Education communications are fragmented. Faculty members meet in one application, administrative teams message in another, contact centers use separate routing tools, and classrooms still depend on phones installed years ago. Emergency communications may operate through yet another environment. Unified Communications as a Service brings cloud telephony, meetings, messaging, collaboration, and contact-center capabilities into a managed service. The appeal is fewer isolated systems, more consistent administration, and communications that can follow users between offices, classrooms, homes, and mobile devices. Education presents complications that a conventional enterprise deployment may not encounter. Institutions serve varied populations, operate public-facing facilities, retain legacy endpoints, and manage identities that change with academic terms. Their UCaaS decisions also intersect with accessibility, emergency response, student privacy, and data governance. This paper examines how education leaders can define the problem, evaluate architectures, structure vendor selection, and implement UCaaS without treating migration as a simple technology swap. A university communications environment rarely starts from a clean slate. It accumulates. insight.tmcnet.com/insight/c…

China’s sharp AI stock correction has given U.S. technology investors a timely warning: enthusiasm can reverse quickly when valuations, commercial demand and infrastructure economics stop moving in sync. The AI-linked STAR50 index fell 40% in less than four months, wiping out approximately $480 billion in market value, according to DealStreetAsia. Concerns about elevated valuations, uncertain demand and export controls contributed to the decline. Now Microsoft, Alphabet and Amazon are set to report earnings this week, putting the durability of the U.S. AI trade under closer scrutiny. The comparison is not exact. China’s capital markets, industrial policy and access to advanced chips differ materially from conditions in the United States. Still, the correction illustrates what can happen when investors price in years of expansion before underlying revenue catches up. The central question is whether adoption will generate sufficient revenue, productivity gains and infrastructure returns to support current spending levels. Global corporate AI investment reached $581.7 billion in 2025, an increase of 129.9% year over year. Private investment accounted for $344.7 billion, while generative AI attracted $170.9 billion, according to the Stanford HAI AI Index 2026. AI infrastructure, models, research and governance collectively received $143.2 billion in global private funding. insight.tmcnet.com/insight/m…

Morgan Stanley expects spreads on asset-backed securities funding data-center developments to widen further as developers and operators tap a broader mix of debt markets. The warning reflects a shift in investor attention: demand for computing capacity remains substantial, but financing structures are receiving more scrutiny as issuance accelerates. According to Bloomberg, Morgan Stanley sees continued widening in data-center ABS spreads as companies race to fund new developments. Investors are reassessing tenant concentration, hyperscaler leverage, structural complexity, and the uncertain residual value of facilities built around rapidly evolving computing requirements. The market has grown quickly. From 2018 through early 2025, more than $34 billion of data-center ABS debt was issued across 75 transactions, according to KBRA data cited by APSEC in 2026. That expansion has helped establish securitization as an important funding channel alongside corporate bonds, bank loans, commercial mortgage-backed securities, and private credit. Exposure to a well-known technology tenant is not necessarily priced like exposure to that tenant's corporate balance sheet. CLDHQ 2026-1A A22, backed by a data center where Meta and Microsoft are anchor tenants, traded at 148 basis points over the five-year Treasury. Comparable Meta corporate debt traded at 89 basis points over the same benchmark. The 59-basis-point gap shows how investors distinguish between direct corporate credit and a securitized claim dependent on leases, property performance, transaction protections, and the facility's longer-term competitiveness. insight.tmcnet.com/insight/m…

What is the future of communications? See my interview with Mitel CTO Luiz Domingos to find out what comes next! "The message for end users is simple: if you aren’t exploring what workflow-driven, AI-enabled communications can do for your frontline workers, your competitors probably are." blog.tmcnet.com/blog/rich-te…

