Summary Bullets:

• Telecom companies are facing many challenges moving beyond their legacy business and adopting digital solutions including AI to drive business transformation.
• Amdocs is helping telcos to drive transformation with AI and its consulting-led services play a key role to accelerate the process from customer engagement to backend operations.

Telecommunications companies (telcos) are in various stages of transforming their businesses. The industry as a whole faces several challenges that have hindered progress.

These include regulations (e.g., to meet quality of service, data privacy, consumer protection, etc.); the need to constantly invest in their networks (e.g., upgrading mobile networks to 5G and 5G-A), legacy systems, and processes (including IT, network, and operations support system); and growing competitive pressures from traditional competitors to new telco start-ups and disruptive players (e.g., over-the-top providers, cloud providers, LEO satellite companies, etc.). They also have a huge workforce that may not be ready to transition into new technology areas such as AI, data science, cybersecurity, and cloud computing. While telcos’ leadership teams are well aware of the opportunities of emerging technologies, they have to take a more holistic approach in transforming the business, not just adding new digital capabilities. They need to reimagine their business (i.e., define the core business and operating model), right-size the organization with the right talent, adjust the company culture, and ensure effective change management.

This means opportunities for technology services providers including consulting firms, systems integrators, and other telco vendor partners to help telcos modernize their technology and transform their business. Amdocs is a key player within the telco partner ecosystem. It already serves 350 communications and media providers across more than 85 countries, including many tier-1 telcos (e.g., AT&T, BT, Telefonica, and Globe) with long-standing relationships. The company offers a range of products for catalog management, commerce and customer care, billing/monetization, network deployment and optimization, service & network automation, and more. Amdocs has also embedded AI (including GenAI and agentic AI) into its solutions. For example, its customer engagement platform is a customer relationship management (CRM) solution to deliver AI-driven customer journeys and personalized services serving both consumer and B2B customers. This is developed in partnership with Microsoft, leveraging Microsoft Dynamics 365 and Microsoft Azure, verticalized for telecoms by Amdocs. Amdocs amAIz suite lays the foundation for telco data management, AI control and governance, and AI application and AI agent deployment. More importantly, since Amdocs is already embedded in telcos’ operations, the company has a deep understanding of the telco business and operational requirements. This places the company in a better position to help telcos adopt AI, particularly agentic AI, to automate workflows (from IT operations to business operations and network operations) to deliver the desired business outcomes.

However, due to the aforementioned challenges, many telcos are facing in transforming their business: They are not merely looking for more technologies but partners that can help them drive business outcomes. Many technology vendors choose to partner with service providers to help telcos close their capability gaps, recognizing the need to work across technologies from different vendors, which may require systems integration. Amdocs has taken a different approach by building a more comprehensive set of services to support telco customers, which it can also extend to customers in more verticals over time. Besides services to support network management and operations, the company is also helping telcos to transform various aspects of their business from CX to the modernization of backend systems. This is through Amdocs Studios, which has broad expertise across cloud services (e.g., strategy, migration, and operations), data and AI (e.g., data strategy, AI & analytics, and GenAI), and consulting services (e.g., experience design, product development, cybersecurity, and risk management). Amdocs is developing agentic services to support operational aspects of the Amdocs Studios’ main practices, including application modernization, data modernization, quality engineering, and more. The company has an extensive partner ecosystem to deliver the right outcomes for customers. For example, it has strategic partnerships with AWS, Google Cloud, Microsoft Azure, Oracle, and Red Hat to offer cloud services.

Consulting services in particular are crucial in aligning technologies with business outcomes and helping drive change especially in using cloud, data, and AI to improve customer experience, employee experience, and operations experience (the processes involved to facilitate the interaction between a customer and a brand). Successful implementation will require enterprises to focus on the experiences they want to deliver and the brand image they want to establish. In particular, a human-centered design is crucial especially in AI initiatives to promote trust and focus on the benefits to enhance human capabilities (not to replace them).

Amdocs has invested significantly to develop experience design capabilities, which will be pivotal to compete with other service providers. Some global systems integrators also have strong creative design consulting capabilities (e.g., Accenture Song, Deloitte Digital, and TCS Interactive). As businesses are adopting digital solutions to drive business and operational changes, it is imperative for service providers to have an industry-focused approach for their go-to-market. This is already the case for most global systems integrators. While Amdocs does not have the scale of some of the largest global systems integrators, it has deep expertise in the telco sector. However, the company will continue to face stiff competition from systems integrators, especially Accenture, Infosys, and HCLTech, which have made acquisitions, high-profile customer examples, and extensive partnerships with vendors important to telcos.

Summary Bullets:

• The combination of omni-channel capability, effective data management, and AI will drive better customer experience.
• As Twilio’s business evolves from CPaaS to customer experience, the company focuses its product development on themes around trust, simple, and smart.

The ability to provide superior customer experience (CX) helps a business gain customer loyalty and a strong competitive advantage. Many enterprises are looking to AI including generative AI (GenAI) and agentic AI to further boost CX by enabling faster resolution and personalized experiences.

Communications platform-as-a-service (CPaaS) vendors offer a platform that focuses on meeting omni-channel channel communications requirements. These players have now integrated a broader set of capabilities to solve CX challenges, involving different touch points including sales, marketing, and customer service. Twilio is one of the major CPaaS vendors that has moved beyond just communications applications programming interfaces (APIs), including contact center (Twilio Flex), customer data management (Segment), and conversational AI. Twilio’s product development has been focusing on three key themes: Trusted, Simple, and Smart. The company has demonstrated these themes through product announcements throughout 2025 and showcased at its SIGNAL events around the world.

Firstly, Twilio is winning customer trust through its scalable and reliable platform (e.g., 99.99% API reliability), working with all major telecom operators in each market (e.g., Optus, Telstra, and Vodafone in Australia). More importantly, it is helping clients win the trust of their customers. With the rising fraud impacting consumers, Twilio has introduced various capabilities including Silent Network Authentication and FIDO-certified passkey as part of its Verify, a user verification product. The company is also promoting the use of branded communications, which has shown to achieve consumer trust and greater willingness to engage with brands. Twilio has introduced branded calling, RCS for branded messaging, Whatsapp Business Calling, and WebRTC for browser.

The second theme is about simplifying developer experience when using the Twilio platform to achieve better CX outcomes. Twilio has long been in the business of giving businesses the ability to reach their customers through a range of communications channels. With Segment (customer data platform), Twilio enables businesses to leverage their data more effectively for gaining customer insights and taking actions. An example is the recent introduction of Event Triggered Journey (general availability in July 2025), which allows the creation of automated marketing workflows to support personalized customer journeys. This can be used to enable a responsive approach for real-time use cases, such as cart abandonment, onboarding flows, and trial-to-paid account journeys. By taking actions to promptly address issues a customer is facing can improve the chance of having a successful transaction, and a happy customer.

The third theme on ‘smart’ is about leveraging AI to make better decisions, enable differentiated experiences, and build stronger customer relationships. Twilio announced two conversational AI updates in May 2025. The first is ‘Conversational Intelligence’ (generally available for voice and private beta for messaging), which analyzes voice calls and text-based conversations and converting them into structured data and insights. This is useful for understanding sentiments, spotting compliance risks, and identifying churn risks. The other AI capability is ‘ConversationRelay’, which enables developers to create voice AI agents using their preferred LLM and integrate with customer data. Twilio is leveraging speech recognition technology and interrupt handling to enable human-like voice agents. Cedar, a financial experience platform for healthcare providers is leveraging ConversationRelay to automate inbound patient billing calls. Healthcare providers receive large volume of calls from patients seeking clarity on their financial obligations. And the use of ConversationRelay enables AI-powered voice agents to provide quick answers and reduce wait times. This provides a better patient experience and quantifiable outcome compared to traditional chatbots. It is also said to reduce costs. The real test is whether such capabilities impact customer experience metrics, such as net promoter score (NPS).

