London-based Trace has raised $3 million in seed funding to address a critical gap in enterprise AI: not the technology itself, but the challenge of getting employees to adopt and trust AI agents Trace raises $3M to solve the AI agent adoption problem in enterprise. This isn’t just another AI funding announcement—it’s a strategic bet on solving the operational and human barriers that prevent AI agents from delivering real business value.
For CTOs and product leaders, Trace’s approach offers a practical framework for moving beyond pilot projects. The platform maps complex corporate environments, assigns tasks between humans and AI, and ensures scalability and security—all while addressing the trust and workflow integration issues that derail most AI deployments.
Here’s what Trace’s solution means for your AI strategy, and why its focus on adoption, not just algorithms, should reshape how you think about scaling AI in your organization.
The Core Problem: AI Agents Fail When Humans Don’t Use Them
Trace’s CEO, Tim Cherkasov, frames the challenge clearly: “OpenAI and Anthropic are building these brilliant interns that can be leveraged within the company. We’re building the manager that knows where to put them.” Trace raises $3M to solve the AI agent adoption problem in enterprise. This distinction is critical. While most enterprises focus on technical integration, Trace’s research highlights that the real barriers are operational and human:
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Lack of Contextual Awareness AI agents are often deployed without clear process maps or access controls, leaving them unable to navigate real-world workflows. Trace solves this by dynamically mapping how work actually happens—including unofficial steps and exceptions—so agents can operate effectively within existing systems.
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Trust Gaps Employees resist AI when they don’t understand how it makes decisions, where it fits into their roles, or how to handle its mistakes. Trace argues that the biggest barrier to adoption isn’t the technology—it’s the people Trace raises $3M to solve the AI agent adoption problem in enterprise.
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No Clear Delegation System Without a way to assign tasks dynamically between humans and AI, agents either overstep (creating compliance risks) or underperform (wasting resources). Trace’s platform allows users to prompt the system with high-level tasks, and it automatically breaks them into steps, delegating to AI or humans as needed.
For European enterprises, this is particularly relevant. The EU AI Act requires transparency, human oversight, and audit trails—standards that most pilot projects aren’t designed to meet. Trace’s emphasis on security, scalability, and compliance aligns with these regulatory demands, making it a critical enabler for production-ready AI Trace raises $3M to solve the AI agent adoption problem in enterprise.
How Trace’s Platform Works: A Manager for AI Agents
Trace’s solution stands out by addressing three key gaps that traditional workflow tools ignore:
1. Dynamic Process Mapping
Most enterprises rely on static process documentation, which quickly becomes outdated. Trace continuously maps how work actually flows, including unofficial steps and exceptions, to give AI agents the real-world context they need to function effectively.
Example: In a global manufacturing environment, an AI agent might fail to process supplier disputes if the official workflow doesn’t account for informal approvals via email or chat. Trace’s system surfaces and incorporates these hidden steps, ensuring the agent can handle real-world complexity.
2. Human-AI Task Delegation
Trace’s interface allows users to input high-level tasks (e.g., “Resolve this customer complaint”), and the system automatically routes subtasks to the right mix of AI and human workers. This reduces manual handoffs and decision fatigue by clarifying who (or what) should act next.
Data Point: In early pilots, this approach reduced manual handoffs by 40% by eliminating ambiguity in task ownership Trace raises $3M to solve the AI agent adoption problem in enterprise.
3. Built-in Compliance and Security
Trace’s architecture ensures:
- Role-based access controls (e.g., AI agents can draft but not approve contracts).
- Full audit logs for all AI decisions (critical for EU AI Act compliance).
- Human-in-the-loop escalations for edge cases.
Why This Matters: In highly regulated industries (e.g., banking, healthcare), these features aren’t optional—they’re essential for avoiding fines and operational risks.
Why This Matters for European Enterprises
Trace’s $3M seed round—backed by prominent VC firms specializing in AI and enterprise software—signals a shift in market priorities Trace raises $3M to solve the AI agent adoption problem in enterprise. Here’s what it means for your business:
1. From AI Projects to AI Operations
European CTOs are under pressure to transition from experimental AI to operational AI. Trace’s funding reflects a broader trend: Investors are prioritizing tools that make AI agents production-ready, not just technically advanced.
Action Item: If your AI agents are stuck in pilot mode, ask:
- Do employees know when to trust the AI’s output?
- Is there a system to escalate tasks the AI can’t handle?
- Can you audit the AI’s decisions for compliance?
2. EU AI Act Compliance Is Non-Negotiable
By 2026, the EU AI Act will require:
- Transparency in AI decision-making.
- Human oversight for high-risk systems.
- Documentation of workflows and data provenance.
Trace’s focus on simplification, security, and scalability aligns directly with these requirements. Enterprises that ignore this risk fines and operational disruptions.
3. Solving the Productivity Paradox
We’ve seen this repeatedly: A Fortune 500 client deployed AI agents to automate IT support, but engineers spent more time reviewing AI suggestions than resolving issues manually. The problem? No system to triage, explain, or escalate the AI’s work.
Trace’s workflow delegation solves this by:
- Pre-filtering AI outputs for confidence levels.
- Routing low-confidence tasks to humans automatically.
- Providing clear ownership for each step.
The Trace Playbook: 3 Lessons for Your AI Strategy
Trace’s approach offers a practical framework for enterprises struggling with AI agent adoption. Here’s how to apply it:
1. Map Workflows Before Deploying Agents
- Tool: Use process mining (e.g., Celonis) or Trace-like systems to document how work actually happens, not how it’s supposed to happen.
- Goal: Identify where AI can augment (not replace) human judgment.
2. Design for Hybrid Teams
- Rule of Thumb: For every AI agent, define:
- Autonomous decisions (e.g., “Approve standard expense reports”).
- Human-review tasks (e.g., “Flag anomalies in financial data”).
- Escalation paths (e.g., “Route unclear cases to a manager”).
- Example: A Nordic financial services firm reduced AI-related errors by 60% by implementing this tiered oversight model.
3. Measure Adoption, Not Just Accuracy
- KPIs to Track:
- % of employees using the AI agent weekly (Trace targets >80% in pilots).
- Time saved per task (not just AI response time).
- Escalation rate (how often humans override the AI).
- Why: An AI agent with 99% accuracy but 5% adoption is a failure.
The Bottom Line: Adoption Is the Next AI Frontier
Trace’s $3M seed round is a clear signal that the next phase of enterprise AI isn’t about building smarter agents—it’s about deploying them in a way that humans will actually use. For European enterprises, this means:
- Mapping AI to real workflows (not just APIs).
- Designing for trust and transparency (especially under the EU AI Act).
- Treating AI agents as part of a hybrid team (not standalone tools).
At Hyperion Consulting, we’ve helped enterprises like Cisco, Renault-Nissan, and ABB navigate this exact transition—from AI experiments to AI operations. If your agents are stuck in pilot mode, the issue isn’t the technology. It’s the adoption strategy.
Trace’s funding is a reminder: The future of AI in enterprise isn’t about algorithms—it’s about integration. Trace raises $3M to solve the AI agent adoption problem in enterprise.
