source.

Relying solely on networks is not enough; spoofed calls can still slip through. Businesses require extra layers like call identification and authentication. Conversely, for outgoing calls, brands must ensure their outreach isn’t mistaken for spam. This is achieved by displaying a branding logo, verified number, and the reason for the call. Notably, ecosystem requirements are stringent—calls without the highest certification grade (Grade A) cannot utilize call branding.

A robust partner ecosystem in the US brings these pieces together:

• SecureLogix orchestrates the screening of incoming calls.

• TransUnion (via its Neustar division) operates a massive database of legitimate business numbers to match callers accurately.

• Pindrop addresses audio authenticity and caller verification once calls pass the carrier gateway and reach the business phone system.

• Hiya and First Orion provide branded calling for outgoing traffic, displaying logos and call reasons.

• TNS, a carrier network hub, operates the data highways that route cryptographic signatures for both initial screening and outbound branding.

While these building blocks are in place, the broader architecture needs attention. Point solutions remain fragmented and are generally sold as add-ons, leaving gaps and placing a costly, complex configuration burden on enterprises. Partner integration should ideally happen upstream on the carrier network routing level to seal the gap at the origin.

Furthermore, stronger verification is required at the business phone system layer, as AI voice cloning makes it easier than ever to impersonate customers. Implementing multi-factor authentication can reinforce this safety net. Finally, for outgoing calls, while cross-carrier interoperability has improved, branded calling can still experience fragmentation depending on the recipient’s mobile operating system and carrier ecosystem.

The post Voice Security in Customer Engagement Has Missing Pieces appeared first on IT Connection.

Summary Bullets:

• The UK’s Competition and Market Authority released its interim findings on nexfibre’s proposed acquisition of Netomnia, in which it raised concerns that the acquisition could reduce wholesale competition.

• Deploying multiple overlapping networks have put smaller altnets under financial pressure, with consolidation seemingly the only way forward.

It’s been humorously said that the UK has roughly one altnet for every postcode – well over 100 to be exact – each armed with big ambitions and a digger. It’s therefore no surprise that consolidation is now firmly on the agenda, with M&A activity set to reshape the alternative network landscape. The market that emerges could look very different from the landscape we see today. With the UK’s Competition and Markets Authority (CMA) releasing its interim findings on nexfibre’s proposed acquisition of Netomnia, raising concerns that the deal could reduce wholesale competition, particularly where the two networks overlap. Yet consolidation could create a larger stronger challenger to take on BT’s Openreach. Though this raises the broader question: Does the UK need more fiber networks, or fewer but stronger networks capable of competing at scale with Openreach?

The CMA’s interim findings highlight a fundamental tension in the UK fiber market. Where nexfibre and Netomnia’s networks overlap, the proposed acquisition would remove a wholesale infrastructure competitor, potentially reducing choice for broadband providers and reducing the competitive pressure on wholesale services and pricing. The CMA’s alternative scenario, in which CityFibre acquires Netomnia, would retain three significant competing infrastructure providers in these areas: Openreach, nexfibre, and CityFibre.

However, nexfibre argues that consolidation would create a larger and financially stronger alternative to Openreach, providing greater scale to invest in network expansion and provide real competition for wholesale customers. The issue therefore extends beyond having more networks. The bigger question is where the UK is better served bymultiple smaller infrastructure providers, or by fewer but stronger operators capable of competing at scale.

The fiber market in the UK has been shifting from network deployments to consolidation. After years of investment by alternative network operators, it has contributed to increased competition with Openreach and has expanded the choice available to consumers. However, the economics of deploying multiple overlapping networks are becoming more challenging, placing greater emphasis on scale, customer acquisition, and returns. The proposed nexfibre-Netomnia transaction could therefore represent a pivotal moment for the sector, providing an early test of whether this altnet model can deliver sustainable returns or whether consolidation becomes a consequence of market maturity.

Looking at other international markets, there is no single blueprint for wholesale fiber infrastructure. New Zealand has four regulated fiber wholesalers, but their networks are largely geographically complementary, limiting direct infrastructure overlap. France has taken a more fragmented approach, with multiple private and regional infrastructure operators supporting wholesale access. The UK, by contrast, has deliberately encouraged Openreach, CityFibre, and altnets to build on top of one another, using infrastructure overlap to increase competition. This makes the acquisition decision strategically important for the future structure of the UK fiber market. Too much consolidation could weaken wholesale competition, while excessive fragmentation could leave operators without the scale, capital, and customer base required to generate sustainable returns and challenge Openreach effectively.

Whatever the CMA decides, it will set a precedent for the wave of potential altnet deals to come:

• If the deal is approved, nexfibre gains the scale to become a credible national challenger to Openreach, but overlapping areas lose an independent competitor.

