Google Workspace Developer Release Notes Q3 2026: The Shift Toward Agentic Collaboration

Looking across the Google Workspace developer release notes throughout 2026 it looks like there is a strategic evolution with moves to make Google Workspace an agent-ready, collaborative platform. Three areas stand out: standardising external agent integration via MCP, APIs to allow agents to comment and suggest, and expanding Google Chat into an interactive operational hub.

1. Standardising Agent Integration via MCP: But Is It the Right Model?

Following the initial rollout of Chat and Gmail Model Context Protocol (MCP) servers in Q2, Q3 expanded remote MCP servers to Google Docs, Sheets, and Slides (alongside new membership and read-state tools in Chat). The promise of MCP is attractive, providing a standardised connector that allows AI agents to query and manipulate Workspace resources as the user.

However, as the broader tech community is increasingly pointing out, MCP raises serious architectural and security questions in enterprise environments. I feel while MCP excels as a developer protocol for local sandboxes, questions remain whether this is the right abstraction for enterprise workspaces. Giving an external agent broad access across entire document trees for me creates an uncomfortable blast radius. 

2. Programmatic Collaboration: AI & Bots Entering the Review Loop

Historically, Workspace APIs focused on inserting and editing document content. Recently new APIs are unlocking the entire editorial feedback layer with programmatic comments and suggestions across the Docs, Sheets, and Slides APIs. This is a major enhancement for AI copy-editing solutions. Bots and agents no longer have to overwrite or add content, they can suggest changes and leave contextual comments for human approval.

3. Google Chat as the Universal Workflow Hub

Google Chat received major quality-of-life upgrades in Q3, including native Markdown formatting (MARKUP_SYNTAX_MARKDOWN), interactive citations and footer sources, context-menu message actions, and simplified read-only OAuth (GCP not required). This lowers integration friction for internal tools while elevating Chat bots into interactive enterprise assistants.

Gemini Side Panel in the Apps Script Editor (Beta)

Outside of these three areas Q3 also saw the release of the Gemini side panel in the Apps Script editor. This development is making it easier for users to collaborate with Gemini to author solutions with context or the document and other script files in your project. One area I see this already being beneficial is co-author Workspace and Editor Add-ons with Gemini. This allows businesses to deploy domain solutions in a managed way.

Summary

Q3 2026 for me marks a decisive shift from data manipulation to agent-ready co-authoring across Google Workspace. Between standardized MCP server endpoints, non-destructive suggestion APIs, and upgraded Chat interactivity, it looks like the platform is laying the groundwork for agentic collaboration.

Explore the full list of changes and migration guides on the official release notes page, or subscribe to the feed to stay informed on upcoming releases.

Source: Google Workspace developer release notes — Google Workspace Developers

Exploring Workspace Intelligence Skills using the Workspace CLI and Gemini CLIs with Apps Script API Executables

The most striking demonstration at Google Cloud Next ‘26 was the “Chat Command Centre”. Julie Quan Kim showed how a user might invoke a “regional campaign skill” to orchestrate complex tasks across Docs, Slides, and external CRM data. While we have yet to see how “skills” will be created and managed, developers can already prototype these agentic behaviours using the Workspace CLI (gws) and the Gemini CLI.

These tools are the perfect laboratories for developing “skills” that could eventually be used in your own Workspace Intelligence platform. To test the boundaries of this, I have been building a “recipe” that fetches a webpage, converts the content into a natively formatted Google Doc, and then executes additional logic via Google Apps Script.

The Practical Bridge: The Web-to-Doc Recipe

This recipe, published in this repo, hopefully demonstrates how we can orchestrate a multi-step workflow using different tools. It is worth noting that the development of this very skill was facilitated by a specialised skill-creator subagent included in the latest Gemini CLI. This dedicated assistant handles the initialisation and packaging of new capabilities, which in itself makes the transition from a manual workflow to a reusable agentic skill remarkably easy.

The core logic of the Web-to-Doc recipe is defined in a simple SKILL.md file, which instructs the local agent to follow a strict pipeline:

  1. Extraction via Node.js: The skill instructs the CLI to execute a local Node.js script (extract_article.js), which uses Mozilla’s readability and turndown libraries. This gives us a clean, “Reader View” markdown version of the article, stripping away the navigation menus and ads. With the announcement of the Gemini Enterprise Agent Platform I think it is very probable that Workspace customers would be able to use similar functions hosted in Cloud Run.
  2. Native Conversion via CLI: Next, the agent passes this markdown file directly to the Workspace CLI. By explicitly setting the MIME type to application/vnd.google-apps.document, the CLI takes advantage of the Drive API’s ability to convert markdown into formatted Google Docs. With the announcement Workspace MCPs, including Drive, a similar conversion should be possible without the Workspace CLI
  3. Refinement via Apps Script: This is my favourite part of the process. To ensure the embedded images do not overflow the document margins, the skill triggers a deployed Google Apps Script function (resizer.gs) using the gws script scripts run command. This is a level of precise document manipulation, specifically calculating page margins to ensure images do not overflow, is one that the high-level Drive API cannot perform during a simple import.