Today, many businesses use Twilio to enhance customer engagement. At the Twilio SIGNAL Sydney event for example, Twilio customers spoke about their success with Twilio solutions. Crypto.com reduced onboarding times from hours to minutes, Lendi Group (a mortgage FinTech company) highlighted the use of AI agents to engage customers after hours, and Philippines Airlines was exploring Twilio Segment and Twilio Flex to enable personalized customer experiences. There was a general excitement with the use of AI to further enhance CX. However, while businesses are aware of the benefits of using AI to improve customer experience, the challenge has been the ability to do it effectively.

Twilio is simplifying the process with Segment and conversational AI solutions. The company is tackling another major challenge around AI security, through the acquisition of Stytch (completed on November 14, 2025), an identity platform for AI agents. AI agent authentication becomes crucial as more agents are deployed and given access to data and systems. AI agents will also collaborate autonomously through protocols such as Model Context Protocol, which can create security risks without an effective identity framework.

It has come a long way from legacy chatbots to GenAI-powered voice agents, and Twilio is not alone in pursuing AI-powered CX solutions. The market is a long way off from providing quantifiable feedback from customers. Technology vendors enabling customer engagement (e.g., Genesys, Salesforce, and Zendesk) have developed AI capabilities including voice AI agents. The collective efforts and competition within the industry will help to drive awareness and adoption. But it is crucial to get the basics right around data management, security, and cost of deploying AI.

Summary Bullets:

• Huawei aims to accelerate Digital Africa with wider connectivity, 5G, and sustainability.

• Industrial 5G, especially in mining, can drive 5G monetization in Africa. This is supported by Huawei’s broad portfolio and ecosystem.

Huawei held its MBBS Africa in Cape Town, South Africa in November 2025. As one of the leading telecom network vendors, Huawei shared its regional vision – to drive ‘Digital Africa’ through wider connectivity, 5G, and sustainable solutions.

GlobalData’s Africa & Middle East Mobile Broadband Forecast shows that mobile data subscription has been growing steadily at high single-digit rates over the past several years and is expected to rise at a 7.2% CAGR over the next five years. While 5G adoption is increasing, the penetration rate is still low compared to other regions. Huawei highlighted its initiatives and broad capabilities to accelerate growth in Africa. These include multi-band massive MIMO for additional capacity, active antenna solutions for efficient and flexible deployments, FWA for new use cases, cost-efficient solutions for rural deployments, and various energy-saving technologies such as adaptive power backup. Several operators including Telkom SA, Safaricom Kenya, and Airtel Tanzania showcased how they are leveraging these technologies in their networks. Huawei is also transforming its engagement model with African operators, moving beyond the role of a network vendor to become to a digitalization partner by delivering innovative solutions aligned with business needs and monetization strategies.

As 5G deployment gathers pace, monetization will become critical for operators. GlobalData research estimates 5G users will account for 7.1% of all mobile users by the end of 2025 and will grow to 26.7% by 2030. However, 5G monetization remains a global challenge even in advanced markets. The challenge will be an even bigger hurdle for African operators due to slower overall adoption and the relatively lower spending power. This makes the importance of enterprise 5G as a key monetization engine. Horizontal services such as FWA, private 5G, and IoT are essential. These use cases can help enterprises address various needs such as increasing reliability and security for critical applications, agile connectivity for temporary sites (e.g., events, remote operations), SD-WAN underlay, and large-scale IoT deployments. Meanwhile, 5G-enabled industrial solutions represent an even larger opportunity. Mining and resources, one of the region’s largest sectors, can benefit from applications like autonomous drilling, remote operation/maintenance, and worker safety. Globally, 5G adoption in mining is maturing and widely adopted. GlobalData’s 5G & Private Network Deployment Tracker shows that 7% of global deployments are in the mining sector. Other major verticals are construction, tourism, and hospitality are among other major verticals in the region. There is a growing number of use cases including drones and surveillance, digital twins, and safety in construction; and mixed reality, robots, and smart facilities in tourism/hospitality.

While the opportunity for 5G-enabled industrial services is increasing solidly, the solutions are far more complicated. They span across broader ICT stacks and require IT-OT integrations. Nevertheless, this plays to Huawei’s strengths. The vendor has comprehensive portfolio from cellular and fixed networks, to cloud, server, end points, AI, and industrial capabilities. For example, autonomous drilling in mining requires private network, but also edge computing, AI/analytics, and vertical expertise. Besides, the company has wide partner ecosystem including industrial players and end-point manufacturers. And more importantly, Huawei has extensive references and experience delivering these solutions cost-effectively in other emerging markets like Asia and South America. It can showcase its other successful deployments to gain market trust and drive its brand share in the enterprise 5G space.

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Summary Bullets:

• To protect an expansive mobile environment attack surface in the face of a very dangerous threat environment, organizations are ramping up their security investments, with 75% of the 762 polled in a recent Verizon study reporting they had increased spending this year.
• But concerns still loom large threat actors using AI and other technologies and tactics to breach the enterprise; and only 17% have implemented security controls to stave off AI-driven attacks.

Mobile and IoT devices play an essential role in most organizations’ operations today. However, the convenience and flexibility they bring comes with risk, opening new points of exposure to enterprise assets. Organizations that were quick to embrace bring your own device (BYOD) strategies often didn’t have a solid plan for safeguarding this environment when so many of these devices were under-secured. Enterprises have made progress in layering their defenses to better protect mobile and IoT environments, but there is still room for progress.

In Verizon’s eighth annual Mobile Security Index report, 77% of the people surveyed said deepfake attacks that tap AI-generated voice and video content to impersonate staff or executives, and SMS text phishing campaigns are likely to accomplish their objective. Approximately 38% think AI will make ransomware even more effective.

Despite the increase in cybersecurity spending in most organizations, only 12% have deployed security controls to safeguard their enterprise from deepfake-enhanced voice phishing. Just 16% have implemented protections against zero-day exploits.

Enterprise employees are welcoming AI-driven apps to their mobile devices – with 93% using GenAI as part of their workday routine. They raised red flags, with 64% calling data compromise via GenAI their number one mobile risk. Of 80% of enterprises that ran employee smishing tests, 39% fell for the scam.

AI aside, user error is the most frequently noted contributor to breaches in general, followed by application threats and network threats. Some 80% said they had documented mobile phishing attempts aimed at staff.

While prioritizing cybersecurity spending is important, organizations need to look at whether they are allocating this investment on the right areas. Just 45% said their organization provides comprehensive education on the potential risks mobile AI tools bring. Only half have formal policies regarding GenAI use on mobile devices, and 27% said they aren’t strictly enforced.

Summary Bullets:

• Don’t use AI browsers or AI browser extensions – the loss of privacy isn’t worth the functionality.

• AI companies mean well, but the privacy implications of these products are unsuitable for enterprise or personal use.

“If you are not paying for it, you’re not the customer; you’re the product being sold.” – Andrew Lewis (blue_beetle), MetaFilter comment (2010)

It’s not news that AI is being talked about everywhere. It’s also not news that the websites and applications you use regularly are doing their level best to spy on you or obtain data that can be used internally or be sold to advertisers. Nor is it news that the state of privacy laws across the world is pretty poor, despite the EU giving its best attempt and the US pretending that three lines of legalese in a 15-page disclaimer somehow magically sets the ‘informed’ flag on users.

