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:

• 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.

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:

• 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:

• 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?

Summary Bullets:

• RingCentral continues to aggressively grow its portfolio of AI-infused features both for team collaboration and communication as well as for contact center.
• The most significant enhancement to RingCentral AI Receptionist (AIR) is the introduction of a standalone version called AI Receptionist Everywhere (AIR Everywhere).

RingCentral has maintained a very aggressive cadence of compiling AI-infused features in its portfolio. That is true with respect to both its team collaboration and communication platform called RingCentral RingEX (RingEX) as well as its contact center platform known as RingCentral RingCX (RingCX). Mirroring a growing trend, it has also implemented features that link team collaboration/communication and contact center, allowing for employees throughout an organization to influence the customer experience. RingCentral’s latest portfolio fortification has arrived in the form of new features for RingCentral AIR.

RingCentral AIR is an AI phone agent that can answer or route customer calls using natural language capability. RingCentral AIR now includes appointment booking with Google Calendar and Microsoft Outlook, and it now supports British and Australian English, Spanish, and French, including Canadian French. In addition, RingCentral AIR will be available in the UK and Australia by the end of September 2025.

The most significant enhancement to RingCentral AIR is the introduction of a standalone version called RingCentral AIR Everywhere. RingCentral AIR Everywhere is especially intriguing because it brings the capabilities found in RingCentral AIR to third-party telephony systems – and thus to users who are not RingCentral customers. RingCentral AIR Everywhere is currently in controlled availability.

The addition of new languages to RingCentral AIR coupled with the compatibility of RingCentral AIR Everywhere with third-party telephony systems will allow RingCentral to expand the RingCentral AIR customer base. That is saying a lot because RingCentral AIR has already seen rapid adoption and growth since its release only six months ago. RingCentral AIR boasts more than 3,000 customers at the end of Q2 2025, triple the number compared to Q1 2025.

RingCentral AIR is also noteworthy because it marks RingCentral’s entry into the latest phase of AI, agentic AI. Agentic AI is an advanced form of the technology that stretches beyond merely generating content, featuring agents that perform tasks independently on behalf of users ranging from the mundane to the complex. Agentic AI can act autonomously, make decisions, and take actions without human intervention. It can adjust its approach based upon new information or changing circumstances.

RingCentral is tipping its toe into the agentic AI waters on the early portion of the adoption curve, reversing a tendency to play the role of follower and unspooling a long lag time versus competitors. RingCentral AIR joins the ranks of rival offers such as Agentforce from Salesforce, Copilot Agents from Microsoft, and Webex AI Agent from Cisco.

Although positive on balance, the launch of RingCentral AIR enhancements suffers a blemish, with no expiration date indicated for the controlled availability period for RingCentral AIR Everywhere. RingCentral is losing an opportunity to generate a dash of market momentum. Even a vague launch date such as ‘Q4 2025’ would be preferable over staying mute. Still, once RingCentral AIR Everywhere does become generally available, RingCentral will officially have yet another impactful offer in its portfolio.

Summary Bullets:

• Google Cloud has developed a comprehensive tech stack to help businesses embrace GenAI and agentic AI.

• There are now many examples of AI delivering commercial benefits including AI-enabled software and automation of workflow for traditional enterprises.

Google Cloud held its annual event in Sydney (Australia) recently which had a keen focus on AI and more specifically agentic AI. The event highlighted the possibilities and benefits of AI through Australian businesses that have deployed the technology commercially to improve customer experience and develop new business opportunities. While the AI models such as Google Gemini have been capturing market attention, there are many other initiatives within Google Cloud to develop capabilities to simplify the adoption of AI. The company now offers an AI-optimized technology stack, which includes the AI infrastructure, data platform, models, platforms, and AI agents and applications. This does not mean that customers are confined to Google Cloud’s tech stack. One of the key value propositions of the company has been its willingness to support hybrid and multi-cloud environments. Another is a view to work with partners on open source and decoupling architectures to avoid lock-in and pivot with new requirements in 12 to 18 months. In the case of AI, it allows businesses to connect their tools developed on Google Cloud to data and applications in other cloud environments.