Omnichannel engagement requires linking phone, portals, Short Message Service (SMS), email, telehealth, and scheduling around one patient context. When executed correctly, these channels share identity, history, and routing instead of operating as separate queues. A patient receives an SMS reminder, replies with a scheduling question, calls the contact center after receiving no answer, and then repeats the issue at check-in. Each channel worked technically, but the engagement model did not preserve the conversation. That fragmentation matters because 68% of U.S. healthcare consumers reported access barriers, while 65% delayed care until a concern became urgent, according to a 2025 PwC survey published in 2026. Respondents also identified communication breakdowns as a major source of dissatisfaction. Buyers should therefore map complete patient journeys rather than count supported channels. A useful map follows an appointment from initial search through booking, eligibility verification, reminders, arrival, follow-up, and billing. It should identify where electronic health records (EHRs) like Epic MyChart, contact-center software, telehealth applications, and in-person teams create or consume data. The practical question is simple: can context follow the patient? If an SMS conversation remains isolated from the customer relationship management (CRM) system and agent desktop, adding texting may create another inbox rather than improve access. insight.tmcnet.com/insight/h…

Home-service workflow automation uses rules and system integrations to move customer requests from booking through dispatch and billing. Teams protect dispatch operations by automating one bounded process, retaining human review for exceptions, and testing integrations before rollout. A customer calls about a failed air conditioner. The contact-center agent gathers the address and equipment details, checks service coverage, finds a qualified technician, confirms a time window, and takes a deposit. In many home-service businesses, each action occurs in a different screen. That fragmentation matters because the workflow does not end when the call does. A changed appointment may require a customer relationship management (CRM) update, a dispatch-board adjustment, a customer notification, and a payment authorization change. If one update fails, office staff often discover it only after a technician reaches the wrong address or lacks the correct part. The ServiceTitan and Thrive Analytics 2025 contractor survey, as reported by HomePros News, found that 46% of respondents were already using or experimenting with artificial intelligence. Administration had the highest reported adoption at 59%, followed by marketing and sales at 51%; customer service and field operations each stood at 39%. Those figures point buyers toward repetitive, rules-heavy work rather than open-ended decision-making. Appointment reminders, call summaries, invoice creation, status synchronization, and maintenance-plan renewals are usually more suitable starting points than autonomous diagnosis or unsupervised route changes. insight.tmcnet.com/insight/h…

Kelly Services is highlighting managed workforce options including external workforce programs, commonly known as managed service provider programs, alongside recruitment process outsourcing, statement-of-work and payroll process outsourcing services. The menu reflects a broader change in how employers approach talent acquisition: hiring is becoming less about filling isolated vacancies and more about coordinating skills, suppliers, technology and workforce demand. That distinction matters. Nearly 70% of organizations still struggle to recruit for full-time roles, according to SHRM’s 2025 Talent Trends research. Meanwhile, 28% need new skills for those roles, and 47% are updating existing positions to include new capabilities. Employers are not simply looking for more candidates. Many are reconsidering what work needs to be done, which skills it requires and whether it belongs with an employee, contractor or project-based provider. Managed programs can support that shift by giving procurement, human resources and business leaders a more consistent view of hiring activity. An MSP typically coordinates external labor suppliers and contingent-workforce processes. RPO transfers some or all recruitment operations to a specialist, while SOW management focuses on project deliverables rather than individual temporary workers. PPO handles payroll and related administration for designated workers. When hiring channels operate separately, employers can end up competing against themselves for talent, applying inconsistent screening criteria or paying different rates for similar work. A coordinated model can make those gaps easier to spot. It can also create the data foundation required for workforce planning, although the value depends heavily on data quality and governance. Skills intelligence is becoming central to that effort. LinkedIn’s Future of Recruiting 2025 found that more than 90% of talent-acquisition professionals consider accurate skills assessment crucial to quality of hire (source). Companies conducting the most skills-based searches were 12% more likely to make a quality hire. insight.tmcnet.com/insight/k…