But the latest trend involves AI companies either creating browser extensions or, in at least one case, creating their own browser. OpenAI is touting its AI-enabled browser called Atlas, designed to both remember all activity, search that activity, chat, and do any number of AI-enhanced things. OpenAI rival Perplexity has a browser product called Comet. There are even sidebar browser extensions for Microsoft Copilot and Google Gemini. Some browsers, such as Firefox and Brave, come with an AI sidebar but uses your choice of LLM.

The first problem is an AI watching everything – your passwords, all text you type, your URLs… everything. Then that data isn’t stored locally; it’s stored with the AI. The problems here are no different than the problems with Microsoft Recall, an AI-driven search and backup feature that Microsoft released earlier in 2025, much to the consternation of pretty much everyone. All these AI companies have multiple safeguards to protect data, have stated policies on how such data can be used and where, and are being pretty upfront about how and when they use your data. They allow end users to pick and choose when the AI is available or even forget that data after a session. Companies adding these AI features to the browser are legitimately trying to make the lives of users easier with AI and protect user privacy.

They are adding other safeguards as well. OpenAI says that its Atlas AI browser cannot access other applications, download files, and cannot install extensions. Technological limits to prevent AI browsers and extensions from becoming security risks are being taken.

But giving any corporation a detailed record of all activities conducted on the internet, including every click, search, text, or picture and the metadata around it could have disastrous consequences in the long term. Hackers could gain access to the data. Governments could seize the data and use it against a populace or an individual. Companies get bought, end user agreements change, or investors could simply demand that all that personal data is monetized. If companies go out of business, what happens to the data? A fair amount of the world doesn’t have any legal mechanism to force businesses to delete data either.

Then there are the other issues, regarding security on your desktop. Social engineering or AI chat window spoofing is a real issue. That’s just the tip of the iceberg.

Every individual and every enterprise have the choice to decide whether the risks are worth the utility of having AI integrated into your browser. Everyone wants tools that work better; some of the features in AI browsers are impressive, and likely even more features will be coming. But that shouldn’t be at the expense of risking all your personal data or risking the company’s internal data, no matter how nice the tools look or how much you trusts a given AI vendor. This is about ensuring personal privacy and the data security of enterprises. Take a pass on AI browsers and AI browser extensions. Nobody would stand for being under video and audio surveillance every second and everywhere. Don’t allow the same to happen to your digital life.

Summary Bullets:

• Oracle shifts pitch from technology to outcomes, driven by AI-injected tools.

• Oracle APEXLang, slated for 2026, to modernize Oracle’s development practices.

Last week’s Oracle AI World couldn’t have been timelier, attended by customers and partners still buzzing from a corporate earnings report, which triggered the tech giant’s stock to soar based on its mounting investments in AI and cloud infrastructure.

Executive Chairman and CTO Larry Ellison’s high-level keynote speech included profound possibilities and some examples of a variety of outcomes that AI advancements can have on global enterprises. Executives continued to carry his message in other keynotes, noting the company’s shift in focus from CIOs to CEOs through conversations that emphasized outcomes versus products. For example, in one case, Oracle’s ability to apply AI-injected applications, app platforms, and data platforms to a particular healthcare clinic resulted in productivity gains, which saved individual health workers 100 minutes per day.

He and other executives further described situations involving various medical diagnostic imaging and genetic testing with examples of how AI will diagnose, treat, and cure health issues at significantly greater levels and speeds through modernized code bases, medical systems, and ecosystems.

Oracle announced new functionality across Oracle Cloud Infrastructure’s (OCI) comprehensive cloud offering, spanning its developer portfolio: AI Agent Studio, Fusion AI Agent Marketplace, and Agent Hub (preview) AI tools for business users. Oracle’s AI agent studio has been enhanced to build and deploy AI agents across the enterprise including Oracle Fusion Applications. Oracle’s new AI agent marketplace extends the company’s LLM ecosystem and third-party agent-building resources. Ellison noted that Oracle’s low-code and automation technology, Oracle Application Express (APEX), will continue to demonstrate a growing role in AI code generation of applications connected through workflows and shored up by security. Advancements will make applications developed more scalable and reliable. This led to other application development discussions throughout the week, including some on the future of Oracle APEX.

A little-known app development tool, Oracle APEXLang, shows promise in modernizing and extending Oracle’s current development practices. Set for 2026 release, the Apex extension uses a structured, file-based format to build and format Oracle APEX applications, specifically to enable app development to be integrated with enterprises’ latest digitization practices.

Oracle APEX, traditionally used in a browser-based, declarative environment over the past five years, is valued by enterprise developers for its low-code cloud service advantage, traditionally used to build apps on Oracle databases. Oracle APEXLang represents a significant shift for these traditional developers. Features include version control support (e.g., Git) and tools to adhere to CICD pipelines for improved automated test and deployment. It works with developers’ app platforms of choice including code assistants, because the new file-based approach is particularly well suited for GenAI and LLMs. Oracle research notes Oracle APEXLang is not a replacement for Oracle APEX, SQL, or JavaScript, but an enabler for defining components of applications.

Summary Bullets:

• ANS’ acquisition of Sci-Net Solutions expands its portfolio of value-added enterprise technology solutions in a highly competitive UK B2B market
• AI is a hook everyone latches on to – there are even products and solutions out there – but this is an acquisition of a service provider with current revenues

The ANS acquisition of Sci-Net Business Solutions is positioned as a complement to previous acquisitions such as Makutu as part of the ANS strategy to exploit and deliver the opportunities presented by artificial intelligence (AI). Sci-Net is an Oxford-based business solutions specialist with expertise in ERP, CRM, and cloud infrastructure solutions (e.g., 365 Business Central, Microsoft Dynamics NAV, CRM, and Microsoft Azure).

With ANS already having a strong relationship with Microsoft (Services Partner of the Year in 2024 and over 100 certified Microsoft specialists), the combination makes sense and grows the ANS talent base to over 750 including 65 technology consultants from Sci-Net. It offers opportunities to cross- and up-sell to the companies’ existing customer bases, and to continue to move up the value chain as a managed services provider (MSP).

The move also underlines some key trends in the UK marketplace. Competition remains fierce, so being able to act as a trusted advisor is becoming more important to win and retain business. At the same time, technology continues to become more complex, therefore offering a full portfolio of services ‘above and beyond’ connectivity is vital. MSPs and value-added resellers (VARs) recognize this and represent an ever-stronger force in the market as they can work closely with customers to develop technology solutions that directly address their business needs.

That is not to say that the ‘Big Three’ B2B service providers – BT, Vodafone, and O2 Daisy – do not also recognize this. All of them are positioning to become more solutions-oriented with a focus on areas like cloud, security and, increasingly, AI. They have the advantage of significant existing customer bases, deep human and partnership resources, strong brands, and nationwide fixed and mobile networks from which to deliver their services. By contrast, the likes of ANS and other VARs/MSPs can exploit their agility to differentiate themselves in the market.

It will continue to be a highly competitive market to win the custom of enterprises of all sizes in the UK, which is a tough challenge for all service providers. But it is good news for UK plc as businesses stand to benefit from innovation and value.

Summary Bullets:

• Agentic AI streamlines workflow automation and transformations.
• Application and automation platforms to integrate agentic AI capabilities in next 12 months.

Digital transformations will receive a major boost over the next 12 months following new platform integrations with agentic AI. The AI-injected solutions will significantly streamline the creation of workflow automation, which are critical to organizations moving to migrate legacy apps to cloud environments in order to realize CICD and improved application lifecycle efficiencies.