In deploying AI agents, Google Cloud offers customers three options. Firstly, businesses can build their own proprietary agents through Google Vertex AI Agent Builder, which has various models (including third-party models within Google’s Model Garden), tools, and development kit. Secondly, Google Cloud offers a range of pre-built agents that can be quickly deployed (e.g., agents for customer engagement, coding, data engineering, security, content creation, etc.). Finally, the company is also giving customers access to partner agents developed by companies such as Salesforce, ServiceNow, Workday, and more.

While there are many AI projects that failed to move into production, the event showcased some examples of AI being deployed commercially and yielding business value. Global software firms are leading the pack in adoption AI as a matter of survival. As with any platform change, they stand to be disrupted the most by emerging firms who are able to harness the power of AI. Google Cloud is co-innovating with independent software vendors of different sizes to embed AI capabilities into their products. For example, graphic design software firm Canva demonstrated the adoption of Google’s video generation model Veo 3 to add video creation capability to its platform. This move has created a new feature for Canva customers, allowing them to create video content by entering a single text prompt. This can deliver dramatic productivity gains for creative agencies, marketing teams, and educators.

Heidi Health, a software company addressing the challenges of clinicians has been using AI to simplify administrative tasks and giving clinicians more time to engage patients. Heidi Health is an AI-powered medical scribe that frees up clinicians from time-consuming transcription and documentation tasks. The company has leveraged Google Cloud’s AI platform for speech-to-text capabilities, and it is looking to use other tools such as its ‘Agent Builder’ to advance its solution to become a digital health concierge that can, for example, use an AI agent to call patients for follow-up calls to check on symptoms. As a start-up without extensive resources, Heidi Health has leveraged Google Cloud’s global scale to extend its solutions to clinicians in more than 50 countries.

Traditional businesses are also pursuing AI and prioritizing use cases. Orica, a mining and infrastructure solutions provider, applies Google Cloud’s AI platform to its SAP data across its supply chain to forecast customer demand and improve demand planning. This is made possible as the company has consolidated its data to a single source and by re-platforming the SAP environment onto Google Cloud. Optus, the local telco has also launched its ‘Expert AI,’ an agentic AI solution that supports sales and service interactions. It analyzes live customer conversations across channels in real time, provides contextual guidance, summarizes insights, suggests next actions, and executes tasks across multiple backend systems to resolve customer needs. In this market segment, while Google Cloud has the tools and technology, it is working closely with partners to help enterprises achieve their transformation objectives. In the case of Orica, it has highlighted the importance of the partner ecosystem and the migration of SAP workloads from Microsoft Azure to Google Cloud was supported by partners including Accenture, Cognizant, and LTIMindtree.

The event highlights the many possibilities of agentic AI but the reality is that not every business is AI-ready. For AI agents to be capable, a business needs to have a data strategy and the ability to execute it. “Born in the cloud” companies are also more ready as well as companies that have strong digital expertise and have been modernizing their IT. Google Cloud is demonstrating the possibilities with AI but it needs partners to be ready to work with clients to overcome challenges, often due to legacy systems and mindset. Likewise, enterprise will need to also move beyond point solutions, some shared on the day, toward a mindset shift of scaling rollouts by embedding AI across multiple departments, systems, workflows, and buying journeys. While the approach takes on more risks, they provide the highest chances of high impact outcomes.

Summary Bullets:

• OpenAI released GPT-5 on August 7, 2025, a multimodal large language model (LLM) with agentic capabilities.
• This is the latest iteration of the famous chatbot, and the most important upgrade since the release of the previous generation, GPT-4, in 2023.

As it happens sometimes when a product is thrust with such force into the realm of popular culture, the release of GPT-5 sparked a veritable PR crisis, leading CEO Sam Altman to make a public apology and backtrack on the decision to remove access to all previous AI models in ChatGPT. Unlike enterprise customers, which received advanced warnings of such movements, consumer ChatGPT users did not know their preferred models would disappear so suddenly. The ensuing kerfuffle highlighted the strange co-dependency relationship that some people have developed with the technology, creating no end of background noise surrounding this momentous release.