Meta and SpaceX are stepping up their financing efforts as the artificial intelligence race moves from software development into an expensive buildout of physical infrastructure. Compute remains central, but the investment map now includes data centers, energy generation, cooling systems, network equipment, and satellite connectivity. The scale is striking. Gartner forecasts worldwide AI spending will reach $2.7 trillion in 2026, an increase of 49.5% year over year. AI infrastructure represents approximately $1.48 trillion of that total, showing how much of the market is tied to the hardware and facilities beneath AI services rather than applications alone. For Meta, funding capacity supports the expansion of data centers and the computing environments used to develop and operate AI models. Those investments can strengthen products across advertising, content recommendations, developer services, and consumer AI. They also expose Meta to multiyear commitments before the eventual revenue and productivity gains are fully visible. SpaceX occupies a different but complementary position. Its Starlink satellite network can extend AI-enabled applications into areas where terrestrial broadband is limited, unreliable, or expensive to deploy. That gives the infrastructure race another dimension: models need computing power, but useful services also need a path to users, devices, and remote industrial sites. Securing hardware accelerators represents just the first phase of deployment. Advanced systems from Nvidia require servers, networking, storage, power distribution, cooling capacity, and specialized buildings. Each layer has its own supply constraints and financing requirements. A delay involving transformers or grid interconnection can matter as much as a shortage of processors. insight.tmcnet.com/insight/m…

Anthropic’s initial public offering filing has opened a rare window into the financial machinery behind one of the leading artificial intelligence developers. The numbers show extraordinary demand for Claude, but they also expose how expensive it is to train, operate, and expand a frontier-model business. According to CTech, Anthropic generated $16.29 billion in revenue during the six months ended June 2026. That compares with $4.59 billion for all of 2025. Few enterprise technology markets expand at that pace, particularly when the underlying product requires extensive computing capacity every time customers use it. Yet revenue growth tells only part of the story. Anthropic recorded an operating loss of $693 million for the six-month period. Computing power was its biggest expense, including cloud services supplied by Amazon Web Services and Google Cloud, along with data center costs and other infrastructure required to develop and serve its models. AI economics do not closely resemble conventional software economics, at least not yet. A traditional software vendor can often distribute another copy of its product at relatively little incremental cost. Generative AI services incur inference costs as users submit prompts, generate code, analyze documents, or run automated workflows. More adoption can produce more revenue, but it can also create a substantial infrastructure bill. Anthropic’s $80.7 billion first-half net loss appears even more dramatic. As Fortune reported, that figure largely reflected accounting effects associated with convertible securities. Investors will therefore need to distinguish between operating performance, noncash valuation adjustments, and the company’s actual cash requirements. The $693 million operating loss offers a more useful, though still incomplete, view of the economics of the underlying business. insight.tmcnet.com/insight/a…

Oneonta City School District has identified ransomware as the cause of a network disruption discovered late last week, shifting the district’s response from troubleshooting an outage to containing and investigating a confirmed cyberattack. The confirmation answers the first major question about the incident, but several others remain. It does not, by itself, establish whether attackers accessed or removed student, employee, or financial information. Nor does it reveal how the attackers entered the environment, whether they issued a ransom demand, or how long complete restoration may take. Those distinctions matter. Ransomware investigations commonly unfold in stages as technical teams review system logs, examine compromised accounts, identify affected devices, and determine whether information left the network. Public updates can consequently lag behind recovery work, particularly when forensic specialists, insurers, legal advisers, and law enforcement are involved. For Oneonta City School District, operational continuity is likely to be the immediate concern. School systems now support far more than classroom devices. They can include attendance records, family communications, payroll, transportation coordination, food-service operations, building systems, and access to instructional materials. A disruption in one part of that environment can ripple quickly. A school district cannot simply pause its business while technicians rebuild servers. Classes continue, families need updates, employees need access to basic services, and administrators still have reporting obligations. Incident response acts as both a technology exercise and a business-continuity test. insight.tmcnet.com/insight/o…