This next generation of intelligent automation will have far-reaching ramifications among service providers, from traditional PaaS players to leading automation vendors to newer telco/infrastructure providers offering managed Kubernetes services.

Business transformations have been largely stalled over the past few years due to the fact that many enterprises lack the internal expertise necessary to configure the backend integration and connectivity to enable workflows that support critical business processes. Automation leaders – including Automation Anywhere, UiPath, SS&C Blue Prism, ServiceNow, and Pegasystems – have played a pivotal role in advancing workflow automation, particularly predictable and rules-based workflows. Application platform solutions including Microsoft Power Platform and IBM Cloud Pak for Business Automation also compete in this space.

In coming months, these solutions and platforms will be equipped with advanced cognitive capabilities such as generative AI (GenAI) and agentic AI to enable dynamic business processes capable of adapting and reasoning in an autonomous fashion. This will be a welcome relief to those enterprise personas involved in back-office transactional processing where accuracy and quality of solutions are critical. They will be most inclined to rely on their trusted technology partners integrating such agentic capabilities through mature platform services.

Automation and platform leaders are only just beginning to offer the industry glimpses into their agentic AI roadmaps, having spent the last couple of years integrating GenAI into developer tools and workflow solutions. Beta versions of AI agent capabilities are starting to appear, typically in the form of prebuilt templates and ultimately agent building toolkits and agent orchestration management capabilities.

GlobalData will be closely following the slew of later conferences hosted by platform providers including IBM, Oracle, Salesforce, AWS, and multi-vendor Kubernetes/DevOps show KubeCon for advancements in this space. Similarly, IT ops teams should keep an eye out for a constantly changing ecosystem of players and partnerships in this space, which will encourage more service providers to support global companies struggling with digitization integrations.

Summary Bullets:

• AWS Singapore Innovation Hub shows the company’s shift from technology-led to business-driven, turning use cases into commercial applications.

• The F1 partnership showcases AI and cloud innovation, but also AWS’ capabilities with real-time data intensive analytics and insights.

AWS held an analyst day in Singapore, showcasing its Innovation Hub and the partnership with Formula 1 (F1).

AWS Innovation Hub

At the innovation hub, AWS demonstrated a diverse range of AI and cloud enabled use cases – from document analytics and loan processing for BFSI, to preventive maintenance and AI-driven surveillance in manufacturing. The facility also houses many other industry-specific use cases with additional use cases in the pipeline. This initiative reflects AWS’ ongoing shift from a technology-focused to a business-led engagement model. While technologies remain at the core, the company is deepening collaboration with enterprise leaders beyond IT, engaging directly with business executives and functional owners. Leadership, culture, and people are key enablers of successful digital transformation. The Innovation Hub serves as a platform for enterprises to explore and co-develop use cases tailored to their business needs.

Innovation labs are not new. Many other providers like global system integrators, telcos, and tech vendors, have been opening new facilities over the last few years. It is a proven way to drive adoption of emerging technologies through solution co-development, commercialization and ecosystem expansion. Besides, innovation labs can also strengthen providers’ brand share and enable them to gain deeper market knowledge such as understanding customers’ pain points. The use cases demonstrated at the AWS facility were innovative and promising, but most are somewhat comparable to use cases found in other providers’ facilities. But what differentiates AWS is its strong execution. Over 70% of the use cases have been brought into production. This is consistent with its strategy to expand focus on outcome-led engagements, and a strong proof point that the efforts are not just conceptual but outcome-driven.

AWS x F1

In the latter part of the event, there were sessions with executives at the track sites including a visit to the F1 Event Technical Center (ETC), sharing how the AWS and F1 collaboration is driving innovation and enabling various use cases for F1, teams, drivers, fans, and viewers. Since the partnership began in 2018, AWS has evolved from providing core cloud infrastructure to powering advanced AI solutions. Early deployments include leveraging over 1,000 AWS compute cores for computational fluid dynamics (CFD) projects to design race cars. Today, the partnership extends to AI applications including real-time insights, car performance, race strategy, issue resolutions/root cause analysis, fan engagement (e.g., hyper-personalization), game strategy, and safety and reliability.

Sports, as one of the most data-intensive industries in the world, offers an ideal testbed for real-time analytics. For example, an F1 car alone carries around 300 sensors, generating over one million telemetry data points per second with a total of 600TB across entire race. Similarly, a football match generates about 3.6 million data points. Furthermore, data from sports events are often highly fragmented (structured and unstructured) and need to be processed in real-time. Apart from F1, AWS is also an official technology partner in various major global sports events such as the NFL, PGA Tour, Bundesliga, NHL, and many other sports teams. While sports in APAC are not as big as in other regions such as the US and Europe, AWS collaborations with F1 and other sports organizations show its leadership in this industry. More importantly, it can also be seen as a powerful platform to demonstrate its broad capabilities and innovation in complex and data-rich environments.

Summary Bullets:
• T-Mobile added data connectivity to T-Satellite in an announcement pivoting from “emergency messaging” focus of the service’s launch towards app-based data services and higher enterprise relevance

• Early business app integrations, coupled with an expanded set of consumer apps, position T-Satellite as a broader competitive play in satellite-to-mobile, though IoT support and performance limitations remain concerns

When T-Mobile launched T-Satellite commercially in July, the focus was firmly on safety and resilience. SMS, MMS, and text-to-911 formed the core offering, with compelling stories of hikers rescued and emergency alerts broadcast in disaster zones. At the time, the service’s competitive strengths were clear: broad device support, seamless integration with existing smartphones, and unique value in public safety. But there were also several limitations: no data or voice, little to offer enterprises beyond the “resilience” messaging, and no clarity on IoT support.

The company did promise to add support for data in October, and it kept that promise by announcing its availability on the first day of the month. With data now switched on, T-Satellite can support popular apps like WhatsApp, AllTrails, AccuWeather, Google Maps, and T-Mobile’s own T-Life customer portal. The key feature is WhatsApp voice and video chat over satellite, demonstrating that T-Satellite is no longer limited to one-way messaging and can now support real-time communications, albeit with constrained performance compared to terrestrial 5G.

For consumers, it’s a tangible leap in functionality, making T-Satellite relevant not only in emergency scenarios but to outdoor enthusiasts, travelers, or anyone in a rural dead zone (who can now stay connected with their smartphone apps despite the lack of mobile signal). Apple’s Emergency SOS and satellite iMessage have so far offered narrower sets of features, whereas T-Satellite is making mainstream apps usable in off-grid conditions, targeting a wider appeal and one not limited to a single device ecosystem.

On the enterprise side, T-Mobile noted support for several apps relevant to business and public sector users. Dialpad (unified communications), FLORIAN (real-time location monitoring), MultiLine (secure, compliant business communications), and T-Mobile Direct Connect (push-to-talk) are all now supported by T-Satellite. This is a notable step toward the enterprise relevance that was absent at the initial launch. For field services, first responders, and regulated industries like finance or healthcare, the assurance of “always-on” communications, even beyond terrestrial networks, addresses real gaps in continuity and safety.

Still, the new announcement does not address all potential use cases. IoT support remains unaddressed by T-Mobile, and organizations in industries such as logistics, utilities, and agriculture will continue to look to rivals who are further ahead in satellite IoT integration. In addition, while T-Satellite now supports data-based apps, performance (throughput) is limited, and the experience will not replicate terrestrial mobile broadband. T-Mobile acknowledges this openly, describing the connectivity as designed for critical functions rather than data-heavy use.