Governance and Security

Moving to this atomic, skill-based architecture is not without its friction. This homebrew approach of using different CLIs and managing authentication scopes across different local and cloud environments can be a severe headache and is very difficult to scale.

It also remains unclear if the Workspace Intelligence skills will include a script.run functionality found in the Workspace CLI. If it is included, it would be an exceptionally powerful way to extend skill capabilities. It would mean being able to build modular, portable sets of very repeatable code using the existing power, functionality and affordances of Apps Script.

This is where it will be very interesting to see how Workspace Intelligence manages this with the appropriate security and governance, especially considering the risks associated with Workspace MCP servers and the risk of indirect prompt injection attacks.

This is a rapidly evolving space. The ability to prototype these behaviours today using the Workspace CLI provides a direct path to understanding how the Agentic Enterprise could operate.

Atomic Skills and Role-Based Recipes: The Architecture of the Google Workspace CLI

I have spent a significant portion of my career working with various command-line interfaces for Google Workspace, from gcloud to gam. The recent release of the Google Workspace CLI (gws), which is not an officially supported Google product but has a number of Googler contributions, represents a fundamental shift in how we interact with the entire Google API surface.

In this post, I will provide an overview of the gws tool and explain how it differs from the Gemini CLI Workspace Extensions. More importantly, we will examine the modular architecture of “Skills”, “Recipes” and “Personas” that makes this tool in my view a masterpiece in modern, role-based automation.

GWS vs. GAM: Understanding the Overlap

Many Workspace Admins are already deeply familiar with gam (Google Apps Manager), the open-source command-line tool that has long served as a staple for domain administration. While gam is exceptional for bulk administrative tasks, such as creating users, modifying groups, or auditing devices, gws serves a different primary audience.

There is certainly a slight overlap between the two. You will notice administrative skills like gws-admin-reports present in the new repository. However, gws is fundamentally a developer and agentic tool focused on individual productivity applications like Drive, Gmail, and Docs, rather than domain-wide directory administration.

GWS vs. Gemini CLI Workspace Extensions

It is easy to confuse the Google Workspace CLI with the Workspace Extensions available for the Gemini CLI, but they serve entirely different purposes. The Gemini CLI Extension is a high-level interface designed for natural language tasks, such as “Summarize my unread emails” or “Find a file in Drive.” It is a convenient, user-centric tool for common, daily interactions.

In contrast, gws is a developer’s infrastructure tool. While the Gemini Extension is about “doing,” gws is about “orchestrating.” It provides the granular, programmatic control needed to build complex, multi-step workflows that can be automated, tested, and shared across a team.

The Modular Approach: Atomic Skills

The repository breaks down Google Workspace API interactions into highly modular components called “skills”. Instead of a monolithic script handling an entire workflow, the architecture relies on specific, atomic actions. Some examples from the repository include gws-gmail-send and gws-docs-write.

This atomic design means each skill has a single responsibility. For Apps Script developers, this is highly reminiscent of creating focused, single-purpose functions within an Apps Script library. By keeping these functions isolated, the code becomes easier to test, maintain, and reuse across different parts of an application.

Combining Forces: Recipes and Personas

Where this architecture becomes particularly interesting is how it combines these basic building blocks. The repository introduces the concept of “recipes,” such as recipe-backup-sheet-as-csv or recipe-create-expense-tracker. These recipes orchestrate multiple underlying skills to achieve a larger, practical goal.

In addition, these recipes are mapped to specific user “personas” like persona-content-creator or persona-it-admin. By grouping actions based on the user’s operational role, the tool becomes highly intuitive. When building Google Workspace automation tools, adopting a persona-driven approach can significantly improve the user experience: you are no longer just presenting a list of features, but offering targeted workflows tailored to what the user actually needs to accomplish their daily work.

Why this Approach Fits Workspace APIs Perfectly

This architectural choice is particularly effective given the versatile nature of Google Workspace APIs. Consider the Google Drive API, which features a generic endpoint for creating files. A basic integration might simply expose this raw “create file” capability. However, given the massive range of situations where you might want to create a file, a generic tool often leaves users or AI agents guessing how to properly format the request.

This is where skills, recipes, and personas show their true value. They allow us to dictate exactly how a file should be created for a specific context. For instance, if you want to turn a Markdown document into a natively formatted Google Doc, you could try to use the complex Google Docs API to insert text paragraph by paragraph. Instead, a targeted recipe can use the Drive API’s built-in file conversion features. By packaging instructions to upload the Markdown content and explicitly set the target MIME type to application/vnd.google-apps.document, the recipe abstracts away a complex API quirk into a reliable, repeatable action.