In truth, OpenAI handled this launch rather clumsily. But GPT-5 remains an extraordinary achievement, in terms of writing, research, analysis, coding, and problem-solving capabilities. The bête noire of generative AI (GenAI), hallucination, has been addressed (to a limited degree, of course), and GPT-5 is significantly less likely to hallucinate than previous generations, according to OpenAI. With web search enabled on anonymized prompts representative of ChatGPT production traffic, GPT-5’s responses are around 45% less likely to contain a factual error than GPT-4o. The startup claims that across several benchmarks, GPT-5 shows a sharp drop in hallucinations, about six times fewer than o3.

However, safety remains a concern. OpenAI has a patchy record in this area: Altman famously lobbied against the US California Senate Bill SB 1047 (SB 1047), which aimed to hold AI developers liable for catastrophic harm caused by their models if appropriate safety measures weren’t taken. In 2024, members of OpenAI’s safety team quit after voicing concerns about the company’s record in this area.

Meanwhile, there has been talk in industry circles and trade media outlets of artificial general intelligence (AGI) and GPT-5’s position in this regard. However, the AI landscape remains so dynamic that this is missing the point. Google’s announcement on August 5, 2025 (in limited research preview) of Google DeepMind’s Genie 3 frontier world models, which help users train AI agents in simulation environments, positions the company against AI behemoth Nvidia in the realm of world AI. World AI in this context means technologies that integrate so-called “world models,” i.e., simulations of how the world works from a physics, causality, or behavior perspective. It could be argued that this is where true AGI resides: in real-world representations and in the trenches of the simulation realm.

On the other hand, Google’s latest salvo in the enterprise space has involved a fierce onslaught of partnerships, with several deals announced in the last 48 hours. Oracle will sell Google Gemini models via Oracle’s cloud computing services and business applications through Google’s developer platform Vertex AI, an important step to boost its capillarity in corporate accounts. With Wipro, Google Cloud is going to launch 200 agentic AI solutions in different verticals that are production-ready and accessible via Google Cloud Marketplace. And with NTT Data, Google is launching industry-specific cloud and AI solutions, with joint go-to-market investments to support this important launch.

The AI market is advancing at rapid speed, including applications of agentic AI in enterprise environments. This includes a variety of AI-driven applications and platforms that are transforming business processes and interactions. The release of GPT-5 is simply another tool in this direction.

Summary Bullets:

• The Google Cloud summit was held in London (England) on July 9-10, 2025. The company said there are now over seven million developers operating within Google Vertex AI Studio.

• The company has expanded into 42 cloud regions, adding that 90% of AI unicorns are running on Google Cloud.

Google Cloud highlighted a wealth of customer cases during the Google Cloud summit held in London on July 9-10, 2025. Google Cloud has a data center in Waltham Cross (England), which will be fully operational by end-2025 to provide British businesses with high-performance computing (HPC) services. Various new capabilities for agentic AI that are aligned with business needs were highlighted. This shows strong momentum, but there are a few considerations for Google Cloud to strengthen its position in the enterprise, such as the need for stronger skills in the area of consulting, for example.

In line with the market direction, most new services and features are around agentic AI. Google Cloud unveiled plans to introduce a portfolio of purpose-built agents created through its experience in working across different industries, following patterns that are consistent across different sectors. There are other capabilities in the platform that mean users can create their own agents with no-code or low-code just by clicking a button or having a voice conversation. For an in-depth analysis of the event, please see GlobalData’s report, Generative AI Watch: Google Cloud Announces Customer Cases Leveraging Multi Agent Tools, July 11, 2025.

Google Cloud emphasized that is not necessary to build the AI agents within the Google environment. Customers can bring their own models, for example, if the company’s developers have created agents on external platforms such as Salesforce Agentforce and/or Microsoft Copilot, they can still deploy them on the Google Agentspace platform. The company is committed to building an ecosystem that gives users space and has collaborated with professional partners such as Capgemini, Accenture, and Deloitte as well as large software providers such as ServiceNow and Salesforce to ensure interoperability.

