Beyond the Demo: What Automation CoEs Really Want to See

You've sat through enough AI demos by now. Impressive capabilities? Yes. Bold claims of autonomous decision-making? Absolutely.

So what’s stopping you? You aren’t about to ditch hard-earned ROI for uncertainty. Before you commit to agentic transformation, you need answers:

What happens to your existing automation investments?
What does the transition to agentic CoE look like?
What platform capabilities determine enterprise success (versus demo success)?
How will you know it’s working?

The answer to that first question is the foundation: your existing automation investments become MORE valuable, not obsolete. Here’s why.

How agentic automation multiplies your existing investments

Automations your CoE has built, each solving a specific use case — invoice processing, vendor data management, order entry, report generation — required developer time, testing, documentation, and governance approval. This was the linear equation: more use cases took proportionally more development time.

Agentic automation breaks that constraint. Instead of developers coding each new workflow, agents generate potentially unlimited workflows by orchestrating automation components and recombining them based on business context.

That means your automation library is an invaluable core building block of agentic automation. Organizations starting their automation journey today can’t skip ahead because they won't have enough components for agents to work with.

Automation coverage expands and time-to-value shrinks

Typically, automation programs deliver strong ROI in their first few years. But you may have already noticed that the easy wins are behind you. Remaining opportunities tend to be more complex, more variable, and harder to justify with traditional RPA economics.

Agentic automation allows you to extend automation coverage to exactly the processes you've written off as too complex; it's perfect for handling exceptions, judgment calls, and unstructured data. Early adopters are achieving significant coverage expansion without proportional cost increases, tackling special cases and judgment-based work that traditional RPA couldn't handle.

And with a CoE foundation, AI agents can operate right away. The real ROI risk lies in watching your automation capabilities plateau while others pull ahead.

Your team's expertise compounds, it doesn't reset

The teams that built your RPA program understand how enterprise automation actually works in production. They know which integrations break, how exceptions surface, what governance actually requires, and why processes that seem simple in demos fail in your environment.

All of this process understanding, integration knowledge, and governance discipline becomes more valuable when agents are creating workflows, not less. The new pieces — agent instruction design, validation protocols, risk-based oversight — layer on top of your team’s expertise that took years to develop.

Agentic automation redirects this expertise from coding to designing higher-level orchestration patterns and validating what agents generate. The work shifts from "build this specific automation" to "establish the frameworks agents operate within."

What this looks like for your CoE

Instead of distributing finite developer resources across the ever-growing list of opportunities to build useful automations, your CoE moves into curating the toolkit agents orchestrate and maintaining oversight and validation. Agents generate new workflows from components you've already built and validated.

At Cargill, their Global Intelligent Automation CoE built 236 automations over five years, delivering $19M in savings. Each automation solved specific problems: vendor deactivation, order entry, invoice processing. The CoE governance and structure they established positions them to multiply that value across new use cases that until now they haven't been able to tackle.

For Sumitomo Rubber, agentic automation ingests data, optimizes container fill, and applies rules across orders. Manual allocation time dropped 98%, from 20 days to half a day.

These results show the value multiplication when agents can leverage your existing automation library in your actual enterprise environment. However, that last part — "your actual enterprise environment" — is where agentic AI claims are really tested.

Separating demo magic from enterprise reality

Consider an invoice processing scenario. An AI agent needs to extract data from a PDF, validate the vendor in your ERP system, check the purchase order in your procurement platform, route approvals through your workflow system, and trigger payment in your financial application.

That's five different systems: ERP runs on-prem for compliance reasons. Your procurement platform is legacy with limited API access. Your workflow tool is cloud-based SaaS. And your financial application requires specific security protocols.

Point solutions demo well because all the data lives in one system they control. But they tend to fall short at navigating hybrid infrastructure, legacy systems, security boundaries around financial data, and compliance requirements that dictate where data can move.

Platforms like ServiceNow and Salesforce offer enterprise governance and security, but their agentic capabilities are built to operate within their ecosystems. That means agents can orchestrate across ServiceNow modules or Salesforce clouds effectively, but when invoice data sits in SAP, the vendor master lives in Oracle, and approvals flow through a custom application, they can't bridge those gaps.

Their value is real but constrained to specific domains, effectively orphaning automation investments you've made outside those platforms.

What enterprise orchestration actually requires

When an agent orchestrates a workflow, it sequences and manages execution across disconnected systems. The agent determines processing path, triggers each automation in sequence (for an invoice: OCR extraction, vendor validation, PO retrieval, approval routing, payment processing), and coordinates across different security models, deployment environments, and access patterns.

Your developers built these individual automations. You want agents that can coordinate them across systems with different architectures and requirements.