Government organizations face a unique constellation of challenges when adopting cloud platforms. Strict compliance mandates, legacy system dependencies, and the need for continuous operational availability create complexity that differs sharply from commercial deployments. The rollout of Microsoft 365 G7 marks a pivotal moment in federal technology infrastructure, introducing advanced artificial intelligence and security capabilities specifically designed for government use. As agencies evaluate whether these new features align with their mission requirements, many are discovering that successful adoption depends not merely on technical specifications but on strategic planning, compliance validation, and phased implementation, tasks that often exceed internal IT capacity. The G7 tier's enhanced data residency controls and AI governance tools promise to address long-standing concerns, yet navigating eligibility requirements and orchestrating migrations without service disruption often requires specialized expertise that many agencies source externally. The broader managed services sector has become a cornerstone of enterprise technology strategy. Omdia forecasts global managed-services revenue to reach $595 billion in 2025, an increase of 13% year over year, according to Omdia 2025. Organizations increasingly recognize that outsourcing day-to-day infrastructure, cloud operations, and application support under service-level agreements allows internal teams to focus on mission-critical initiatives rather than routine maintenance. Within government technology circles, this shift is accelerating. Omdia reported that 82% of organizations outsource some or all IT functions, with managed services representing roughly 15 to 20% of technology budgets, according to Omdia 2025. For federal, state, and local agencies constrained by hiring freezes and skills shortages, partnering with experienced managed service providers has evolved from a convenience into a strategic necessity. insight.tmcnet.com/insight/m…

Masdar has agreed to acquire and develop a solar photovoltaic project of more than 550 MW in Greece from Lightsource bp, expanding its presence in a European market where solar already supplies a significant share of electricity. The project is expected to enter operation by the end of 2026. Once running, it is projected to generate enough electricity annually to serve more than 200,000 homes. Financial terms of the transaction were not disclosed in the supplied announcement. This is more than a transfer of generation assets. The transaction includes two privately owned substations, giving the project dedicated infrastructure for connecting its output to the grid. Planned battery storage is also part of the development, although Masdar has not disclosed the storage system’s capacity or operating duration. That combination matters. Solar production can rise sharply around midday and fall as evening demand increases, creating operational challenges in markets adding renewable capacity quickly. Batteries can shift at least some generation into later periods, while substations can give developers more control over grid connection infrastructure. Neither eliminates broader network constraints, but both can make a utility-scale project more adaptable. For Masdar, the deal is its first strategic investment through TERNA ENERGY since taking over the Greek renewable-energy business in 2025. TERNA ENERGY provides Masdar with an established operating platform in Greece, rather than requiring the Abu Dhabi-based renewable energy developer to build a local presence entirely from scratch. insight.tmcnet.com/insight/m…

The financing gives Mave fresh resources to compete in a crowded market for artificial intelligence inside the security operations center. Founded in 2025 by former Unit 8200 officers Raziel Oren and Guy Eshel, Mave has raised $34 million in total, comprising a $6 million seed round and a $28 million Series A led by Red Dot Capital Partners in October 2026. The startup had about 30 employees following the financing and plans to recruit for AI research and go-to-market roles, according to SecurityWeek. Mave is positioning its product as an agentic SecOps layer rather than another repository for security data. The platform queries an organization’s existing systems, correlates live context and investigates alerts where the relevant information already resides. It can filter suspected false positives, assemble an investigation record and support response actions that have been approved by the customer. Avoiding a centralized data lake could reduce duplication and deployment friction, particularly for enterprises already managing a substantial mix of detection, identity, endpoint and cloud systems. Connecting an AI agent to many security products is both the attraction and the risk. Broad access gives the system more context, potentially helping it distinguish a routine administrative change from malicious behavior. Yet every integration also expands the agent’s reach. Security leaders will want tight controls over which data it can read, which queries it can execute and whether it can take an action without a person reviewing the recommendation first. Mave says its platform can investigate 100% of alerts within minutes while documenting every query, decision and action. That is a company claim reported by SecurityWeek, not an independently validated benchmark. The distinction matters. Performance can vary with the quality of telemetry, integration coverage, detection rules and the complexity of an enterprise environment. Buyers may therefore look for proof through controlled evaluations using their own alert queues rather than relying only on headline automation rates. insight.tmcnet.com/insight/m…