• If the deal is blocked, wholesale customers keep more choice of infrastructure provider, but the financial pressure on smaller players may persist, and the eventual consolidation could be messier.

The CMA is weighing more infrastructure players against stronger infrastructure competitors. The UK has spent a decade proving it can build multiple fiber networks. The harder task now is proving it can sustain them, and the answer may be fewer networks, each far stronger.

The post nexfibre and Netomnia: Is Bigger Better for UK Fiber Competition? 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.

The post Chinese Cloud Giants Close Enterprise AI Platform Gap with Global Hyperscalers, but Execution Lags appeared first on IT Connection.

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.

The post Agentic AI May Give IP Voice a New Lease on Life as Legacy Decline Continues appeared first on IT Connection.

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.

• Artificial intelligence (AI) is reshaping Australia’s fiber map, driving investment beyond city limits and into emerging data center locations.

• Route diversity is becoming strategic, with terrestrial and subsea network providing greater resilience and flexibility for future digital workloads.

Australia’s fiber build-out is getting a second wind – and this time, artificial intelligence (AI) is changing the rules of the game. In the same week, Vocus and SUBCO announced new terrestrial and subsea cable projects demonstrating that Australia’s connectivity investment cycle is picking up speed. But this is about more than adding fiber. AI, cloud and data center expansions are changing where capacity is required and creating demand for networks with diverse, resilient routes that can withstand outages, congestion and other disruptions.

Vocus announced that it will extend its terrestrial fiber footprint from eastern into northern Australia with a new 4,000km route connecting Brisbane, Townsville, and Darwin. The route expands the Australian Digital Infrastructure platform that the company announced in June 2026, to support increased demand from hyperscaler, neocloud, and enterprise customers. With a capacity for up to 3,456 fiber strands, the build is designed to accommodate future demand as AI and data center investment spreads beyond the capital cities. This highlights that terrestrial fiber will carry the next wave of data center and AI growth, particularly as demand expands beyond Australia’s major metropolitan hubs.

SUBCO is taking its build offshore, with three new 24-fiber pair systems planned to connect Australia with key markets across the Indo-Pacific and North America. APX North will link Sydney to Japan via Guam, and APX East-2 will add a new route from Guam to Los Angeles. APX West, the shortest of the three will connect Perth with Singapore and extend into Indonesia through Jakarta and Batam. SUBCO aims to complete all three systems by 2029. Together, they will add significant capacity and more route diversity between Australia, Asia, and the US. For carriers, hyperscalers and other high-capacity network customers, the extra capacity matters less than the resilience, redundancy, and connectivity options that diverse routes provide.

The connectivity race will not be won by whoever lays the most fiber, but by whoever lays it where demand is heading. Taken together, the Vocus and SUBCO announcements signal a broader strategic shift in Australia’s connectivity market; the focus is moving from adding capacity to building infrastructure around future digital demand. AI, cloud and data center growth are fueling demand for high-capacity networks, and route diversity and access to multiple international gateways now matter just as much. This turns terrestrial and subsea networks from connectivity assets to strategic digital infrastructure. As AI and data centers create new traffic corridors, the providers that anticipate emerging data center hubs and build diverse routes ahead of demand will be best placed to capture future connectivity growth.

The post Australia’s Cable Wars Are Heating Up: Thanks, 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:

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

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

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

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

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

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

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

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

Summary Bullets:

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

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

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

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

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

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

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

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

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

The post Salesforce Dreamforce 2026: Salesforce Combats AI Mistrust via Control Plane appeared first on IT Connection.

Summary Bullets:

• Concerns about missing the AI boat have many corporate executives pushing AI application development mandates.

• However, research indicates that far too few organizations are realizing the kind of expected dividends in terms of cost savings, revenue generation, productivity gains, and innovation. What can they do differently to make the most of their investments?

To say artificial intelligence (AI) has become ubiquitous is almost an understatement. Just under 90% of all enterprises today use AI to support at least one corporate function, according to consulting firm McKinsey and Company, with 56% of all organizations applying the technology across three or more discrete tasks. This pace of adoption is unprecedented. Stanford University’s 2026 AI Index Report says 53% of the global population had been using generative AI (GenAI) within three years of widespread availability. For comparison’s sake, it was 12 years before the personal computer reached 40% of the population.

The appeal is obvious: Executives see AI’s promise as an engine to accelerate cost savings and operational efficiencies while delivering data-driven analytics that produce better outcomes and rapid revenue expansion. On paper, the technology is a game-changer, the kind that has corporations pressing lines of business to quickly develop and deploy AI-driven applications to realize fast returns. But the reality, particularly given the nascent nature of the technology and the relatively limited experience of most organizations in developing and deploying it, presents a much more complicated reality.