The Future of Agentic AI: The CLI + Skills Paradigm

As a recent VentureBeat article highlighted, the real breakthrough of gws is not that it makes Google Workspace programmable, given those APIs have existed for years. Instead, the innovation is the interface itself. By eliminating the need for custom wrappers and endless glue code, the CLI transforms Workspace from a collection of siloed applications into a single, unified programmable runtime.

This touches on a growing industry debate around the Model Context Protocol (MCP). Interestingly, while gws initially launched with a native MCP server mode, the maintainers explicitly removed it in version 0.8.0. Justin Poehnelt, one of the main contributors behind gws, refers to this overhead as the “MCP Abstraction Tax”. Exposing hundreds of granular API tools via a standard MCP server requires passing massive JSON schemas that bloat the AI’s context window and often lead to complex mapping bugs.

Instead, gws is now focused on a pure “CLI + Skills” architecture. In a related post on why developers should “Rewrite your CLI for AI Agents”, Justin outlines a shift from imperative to declarative design. An AI agent should “think” in terms of the high-level business goal (the “what”) rather than worrying about the underlying API steps to edit a document (the “how”). By moving the complex reconciliation logic directly into the CLI library, agents can simply use bash commands, a language they are already highly fluent in, to execute tasks efficiently.

Agent skills supply the “recipes” for these declarative commands, acting as modular, portable sets of instructions that teach an AI agent exactly which CLI tools to execute. The repository already incorporates over 100 of these Markdown skills, following a standardised structure. This framework is a powerful example of how we are moving away from simple prompt engineering toward “Skill Engineering”. By encoding our organisational knowledge and engineering judgement into these portable files, we ensure that AI agents follow token-efficient, reliable workflows every time they interact with our data.

Summary

The Google Workspace CLI is a glimpse into a future where our development environments are natively aware of our roles and workflows. For those interested in mastering this new framework, Anthropic recently shared a Complete Guide to Building Skills for Claude which explores the technical patterns and best practices for creating your own high-order skills.

Whether you are looking to automate your local development loop or build complex agentic workflows, the googleworkspace/cli provides the infrastructure you should keep an eye on!

Beyond the Sidebar: Re-imagining Google Workspace Add-ons as Gemini for Workspace Actions

The conversation around Enterprise AI is shifting. In 2024, we were preoccupied with the novelty of the prompt box, marveling at the ability of large language models to draft emails or summarise long documents. By 2026, that novelty has faded. The real frontier is no longer about asking an AI to “write this”; it is about instructing it to “execute this process.” We are witnessing the birth of a managed Action Layer within our digital environments.

The DORA Reality Check: AI as an Amplifier

To understand why this shift is happening, we must look at the data. The 2025 DORA Report provides a sobering reality check for technology leaders. Its central finding is that AI is an amplifier of your existing organisational systems. If your workflows are streamlined and your data is clean, AI accelerates excellence. However, if your processes are fragmented, AI simply creates “unproductive productivity,” generating isolated pockets of speed that eventually lead to downstream chaos.

DORA’s insights suggest that the bottleneck for AI value isn’t the model’s intelligence; it is the “Context Constraint.” AI cannot be effective if it is trapped in a silo, unable to see the broader business environment or act upon the tools that employees use every day. To move from hype to value, we must stop treating AI as a separate chatbot and start treating it as a governed co-worker.

Demystifying MCP

An emerging key component at the heart of this transition will be the Model Context Protocol (MCP). This protocol acts as a universal bridge that allows AI models to connect directly with data sources and local tools without the need for custom code for every new integration. It works by standardising how a model requests information from a database, a file system, or a specific software application. Because this protocol creates a common language between the AI and the external world, developers can swap different models or data sets in and out of their workflows with much less friction.

We are moving away from the era of closed silos. Instead, we see a future where an AI assistant can securely reach into your local environment to read a specific document or query a live database using a single, unified interface. It is a fundamental shift in how we think about connectivity.

Two Philosophies of Action: Managed Features vs. Configurable Platforms

Given the scale and pace of change, it is not surprising that Google’s AI efforts are not singular. The reality is that Gemini Enterprise (the platform formerly known as Agentspace) and Gemini for Workspace are two distinct products developed by separate teams with very different philosophies of control.

Gemini for Workspace primarily operates as a “managed service.” We see a steady expansion of features, but these remain “black boxes” entirely defined and controlled by Google. The user receives the feature, but the administrator has very little say in how that tool is connected, calibrated, or exposed to the model’s reasoning engine. Gemini Enterprise, by contrast, is a more configurable platform. It allows organisations to define their own data connectors, tailor system prompts and even deploy enterprise-owned agents.

From Black Boxes to Federated Actions

Whilst Google continue to expand the actions Gemini for Workspace can perform in products like Google Sheets, these tools remain non-configurable for the domain administrator. The January 23, 2026, Gemini Enterprise release reveals a more radical direction: the expansion of Configurable Actions.

