Companies are pouring billions into AI initiatives with the expectation that something transformative will happen. But the obvious question—“what’s the ROI?”—is one that few can answer.
Instead, we use a set of well-intentioned proxy metrics to explain AI’s value: adoption rate, decision quality, grounding, user trust. These metrics tell us how AI is being used by employees and whether it’s accurate, but reveal next to nothing about actual business outcomes.
AI’s true transformative potential shows up in the same places every other investment does: faster delivery, shorter resolution times, higher conversion rates, stronger pipeline, and healthier revenue. In other words, the KPIs your company already uses to measure success.
If every other initiative is measured against business KPIs, why should we treat AI any differently?
Rather than working backwards to justify AI spend with new metrics, the better approach is to start with the outcome you want to influence and work forward from there. Pick a KPI. Improve it with AI.
After working closely with customers deploying AI in real operational workflows, Workato CEO Vijay Tella identified three fundamental “unlocks” that make it possible to measure AI impact with KPIs, which he shared at the most recent World of Workato user conference in Las Vegas, Nevada:
1. AI belongs in the orchestration layer.
Most AI initiatives don’t even have a chance to impact KPIs. Why? Because AI alone isn’t enough.
AI models may be powerful, but they’re isolated and can’t take action on their own. For example, say you build a customer support chatbot—without orchestrated access to back-end systems, it can’t check order status or process a refund. Or, say, you have an AI project that can process business data and make recommendations almost instantaneously—it still isn’t able to take action on them without orchestration.
AI won’t make much of an impact on KPIs if it doesn’t have a clear role in your most important processes. The orchestration layer defines how AI interacts with your systems, using features like access permissions, auditability, compliance controls, guardrails, and human-in-the-loop approvals.
Workato One is the control plane that defines how AI interacts with your data, systems, and people.
To create real business value, you must move AI from closed systems into your orchestration layer, where you connect systems and enforce permissions. When AI can see, understand, and act with trust across multiple applications, it becomes capable of improving your most important metrics.
Read about agentic orchestration.
2. Govern AI with an Enterprise MCP.
Many business leaders hesitate to give AI access to core systems and don’t want AI to take action. The reason is simple: AI is probabilistic. It improvises. It hallucinates. It doesn’t behave the same way twice.
And enterprises can’t afford that kind of unpredictability.
Model Context Protocol (MCP) is meant to solve this by introducing a universal way to connect AI to enterprise systems—a “USB-C for AI.” But basic MCP standardizes without adding safety measures.
Basic MCPs can expose data and trigger actions, but still lack process logic, real-time signals, permissions, and controls. They still operate alongside your enterprise architecture, not in it.
Workato Enterprise MCP closes that gap. It routes every action through enterprise “skills” that map out a secure, repeatable workflow like issuing a refund, updating a record, or provisioning access—things AI can do, but not reliably on its own. AI refers to these skills to execute the process correctly every time.
Enterprise MCP adds:
- Security and identity: fine-grained permissions, encryption, anomaly detection
- Resilience: retries, transaction guarantees, performance SLAs
- Transparency: a complete audit trail of every AI decision and action
Learn about Enterprise MCP.
3. Tie agents to business outcomes.
We’ve spent years asking, “What can AI do?” It’s time to ask, “What can AI change?” That shift turns AI from an experiment into a business imperative focused on outcomes instead of possibilities.
Workato’s approach centers on KPI-driven Genies, AI systems whose sole purpose is to move the most meaningful business metrics. This methodology aligns business and IT through a shared interface called Playbook UX.
Business leaders define the KPIs and desired outcomes. IT provides the orchestration, skills, and data context. Together they build agents designed to achieve measurable impact.
These KPI-driven agents combine:
- Job Descriptions: what the agent is responsible for
- Enterprise Context: access to process, data, and signal information
- Enterprise Skills: the trusted actions it can perform
- Agent Acumen: Workato’s analytics layer that monitors KPI movement in real time
By designing agents around KPIs, enterprises ensure AI does not just act, but acts with purpose.
Examples of KPI-driven agents
These agents are orchestrated through Workato’s Enterprise MCP, governed by enterprise skills, and measured against clear KPIs. The result: real, board-level impact, not just demos or prototypes.
The new AI stack
AI transformation is not about replacing your stack. It is about modernizing the orchestration layer that ties it all together. The new enterprise foundation includes:
- Workato Enterprise MCP: the secure, trusted interface between AI and enterprise systems
- Workato Genies: Workato Genies are prebuilt AI agents designed to handle high-impact work within key business functions—like CPQ in Sales or onboarding in HR. They integrate with your systems, operate securely, and are customizable to your needs—so you can put AI to work, fast.
- Workato Agentic: Design, test, and deploy enterprise-grade agents in a low-code studio grounded in your systems, logic, and business processes.
- Universal Connectivity: From modern SaaS to legacy mainframes, connect every system—including custom apps, databases, data lakes, LLMs, and on-prem infrastructure.
This unified platform gives enterprises one runtime, one experience, and unified governance, finally bridging the gap between generative power and operational reliability.
KPI impact is the true measure of AI maturity
The agentic era will separate those who experiment with AI from those who master it. Those that survive the upheaval will be the organizations that make AI accountable for business outcomes, orchestrate AI models, secure them with Enterprise MCP, and drive AI with the metrics that define success.
And that means every AI strategy must start and end with core KPI impact.