From Tools to Orchestration: Rethinking How Work Gets Done

The Workplace Has a Tool Problem… But Not the One You Think

Workplace technology strategies have long focused on providing access to tools, from cloud and collaboration to AI. Organizations invested heavily in digital platforms and assistants to make work faster, smarter and more connected. Today’s workplace is richer in tools than ever before.

Yet work still feels fragmented. Employees must move between multiple applications to complete even simple tasks, switching contexts as they change systems. FDM CCS Insight’s Survey: Employee Workplace Technology, 2025 shows that employees routinely juggle multiple tools, with fragmentation a persistent source of friction. This lack of integration and uncertainty continues to frustrate employees as AI becomes more embedded, as we discussed in our recent report, The Hidden Organizational Costs of Uneven Workforce AI Maturity.

The problem is no longer just access to tools, but how work flows between them. The challenge for organizations is how well tools work together.

The Reality: Work Is Fragmented by Design

This fragmentation reflects how the modern workplace has evolved. Organizations have adopted many specialized tools across functions and teams, creating environments where work rarely happens on a single platform but over multiple systems with different interfaces, logic and data.

In practice, this means employees build their own workflows to complete tasks, acting as manual integrators between systems that weren’t designed to work seamlessly together. FDM CCS Insight’s research suggests that this dynamic persists, revealing a gap between how work is designed and how it actually happens. This may be effective in the short term, but it introduces duplication, inconsistency and additional cognitive load.

This has direct implications for AI. Where workflows are fragmented, AI is confined to individual tools, limiting its ability to operate across tasks. Fragmentation creates friction for employees and can limit how effectively AI can be applied. The challenge is enabling AI to operate in many workflows, rather than in individual tools.

The State of Play: AI Is Embedded, But Is Still Task-Level

Generative AI has moved quickly from experimentation to everyday use, becoming embedded in common tasks like drafting content, summarizing information and supporting analysis. Our research shows widespread adoption of generative AI, with 87% of employees using it in some capacity at work, reflecting how naturally AI fits into existing ways of working (see Survey: Employee Workplace Technology, 2025).

However, its impact remains largely at the task level. Most scenarios focus on accelerating individual actions in specific tools to deliver meaningful gains in areas like communication and content creation. However, these are typically confined to discrete steps rather than spanning workflows.

As a result, outcomes remain uneven, with higher productivity gains concentrated among more-advanced users, showing that access alone doesn’t guarantee consistent impact. This creates a gap between what AI can do and what organizations need it to deliver: connected, end-to-end support across workflows.

The Shift: From Tools to Orchestration

The first phase of enterprise AI focused on access and acceleration, embedding AI into existing tools to improve task efficiency. As adoption expanded, the limitations of this approach are more visible, prompting a focus how AI can be coordinated throughout workflows.

Rather than adding more intelligence into individual tools, attention now turns to how that intelligence can operate across work. This marks a transition from isolated interactions to more orchestrated models, where AI contributes throughout tasks and systems. In this new phase, AI no longer treats work as a series of disconnected steps, but as a continuous process where context is maintained and reused. It moves from being a reactive assistant to an active participant, supporting not just individual actions, but the connections between them.

As AI becomes more embedded, its integration with workflows becomes more important than just giving employees access. Organizations that align AI with how work happens are better positioned to realize consistent value. Fragmented approaches limit impact.

Orchestration is critical. The challenge is no longer deploying AI but ensuring it can operate across the structures that define everyday work.

The Approach: Connecting Intelligence to the Flow of Work

One of the central challenges for enterprise AI isn’t generating intelligence but applying it where work happens. Google’s approach is to bring these layers closer together, linking AI orchestration with the places where work is carried out. This reflects a broader shift toward more agent-driven models of work, where AI generates outputs but also coordinates actions across workflows.

At the core of this is Gemini Enterprise. Positioned as the central orchestration layer for an organization’s AI agents, it coordinates how intelligence is applied to data, systems and workflows. For AI to deliver meaningful impact, orchestration must be embedded into everyday work. This is where Google Workspace becomes critical.

Workspace provides the primary environment where employees work, spanning tools such as Docs, Sheets, Meet and Gmail. By integrating Gemini directly into these surfaces, Google positions AI as part of existing workflows rather than a separate destination, reducing context switching and enabling more continuous contribution.

In practice, this is reflected in how AI is applied throughout Workspace, from generating content in Docs and Gmail to supporting collaboration in Meet and coordinating tasks between applications. Rather than introducing separate tools, these capabilities operate in existing workflows, reducing the need to switch contexts.

It’s also reflected in capabilities like Canvas Mode, which brings an interactive workspace to Gemini Enterprise for creating and refining content. Support for external tools, including Microsoft 365 applications, shows an emphasis on interoperability, and Agent Designer enables employees to create workflow-driven agents without specialist development skills. Capabilities like Drive Projects extend this by helping to organize work spanning tasks and resources, supporting more structured, continuous workflows.

A key enabler of this approach is shared context. Instead of isolated interactions, AI carries context between documents, conversations and activities, enabling a more connected experience where work moves more seamlessly between tasks and tools. The value lies in how these layers work together, with Workspace being the place where this intelligence is applied in day-to-day work.

Workspace Intelligence introduces a semantic layer to Google Workspace, improving its understanding of relationships between content and activity. By building a more persistent understanding of context, it enables AI to span tasks without requiring users to repeatedly re-establish information.

This approach addresses key challenges of fragmentation and coordination, but its impact depends on how effectively AI extends beyond individual tools and aligns with existing organizational systems. In many environments, challenges like out-dated infrastructure, governance requirements and uneven workforce maturity can limit how consistently these capabilities are applied.

What This Means for Work

The shift toward orchestration signals a broader change in how organizations need to think about AI in the workplace. This places less emphasis on individual tools and more on the systems, workflows and behaviours toward them. As AI becomes more integrated, differences in organizational readiness, from workforce capability to governance and infrastructure, play an increasingly important role in determining outcomes.

This also reshapes how enterprise technology is evaluated. Rather than focusing on features or model performance, organizations must assess how well platforms support coordination throughout work, not just in specific tasks.

The direction of travel is clear, but execution will vary. The next phase of AI won’t be defined by the number of tools employees use, but by how effectively those tools support more-connected, consistent ways of working.