DaVita’s proposed settlement would resolve class-action claims arising from the Interlock ransomware attack, but the agreement offers only a partial accounting of the incident’s cost. The settlement is capped at $15 million, while the dialysis provider separately recorded $25 million in cyber-incident charges during 2025 (source). Against more than $13.6 billion in 2025 revenue, those figures appear manageable at the group level. The longer-term expense profile is less tidy. According to BankInfoSecurity, the attack affected nearly 2.7 million people and involved patient, clinical and financial information. That combination creates several layers of exposure. Personal and financial records can support identity theft and fraud, while clinical data may carry lasting sensitivity because patients cannot simply replace their medical histories as they would a payment card. The proposed payout also should not be treated as a ceiling on DaVita’s total costs. Legal fees, forensic work, notifications, credit-monitoring services, infrastructure changes and additional security staffing can all sit outside a class-action fund. Insurance recoveries may offset some expenses, but the timing and scope of those recoveries can vary. Future controls can then turn a one-time breach into recurring general and administrative spending. Healthcare cybersecurity operates as a core business function rather than a periodic technology problem. HHS OCR data compiled by HIPAA Journal recorded at least 772 large healthcare breaches in 2025, an all-time high. Providers depend on connected clinical systems, billing infrastructure, third-party services and large stores of regulated data. Disruption can therefore affect both information security and the delivery of care. The Change Healthcare ransomware incident illustrates the scale at the far end of that risk. Its 2024 breach affected 192.7 million people, far more than the reported DaVita population, but the comparison still matters. Both cases show why healthcare organizations have become attractive targets: valuable data, complex technology estates and limited tolerance for extended downtime. Interlock and Qilin are among the ransomware groups cited in healthcare attacks. insight.tmcnet.com/insight/d…

Meta has launched a $35 billion stock offering to finance an AI expansion that stretches well beyond model development. The planned investment covers data centers, computing capacity, cloud infrastructure, and AI products, reflecting the increasingly physical and capital-intensive nature of the AI market. The financing gives Meta more room to build infrastructure while competing with OpenAI and Anthropic. It also signals that the next phase of AI competition may be determined as much by access to electricity, land, networking equipment, and Nvidia GPU systems as by model quality. Meta also plans a separate share-sale program of up to $60 billion. The two programs serve as a reminder that even companies with large operating businesses are seeking broader financing options as AI capital requirements climb. Equity funding can preserve cash and borrowing capacity, although issuing shares can dilute existing investors and invite closer scrutiny of returns. Unlike conventional software investments, AI infrastructure requires significant physical and logistical planning. Data centers take time to construct. Accelerators, cooling systems, networking equipment, and power connections can become bottlenecks. Once deployed, the capacity also generates continuing electricity and maintenance costs. The wider spending environment helps explain the scale of Meta's move. Gartner forecasts worldwide AI spending of $2.67 trillion in 2026, including about $1.48 trillion for AI infrastructure. That means infrastructure represents roughly 56% of the projected total, putting the industry's attention squarely on the systems beneath AI applications. insight.tmcnet.com/insight/m…

The easy-money phase of the artificial intelligence infrastructure boom may be approaching a more selective chapter. Elon Musk has warned that smaller cloud computing providers could face “guppy-level death” as the funding that powered rapid industry expansion becomes harder to secure. Building an AI cloud requires expensive accelerators, high-density data centers, networking equipment, cooling systems, and long-term electricity supplies. Much of that spending occurs before customer revenue arrives. If borrowing costs rise, investors become cautious, or expected demand takes longer to materialize, smaller operators can quickly find themselves squeezed. Demand itself remains substantial. IDC reported that AI infrastructure spending totaled $318 billion in 2025, more than double the $153 billion recorded in 2024. The research group forecasts that annual spending will exceed $1 trillion by 2029. In Q3 2025 alone, spending reached $86 billion, while cloud and shared deployments represented more than 86% of the market. Fast market growth does not mean every supplier benefits equally. AI infrastructure is unusually capital-intensive, and the economics depend heavily on utilization. A GPU sitting idle still carries financing, depreciation, maintenance, and power-related costs. Providers therefore need a steady stream of workloads at prices high enough to recover their investment. That dynamic favors Amazon Web Services, Microsoft Azure, and Google Cloud. Together, AWS, Microsoft, and Google Cloud accounted for 68% of enterprise cloud infrastructure spending in Q4 2025, when the overall quarterly market reached $119 billion, according to Synergy Research Group. Their advantage extends beyond market share. Each can combine financing capacity, chip procurement, global data-center footprints, software ecosystems, and established enterprise sales channels. insight.tmcnet.com/insight/e…