In terms of competitive positioning, the service is now more than a safety/emergency add-on. It strengthens T-Mobile’s differentiation versus AT&T’s FirstNet and Verizon’s Frontline, demonstrating both consumer value and initial enterprise relevance. The fact that it is included in top-tier T-Mobile plans at no extra cost (and even available to AT&T and Verizon customers for $10 per month) is an aggressive marketing move. By broadening access, T-Mobile appears confident that the real differentiator will be the user experience and partner ecosystem, not exclusivity.

Looking ahead, T-Satellite’s trajectory in the enterprise space will hinge on two factors: how quickly T-Mobile can move from early enterprise applications to full vertical solutions, and whether it can articulate a credible IoT roadmap. Without those, the service risks being viewed primarily as a consumer perk with limited depth for business users. With them, it could become a crucial feature in industries that depend on connectivity anywhere.

For now, the new announcement is a genuine milestone, achieving a goal that T-Mobile set for itself. T-Satellite has moved from a narrow emergency communications tool to a platform supporting both everyday consumer apps and the first enterprise-oriented solutions, strengthening T-Mobile’s relevance in direct-to-device and satellite-to-mobile conversations.

Jaguar Land Rover, the iconic British car manufacturer has had virtually no production in its plants since the end of August 2025. A devastating cyberattack shut the company down – details on how the attack happened, who initiated the attack, and why it so thoroughly shut down Jaguar Land Rover have not been released to date. The postmortem will be an interesting read, more so to find out how much of the effect of this cyberattack was Jaguar Land Rover’s fault. No, this isn’t indulgent victim-blaming, and right now there is no proof the Jaguar Land Rover was anything but diligent. But the length of the shutdown and the secrecy does arise suspicions. Under principles of good business continuity and disaster recovery, Jaguar Land Rover should have been at least somewhat back in production by now. But analysis will really have to wait until details emerge.

This does highlight an issue that most organizations struggle with. Cybersecurity, as well as disaster recovery and business continuity, are preventative – they shouldn’t be noticed unless they are needed… or if they didn’t work. It’s hard to get satisfaction creating business continuity/disaster recovery (BC/DR) systems that you may never get to actually use. Security has a much higher profile… but ‘everything is running smoothly’ doesn’t often gain accolades.

Cybersecurity, and especially BC/DR are often pressured to compromise, for finance, for convenience, and because neither function will ever make money for the organization. Often there is a push to compare cybersecurity and BC/DR to an automotive or homeowner’s insurance policy, that they offer peace of mind. There is a better way to think about it. Think of cybersecurity and BC/DR like law enforcement thinks about bomb squad units. Bomb squad units get all the training and practice they want. Bomb squad units are encouraged to get the latest training, learn the latest advances, and to keep their equipment as up to date as possible. Nobody thinks that the bomb squad has it easy when they render an explosive safe, or in the best of times are not called on. Nobody suggests that the bomb squad does more with less. Because the consequences are so extreme, both for the bomb squad and for the law enforcement organization.

Budget holders need to start viewing cybersecurity, BC/DR, and BC/DR testing like the bomb squad. Yes, they provide peace of mind. But what they really provide is protection from extreme consequences. Nobody wants the organization in the news for having been knocked offline for a month in every major news outlet. Nobody wants to have to create the postmortem and present it to the board and likely various government officials, insurance executives, investor representatives and lawyers. Let’s not let this plea to take cybersecurity and BC/DR seriously fall on deaf ears like it has in the past.

Summary Bullets:

• Data center cooling has become an increasingly insurmountable challenge because AI accelerators consume massive amounts of power.

• Liquid cooling adoption is progressively evolving from experimental to mainstream starting with AI labs and hyperscalers, then moving into the colocation space and later enterprises.

As Generative AI (GenAI) takes an ever-stronger hold in our lives, the demands on data centers continue to grow. The heat generated by the high-density computing required to run AI applications that are more resource-intensive than ever is pushing companies to adopt ever more innovative cooling techniques. As a result, liquid cooling, which used to be a fairly experimental technique, is becoming more mainstream.

Eye-watering amounts of money continue to pour into data center investment to run AI workloads. Heat management has become top of mind due to the high rack densities deployed in data centers. GlobalData forecasts that AI revenue worldwide will reach $165 billion in 2025, marking an annual growth of 26% over the previous year. The growth rate will accelerate from 2026 at 34%, and in subsequent years; in fact, the CAGR in the period 2004-2025 will reach 37%.

Source: GlobalData

The powerful hardware designed for AI workloads is growing in density. Although average density racks are usually below 10 kW, it is feasible to think of AI training clusters of 200 kW per rack in the not-too-distant future. Of course, the average number of kW per rack varies a lot, depending on the application, with traditional IT workloads for mainstream business applications requiring far fewer kW-per-rack than frontier AI workloads.

Liquid cooling is a heat management technique that uses liquid to remove heat from computing components in data centers. Liquid has a much higher thermal conductivity than air as it can absorb and transfer heat more effectively. By bringing a liquid coolant into direct contact with heat-generating components like CPUs and GPUs, liquid cooling systems can remove heat at its source, maintaining stable operating temperatures.

Although there are many diverse types of liquid cooling techniques, direct to chip is the most popular cooling method, also known as “cold plate,” accounting for approximately half of the liquid cooling market. This technique uses a cold plate directly mounted on the chip inside the server, enabling efficient heat dissipation. This direct contact enhances the heat transfer efficiency. This method allows high-end, specialized servers to be installed in standard IT cabinets, similar to legacy air-cooled equipment.

There are innovative variations on the cold plate technique that are currently under experimentation. Microsoft is currently prototyping a new method that takes the direct to chip technique one step further by bringing liquid coolant directly inside the silicon where the heat is generated. The method entails applying microfluidics via tiny channels etched into the silicon chip, creating grooves that allow cooling liquid to flow directly onto the chip and more efficiently remove heat.

Swiss startup Corintis is behind the novel technique, which blends the electronics and the heat management system that have been historically designed and made separately, creating unnecessary obstacles when heat has to propagate through multiple materials. Corintis created a design that blends the electronics and the cooling together from the beginning so the microchannels are right underneath the transistor.

Summary Bullets:

• As part of a larger global cross-industry study, LevelBlue surveyed executives in 220 manufacturing companies to gauge the state of their cyber resilience strategies in the era of AI-driven threats and other risks
• Awareness is high but also so are concerns, with 37% saying they are seeing a significantly higher volume of attacks; just 30% said their organization is prepared for deepfake attacks, even as 47% are anticipating them

Threat actors are savvy when choosing their targets. Manufacturing holds a strong appeal to cyber criminals because the profit potential associated with intellectual property is high and, thanks in part to supply chain vulnerabilities, there are plenty of points of exposure. A recent LevelBlue survey of 220 manufacturing executives found that while awareness about the threat environment is high, preparedness, especially for AI-driven attacks, is not.

Only 32% of manufacturing executives are ready for AI-powered threats, even though 44% expect them to occur. On the supply chain front, 54% admitted to having a very low to moderate visibility into their supply chains. Just 26% said working with their software suppliers to vet their credentials will take precedence in the next year.

In spite of the fact that 28% of manufacturing executives say their organization suffered a breach in the past 12 months and more than one-third are expecting that attack volume to increase, 51% said they are highly or very highly competent to protect their enterprise against threat actors. Fifty-five percent gave themselves the same competence when it comes to implementing and using AI to enhance cybersecurity.

