--- url: "https://xcademia.com/news/google-named-a-leader-in-the-forrester-wave-ai-platforms-q3-2026" title: "Google Named a Leader in The Forrester Wave: AI Platforms, Q3 2026" description: "Google is named a Leader in The Forrester Wave: AI Platforms, Q3 2026, highlighting Gemini Enterprise, AI agents, governance and enterprise AI." publishedAt: "2026-08-11T09:50:03.105+00:00" updatedAt: "2026-08-11T10:47:50.363833+00:00" type: news category: "ai-ml" source_name: Google Cloud Blog source_url: "https://cloud.google.com/blog/products/ai-machine-learning/google-named-a-leader-in-the-forrester-wave-ai-platforms" tags: - "#GoogleCloud" - "#GeminiEnterprise" - "#ArtificialIntelligence" - "#AIPlatforms" - "#EnterpriseAI" - "#AgenticAI" - "#MachineLearning" - "#Forrester" --- # Google Named a Leader in The Forrester Wave: AI Platforms, Q3 2026 > Google has been named a Leader in The Forrester Wave: AI Platforms, Q3 2026. Google Cloud highlights Gemini Enterprise, agent development, multimodal AI, governance and enterprise controls as key parts of its platform strategy. Source: **Google Cloud Blog** · 11 August 2026 ## Google’s Enterprise AI Strategy Gains Industry Recognition Google has been named a Leader in **The Forrester Wave: AI Platforms, Q3 2026**, according to a Google Cloud announcement published on August 11, 2026. Google Cloud also says it received the **highest score in the Strategy category** in the report. The recognition comes as enterprises increasingly explore AI agents that can support business processes, developer workflows and operational tasks. Google Cloud positions **Gemini Enterprise** as the central platform for this approach, bringing together enterprise data, AI models, development tools and IT operations. The announcement focuses on Google’s strategy for building and deploying enterprise AI rather than presenting a new standalone AI model or product release. ## Gemini Enterprise as the centre of Google’s enterprise AI strategy Google Cloud describes Gemini Enterprise as a unified platform intended to serve as the “front door” to AI across an organisation. The platform is positioned around different groups involved in enterprise AI adoption, including business users, developers and IT leaders. According to Google Cloud, the objective is to reduce separation between these groups by providing shared context and access to common AI capabilities. Google says Gemini Enterprise combines: - Enterprise data - AI model capabilities - Developer tooling - Agent development - IT operations - Governance and security controls The company says this integrated approach is designed to help organisations build and operationalise agentic workflows while maintaining centralised control. ![info-1](https://0a515t3ure77wbvx.public.blob.vercel-storage.com/articles/1786441367957-info-1--78-.webp) ## Building for the agentic enterprise A major theme in the announcement is the growing role of AI agents in enterprise environments. Google Cloud describes **Gemini Enterprise Agent Platform** as the foundation for technical teams and IT leaders to build and deploy production-grade agents. The company says agents developed through Agent Platform can be deployed across the workforce through the Gemini Enterprise application. This approach is intended to connect agent development with broader organisational access. Instead of limiting custom agents to technical teams, Google positions the platform as a way to make these capabilities available to employees through a common enterprise environment. The announcement also highlights the importance of centralised management as organisations experiment with multiple AI applications and agentic workflows. For enterprises, this could mean that AI adoption increasingly depends not only on model capabilities but also on how effectively organisations manage development, deployment, access and operational oversight. ## Supporting different development approaches Google Cloud acknowledges that development teams do not all build AI agents in the same way. Some developers prefer traditional coding environments, while others may use low-code or no-code tools. Organisations may also work with multiple AI models rather than relying on a single model. Gemini Enterprise is presented as supporting this range of development approaches. Google says the platform provides tools for: - High-code development - Low-code development - No-code workflows - Multiple AI models - Pre-built agent templates The announcement states that Gemini Enterprise provides access to **more than 200 native and third-party models**, alongside pre-built agent templates. This model flexibility is positioned as a way for organisations to accommodate different development requirements and existing technology strategies. The company does not provide further details in the announcement about the individual models, their performance characteristics or how organisations should select between them. ![info-2](https://0a515t3ure77wbvx.public.blob.vercel-storage.com/articles/1786441347577-info-2--58-.webp) ## Multimodal AI beyond text Another part of Google’s platform strategy is its emphasis on multimodal AI. Google Cloud says Gemini Enterprise is designed to work across **text, code, audio, image and video inputs** within a single workflow. The company presents multimodal capabilities as particularly relevant for enterprise environments where valuable information can exist across different formats. The announcement also says organisations can connect this multimodal intelligence with enterprise data wherever that information resides. The stated objective is to help engineering teams build more contextual business experiences from enterprise information. However, the announcement does not provide detailed technical specifications about the underlying multimodal architecture, model benchmarks or performance measurements. **Additional details were not disclosed in the announcement.