SoftBank is reportedly considering a $50 billion technology investment program in Malaysia, a potential commitment that would sharply raise the stakes in Southeast Asia’s race to build artificial intelligence and cloud infrastructure. The proposed spending would cover AI, data centers, and other advanced technologies, according to CNBC. Potential locations include Kuching in Sarawak as well as sites across Peninsular Malaysia. For now, however, the $50 billion figure represents a reported plan rather than a finalized or completed investment. That distinction matters. Large infrastructure programs are generally deployed in stages, with construction schedules, financing, energy procurement, and customer demand influencing how much capital is ultimately committed. SoftBank’s proposal could encompass direct investment, partnerships, and computing services rather than a single data center project. Even so, the scale is striking. Google has committed $2 billion to establish its first Malaysian data center and cloud region, while Microsoft has announced $2.2 billion for cloud and AI infrastructure and related digital initiatives. Amazon Web Services plans to invest $6.2 billion in Malaysia through 2038. SoftBank’s reported figure is far larger, although the scope and implementation period may also be broader. Malaysia is not starting from scratch. Data-center-related investments reached RM385.7 billion between 2021 and the first half of 2026, giving prospective investors an expanding base of facilities, contractors, connectivity providers, and enterprise customers. insight.tmcnet.com/insight/s…

Carrefour’s recent cyber incident has sharpened attention on a risk that extends well beyond payment terminals and customer databases. For supermarkets, a cyberattack can reach refrigeration controls, warehouse systems, temperature sensors and logistics platforms. Those systems affect whether food remains safe and whether stores can keep shelves stocked. The distinction matters. A retailer might continue accepting payments while losing confidence in temperature readings from freezers or refrigerated trucks. Manipulated telemetry could indicate that products remained within approved ranges when they did not. Ransomware could make monitoring dashboards unavailable just as employees need to determine which goods can still be sold. Digital food systems increasingly rely on cold-chain monitoring and Internet of Things devices. Research published by Wiley in 2026 identified ransomware, distributed denial-of-service attacks, insecure IoT and operational technology, and supplier compromise among the exposures created by that dependence. Refrigeration equipment is no longer just physical machinery. Sensors, gateways, cloud dashboards and maintenance connections have turned it into a cyber-physical environment. The same applies to automated warehouses and transportation fleets. A compromised component can create consequences in the physical world, even if the attacker never gains access to a cash register. The pressure is particularly visible in Asia-Pacific. Akamai reported that commerce bot activity across the region increased 63% during 2025. Retail also represented 51% of API-targeted Layer 7 DDoS activity there. Those figures reflect attacks against the digital interfaces that increasingly connect shoppers, suppliers, inventory applications and store operations. insight.tmcnet.com/insight/c…

Education providers should compare network monitoring options by how well each measures user experience, wireless capacity, multivendor infrastructure, and incident response. Network monitoring is the continuous collection and analysis of availability, performance, traffic, and path data. A school network can appear healthy in the operations center while students struggle to join a lesson, faculty members lose video quality, or an assessment platform slows to a crawl. Device uptime reveals only part of network performance. According to a 2025 BUFFALO survey reported by DX Magazine, 70.9% of education-technology personnel wanted to improve school Wi-Fi, 55.7% reported instability when many devices connected simultaneously, and 44.3% experienced variable video streaming. Centralized management was already in use among 69.8% of respondents, yet license renewals and multivendor management remained major concerns for 46.7% and 38.3% of respondents, respectively. Demand is expanding beyond traditional monitoring as a result. SNS Insider valued the global network-observability market at $3.30 billion in 2025 and projects it to reach $10.65 billion by 2035. Cloud deployments represented 69.2% of the market in 2025. Education providers are no longer monitoring only switches. They support cloud applications, managed devices, guest access, streaming media, remote users, security controls, and aging infrastructure, often with a small team. insight.tmcnet.com/insight/e…