Whilst the internal implementation details of every Gemini feature remain proprietary, Google’s recent announcement of official MCP support suggests that the protocol is becoming the standard connective tissue for their entire AI stack. The Enterprise team is simply the first to expose this plumbing for organisational governance and configuration. To understand the practical power of this protocol when it is stripped of its corporate packaging, we only need look at how MCP is being used in other generative AI tooling.

Lessons from the Gemini CLI

While most Workspace users interact with AI through polished side panels and the Gemini App, some power users are turning to the Gemini CLI. This command-line interface is the playground often being used to explore the automation of AI-driven workflows.

Unlike the “black box” nature of Gemini Enterprise and Gemini for Workspace, the Gemini CLI is easy to extend and customise. Users can easily install and create Extensions which can wrap commands, system prompts and MCP tools. The Google Workspace Extension is a wonderful demonstration of the power of MCP. By providing a few lines of configuration, a user grants the model permission to query and manage core productivity data: it can search files in Drive, draft emails in Gmail, dispatch Chat alerts, or organise Calendar appointments directly from the command line.

The value of the CLI lies in its demonstration that generative AI is capable of much more than just summarisation and analysis. By extending its capabilities through MCP, a user can hand-pick the actions they need based on their specific persona and the systems they inhabit. This shift toward action-oriented tools allows the model to move from passive reasoning to active execution without the user ever leaving their primary environment.

The Aspiration: A Managed Action Marketplace

The challenge now is to take this raw, unfiltered power seen in the CLI and translate it into a governed enterprise experience. Individual productivity is a start, but true organisational scale requires a bridge between these flexible protocols and the managed safety of the Workspace environment. I believe the defining moment for enterprise productivity will arrive when Google closes this gap by utilising its existing, battle-tested infrastructure.

For Gemini for Workspace to truly align with the DORA findings on systemic stability, it must move away from the current “one-size-fits-all” managed feature-set and toward a framework of granular administrative control. This is the aspiration for a truly professional Action Layer.

The Google Workspace Add-on Manifest is an existing way for enterprises to customise Google Workspace. Today, developers use a manifest to define which products an add-on supports, such as Gmail or Docs. To enable the next wave of productivity, Google could add the Gemini App (gemini.google.com) as a first-class, configurable product within this manifest. This would effectively turn the “Add-on” from a static sidebar tool into a dynamic Action that Gemini can call upon.

In this vision, a developer defines the location of their MCP server directly within the manifest. This creates a standardised way to deliver agentic tools through the existing Marketplace. This is not about building another static side panel. Rather, it is about injecting new capabilities directly into the core reasoning engine of the Gemini App and the integrated Gemini side panels across Workspace.

Imagine a “Tableau for Workspace” add-on. Instead of a user switching tabs to query a dashboard, the add-on uses the Tableau MCP toolset to allow Gemini to “see” live visualisations. The user can simply ask their Gemini side panel in Google Docs to “Insert a summary of our regional performance from Tableau,” and the agent executes the query, interprets the data, and drafts the text in situ.

This transformation would provide the necessary governance layer to ensure that AI-accelerated workflows remain under human control. By allowing admins to curate and configure these agentic tools, Google can turn Workspace into a high-quality internal platform. It would magnify the strengths of its users without introducing the chaos of ungoverned automation. The path to enterprise productivity is paved by protocol, not just better models. The winners of the next decade will not be the companies that buy the most AI licenses, but those that have the best platforms for those AI agents to inhabit.

Google Hints at AI Advanced Services in Apps Script: Validation for a Mystic Marty Prediction

At the Devoteam UK GDC event on the 16, October 2025 I debuted, ‘Mystic Marty’ and made three AppSheet and Apps Script predictions for H1 2026.

The Predictions

These predictions were:

  1. Deep Integration of AppSheet into Google Workspace Flows: By allowing AppSheet applications to act as both triggers and actions within Workspace Flows, AppSheet transitions from an isolated tool into a managed step within a larger, automated business value stream, preventing the creation of new data silos and process bottlenecks.
  2. AppSheet Actions within Gemini Enterprise (aka Agentspace): Integrating AppSheet into Gemini Enterprise positions it as a mechanism for structuring and exposing proprietary business data, enabling the creation of custom, data-aware agents at enterprise scale.
  3. Gemini App as a Google Apps Script Service: By making it simple for AppSheet automations to call Apps Script functions that interact directly with Vertex AI and Gemini models, this feature addresses complex, domain-specific enterprise needs that go beyond the scope of no-code solutions.

The Validation

Roll forward one week to the Google Workspace Developer Summit in Paris, and we had confirmation in a presentation by Luke Camery (Product Lead, Enterprise Collaboration at Google) that Google is in the early planning stages of an AI Advanced Services in Apps Script which would be launched in 2026.

Not only that, but a free quota was being considered, which would avoid the need for users to create an associated Google Cloud Project (GCP). The details are still being worked out and this offer is subject to change, but the fact it is being considered answers a lot of the questions I hear on this topic from the community.