As part of the drive for interoperability, Google Cloud has launched the agent-to-agent (A2A) protocol within Google Vertex AI, its ML platform. While the model context protocol (MCP) is an open framework created by Anthropic that standardizes how large language models (LLMs) integrate data with external tools, and has enjoyed widespread adoption in the industry, Google wanted to take this a step further. To help firms integrate its prebuilt AI agents into their workflows, the A2A protocol aims to help AI agents be able to collaborate in a multi-agent ecosystem across siloed data systems and applications. A2A has had contributions from more than 50 technology partners like Langchain, MongoDB, Salesforce, SAP, ServiceNow, Accenture, BCG, Capgemini, Cognizant, Deloitte, KPMG, McKinsey, and more. The A2A protocol helps AI agents communicate with each other securely.

Fittingly for a UK summit, one of the presentations by customers included the UK government. Google Cloud EMEA has become a strategic partner of the UK Government to drive an ambitious digital transformation project to modernize the British government’s AI strategy and assets. Peter Kyle, Secretary of State for Science, Innovation and Technology (DSIT), took to the stage during the keynote to explain how his department secured almost GBP2 billion from the UK Spending Review 2025 (SR25) to implement the plan, moving straight to a hands-on product with the new trial version of the gov.uk app. Google Cloud will help it deal with a huge technical debt in the form of antiquated legacy systems that will be gradually replaced with a cloud-based service. The current contracts are very expensive, and the project is supposed to save the British taxpayer millions.

To sum up, some of the capabilities demonstrated during the event show Google Cloud’s strong momentum in the rapidly evolving cloud and AI markets. The company also showcased many customer references across multiple industries. However, competitors are also moving at a similar pace. For example, Amazon Web Services (AWS) recently added multi-agent collaboration to its Amazon Bedrock platform and is increasingly promoting open-source by adding support for Strands Agent (i.e., an open-source agent software development kit), and Model Context Protocol.

Summary Bullets:

• Comcast has nearly doubled the energy efficiency of its network ahead of its 2030 target while also carrying 76% more data.
• Other examples of greater energy efficiency through new technology include BT Global Fabric, where the replacement of legacy platforms will see a 79% energy consumption reduction.

Comcast announced that it is near to reaching its goal of doubling its network energy efficiency ahead of its 2030 target, stating that it is “delivering dramatically more data at faster speeds and greater reliability at the highest quality for our customers, all while conserving the amount of energy needed to power our network.”

Comcast reported that it has achieved an 11% reduction in energy usage between 2019 and 2024, while at the same time carrying 76% more traffic over the same period as all customer segments use their connections for applications and services needing higher bandwidths – ranging from streaming videos to unified communications. As a result, the energy savings combined with network growth have delivered a 49% reduction in electricity per consumer byte since 2019 (from 18.4 kWh [kilowatt hour] per Terabyte to 9.3 kWh in 2024). Like many others, Comcast has noted both the increase in data as a result of the artificial intelligence (AI) revolution as well as its potential to optimize network performance, including enhanced monitoring/network diagnostics, and optimization.

The other trend driving improved sustainability and efficiency in networks is the latest generation of equipment, with decommissioned legacy technology having been far less efficient. GlobalData analysis has found that replacing copper lines with fiber can be up to 85% more efficient, and power-saving measures using AI can lead to energy savings of up to 40%.

Another notable example is BT’s move to the BT Global Fabric Network-as-a-Service (NaaS) platform, which replaces multiple previous technology platforms and will result in a 79% energy consumption reduction. These technology developments and evolutions are all helping to keep telecoms service providers – national and international – in the vanguard of reducing greenhouse gas (GHG) emissions. Given recent flash floods in Texas (US) and wildfires across Europe and Canada, alongside further destructive climate change impacts on society and nature, these examples of progress should be celebrated and encouraged.

