At Google Cloud Next 2025, held from April 09 to 11, 2025, in Las Vegas, top executives emphasized how AI  has moved beyond hype to become the foundation of modern enterprise operations. Google is positioning its tools and platforms to reflect this shift, designing solutions that don’t just support AI usage, but which also make AI central to how businesses function, strategize, and compete.

A new report by Bain and Company gives an overview of the key takeaways from the event, outlining how AI is reshaping every layer of enterprise operations.

AI now core to the business roadmap

AI should no longer be considered as a side project. Instead, AI should be embedded directly into the business roadmap across operations, product development, marketing, finance, and customer experience.

Google is applying this approach to Workspace, its collection of cloud-based productivity and collaboration tools. With tools like Workplace Flows, which allows for tasks and workflows automation, Google Vids, an AI-powered video creation app, as well as Gemini-powered writing tools, Workplace is evolving from a productivity suite into a true coordination layer for everyday work. The suite allows agents to generate, refine, and automate tasks across Google Workplace’s key applications.

A focus on business impact

AI success depends less on using the latest models and more on integrating them meaningfully into business processes. Models should be integrated into business processes, supported by adoption, and measures against real goals, such as improved speed, quality, efficiency, or customer experience.

Google’s platform allows organizations to use multiple models together in a single environment. For example, Gemini can be paired with third-party options such as Claude and AI21 or with open models like Llama. Different models serve different needs, and selecting the right model for each workflow is becoming a key element of AI strategy.

Clean, AI-ready data

Modern AI models depend on data that is structured and contextual, not just available. Tools exist to clean, label, and align data, but they must be used deliberately and early in the development process.

To get strong results, business and technical teams must work together to design data pipelines that use clear schemas, metadata tagging, embedding strategies, and retrieval logic matched to task complexity and model context.

A complete AI stack

Google has developed a complete AI stack across chips, infrastructure, and orchestration. At the software level, Google offers Gemini, its family of advanced AI models, along with developer tools to build, fine-tune, or integrate AI into applications. Organizations can use these pre-trained models, bring their own, or customize them with proprietary data. Deployment options are flexible, including APIs, containerized services, or direct integration into Google Workspace tools.

Security is integrated directly into the platform through Google Unified Security, which centralizes threat detection, policy enforcement, and incident response, all powered by AI. This system enables intelligent agents to triage alerts, analyze malware, and trigger automated workflows. Meanwhile, real-time monitoring and integrations with tools like Mandiant and VirusTotal provide broader visibility and faster threat response.

Built-in governance

Organizations are now building AI agents with governance and safety in mind from the start. Google offers tools to create these agents, alongside reusable templates and prebuilt agents for ideation and research. However, these require strong governance to ensure responsible use.

Forward-thinking organizations are adopting agentic structures that define roles, permissions, and oversight, along with layered architectures featuring validation agents and supervisory logic for accuracy and compliance. Google is applying the same principles to its own model portfolio.

Trust and customization

Enterprise AI is moving from pilot to production in areas that impact revenue and customer experience. As AI agents take on customer-facing roles, such as in call centers, organizations must carefully control their behavior, especially in sensitive industries.

Google’s Vertex AI helps organizations in this regard by offering tools to shape agent behavior, monitor outcomes, and ensure systems operate within business and compliance constraints. These capabilities are especially important in regulated industries such as healthcare and financial services, where auditability, explainability, and human oversight are required.

AI becomes part of the creative workflow

Creative teams are now using generative tools as part of daily production. To address this growing demand, Google continues to expand the multimodal capabilities of Gemini across text, image, code, and structured outputs. Meanwhile, Google’s new Veo 2 model brings high-quality, prompt-based video generation into reach for brand and campaign teams.

These tools are already being adopted across industry to accelerate campaigns, reduce production costs, and localize assets at scale.

Use of AI continued to increase in 2024. In the latest McKinsey Global Survey on AI, 78% of respondents said their organizations used AI in at least one business function, up from 72% in early 2024 and 55% a year earlier.

Organizations are also using AI in more business functions. For the first time, most survey respondents reported the use of AI in more than one business function, with an average of three business functions.

Featured image credit: edited from freepik