Google is Keeping Workspace on the Cutting Edge

A close-up portrait of a smiling man wearing glasses, with a neutral background.
G. Willsky

Summary Bullets:

• Three voice-activated features coming to Google Workspace reflect a broader trend of AI becoming incorporated into everyday workflows.

• The features also mirror how vendors are opening their team collaboration platforms to support the multi-platform environment in which their users operate.

Google recently announced three voice-activated AI features coming soon to Google Workspace. ‘Gmail Live’ lets users ask conversational questions to retrieve information from their Gmail inbox such as “What’s my flight’s gate number?”. Users can talk to ‘Keep Live’ to capture ideas that cross their mind and turn them into organized, actionable notes. With ‘Docs Live’ users can engage in real-time conversation to create documents on the fly. Although Workspace users cannot avail themselves of the features quite yet, ‘Gmail Live’ and ‘Keep Live’ are available to Google AI subscribers on the Plus, Pro, and Ultra plans while ‘Docs Live’ is reserved for Pro and Ultra subscribers.

Google, like all vendors in the team collaboration space, is integrating AI agents and assistants into everyday workflows. In theory this allows users to remain head-down on their work and avoid shifting gears to access AI in a separate space. The new features are intended to fulfil that vision, and they do to a degree but there is a cleaner approach.

Rather than users invoking a given feature, in the course of conducting their work users could simply describe what they would like to accomplish and AI, working in the background, would decide which feature should be leveraged to complete that goal. If the user is best served with information living in Gmail, then AI would tap Gmail Live. If a set of well-organized notes would do the trick, then AI would leverage Keep Live. If the users task could be best represented with a concisely written document, then AI would engage Docs Live. Regardless of the route AI chooses, everything would happen behind the scenes, transparent to the user.

In addition to reflecting how vendors are merging AI and the flow of work, the features mirror a second trend in the team collaboration space.

Rivals have acknowledged the need to open their platforms to support the multi-platform environment in which their users operate. Specifically, AI is moving from use in silos to being leveraged on a far grander scale – across vendor platforms, joining parts of organizations, and linking organizations with external partners, suppliers, and the like. AI is increasingly serving as connective tissue, threading sections of vendors’ platforms such as meetings, chat, and calling; establishing links between those platforms and third-party applications used in various parts of the business such as CRM; and integrating platforms from different vendors making things possible such as attending a meeting on one platform with an external partner that uses a competing platform.

The expanding reach of AI comes with a heavy price. Compliance, confidentiality, and security issues, among others, multiply when platforms are intra- and inter-connected with data flowing across boundaries and AI agents manipulating and disseminating that data. Vendors have made assurances regarding the integrity of their platforms, but there remains a sense that these issues have not been adequately addressed. Vendors need to take a much deeper look.

Without Saying So Directly, Nokia Asserts its Right to Play in Physical AI

Headshot of a man with short gray hair and a friendly smile, wearing a black jacket over a collared shirt, against a soft blue background.
John Marcus – Senior Principal Analyst, Enterprise Mobility and IoT Services.

Summary Bullets:

  • Nokia’s Cognitive Operations launch is less about another edge platform and more about staking a claim in physical AI, bringing intelligence and connectivity to real operational environments.
  • But “physical AI” is already getting crowded and cloud-led, with Nokia’s platform story at risk of being dismissed as repackaging connectivity plus analytics unless it leads to visible, scaled deployments.

Nokia didn’t use the term “Physical AI” in its announcement, but its new Cognitive Operations (CO) platform reads like a carefully constructed argument for why it belongs in that conversation. Physical AI is not just AI applied to industrial data; it is AI that perceives, decides, and supports action in the physical world, often in real time, often with incomplete connectivity, and often where failure carries operational, financial, or human consequences. This is the exact scope that Nokia is targeting with CO, initially for mining, construction, public safety, and defense. Specific examples of platform capabilities include vehicle-as-a-node, local inferencing (GPU/TPU), video/sensor analytics, safety interventions (driver fatigue/collision), and independent continuity (where operations continue despite disconnection).

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A New Breed of Conversational AI is Leading to Voice Resurgence in Customer Service

Oru Mohiuddin

Summary Bullets:

• Conversational AI is driving a major voice resurgence in customer service by improving the IP voice experience.

• Long call queues on account of human agents being thinly stretched across large call volumes have led to customer frustrations and poor CX, but modern versions of conversational AI are set to change that by taking away the frictions associated with voice.

It was believed that voice would diminish in value as digital channels started to emerge as an alternative to the voice channel. While messaging and chatbots offer cost and scale advantages, GlobalData’s research shows that voice continues to remain a vital channel for connecting with customers despite the frictions. Voice provides instant gratification and tends to be more straightforward to use as it does not involve navigating through messaging channels, websites and so and having to type messages. While the elderly demography tends to prefer voice due to greater familiarity, voice is seen to be popular even with the younger generation.

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Public Opposition to New Data Centers Disrupts but Doesn’t Derail US Facilities’ Expansion

Amy Larsen DeCarlo – Principal Analyst, Security and Data Center Services

Summary Bullets:

  • As rapid advances in AI application development drive demand for more processing capacity, US-based cloud providers are investing heavily in building out facilities to support these deployments.
  • But not everyone is on board with expansion plans, with public criticism stalling some development efforts, forcing hyperscalers to pivot to new locations, often in more remote areas.