Summary Bullets:

• Password managers do tend to make logging in easier – but it’s a change that people must get used to…

• To really embrace cybersecurity, there needs to be a reckoning to correct old thinking and ideas.

Sixteen (16) billion. That’s a number that isn’t comprehendible. It’s a number you hear on the news, usually in a science segment or in a finance segment talking about the ultra-wealthy. But this time, 16 billion is the number of exposed login credentials researchers from Cybernews found in an exposed dataset. This dataset contains stolen login credentials, mostly gained via malware. The credentials come from everywhere – from websites around the world, including popular websites and cloud services.

What is known is that the dataset was visible for a short time before being taken down. We know that some or all of the data in the dataset is not new but comes from earlier breaches and infostealers. We do not know where the data was being held/exposed from. The data wasn’t stolen from any one site breach, but likely a compilation of earlier stolen credentials. Initial reports seem to indicate that much of the discovery is net-new, but that has since been disputed. Still, that many credentials in one spot is a worry.

What was interesting about this information was essentially the lack of reaction from the public. Sure, skepticism of the discovery happened quickly – many security experts feel that this was a bit of a case of crying wolf. But the initial reaction by the public was more of a shrug. After all, how many times can a person’s login credentials get stolen? How many times should an individual go through the cumbersome process of updating passwords? Especially when it seems like there are more breaches every day. Keeping one’s credentials up to date after breaches begins to look like a Sisyphean task.

Cybersecurity fatigue is real, and the public is becoming increasingly numb about cybersecurity incidents. Reminders to update passkeys, use password managers, don’t reuse passwords, and enable multi-factor authentication are a constant drumbeat. With every hysteria-filled announcement of another breach that spills user data and login credentials, more people tune it out entirely – after all, *they* have never been hit.

The ugly truth: Good cybersecurity is difficult, even when just talking about login and passwords. Passwords should be long, 20-30 characters, randomly generated, and contain upper- and lower-case letters, numbers, and symbols. Each site should have its own password. People resist that – extremely difficult to remember a password like that, and it’s much easier to simply have a single password to use everywhere. A password manager is required to generate and store these passwords, as well as enter them when it comes time to log in. That password manager needs to work across platforms – e.g., Apple (e.g., phones, tablets, macs), PC, Android, and Linux.

But a password manager is yet another thing – one that requires its own password. To make it worse, the very public breach of LastPass, a popular password manager, makes people distrust password managers, especially those with a cloud component. There is also the learning barrier – using a password manager requires effort and changes how you log in. Password managers do tend to make logging in easier – but it’s a change that people must get used to, and people hate change to daily routines like logging in. Changing habits is hard, and not being able to just instantly enter a memorized password feels frustrating at first.

To really embrace cybersecurity, there needs to be a reckoning to correct old thinking and ideas. Let’s take a look:

• Password managers are not hard or scary – they are designed for ease of use, and there are tons of tutorials.

• Your personal password generation is vulnerable, no matter how clever the scheme you created is. Brute force techniques are far better than you imagine. And no, the word ‘password’ backwards isn’t clever.

• Password re-use is a vulnerability, no matter how easy it makes things.

• The fact that a person has never been hacked or doesn’t know anyone who has been isn’t a reason to keep old practices.

• This isn’t about having perfect security. It’s about protecting yourself and limiting damage if a breach occurs. Just like locking your doors and putting your blinds down at night.

Take the plunge yourself, get a password manager, then show a friend that it isn’t that hard and, in the end, never forgetting a password is a time-saver too! Proactive action with a password manager and password hygiene is important, and we cannot let the slew of high-profile breaches numb us from upping the quality of our own cybersecurity regimen.

Summary Bullets:

• New Zoom CX features are noteworthy for their value and symbolize the rise of Zoom.
• The features connect two key trends – the arrival of agentic AI and the transformation of contact centers.

Earlier this month, Zoom announced new features that are noteworthy for the value they add to its customer experience (CX) platform, Zoom CX. However, the real headline is that the features sit at the intersection of two key trends – the advent of agentic AI and the transformation of contact centers – and are emblematic of the rise of Zoom as a competitor.