The Method Behind the Mysticism

These predictions weren’t just guesswork. They stemmed from both historic product knowledge and more significantly from an analysis with Gemini of the ‘DORA: The 2025 State of AI-assisted Software Development’ report and in particular exploring how high-quality internal platforms can unlock and amplify the value of AI.

The report’s emphasis on internal platforms as a key enabler for AI was a clear indicator. It suggested that Google would inevitably need to provide more powerful, integrated tools—like AI services in Apps Script—to allow enterprise developers to build these exact kinds of high-value platforms.

For a deeper dive, you can read my full analysis here: DORA 2025 Insights: Is Gemini Enterprise the Answer to the AI Challenges?

What This Means for Apps Scripters

The hint of a free quota, is more than just interesting news—it signals a significant potential shift for the Apps Script community. The removal of the GCP barrier, in particular, would allow seamless access to powerful AI tools, allowing Google Workspace users an opportunity to experiment with generative AI in their automations.

While Google’s plans are “subject to change”, the direction seems clear. The integration of AI Advanced Services directly into Apps Script could represent the next major evolution of the platform, moving it from a tool for automation to an integrated high-value platform for intelligent application development. As always, we’ll be watching this space closely.

Google Workspace Flows: A Developer’s First Look After Cloud Next ’25

Image credit: Google

Last week at Google Cloud Next ’25 was packed with announcements, but one that particularly grabbed my attention was the unveiling of Google Workspace Flows. As Google Apps Script developers, many of us are familiar with automating simple tasks. However, the Flows demo hinted at a more accessible approach for tackling complex business processes.

Think about those workflows that go beyond basic if-this-then-that and into a world where you can easily configure Gemini to be your virtual assistant. Updating specific spreadsheet entries based on nuanced analysis, or finding and summarising information scattered across different files before replying to a customer. Traditional automation often hits a wall here because these tasks require context, reasoning, and sometimes even creative generation – capabilities standard automation tools lack.

Workspace Flows: AI Agents Joining the Workflow

What Google presented with Flows is a new solution for Google Workspace designed to automate these kinds of multi-step processes with AI providing assistance. Instead of just triggering actions, Flows uses AI models, including Gemini, as agents within the loop. This means the AI isn’t just kicking off a process; it’s actively participating – researching, analysing, creating, and reasoning to help get work done more efficiently and intelligently.

Having used tools like IFTTT it feels like a conceptual shift from simple automation to building intelligent, agentic workflows directly within the Workspace environment, without writing a single line of code.

Image credit: Google

If you would like to see Flows in action I highly recommend you check out the Google Cloud Next session ‘New ways to automate your work and integrate with Google Workspace’.

Why Flows Looks Interesting for Developers: Current Capabilities and Future Vision

Beyond the core AI capabilities shown in the demo, Google outlined a vision for Flows that is particularly relevant for developers, indicating where the platform is heading:

  • No-Code/Low-Code Interface (Current): The initial preview allows configuring triggers (like new emails, form responses, etc.) and actions across core Workspace apps and integrating Gemini and Gems for AI-driven steps, removing the need to find alternative solutions (code you write or third party solutions).
  • Apps Script Extensibility (Future Vision): Google announced plans to allow developers to build their own custom triggers and actions using Apps Script. This creates the opportunity to integrate your own systems or add specific logic to get more out of Flows. The presentation briefly showed an example appsscript.json manifest snippet for declaring these elements around 13:08).
  • Workspace Connectors Platform (Future Vision): A dedicated platform for third-party integrations was also announced in the presentation as part of the roadmap. The plan is to enable connections to tools like Jira, Asana, Salesforce, HubSpot, etc., allowing them to be used as triggers or actions. The stated goal is to include the ability to build end-to-end workflows spanning beyond Google Workspace, with connectors built once potentially working in both Flows and Gemini Extensions.
  • Bring Your Own Models via Vertex AI (Future Vision): For advanced AI needs, Google shared the vision for integrating your own custom or fine-tuned models hosted on Google Cloud’s Vertex AI. The concept shown involved an ‘Ask an LLM’ step where you could select a ‘Custom Model’ directly within the flow builder, pointing towards future capabilities for incorporating highly specialized AI into Workspace automations.

Looking Ahead

Google Workspace Flows is definitely a platform I’ll be watching closely. The initial preview, focusing on AI agents-in-the-loop and core Workspace automation, is already compelling. But the announced roadmap for developer extensibility – adding Apps Script support, a robust connector platform, and the ability to call custom Vertex AI models – is what makes Flows truly exciting from a development perspective.

Flows is currently in alpha with its initial feature set. If this vision sounds as interesting to you as it did to me, you can sign up for the early access waitlist.

Beyond Basic Q&A: What is the future of the Google Apps Script community and development?

The data reveals a notable decrease in Stack Overflow activity related to Google Apps Script from 2023 to 2024. There’s a reduction in total questions, questioners, answerers, and tags used. Interestingly, the ratios of answered and solved questions have increased. This suggests that while there’s less activity, a higher proportion of questions are getting resolved. A key factor influencing these trends is likely the rise of generative AI.