The contradiction between this high level of confidence in their own competencies and their preparedness for AI-driven and other types of attacks points to potential overoptimism that adversaries could easily exploit. But there are also signs that some of the traditional internal organizational cybersecurity challenges in manufacturing are being addressed. Sixty-eight percent described their cybersecurity team as being aligned with lines of business. Sixty-five percent those in leadership positions are assessed against cybersecurity KPIs, which is higher than the cross-vertical results (60%).

Seventy percent are engaged in end-user education on social engineering, again higher than the entire sample (62%). Manufacturing companies are also more willing to tap third-party security providers for security training and awareness than in the past. Thirty-eight percent said they will augment their own internal resources with external training support in the next two years versus the 30% that have engaged with a third-party in the last year.

Manufacturing organizations are investing in cybersecurity to prepare for emerging threats. Top priorities are machine learning for pattern matching (71%); cyber resilience processes across the organization (69%); GenAI to combat social engineering attacks (64%); application security (67%) and enhanced supply chain security (63%).

While investment is important, awareness, pragmatism, and solid policy execution are essential. Without these, there is no way for any enterprise to mount an effective defense against cyber adversaries.

Summary Bullets:

• Though just 12% of public safety workers currently use AI everyday, 46% anticipate it will become part of their daily work by 2030.

• With an increasing emphasis on using network-dependent technologies like connected cars and drones, network reliability – or the lack thereof – is the top concern of 67% of those surveyed.

As essential as first responders are, public safety officials aren’t necessarily known for deploying leading-edge technology. But results from the fifth annual Verizon Frontline Public Safety Communications Survey suggest this may be changing. The survey results of 1,028 first responders – i.e., EMS, fire, police, emergency management, public safety, and emergency communications workers – find that while advanced technologies like AI and drones are broadly used today, they expect wider implementation through 2030.

While today, only 35% have implemented AI-driven applications in their agencies, 20% are investigating the technology for future use. Seventy-one percent (71%) described AI as either important or a top priority for their organizations, with 22% calling it the latter.

Today, 84% rely on smartphones during emergency response operations. Thirty percent (30%) use drones to provide visual support during emergency response maneuvers. Though currently, drones are only part of daily operations for 15% of the organizations surveyed, and that figure is expected to soar by 2030 to 48%. Wearables including body cameras, which are part of daily operations in 28% of organizations now, will jump to 50% by 2030. Other advanced technology will play more prominent roles in daily work in the coming years, including augmented and virtual reality applications, which currently are only part of daily operations in 8% of agencies, but they will be adopted by a third of first responder organizations by 2030.

All of this underscores the importance of the stability and service quality of the underlying communications network. Seventy-five percent (75%) say a reliable and resilient communications network is the most important element to day-to-day professional communications. Seventy-three percent (73%) cite it as most essential during emergency operations.

Cybersecurity is an important component of network continuity and productivity. More than two-thirds – i.e., 67% – of first responder organizations have deployed new cybersecurity protections in the last twelve months.

Seventy-eight percent (78%) of respondents cite better communications in the field as the biggest payoff of access to a reliable network, nearly double any other benefit, including better situational awareness (46%) and improved response time (45%).

Summary Bullets:

• In its annual Global Managed Security survey of 1,019 managed services providers (MSPs) in the US, Canada, and the UK, security vendor OpenText uncovered a big delta between the desire to exploit SMB demand for AI-driven solutions and the capability of these providers to deliver the essential support.

• Approximately 92% said they are seeing growth driven by client interest in AI but only half have the adequate resources and expertise to help clients deploy these solutions.

Organizations of all sizes are boarding the AI bandwagon. For smaller businesses lacking internal AI expertise, adoption often requires the support of an external provider. Unfortunately, that same resource limitation also plagues many of the MSPs SMBs seek out for AI support. In a recent OpenText survey of 1,019 security practitioners, IT managers, and customer relationship managers, in the coming year 96% expect to see growth in demand driven by interest in AI. However, half said a combination of factors leaves them under-prepared to support SMB AI needs, including a lack of internal expertise, too many disparate tools to manage, and the lack of standardization across different client environments.

Fewer than 50% of the surveyed organizations have developed or implemented AI cybersecurity for their clients. That said, the majority are using AI for a variety of customer-facing applications, with 67% leveraging the technology for customer support and 66% rely on AI for technical support and triage. Over half (58%) apply AI for threat detection and response.

All MSPs are aware of the urgency of upskilling staff on AI, noting it is now the third most important capability behind threat prevention and 24/7 support for its SMB clients. SMBs are seeking out bundled security packages in droves. Seventy-one percent are looking for combined prevention, detection, and response solutions. Forty-one prevent want endpoint, network, and email security offerings.

Most of the surveyed MSPs are on a growth path with 95% expanding their portfolios in the coming year. Integration across discrete tools is of highest importance (38%). Eighteen percent said attach-rate to core services is crucial. Sixteen percent cited the criticality of having solutions that work across vertical industries.

A proven reputation is essential for MSPs to compete and win new clients. Thirty-two percent said customer referrals are the primary way to gain new customers, while 29% of prospects come to MSPs through digital searches or advertisements.

Cisco’s Quantum Labs research team, part of Outshift by Cisco, has announced that they have completed a complete software solution prototype. The latest part is the Cisco Quantum Complier prototype, designed for distributed quantum computing across networked processors. In short, it allows a network of quantum computers, of all types, to participate in solving a single problem. Even better, this new compiler supports distributed quantum error correction. Instead of a quantum computer needing to have a huge number of qbits itself, the load can be spread out among multiple quantum computers. This coordination is handled across a quantum network, powered by Cisco’s Quantum Network entanglement chip, which was announced in May 2025. This network could also be used to secure communications for traditional servers as well.

For some quick background – one of the factors holding quantum computers back is the lack of quantity and quality when it comes to qubits. Most of the amazing things quantum computers can in theory do require thousands or millions of qubits. Today we have systems with around a thousand qubits. But those qubits need to be quality qubits. Qubits are extremely susceptible to outside interference. Qubits need to be available in quantity as well as quality. To fix the quality problem, there has been a considerable amount of work performed on error correction for qubits. But again, most quantum error correction routines require even more qubits to create logical ‘stable’ qubits. Research has been ongoing across the industry – everyone is looking for a way to create large amounts of stable qubits.

What Cisco is proposing is that instead of making a single quantum processor bigger to have more qubits, multiple quantum processors can be strung together with their quantum networking technology and the quality of the transmitted qubits should be ensured with distributed error correction. It’s an intriguing idea – as Cisco more or less points out we didn’t achieve scale with traditional computing by simply making a single CPU bigger and bigger until it could handle all tasks. Instead, multiple CPUs were integrated on a server and then those servers networked together to share the load. That makes good sense, and it’s an interesting approach. Just like with traditional CPUs, quantum processors will not suddenly stop growing – but if this works it will allow scaling of those quantum processors on a smaller scale, possibly ushering in useful, practical quantum computing sooner.

Is this the breakthrough needed to bring about the quantum computing revolution? At this point it’s a prototype – not an extensively tested method. Quantum computing requires so much fundamental physics research and is so complicated that its extremely hard to say if what Cisco is suggesting can usher in that new quantum age. But it is extremely interesting, and it will certainly be worth watching this approach as Cisco ramps up its efforts in quantum technologies.

Summary Bullets:

• Organizations are spending more on technology across business functions, and it is imperative for them to understand and optimize their tech spending through technology business management (TBM).
• IBM is a key TBM vendor helping organizations to drive their IT strategy more effectively; it is making moves to extend the solution to more customers and partners.