A quick examination of the features is in order to better understand their weight. Zoom Virtual Agent 2.0, a self-service customer support agent, can complete complex tasks without the need for human intervention such as processing returns, updating account details, or booking appointments. It also demonstrates reasoning capability, understanding context across interactions, and recalling recent conversations to provide personalized support.

In addition to Zoom Virtual Agent, capabilities that enhance contact center operations were unveiled. Zoom CX Analytics helps assess operational efficiency and service quality; Zoom CX Insights, planned for availability later in 2025, provides recommendations for improving agent performance; Zoom AI Scheduling forecasts demand for agents and generates scheduling accordingly; Zoom AI Topic Detection identifies trending themes in customer interactions so that issues can be isolated and analyzed in real time; new features for its advanced quality management score customer interactions and enable supervisors to surface insights into agent interactions through natural language.

The common thread running through the announced features is agentic AI. Agentic AI debuted in H2 2024 and is already considered to be the next big phase of AI. Agentic AI is an advanced form of AI that stretches beyond merely generating content, featuring agents that perform tasks independently on behalf of users ranging from the mundane to the complex. Agentic AI can act autonomously, make decisions, and take actions without human intervention. It can adjust its approach based upon new information or changing circumstances. Zoom and every competitor, it seems, is leveraging agentic AI in some shape or form.

Agentic AI is not the only link connecting the features. They also reflect the profound transformation contact centers have been undergoing with the concept of a ‘contact center’ yielding to the broader concept of ‘customer experience’. Contact centers are converting from having human agents to including AI agents, from reactive to proactive, from transaction-oriented to relationship-oriented, and from generic to deeply personalized. Zoom and rivals such as Cisco, Microsoft, and 8×8 have been rolling out a steady stream of capabilities to help organizations make the transition.

Most significantly, the new features are symbolic of the profound metamorphosis taking place at Zoom over the past 20 months. After its video meetings capability became renowned virtually overnight in the dark, nascent days of the pandemic, Zoom ignited a steady evolution of its platform. With the October 2023 introduction of Zoom AI Companion, that evolution took a sharp trajectory upward and morphed into a full-blown renaissance marked by the introduction of GenAI features. With the implementation of agentic AI capabilities, Zoom has now entered yet another a new chapter. It is safe to say Zoom is poised to continue its ascent.

Summary Bullets:

• BT, Orange, and Vodafone have launched significant multi-channel advertising campaigns targeted at the small and medium-sized business (SMB) market, underlining its importance to future growth.

• They all emphasize the evolving role of the service provider beyond connectivity, with a focus on security and digital business-enabling solutions that characterize the future of enterprise.

Traditionally, telecom companies have focused mass media advertising campaigns on the consumer market. But now major European service providers are advertising to target the enterprise market, focusing on smaller businesses (e.g., SMBs). This reflects both their strategic shift toward SMBs as offering the best potential for revenue growth, and that their portfolios of technology solutions have become far more relevant in running businesses of all sizes – the market has moved beyond connectivity.

BT Business, Orange Business, and Vodafone Business (there is a trend here in the naming conventions) have all been using adverts across TV, online, and other digital media to promote their business solutions. All their adverts cover similar messages but are also distinctive and memorable.

The BT Business campaign is based on the line ‘We’ve Got Your Back.’ It aims to “showcase BT’s support for every type of business, from the person just starting out at their kitchen table, to major multinationals and critical public services.” The focus of the campaign is to recognize that every business today is a digital business. The goal for BT is to position itself not merely as a supplier, but as a partner offering digital solutions and security alongside reliable connectivity:

BT Business campaign – screengrab

Orange Business aims to underline “the importance of network and digital integrators in the digital ecosystem.” The campaign illustrates the challenges of interconnected components in a complex digital landscape as well as underlines Orange Business’ ability to help across technology areas such as AI, IoT, connectivity, cloud, data, and cybersecurity, promoting the concept that ‘it works better when we work together.’ The campaign avoids the dullness often associated with technology by taking a comedic angle across its adverts, with the goal of making businesses think ‘maybe they should have asked Orange Business?’