Figures like the 95% AI project failure rate quoted by the Massachusetts Institute of Technology (MIT) in the research university’s 2025 study of 300 public AI implementations accounting for a $30 billion to $40 billion total investment raise widespread concerns about poor outcomes and an imminent AI bubble implosion. Broader studies show that while less drastic, early AI results have delivered less than stellar results. IBM Institute for Business Value (IBV) research reports that the 1,250 IT executives surveyed in mid-2026 had seen a return on their AI investment of 17%. The result: Nearly two-thirds of all AI projects fell short of corporate goals. Separate IBM research points to internal technical oversight and management issues often beyond IT’s control, which can cut into 20% of AI returns. These issues range from disjointed processes, erratic measurement, limited visibility, and high upfront costs.

The source of much of this friction is trying to apply AI project budgets across multiple departments where priorities and end goals may be wildly different. The simple guidance for enterprises, particularly those with limited AI experience and even lower success rates, is to allocate resources to smaller, more focused AI projects initially. The goals should closely align with the unit’s requirements, and should by their nature, be easier to monitor and measure and need fewer resources. Enterprises can leverage learnings from these individual engagements over time, which they can then scale for larger inter-departmental projects.

As with any cutting-edge technology investment, failures are bound to happen more frequently than any organizations want to see. The key is to strike the right balance of adequate resource allocation and manageable implementations with clearly established governance over monitoring, management, and overarching objectives.

The post Under Pressure to Demonstrate AI ROI, Too Often Organizations Come Up Short appeared first on IT Connection.

Summary Bullets:

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

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

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

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

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

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

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

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

The post Zoom Continues to Flourish appeared first on IT Connection.

Summary Bullets:

• In August, Meta admitted liability regarding social media’s addictive qualities on children, reaching a $17.1 billion settlement with US states and the District of Columbia.

• While seen as a victory by critics, the settlement is insufficient to introduce the sweeping changes needed to have a measurable effect.

Just weeks after Meta, the parent company of social media giants Instagram and Facebook, promised to restrict access to some of the more controversial features of its platform to settle a multi-billion dollar lawsuit, California Governor Gavin Newsom signed a bill tightening social media controls for children. The California law, which goes into effect in 2028, mandates social media accounts for users under age 16 be private, limits algorithmic feeds for minors, masks children’s location data, and turns off overnight notifications for underage users. Unlike similar legislation passed by other states like Illinois, the California law allows parents to amend social media restrictions. Newsom also signed a bill that requires AI companies to reveal to minors whether they are communicating with a non-human entity. The legislation also prevents children from accessing risky content on subjects like self-harm.

The California law and the Meta settlement come at a time when governments across the US and around the world are imposing regulations on social media and AI companies. The goal is to protect minors from damaging content and addictive online platform characteristics that interfere with the mental health of minors. Governments ranging from Australia and Indonesia to the EU have enacted restrictions barring children and young teenagers from unlimited content access. In the US, government social media regulatory and legal activity is occurring at the state and local levels.

Social media platforms have been under fire for years as parents and researchers voice concerns about hazardous effects of social media on younger users. Research from both public and private sector entities finds that social media use can have wide-ranging repercussions on children’s mental health and brain development. Advisories from the US Surgeon General and the American Psychological Association also cite negative physical and social consequences to minors from sites like Instagram and Snapchat, including sleep deprivation and exposure to content promoting eating disorders.

While social media companies reject accusations that their platforms propagate addictive usage patterns, successful litigation and settlements favoring plaintiffs such as the August Meta resolution show they are on the defensive. As part of the resolution of that settlement, Meta agreed to set a two-hour daily platform limit for users under 18 on Facebook and Instagram. The company will also block overnight access to the online sites and eliminate some features such as ‘like’ counts that have negative effects on users’ mental health. Meta will pay roughly $12 billion initially to the states, with the remaining $5 billion disbursement dependent on TikTok, Google (YouTube’s parent), and Snap (SnapChat’s parent) also settling and agreeing to pay financial penalties and make similar feature access changes.

On paper, experts critical of the effects of social media use on children and teenagers welcome the feature changes. But while large-scale settlements and platform adaptations show at least implied admission of responsibility by platform vendors, it is not necessarily an indicator of future feature development plans or end user behavior. Regulatory restrictions imposed by governments such as Australia have had limited impact on user access as teenagers find ways to navigate around controls.

The post Despite Meta’s $17.1 Billion Lawsuit Settlement, Questions Linger About Reducing Negative Social Media Effects for Children 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:

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

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

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

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

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

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

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

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

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

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

Summary Bullets:

• 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.

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