The latest report on the google-apps-script tag on Stack Overflow, published by Kanshi Tanaike, paints a fascinating picture: Google Apps Script is a maturing platform, but it’s also undeniably at a critical juncture. As Tanaike notes, “the rise of generative AI” is reshaping the developer landscape, and this is clearly reflected in the data.

The declining volume of basic Google Apps Script questions on Stack Overflow is arguably not a sign of the platform’s decline, but rather a signal of a significant evolution. This shift demands our attention and a willingness to explore the new opportunities and challenges it presents. This means actively seeking out new opportunities, adapting our skillsets, and rethinking traditional approaches to Google Apps Script development.

I believe the future of Apps Script rests on the skills of its developers and the strength of its community. Fostering a community capable of tackling complex integrations, building sophisticated automation within Google Workspace, and leveraging AI to enhance Apps Script’s capabilities is paramount. Supporting both current and new Apps Script developers, enabling them to unlock the platform’s full potential, is a key part of this future. The core value proposition, Apps Script’s unparalleled integration with the Google ecosystem, remains strong.

The real challenge, and the opportunity, lies in how the Google Apps Script community adapts, supporting knowledge sharing that goes beyond basic Q&A and cultivating a culture of deep understanding, critical thinking, and collaborative problem-solving. For us at AppsScriptPulse, this means continuing to share not just code snippets, but also design patterns, architectural best practices, and strategies for effectively integrating and evaluating AI-generated code.

Source: Trend of google-apps-script Tag on Stackoverflow 2025

The Transformative Power of Generative AI: Reimagining the Enterprise with Gemini for Google Workspace

This article was co-authored with Gemini for Google Workspace. You can read the full transcript of our conversation.

I appreciate this isn’t the typical Pulse post, but in my mind a lot of Google Workspace Developers who subscribe to this site have a wider interest in Google Workspace. Having worked with and supported a number of customers exploring Gemini for Google Workspace I thought it would be useful to sharing some of my thoughts and observations regarding the transformative power of Generative AI. As part of this I share insights around:

  • The importance of change management in adopting GenAI.
  • Mastering the art of prompting and interaction.
  • The need for continuous learning in a rapidly evolving landscape.
  • The future of human-AI collaboration.

Introduction

Imagine collaborating with an AI to draft a compelling marketing campaign, brainstorming ideas in a fluid conversation, and watching as it generates compelling copy and visuals in real-time. This is the power of generative AI tools like Gemini for Google Workspace, and it’s transforming how enterprises work.

My own journey with generative AI began in late 2023 when I had the opportunity to be part of Google Cloud’s trusted tester program. Working with early models in MakerSuite, Bard, and Duet AI gave me a glimpse of this transformative power. I was immediately struck by the shift to a conversational paradigm. We’re no longer just issuing commands to machines; we’re engaging in dynamic dialogues to create and innovate.

Having now supported several businesses with Gemini for Workspace ‘proof of value’ pilots, I’ve come to realize that simply introducing these powerful tools isn’t enough. To truly unlock the potential of generative AI like Gemini, organizations need a multifaceted approach. This includes investing in training, fostering internal “champions,” strategically identifying pain points for improvement, measuring success, and thoughtfully disseminating new ways of working. This is the key to successfully integrating this revolutionary technology into the enterprise.

The Promise of GenAI for Enterprise Productivity

McKinsey’s research paints a compelling picture of generative AI’s potential to revolutionize productivity. They estimate that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases they analysed. This staggering figure underscores the transformative impact this technology could have on businesses across various sectors, from automating mundane tasks to unlocking entirely new levels of creativity and efficiency.

But the true promise of GenAI extends far beyond simply saving time. Tools like Gemini empower employees to:

  • Improve quality: By providing intelligent suggestions, generating alternative approaches, and catching errors, GenAI helps elevate the overall quality of work.
  • Upskill and learn: Gemini not only provides answers but often explains the “why” and “how” behind them, offering valuable learning opportunities and fostering deeper understanding.
  • Conduct advanced analysis: With the ability to analyse vast amounts of data, GenAI unlocks new levels of insight and understanding that were previously impossible.

Just this week, I was working with a pilot participant from a company’s People team. They needed to analyse an employee satisfaction survey with hundreds of free-text responses. Imagine the task: categorizing those responses by theme, extracting key quotes, and generating a comprehensive report with recommendations. Gemini was able to analyse all the responses and create a draft report in minutes – a task that would normally take a full day. While the person still needed to check the data and refine the report, having this head start, complete with accurate thematic categorization and relevant quotes, significantly enhanced their efficiency and allowed them to focus on higher-level analysis and recommendations.

These benefits highlight the transformative potential of GenAI to not just speed up work, but to fundamentally enhance the way we work and the value we create.

