Siloed Automation to Ambient AI: Next Phase of AI Transformation

Most organizations today are stuck in a frustrating pattern. They’ve adopted AI tools. They’ve built automations. They’ve launched impressive proofs of concept inside individual teams. Yet somehow, the transformative productivity gains they expected remain elusive.

The problem isn’t the technology.

It’s that most companies are still operating in the first phase of AI maturity: siloed automation.

And the gap between siloed automation and the next phase, where AI becomes truly ambient, proactive, and contextually aware, represents one of the most significant competitive inflection points in business today.

From Siloed Automation to Ambient AI: A Solutions Engineering Perspective on the Next Phase of AI Transformation

The Signal You’re Ready to Evolve

How do you know when your organization is ready to move beyond siloed automation? There’s a specific pattern I’ve observed that indicates readiness.`

End-to-end workflow: Designed for specific use cases, such as support ticket management or code generation and review, utilizing agentic AI
Need for functional connectivity: The drive to connect workflows with upstream and downstream functions, such as release management, product roadmaps, training, and implementation.
Tool consolidation: Reducing tool sprawl and overlapping AI licenses

When workflow maturity and consolidation occur together, your organization is ready to evolve beyond siloed automation toward unified, ambient AI.

This convergence moment matters.
It’s when teams stop asking “What can this tool do?” and start asking, “How do we create an environment where AI understands our entire operation?”

The Barriers That Hold Teams Back

Even with clear readiness, most organizations hit the same bottlenecks:

Technology consolidation: Without a shared environment for AI to observe, learn, and act, teams cannot move forward
Cultural barriers: The absence of cross-functional knowledge sharing, intentional enablement, and a safe space to practice utilizing AI hinders progress
No center of gravity: The lack of a leadership-sponsored AI strategy keeps organizations confined to silos

This is where the AI Transformation Matrix becomes essential.

Organizations need high maturity on both axes: AI maturity and context maturity. You can have the most sophisticated AI capabilities in the world, but if your context is fragmented across dozens of disconnected tools, your AI will remain blind to the patterns that matter most.

Clearing Up Common Misconceptions

Before teams can advance, they need to rethink what AI actually is.

Generative AI vs. Agentic AI:

Generative AI handles one-off tasks.
Agentic AI requires structure, constraints, and clear job definitions for repeatable outcomes

Ambient AI doesn’t “just happen.”
It requires intentional design for context sharing, orchestration, and clear rules of engagement.

Think about it this way: having a ChatGPT-like interface doesn’t make something an agent any more than having a steering wheel makes something a self-driving car.

True agentic AI operates within defined parameters, executes multi-step workflows, and makes decisions based on accumulated context. Ambient AI goes even further, operating invisibly in the background across your entire operation.

When Connected Workflows Unlock Real Value

Let me give you a concrete example of what changes when you move from siloed automation to connected, agentic workflows.

Automatic context capture: Agents gather and centralize crucial details from sales cycles
Seamless handoffs: Context is retrievable for delivery and customer success teams, closing the communication gap
Knowledge compounding: Information is preserved and enhanced, not lost across tools

This isn’t just about efficiency.

It’s about creating institutional memory that actually persists and becomes more valuable over time. In the old model, the sales context gets trapped in email threads, chat messages, and meeting notes scattered across different systems. In the ambient AI model, that context flows automatically to where it’s needed, when it’s needed.

Context is the real accelerator

Once AI can access your organizational context, your tasks, timelines, conversations, and decisions, it stops behaving like a writing tool and starts acting like an analyst. ClickUp BrainGPT leverages this by drawing from your entire Workspace, surfacing patterns that people often miss, and making connections that you didn’t manually provide.

You think out loud, it listens with Talk-to-Text, it correlates, and the insights reflect how work actually moves across your company.

The Evolution of Leadership’s Role

As organizations progress from basic automation to truly ambient AI, the role of leadership undergoes a fundamental change.

Leadership Sponsorship: CEOs and CTOs must elevate AI from a technical experiment to a non-negotiable business mandate
Strategic Alignment: Establishing a unified roadmap that drives technology convergence and standardizes the tool stack across the enterprise
Cultural Evolution: Investing in the workforce through communities of practice, active enablement programs, and continuous learning

This is not about technical expertise. It’s about creating the organizational conditions that enable ambient AI to thrive. That means committing to convergence even when individual teams resist giving up their preferred tools. It means investing in the infrastructure and governance that enable safe, cross-functional AI operations. Most importantly, it means treating AI transformation as a strategic priority, not a series of tactical experiments.

Two Approaches to Building Ambient AI

Devin Stoker, Director of our AI Center of Excellence at ClickUp, has worked extensively with organizations navigating this transition. He sees two distinct approaches that can lead to company-wide ambient AI.

1. Aggregation of marginal gains

Essentially, it’s similar to the approach of the British Cycling team, led by Sir Dave Brailsford, which focused on the aggregation of marginal gains,” Devin explains. “I view each new high-quality agent or AI workflow as contributing a 1% marginal gain for your company. As you continue investing in these enhancements, it culminates in the significant outcome of having Ambient AI seamlessly integrated across all your processes.

Devin Stoker

In this model:

Each well-designed agent or workflow adds incremental value
Actual change happens as improvements stack across teams and functions

2. Ambient AI operating in the background

The second approach Devin describes focuses on AI automatically working in the background to perform tasks on your behalf. These ambient agents don’t require direct commands to provide support.

ClickUp includes multiple types of these ambient agents that can answer questions in chat, take actions as a part of your workflows, adapt to user feedback over time, and even update your company knowledge in the background.

Context-aware: It operates based on the user’s environment and ongoing activities
Background operation: It functions discreetly in the background, avoiding constant user interaction
Personalized: It can learn from user behavior and adapt to individual needs
Knowledge maintenance: Ambient AI can automatically update and enrich organizational knowledge

Both approaches share a critical requirement: they need a converged environment where AI can access complete context across all work, communication, and collaboration.

The quiet power of ambient agents

The most underrated advantage of ambient agents is that they operate autonomously, eliminating the need for manual instructions. They collect context in the background, route information where it belongs, capture knowledge before it’s lost, and maintain the connective tissue teams never have time to document.

ClickUp Agent delivers answers from information across the Workspace — ClickUp Agent delivers Workspace from information across the Workspace

When these agents operate inside a converged environment, they become the backbone of a system that learns continuously and improves without prompting.

The Path Forward

The journey from siloed automation to ambient AI isn’t just about adopting better technology. It’s about creating conditions where AI can operate with clarity, context, and continuity.

Here’s what the most successful organizations commit to:

Commit to Convergence: Unify work, knowledge, and collaboration in a single environment.
Share AI learnings: Build a culture of cross-functional knowledge sharing
Make AI strategic: Treat AI transformation as a top priority, not just another IT project

Organizations that make this transition don’t just get better productivity. They unlock a compounding effect where each improvement makes the next one easier and more valuable.

Their AI gets smarter because it has more context. Their teams get faster because they spend less time searching and more time creating. Their competitive advantage grows because they can execute at a pace their competitors can’t match.

The question isn’t whether to make this transition; it’s how to do it effectively. It’s a matter of whether you’ll lead or watch your competitors pull ahead.

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