AI is changing the cloud landscape, creating the near-term need for a vast increase in processing power and storage space. Hyperscalers are responding with substantial facility construction plans. Just this year alone, Amazon Web Services, Google, and Microsoft Azure are pouring a total of $500 to $700 billion into extensions of their data center footprints. Given AI’s dominance in enterprise technology investment plans, this is a logical track. However, not everyone is on board with these aggressive development plans -and AI plays a role in that resistance.

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Google’s New Tools Support ‘Value Maxxing’ to Address Organizations’ Growing Concern Over AI ROI and Tokenomics

R. Bhattacharyya

Summary Bullets:

• After a period of ‘token maxxing,’ organizations are looking to reign in and better control inference costs.

• Instead, enterprises are now embracing ‘value-maxxing,’ which focuses on outcomes.

Last week, Google announced several enhancements to Gemini Enterprise designed to help enterprises obtain greater and faster ROI on their AI projects. The improvements address one of the biggest frustrations expressed by organizations today, namely that the benefits promised by AI are taking too long to realize. Companies are clamoring for domain specific solutions in order to speed the deployment, reduce the integration complexity, and increase the value obtained from AI projects. Additionally, business leaders are eager for better tools to help them manage AI costs. They are looking for improved visibility on token use and costs, more proactive spending controls, and more flexible payment options.

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AI Changing the Commercial Model for Fiber Build

B. Swan

Summary Bullets:

• Zayo will build 8,000 miles of new long-haul fiber across key AI corridors, with Nvidia becoming its anchor customer.

• Nvidia’s extends beyond GPUs and compute, with partnerships spanning the optical and networking ecosystem underpinning AI infrastructure.

Until now, the AI Infrastructure race has predominately been focused on GPUs, data centers and access to reliable power, yet beneath all three sits a less visible, but increasingly critical, layer – connectivity. As AI workloads become larger, more distributed and dependent on moving large volumes of data between locations, fiber is emerging as a fundamental component of the AI Stack. Zayo’s recent announcement to build 8,000 miles of new long-haul fiber across key AI corridors, backed by Nvidia as its anchor customer, could mark the new beginning of a new investment cycle for terrestrial networks. The bigger question is whether AI-related companies could become the anchor customer needed for the next generation of fiber investment?

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AI Requires a Reinvention of the Modern Data Center

B. Valle

Summary Bullets:

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

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

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

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8×8 AI Routing Takes a Sad Song and Makes It Better

G. Willsky

Summary Bullets:

• 8×8 AI Routing identifies the right expert anywhere in an organization that can resolve a customer’s inquiry, not just the contact center.

• While 8×8 AI Routing is marginally better than legacy systems it still merits a try out.

We’ve probably all found ourselves reciting this famous opening line to a classic song when trying to connect with someone in customer support: “Help! I need somebody. Help! Not just anybody. Help! You know I need someone. Help!”

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Google Cloud Summit Sydney: Putting Agentic AI into Action

S. Soh

Summary Bullets:

  • Enterprises are deploying AI agents leveraging Google Cloud’s solutions and achieving positive business outcomes.
  • Google Cloud offers the full AI stack, and its sovereign cloud and cyber solutions are especially crucial for enterprise customers.

AI agents are no longer an idea. They are now being deployed by enterprises to improve internal workplace productivity and external customer experience. At Google Cloud Summit Sydney (held on June 25, 2026), more examples of agentic AI in operations were presented, moving from deterministic AI chatbots to more autonomous systems. Bunnings, a home improvement, gardening, and hardware products retailer in Australia, upgraded its Buddy AI chatbot that helped customers with product search to an AI agent that takes customers’ descriptions of their projects and fills the shopping carts with the products that they need. Bunnings indicated an uplift of conversion rates and basket sizes when customers engage with Buddy. Similarly, Woolworths supermarket has an agentic AI powered Olive assistant that is able to build shopping baskets from recipe photos and assist with proactive meal planning. These two examples demonstrate how AI agents trained with proprietary knowledge (e.g., Bunnings’s DIY catalog and Woolworths’ recipe catalog) can deliver greater customer outcomes.

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Boomi Targets Agentic AI Governance, but Orchestration Remains Its Raison d’Etre

B. Valle

Summary Bullets:

• Boomi is evolving from an iPaaS into an enterprise platform combining integration, automation, API management, data management, and AI agent governance.

• GlobalData recently attended Boomi’s World Tour London 2026, where agentic AI was discussed at length around announcements including Boomi Connect, Boomi Orchestrate, and Boomi Companion.

Although Boomi has historically been best known as an integration platform as a service, or iPaaS, the company is going to great lengths to emphasize that it has evolved into an enterprise platform which activates data and workflows for customers and combines integration, automation, API management, data management, and AI-agent governance. The Boomi platform acts as the connective and orchestration layer between an organization’s applications, data, and AI systems, but is increasingly moving towards management of AI agents to help data enhance business processes.

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