Every company is a tech company. While this is a cliché, especially in the tech industry, it is becoming real in the era of data and AI. For some time, businesses have been gathering data and analyzing them for insights to improve processes and develop new business models. By feeding data into AI engines, enterprises accelerate transformation by automating processes and reducing human intervention. The result is less friction in customer engagement, more agile operations, smarter decision-making, and faster time to market. This is, at least on paper, the promises of AI.

However, enterprises face challenges as they modernize their tech stack, adopt more digital solutions, and move AI from trials to production. Visibility into tech spending and the ability to forecast costs, especially with many services consumed on a pay-as-you-go basis is a challenge. While FinOps addresses cloud spend, a more holistic view of technology spend is necessary, including legacy on-premises systems, GenAI costs (pricing is typically based on the tokens), as well as labor-related costs.

This has made the concept of TBM more crucial today than ever. TBM is a discipline that focuses on enhancing business outcomes by providing organizations with a systematic approach to translating technology investments into business values. It brings financial discipline and transparency to their IT expenditures with the aim of maximizing the contribution of technology to overall business success. Technology is now widely used across business functions such as enterprise resource planning (ERP) for finance, human capital management (HCM) for HR, customer resource management (CRM) for sales, and supply chain management (SCM) for operations. Based on GlobalData’s research, about half of the tech spend today is already from budgets outside of the IT department. It is becoming more crucial as the use of technology becomes even more pervasive across the organization especially with AI being embedded into workflows. Moreover, TBM capability also help to elevate the role of tech leaders within an organization, as a strategic business partners.

IBM is one of the vendors that offer a comprehensive set of solutions to support TBM in part enabled by acquisitions such as Apptio (which also acquired Cloudability and Targetprocess) and Kubecost. Cloudability underpins IBM’s FinOps and cloud cost management, which is a key component that is already seeing great demand due to the need to optimize cloud workloads and spend as companies continue to expand their cloud usage. Apptio offers IT financial management (ITFM) which helps enterprises gain visibility into their tech spend (including SaaS, cloud, on-premises systems, labor, etc.) as well as usage and performance by app or team. This enables real-time decision-making, facilitates the assessment IT investments against KPIs, makes it possible to shift IT budget from keeping the lights on to innovation, and supports showback/chargeback to promote fairness and efficient usage of resources. With Targetprocess, IBM also has a strategic portfolio management (SPM) solution that helps organizations to plan, track, and prioritize work from the strategic portfolio of projects and products to the software development team. The ability to track work delivered by teams and determine the cost per unit of work allows organizations to improve time-to-market and align talent spend to strategic priorities.

Besides IBM, ServiceNow’s SPM helps organizations make better decision based on the initiatives to pursue based on resources, people, budgets, etc. ServiceWare is another firm that offers cloud cost management, ITFM, and a digital value model for TBM. Other FinOps and ITSM vendors may also join the fray as market awareness grows.

Moreover, TBM should not be a practice of the largest enterprises but rather depends on the level of tech spending involved. While IBM/Apptio serves many enterprises (e.g., 60% of Global Fortune 100 companies) that have tech spend well over $100 million, there are other vendors (e.g., MagicOrange and Nicus) that have more cost-effective solutions to target mid-sized enterprises. IBM is now addressing this customer segment with a streamlined IBM Apptio Essentials suite announced in June 2025 which offers fundamental building blocks of ITFM practice that can be implemented quickly and more cost-effectively. Based on GlobalData’s ICT Client Prospector database, in the US alone, there are over 5,000 businesses with total spend exceeding $25 million, which expands the addressable market for IBM.

For service providers, TBM is also a powerful solution for deeper engagement with enterprises and delivers a solution that drives tangible business outcomes. Personas interested in TBM include CIOs, CFOs, and CTOs. While there are TBM tools and dashboards that are readily available, service providers can play a role in managing the stakeholders and designing the processes. Through working with multiple enterprise customers, service providers are also building experiences and best practices to help deliver value faster and avoid potential pitfalls. Service providers such as Deloitte and Wipro already offer TBM to enterprise customers. Others should also consider working with TBM vendors to develop a similar practice.

Summary Bullets:

• Mistral AI’s valuation went up to EUR11.7 billion after a funding round of EUR1.7 billion spearheaded by Netherlands-based ASML.

• The French company has the edge in open source and is well positioned to capitalize on the sovereign AI trend sweeping Europe right now.

Semiconductor equipment manufacturer ASML and Mistral AI announced a partnership to explore the use of AI models across ASML’s product portfolio to enhance its holistic lithography systems. In addition, ASML was the lead investor in the latest funding round in the AI startup and now holds 11% share on a fully diluted basis in Mistral AI.

The deal holds a massive symbolic weight in the era of sovereign AI and trade barriers. Although not big in the great scheme of things and especially compared with the eye-watering sums usually exchanged in the bubbly AI world, it brings together Europe’s AI superstar Mistral with the world’s only manufacturer of EUV lithography machines for AI accelerators. ASML may not be a well-known name outside the industry, but the company is a key player in global technology. Although not an acquisition, the deal reminds of the many alliances between AI accelerator companies and AI software companies, as Nvidia and AMD continue to buy startups such as Silo AI and others. Moreover, Mistral, which has never been short of US funding through VC activity, has received a financial boost at the right time when US bidders were rumored to be circling like sharks. Even Microsoft was said to be considering buying the company at some point. For GlobalData’s take on this, please see Three is a Crowd: Microsoft Strikes Sweetheart Deal with Mistral while OpenAI Trains GPT-5. ASML becomes now its main shareholder, helping keep at bay the threat of US ownership at a critical time to reinforce one of its unique selling points: its credentials in “sovereign AI” by remaining independent from US companies.

From a technological perspective, Mistral AI has also developed a unique modus operandi, leveraging open-source models and targeting only enterprise customers, setting it apart from US competitors. Last June, it launched its first reasoning model, Magistral, focused on domain-specific multilingual reasoning, code, and maths. Using open source from the outset has helped it build a large developer ecosystem, long before DeepSeek’s disruption in the landscape drove competitors such as OpenAI to adopt open-source alternatives.

The company’s use of innovative mixture of experts (MoE) architectures and other optimizations means that its models are efficient in terms of computational resources while maintaining high performance, a key competitive differentiator. This means its systems achieve high performance per compute cost, making them more cost effective. Techniques such as sparse MoE allow scaling capacity without proportional increases in resource usage.

In February 2024, Mistral AI launched Le Chat, a multilingual conversational assistant, positioning itself against OpenAI’s ChatGPT and Google Cloud’s Gemini but with more robust privacy credentials. The company has intensified efforts to expand its business platform and tools around Le Chat, recently releasing free enterprise features such as advanced memory capabilities and capacity, and extensive third-party integrations at no cost to users. The latter includes a connectors list, built on MCP, supporting platforms such as Databricks, Snowflake, GitHub, Atlassian, and Stripe, among many others. This move will help Mistral AI penetrate the enterprise market by democratizing access to advanced features, and signals an ambitious strategy to achieve market dominance through integrated suites, not just applications.

Of course, the challenges are plentiful, Mistral AI’s scale is really far behind its US counterparts, and estimates on LLM usage seem to indicate that it is not nibbling market share away from them yet. It has a mammoth task ahead. But this deal can carve a path for European ambitions in AI, and for the protection of European assets in an increasingly polarized world divided across geopolitical lines. Some of the largest European tech companies including SAP and Capgemini have tight links to Mistral AI. They could make a bid to expand their ecosystems with acquisitions of European AI labs, that have so often fallen in US hands, in the future. For ASML, which has so many Asian customers, and whose revenues are going through a rough patch, the geopolitical turmoil of late has not been good news: this partnership brings a much-needed push in the realm of software, a key competitive enabler. After the US launched America’s AI Action plan last July, to strengthen the US leadership in AI with a plan based on removing red tape and regulation, the stakes are undoubtedly higher than ever.