The Importance of Change Management

Introducing a powerful tool like Gemini into an organization is more than just adding a new piece of software; it requires a fundamental shift in how people work and think about their roles. This is where change management becomes crucial. The ADKAR model, which focuses on Awareness, Desire, Knowledge, Ability, and Reinforcement, provides a useful framework for navigating this transition.

  • Awareness: First, employees need to be aware of what Gemini is, its capabilities, and how it can impact their work. This goes beyond simply showcasing features; it’s about communicating the ‘why’ behind the change and painting a clear picture of how Gemini can benefit them individually and the organization as a whole.
  • Desire: Next, we need to foster a desire to use Gemini. This involves addressing concerns, highlighting the advantages, and creating a sense of excitement about the possibilities. One of the biggest hurdles is overcoming the mindset of ‘I need to do this myself.’ Many employees have never had the experience of having an assistant, so it’s about encouraging them to embrace Gemini as a valuable partner that can augment their capabilities and free them to focus on higher-value tasks. In one of my pilot programs, an employee was hesitant to use Gemini in Gmail because it felt like ‘cheating’ to spend less time writing emails. To address this, I explained that Gemini wasn’t about cutting corners; it was about freeing up time for more valuable activities. By using Gemini to generate a first draft, the employee could then dedicate more time to refining the message, adding personal touches, and ensuring the communication was truly impactful. This reframing helped the employee see the value of GenAI not as a replacement for their skills, but as a tool to enhance them.
  • Knowledge: Once the desire is there, employees need the knowledge to use Gemini effectively. This involves providing comprehensive training on the tool’s functionalities, best practices for prompting, and strategies for integrating it into their workflows. It’s about equipping them with the skills and understanding to truly leverage Gemini’s potential.
  • Ability: Knowledge is not enough; employees need the ability to apply what they’ve learned. This requires hands-on practice, opportunities for experimentation, and ongoing support. It’s about creating a safe environment where people can explore, make mistakes, and learn by doing.
  • Reinforcement: Finally, to ensure lasting adoption, reinforcement is key. This involves recognizing and rewarding successful use of Gemini, providing ongoing support and resources, and continuously communicating the benefits and impact of the tool. It’s about creating a culture where GenAI is seamlessly integrated into the way work gets done.

Addressing the “Assistant Mindset”

A key element of this change management process is helping employees embrace the “assistant mindset.” This means moving away from the idea that they need to do everything themselves and towards a collaborative approach where they leverage Gemini as a powerful ally. It’s about reframing their roles, recognizing that delegating tasks to Gemini can free them to focus on more strategic, creative, and fulfilling aspects of their work.

Mastering the Art of Prompting and Interaction

While generative AI tools like Gemini are incredibly powerful, their effectiveness hinges on our ability to communicate our needs and intentions clearly. This goes beyond simply typing in a text prompt; it’s about mastering the art of interaction and understanding the full range of ways to make the most of Gemini’s capabilities within Google Workspace.

Prompt Engineering: The Basics

Effective prompting is essential for getting the most out of Gemini. Here are a few key principles to keep in mind:

  • Be clear and specific: The more specific your instructions, the better the results. Instead of asking “write a training plan,” try “create a training plan for a new sales onboarding program, including modules on product knowledge, sales techniques, and company values. The plan should be structured for a 3-day program with a mix of presentations, role-playing activities, and online assessments.”
  • Provide context: Give Gemini the information it needs to understand your request. If you want it to summarize a document, provide the document or a link to it.
  • Experiment and iterate: Don’t be afraid to try different prompts and refine your approach based on the results. Prompt engineering is an iterative process.

Beyond Text Prompts: Unlocking Advanced Capabilities

But mastering Gemini goes beyond just text prompts. It’s about understanding how to fully utilize its potential within the Workspace environment. Here’s where things get really interesting:

  • The Gemini Side Panel: This powerful tool allows you to interact with Gemini in more sophisticated ways. You can drag and drop files, incorporate information from multiple sources, and apply Gemini’s analytical capabilities to generate insights and create new content. For example, I recently worked with a client who was impressed with Google Meet’s “take notes for me” and transcript features but found themselves spending significant time reformatting these into structured meeting minutes. I showed them how to use the Gemini Side Panel to combine three files – the meeting agenda, the Google Meet transcript, and an example of minutes from a previous meeting – and then use a prompt to generate new minutes from the transcript, formatted in the same way as the previous minutes. This saved them a huge amount of time and effort.
  • Contextual Awareness: Gemini is designed to understand the context of your work within Workspace. This means it can access and process information from your emails, documents, and other files, allowing for more intelligent and relevant responses.
  • Multimodal Interaction: Gemini is not just about text. It can also process images and potentially other forms of media in the future, opening up even more possibilities for creative expression and problem-solving.

By mastering these advanced interaction techniques, you can unlock the true power of Gemini and transform the way you work.