To deliver enterprise value, agentic platforms must be ready for enterprise reality:

Agent-automation orchestration: Agents must call and coordinate your current automation library as executable tools, regardless of where they run
Enterprise connectivity: Pre-built connectors for core systems and the ability to integrate with legacy applications that lack modern APIs, including on-prem systems
Production governance: Audit trails for every agent action across multiple systems, with controls over data access and decision tracking
Deployment flexibility: The ability to operate where your systems actually live—cloud, on-prem, or hybrid—based on security and compliance requirements
Security architecture: Credential management across systems with different security requirements and safeguards against unauthorized access

Most vendors will claim they meet these requirements. Your job is demanding they prove it with your systems, your security model, and your compliance requirements — not just their demo environment.

Can agents orchestrate your existing automations across your actual enterprise landscape? Does governance track agent actions across all your systems? Can you deploy where security requirements demand?

The evaluation discipline your CoE has already built —the frameworks that prevent shadow automation and ensure production reliability — allows you to separate real agentic capability from AI promises, and protects the organization from accumulating technical debt disguised as innovation.

What does your agentic CoE transformation look like?

The capabilities that make your CoE effective — process discipline, governance frameworks, integration architecture, stakeholder relationships — remain your foundation. What changes is how work gets done and where your team spends time.

For workflows using existing automation components, your team shifts from coding to validation. But you'll still build automations where components don't exist yet. That means context-switching between building and validating, which takes adjustment.

The other shift is prompt engineering, but not the creative kind. Agents need structured instruction frameworks that produce consistent behavior. Developers understand process logic; translating that into instructions agents execute reliably is different work.

Evolving roles

Building a team of experienced agentic automation specialists is largely about evolving your current team.

Your agentic orchestration lead is probably your most senior automation architect or a developer with deep business process understanding. This person designs instruction frameworks, establishes orchestration patterns, and troubleshoots when agent behavior doesn't match intent. The skill combination is rare: technical depth plus business expertise plus thinking in agent instructions rather than procedural code.

AI workflow validators come from your QA or senior developer pool. They validate agent-generated workflows meet production standards, which is part technical review, part business logic verification. The key shift is evaluating workflows they didn't write and logic agents generated, not code developers wrote.

Agentic workflow specialists are RPA developers ready to work at a higher level: designing orchestration patterns, validating agent outputs, building components when needed. Though this isn't a simple skill swap; instructing and validating is different from developing.

The rest of your team evolves, too. Process analysts, governance teams, and support staff adapt but keep their core work. Analysts identify opportunities, but now work with orchestration leads on instruction design rather than handing specs to developers. Governance reviews agent-generated workflows alongside developer code. Support expands from "why did this break" to "why did the agent make this decision."

New governance requirements

Monitoring agent decisions is harder than monitoring traditional automation. When an agent processes an invoice and flags it for human review, your governance framework needs to capture why. Threshold exceeded? Vendor data anomaly? Low confidence score? Something else?

This requires instrumentation you probably don't have today. Your current RPA automation monitoring tracks success/failure, runtime, error logs. Agent monitoring requires decision-level transparency — what path did the agent choose, what was its confidence level, what data informed the decision, where in the orchestration did it determine human review was needed?

Monitoring infrastructure will be part of your selected platform, but defining what to monitor is a governance decision. Your CoE needs to answer: which agent decisions require logging and review? What confidence thresholds trigger escalation? Who has access to review agent reasoning? Validation protocols for agent-generated workflows and escalation frameworks require the same governance decisions.

These aren't questions your platform answers for you. You need to decide based on your risk tolerance, compliance requirements, and organizational culture.

Keep in mind that getting stakeholder alignment typically takes longer than technical implementation, so it helps to establish these protocols during pilots to set the groundwork for scale.

How to measure agentic impact

Time saved and cost reduction still matter, but they don't tell you whether transformation is working. Track metrics that show whether agents are effectively extending your CoE's capabilities:

Agent-generated workflow quality: Track what percentage of workflows pass validation on first review. Low pass rates signal that either instruction frameworks need work or the workflows agents are tackling are too complex for your current capability.
Time from opportunity to production: For workflows that leverage existing automation components, look for timeline compression, e.g., weeks to days. If you're not seeing that, either agents are struggling to orchestrate effectively, validation is a bottleneck, or you're applying agent orchestration to the wrong use cases.
Orchestration complexity: Are you expanding coverage into complex multi-system, multi-step workflows or just making automations slightly easier? If agents are only successfully orchestrating 2-3 step workflows, you're not getting the value.
Developer time allocation: This helps to assess if your team is shifting from building to validating. You want to see traditional development hours declining as orchestration design and validation hours increase. If all three are high, it may signal that transformation is stuck and you're only adding workload.

The key point is that making the move to agentic automation is not starting over, but it's not a trivial shift either. The CoEs succeeding with agentic AI are harnessing their existing foundation as a springboard for transformation.

Start your CoE transformation

Your automation CoE is positioned to become exponentially more valuable. The question now is timing.

Your existing investments, team expertise, and track record represent advantages you've already built. What you do in the next 90 days determines whether you choose your transformation path or have it chosen for you under competitive pressure.

Talk to us about your CoE's transformation path, or see the platform that lets agents orchestrate the automation library you've already built.