Summary Bullets:

• Vocus has launched its business-focused MVNO brand, Vocus Mobile, aiming to disrupt the market and inject fresh competition into an already hypercompetitive landscape.
• Backed by an expanded customer base from the TPG Enterprise acquisition, Vocus is well-positioned to accelerate growth with its existing client base.

Australian telecoms infrastructure provider Vocus has finally launched its own business-focused MVNO brand, Vocus Mobile. Leveraging Optus’s 4G and 5G networks the company aims to position itself as a one-stop provider for enterprise communications, offering connectivity solutions across networking, collaboration, and now mobility as it looks to stand out with a range of self-serve features to create a better user experience for its clients. Will the entrance of another MVNO challenger selling basic mobile connectivity in an already crowded market make a difference?

At launch, Vocus will offer three traditional types of mobile connectivity services, including mobile voice and data for Smartphone use, 5G data plan for broadband, and 4G backup to support business continuity when primary networks are down. Customers will be supported by its self-service mobile fleet management platform, Mobile Fleet 360, giving businesses the ability to manage their mobile fleets with near real-time dashboards, bulk activation of services, and the able to configure roaming settings, reducing the reliance on traditional support channels. Vocus’s foray into the enterprise mobile services market is not surprising following on from its acquisition of TPG Telecom’s fiber network assets and its enterprise, government, and wholesale fixed infrastructure business. It has been anticipated for many years, with the company having a long-standing wholesale agreement with Optus through its consumer and SMB brands Dodo, iPrimus, and Commander. While back in 2019, the company extended its agreement to include its various other brands to provide 5G access to support its growth strategy by expanding and growing market share in large enterprise and SMB segments.

The Australian mobile market has three mobile network operators with Telstra maintaining its superior network leadership for many years. Telstra covers 95% of the country’s population with 5G coverage and 99.7% with 4G coverage, equating to land coverage of approximately three million square kilometers, almost three times the land coverage than any of its nearest rivals. To compete against Telstra, Optus and TPG Telecom (owner of Vodafone in Australia) recently formed a network sharing agreement earlier in 2025, which extends their 5G coverage to 80.5% of the population and 4G reach to 98.4% of the Australian population with over one million square kilometers.

While the Australian enterprise telecommunications market remains in flux, with many providers struggling to achieve growth and facing revenue declines across their connectivity portfolios. The enterprise market is positioned for growth, with GlobalData expecting the business mobile market to grow 5.5% by 2029. Though service providers continue to battle it out to grow their mobile market share, with the country having three enterprise challenger brands including Aussie Broadband, Macquarie Telecom, and now Vocus all leveraging Optus’s mobile network by trying to break the incumbent’s stronghold of approximately 65% of the business mobile market. While all challengers have struggled to make a meaningful impact in the market, to date, all only offer basic mobile connectivity instead of delivering outcome-driven solutions that enterprise customers increasingly expect, such as IoT, asset tracking, and other advanced 5G innovations like network slicing.

Summary Bullets:

• Major telecom companies like Vodafone Germany, T-Mobile US, and Deutsche Telekom are launching distinct 5G network slicing services aimed at business customers, each reflecting unique strategies and market contexts, from Vodafone’s standardized pricing for virtual private campus networks to T-Mobile’s all-in-one premium mobility plan and Deutsche Telekom’s focus on mission-critical services for emergency responders.

• While these launches signify progress in 5G enterprise services, the concept of network slicing is still evolving, with offerings being marketed more as tailored connectivity solutions rather than fully programmable network tools.

The long-promised potential of 5G network slicing—dedicated, virtualized “lanes” in the mobile network—is finally being brought to market in structured offers for business customers. But what exactly is being offered? Vodafone Germany, T-Mobile US, and Deutsche Telekom have all announced distinct slicing propositions in recent weeks, each reflecting a different strategy and market context.

One of the promises of network slicing is that–in theory–it can be used to design an almost limitless number of unique offers based on the feature requirements of individual users and applications. We are still some ways away from dynamic programmability of bespoke network slices on the fly, but even at this early stage of commercialization, each slicing service launch looks completely different from the others. That’s kind of the point, but it could also provoke some head scratching by enterprises trying to understand the concept.

Vodafone Germany has taken perhaps the boldest step towards mainstreaming slicing by publishing standard pricing. Campus Flex Exclusive (EUR2,000/month per location) delivers a virtual private 5G campus network, offering guaranteed uplink and downlink speeds. Campus Flex Starter (EUR10-20 per user/month) is an entry-level, shared slice for light applications like payment terminals or push-to-talk. Vodafone’s use of “Campus Network” branding and positioning is interesting, reflecting its investment in marketing earlier versions of hybrid private 4G and 5G networks leveraging its macro network. The new offers position slicing as a virtual private network alternative, faster and cheaper than deploying a full private network. What’s impressive is the fact that customers can order slices directly from the Vodafone business portal. On the other hand, Vodafone hasn’t detailed service level agreements beyond basic throughput. Transparency on latency, jitter, and other guarantees will be required prior to adoption by more demanding industrial users.

T-Mobile US, meanwhile, is packaging slicing into a broad business mobility plan called SuperMobile, which combines a “nationwide 5G Advanced slice with dynamic, real-time resource optimization” and built-in security (encryption, device authentication, and Threat Protect VPN for smartphones). It also includes T-Satellite, the company’s new satellite-to-mobile service with coverage via more than 650 satellites.

This all-in-one proposition is pitched as a general-purpose premium business solution, not an industrial, mission-critical, or application-specific product. Delta Air Lines and Axis Energy Services are early adopters, showcasing both urban and remote-field use cases.

Unlike Vodafone, T-Mobile has not disclosed pricing, and it remains unclear whether customers can define or request custom quality of service (QoS) parameters per slice (the announcement refers only to data prioritization and latency optimization). That makes the offer more of a broad performance upgrade than a programmable network service.

Deutsche Telekom (DT), by contrast, is going deep into a vertical, announcing slicing-enabled mission-critical broadband services for police, fire, and rescue agencies. Partnering with Motorola Solutions, DT is deploying 3GPP-standard Mission-Critical Services (MCX) protocols, allowing push-to-talk, push-to-video, and prioritized data sharing across LTE, 5G, and traditional radios.

By reserving network capacity through slicing, DT provides emergency responders with a guaranteed “blue light lane” on the network, ensuring reliable communications during congestion. The solution has already been tested with German federal police and proven during the 2024 European Football Championship. Pricing is not disclosed, and the service appears restricted to the public sector. It’s not yet clear whether DT intends to extend MCX-style slicing to commercial industries with critical communications needs.

These three very different commercial launches represent progress in 5G enterprise services, but are we seeing network slicing go mainstream?

Rather than seeing an enterprise “network slicing market” emerge, what is more likely to appear in the near-term are even more examples of market, vertical, or application-specific offerings that benefit in part from network slicing functionality, but which avoid the hype of telecom technology vendors in favor of communicating with business customers in their own language.

Slicing is moving beyond pilots, but its mainstream role remains in flux. Today, it is being marketed less as a programmable network tool and more (as seen in these recent launches) as a value-added connectivity layer, tailored either for specific industries (DT), standardized private network substitutes (Vodafone), or premium broad-market plans (T-Mobile). The key question for the next phase: Will operators empower customers with true programmability and SLAs—or keep slicing as a behind-the-scenes enhancement bundled into premium plans?