Orange Business campaign – screengrab

Vodafone Business’ campaign is based on the strapline ‘Your Business Can’ (echoing Vodafone Group’s ‘Together We Can’ strapline) and is aimed at SMBs that can benefit from digital tools to help boost productivity and security. It also looks to help move the perception of Vodafone as ‘just’ a mobile company to support its strategic push into the broader business market with solutions such as cybersecurity, collaboration tools, and connectivity products.

Vodafone Business campaign – screengrab

Although adverts are often seen as ‘fluffy,’ these three campaigns absolutely underline the seriousness with which these major service providers are focused on the SMB market. Telecom companies globally have realized that the SMB market provides the best opportunity for selling additional services beyond connectivity, with the goal of adding revenues from value-added solutions, leveraging their resources to differentiate against price-focused competitors, and cementing longer-term, stronger relationships with customers. Adverts might just be seen as ‘a bit of fun,’ but this is serious stuff.

Summary Bullets:

• Eight prominent LLM/GenAI competitors are featured in GlobalData’s new LLM Competitive Landscape Assessment. Google Gemini has been named Leader.

• In addition to core model capabilities, other factors are considered, including enterprise tools and partner ecosystems built around the GenAI model.

GlobalData has just released its first LLM Competitive Landscape Assessment, highlighting the strengths and challenges of eight heavy-hitters in this space.

The new report evaluates the competitors’ differentiators of core model technology including context windows, multimodal and multilingual capabilities, vertical and horizontal use cases, AI guardrails, ecosystem, professional services, and go-to-market strategies.

Within this report, Google has been named ‘Leader’ by GlobalData due to a combination of highly developed model capabilities in the Google Gemini family and sophisticated enterprise tooling to build and scale generative AI (GenAI) applications. OpenAI is ‘Very Strong’ thanks to its core model technology with solid code generation and multilingual capabilities, multimodality, and context window size. Microsoft is also rated ‘Very Strong’ for its high degree of penetration in the enterprise and tooling anchored in the powerful capabilities of its exclusive GenAI partner, OpenAI, and for Microsoft’s proprietary model, Phi. IBM is also rated in the ‘Very Strong’ category for its strengths in generating computer code, along with a broad range of native language support and third-party model support. Amazon, Anthropic, and Meta have been ranked ‘Strong,’ and Cohere is ranked ‘Competitive.’

GenAI platforms are largely based on multimodal foundation models and large language models (LLMs); these are borne out of growing interest in accessing natural language processing (NLP) to query computers, following the significant advancements in AI seen in recent years. These include machine learning and deep learning via neural networks, also called generative adversarial networks (GANs), and finally the emergence of the ‘transformer’ architecture in 2017, representing breakthrough efficiencies in training models.

The phenomenon of GenAI builds on the precursor of new software architectures, hybrid cloud, automation, and advancements in AI, resulting in the emergence of LLMs. LLMs are deep learning models trained using vast amounts of text. They are designed to produce new levels of content creation, automation of repetitive tasks, and deliver personalization for improving the customer experience, and their performance depends on the quality and size of the pretraining dataset.

The term LLM was largely unheard of before the end of 2022, which makes its explosive growth and mega investments both startling and somewhat overwhelming. Its immaturity also makes evaluating competitors’ closely guarded training methodologies challenging. The new comprehensive competitive landscape assessment also includes market drivers, buying criteria, and vendor and buyer recommendations, in addition to the eight product evaluations (please see Large Language Models (LLM): Competitive Landscape Assessment.

Following OpenAI’s release of ChatGPT, major cloud and platform providers, hardware/chip manufacturers, and startups moved quickly, recognizing the potential of a revolutionary technology unparalleled since the birth of the internet. Over the past 18 months, these vendors have leveraged their previous AI development efforts and set out building and training models as part of a GenAI portfolio to help address customers’ business transformations.