** ## Enterprise data as shared context Google Cloud also highlights the importance of connecting AI systems to enterprise information. The announcement references **Knowledge Catalog**, which Google describes as a way to establish a universal context engine across an organisation. Google says the capability can aggregate metadata using zero-copy federation to help improve agent accuracy. This reflects a broader challenge in enterprise AI: organisations need AI systems to work with relevant business context while maintaining appropriate control over where data and metadata are accessed. The announcement positions data context as an important component of agent-based systems rather than treating AI models as isolated tools. ## Enterprise governance, security and observability Trust and operational control are another major theme in Google’s announcement. Google Cloud says Agent Platform includes enterprise governance, security and observability capabilities by default. The company highlights several areas: - Built-in guardrails - Continuous evaluation - Cost tracking - Latency tracking - Token usage tracking - Centralised control Google presents these capabilities as mechanisms that can help organisations monitor AI agents as they expand their use across the business. The emphasis on operational visibility is significant because enterprise AI systems can involve multiple agents, models, data sources and users. Monitoring costs, latency and token usage can provide organisations with operational information about how AI workloads are being used. At the same time, the announcement does not provide independent performance measurements or detailed security benchmarks for these capabilities. **The company did not provide specific information about this area.** ![info-3](https://0a515t3ure77wbvx.public.blob.vercel-storage.com/articles/1786441325546-info-3--52-.webp) ## What the Forrester recognition means for Google’s strategy The Forrester recognition gives Google Cloud an external industry reference point for its AI platform strategy. According to Google, the company was named a Leader in the **Forrester Wave: AI Platforms, Q3 2026** and achieved the highest score in the Strategy category. However, the announcement itself is written from Google’s perspective and focuses heavily on the company’s own platform capabilities. Forrester’s disclaimer included in the source states that Forrester does not endorse companies, products, brands or services included in its research publications and does not advise organisations to select products based solely on the ratings. Therefore, the recognition should be understood as part of an analyst evaluation rather than as a guarantee of product suitability for every organisation. For enterprises evaluating AI platforms, factors such as existing infrastructure, data architecture, governance requirements, development skills, model preferences and operational needs would still need to be assessed independently. ## Why the integrated platform approach matters Google Cloud’s announcement highlights a broader industry shift toward integrated enterprise AI platforms. Early enterprise AI adoption often involved individual experiments with chatbots, foundation models or isolated AI applications. As organisations move toward agentic workflows, the technical challenge becomes broader. Enterprises increasingly need to consider how AI systems are: - Developed - Connected to business data - Tested - Governed - Monitored - Deployed - Made available to employees Google’s strategy brings these areas together under Gemini Enterprise and Agent Platform. The company argues that this integrated model can help organisations move from individual AI experiments toward broader enterprise deployment. For enterprises, this could mean that the competitive value of an AI platform is increasingly determined not only by the quality of its models, but also by its development environment, data integration, governance and operational controls. ## Customer examples highlighted by Google Cloud The announcement references several organisations using Gemini Enterprise. Google says **Mars** uses Gemini Enterprise to accelerate marketing campaigns. It also highlights **Kohl’s**, describing its use of Gemini Enterprise for data analysis and customer experiences. Google additionally references **TELUS**, describing an approach that connects data silos into what it calls an Agentic Data Cloud. These examples are presented by Google Cloud as illustrations of how organisations are applying Gemini Enterprise. The announcement does not provide independent validation, detailed performance measurements or comprehensive implementation information for these examples. ## The broader enterprise AI direction The announcement reflects a broader industry shift toward AI platforms that combine models, data, agent development and operational governance. As businesses experiment with AI agents, managing individual models may become only one part of the overall challenge. Organisations also need mechanisms for developing agents, connecting them with relevant information, monitoring their behaviour and managing their use across the enterprise. Google Cloud is positioning Gemini Enterprise around this broader platform model. Its strategy is based on bringing together AI capabilities and enterprise controls while supporting different development approaches and model choices. Whether this approach is appropriate for a particular organisation will depend on its existing technology environment, governance requirements and AI use cases. ## Looking ahead Google Cloud says the Forrester recognition reflects its commitment to an open, scalable and powerful AI platform. The company also says Gemini Enterprise will continue to provide a foundation for developers as agentic systems become more prominent in enterprise software. The announcement does not provide specific future product timelines, performance targets or additional technical commitments. **Additional details were not disclosed in the announcement.** For now, the key message from Google Cloud is clear: the company is positioning Gemini Enterprise as a central enterprise AI platform that connects agent development, multimodal AI, enterprise data and governance. The Forrester Wave recognition adds external analyst context to that strategy, while organisations evaluating the platform will still need to assess its capabilities against their own technical, security, data and business requirements. ## Original source https://cloud.google.com/blog/products/ai-machine-learning/google-named-a-leader-in-the-forrester-wave-ai-platforms ## Tags `#GoogleCloud` · `#GeminiEnterprise` · `#ArtificialIntelligence` · `#AIPlatforms` · `#EnterpriseAI` · `#AgenticAI` · `#MachineLearning` · `#Forrester` --- ## About this content This Markdown news article is the citation-grade twin of [Google Named a Leader in The Forrester Wave: AI Platforms, Q3 2026](https://xcademia.com/news/google-named-a-leader-in-the-forrester-wave-ai-platforms-q3-2026). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite. 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