Looking Ahead: The Future of Work with GenAI

As we’ve seen, generative AI tools like Gemini have the potential to revolutionize the way we work. But the journey has just begun. One of the defining characteristics of this technology is its rapid pace of evolution. New features, capabilities, and ways of interacting with Gemini are emerging constantly. This presents both exciting opportunities and unique challenges for enterprises.

The Need for Continuous Learning

In this dynamic environment, continuous learning is no longer a luxury; it’s a necessity. Employees need to be equipped with the skills and mindset to adapt to new features and functionalities as they become available. Organizations need to foster a culture of continuous learning and provide ongoing support and resources to help their teams stay ahead of the curve.

This rapid evolution was evident in one of our recent pilot programs. At the start of the pilot, Gemini’s ability to analyse quantitative data in the Side Panel within Google Sheets was limited. However, just a few weeks in, Google pushed an update that significantly enhanced these capabilities. This step change opened up a whole new range of possibilities for our pilot participants, but it also highlighted the need for ongoing awareness and education around new features and functionalities.

Human-AI Collaboration: A New Era of Work

The future of work with GenAI is not about humans being replaced by machines; it’s about humans and AI collaborating to achieve more than ever before. This requires a shift in mindset, where we view AI as a partner that can augment our capabilities and free us to focus on higher-value tasks.

Imagine a future where:

  • Marketing teams use GenAI to brainstorm creative campaigns and generate compelling content.
  • Sales teams use GenAI to analyse customer data and personalize their outreach.
  • HR teams use GenAI to streamline recruitment processes and enhance employee engagement.
  • Engineering teams use GenAI to write and debug code, accelerating development cycles.

This collaborative future requires us to develop new skills, embrace new ways of working, and cultivate a mindset of continuous learning.

Responsible AI: Ethical Considerations

As we integrate GenAI into our workflows, it’s essential to consider the ethical implications and ensure responsible use of this powerful technology. This includes:

  • Data privacy and security: Protecting sensitive information and ensuring compliance with data protection regulations.
  • Bias mitigation: Being aware of potential biases in AI models and taking steps to mitigate their impact.
  • Transparency and explainability: Understanding how AI models arrive at their conclusions and ensuring transparency in their use.

This also extends to establishing clear guidelines and policies around the use of GenAI within the organization. In our pilot programs, we’ve been actively helping businesses explore these questions. For instance, in one organization, we facilitated a discussion around the feasibility of having a policy where any content or analysis generated with the assistance of Gemini was clearly noted in the document, presentation, or other output. This type of proactive policy-making can foster transparency, build trust, and ensure that GenAI is used ethically and responsibly.

By embracing these principles, we can harness the transformative power of GenAI while building a future of work that is both innovative and ethical.

Conclusion

The era of generative AI has arrived, and tools like Gemini for Google Workspace are poised to revolutionize the enterprise. But technology alone is not enough. To truly unlock the transformative potential of GenAI, organizations need to invest in change management, foster a culture of continuous learning, and embrace a collaborative approach to human-AI interaction. By doing so, they can empower their employees, enhance productivity, and shape a future of work that is both innovative and ethical.

The journey may be challenging, but the rewards are immense. Let’s embrace this new era of work with open minds, a willingness to learn, and a commitment to responsible AI practices. The future is collaborative, and it’s powered by generative AI.

Source: The Transformative Power of Generative AI: Reimagining the Enterprise with Gemini for Google Workspace

Google Apps Script now includes Google Drive API v3 support in the Advanced Services

If you closely follow the Google Workspace Apps Script Samples repo on GitHub (who doesn’t :) you might have spotted a recent commit which updates the Advanced Drive Services to v3.

The v3 of the Google Drive API has actually been around for a number of years launched on December 14, 2015. One of the reasons Google introduced version 3 was it came with performance improvements.

My impression is that the gap between the v2 and v3 has narrowed over the years with minor release updates, but there are still some areas where you can get more from the v3 API. One example reported by Kanshi Tanaike is to query Google Drive files by createdTime.

Google provide a Drive API v2 & v3 comparison guide, which highlights the main differences between the two versions of the API. To use v3 in your Apps Script project when you enable the Advanced Service for Drive you can select the version number.

Source: Drive API v2 & v3 comparison guide  |  Google Drive  |  Google for Developers

In-person Google Cloud Next ’23 MUST see: Getting the most out of Google Workspace with Apps Script, tips and tricks

In this session learn about combining APIs, automation, configuration as code, and more to improve your productivity using Google Workspace.

If you, or someone you know, is lucky to be at Google Cloud Next ’23 on Day 1 and would like a turbocharge to your Apps Script journey head down to the Moscone South, Lower Level and towards the Innovators Hive Theater for 11am as you’ll be in for a treat as Kara Ireland and Charles Maxson will let you know “How to get the most out of Google Workspace with Apps Script”.

I was part of a similar session at Next ’18 and it was great to meet in person new and established Apps Script users, many of them I’ still in touch with today. If you end up going say ‘hi’ to Kara and Charles from me.

Source: Getting the most out of Google Workspace with Apps Script, tips and tricks