AI Control Plane Solution for Enterprise AI Governance
Get one AI control plane to inventory, govern, and monitor every AI agent, workflow, and automation across your enterprise.
Stop AI sprawl, reduce compliance risk, and make AI spend accountable with centralized runtime governance, policy enforcement, and audit‑ready visibility.
AI sprawl is accelerating
Teams are deploying agents, copilots, and AI features faster than architecture and security teams can track them. That creates a new form of shadow IT: AI connected to enterprise systems without shared ownership, complete inventory, or consistent governance controls.
Security risk is rising
As AI spreads across workflows and systems, the attack surface expands with it. Enterprises are seeing more incidents tied to sensitive data exposure, policy violations, and unmanaged AI activity that traditional controls were never designed to monitor in real time.
ROI breaks without ownership
When no team owns AI governance end to end, projects stall in pilot, costs drift upward, and value becomes hard to prove. Organizations with explicit governance accountability are materially more mature than those still treating AI oversight as a shared afterthought.
According to McKinsey, companies should use digital technology to stand out in customer engagement and innovation, build their own software, data, and AI assets, and use scalable cloud platforms for a competitive edge. They also need to focus on attracting and integrating tech-savvy leaders, especially at the executive level, to overcome ongoing talent challenges
Visibility and Inventory
How it works:
The control plane continuously discovers and registers AI agents, copilots, workflows, models, and third‑party AI tools across your environment - sanctioned and shadow alike. Each asset is tagged with owner, system connections, data access, and risk profile, giving architecture, security, and governance teams a live AI bill of materials instead of a static spreadsheet. That inventory becomes the foundation for every other governance function: without it, you are governing blind.
Runtime Policy Enforcement
How it works:
Put It Forward intercepts AI actions at runtime - prompts, tool calls, data access, and outputs - and evaluates them against enterprise policies before they execute, not after an incident report. Identity, permissions, data‑handling rules, and risk thresholds are enforced in the control plane, which can block, modify, or route actions for human approval when needed. This moves AI governance from a one‑time approval step to a continuous, machine‑speed control loop that keeps agents within their authorized envelope.
Cost Accountability
How it works:
The control plane tracks AI usage and cost per agent, workflow, model, team, and feature, so spend is visible in real time instead of appearing as an unexpected line item after the billing cycle. Budget limits, rate limits, and model‑routing rules can be applied as guardrails - for example, shifting non‑critical workloads to cheaper models when thresholds are approached or shutting down idle workloads automatically. Because costs are attributed to business context, leaders can decide where to invest more and where to pull back based on ROI, not guesswork.
Audit‑Ready Oversight
How it works:
Every AI interaction - the inputs, decisions, actions, policy evaluations, and escalations - is captured as a structured, tamper‑resistant trace in the control plane. These traces form an immutable audit trail that maps directly to frameworks like NIST AI RMF and the EU AI Act, giving risk and compliance teams the evidence they need without manual log‑stitching. When regulators, auditors, or enterprise clients ask how AI decisions were made, you can answer with concrete, time‑stamped records instead of narrative explanations.
Governed AI for Every Critical Function
Governed AI for Every Critical Function
The AI Control Plane matters because every business function scaling AI eventually runs into the same problem: more autonomy, more cross-system activity, and more risk without a shared layer of control.
Revenue Operations - From signals to control
Turn AI-driven revenue activity into governed, auditable workflows your CRO can trust.
The problem:
Revenue teams are rolling out AI to score pipeline, flag deal risk, and trigger outreach across CRM, marketing automation, and support systems, but most of that activity is invisible to governance and risk teams. Actions that change customer data, advance stages, or alter forecasts often run without clear ownership, policy controls, or a traceable record of who approved what.
What Put It Forward does:
Put It Forward’s AI Control Plane inventories every revenue-facing agent and workflow, applies runtime policy to AI actions that touch CRM and revenue systems, and records a full audit trail of changes and approvals. RevOps leaders gain one governed layer above pipeline AI so they can scale automation without losing control of customer data or forecast integrity.
Outcome metrics:
- Fewer AI-driven data changes without clear ownership or approval
- Faster detection and remediation of risky AI behavior in revenue workflows
- Clearer link between AI spend and revenue process performance
Pattern used: Runtime governance across agent-assisted and autonomous revenue workflows.
Finance & Compliance - From risk to assurance
Embed runtime AI governance into high‑stakes financial and compliance workflows.
The problem:
Controllers, risk teams, and compliance leaders are being asked to use AI for reconciliation, close, policy monitoring, and control testing, yet these are precisely the processes where unmanaged AI creates material risk. Static approvals, policy PDFs, and manual sampling cannot keep up with AI that reads contracts, touches ledgers, and runs across ERP and billing systems.
What Put It Forward does:
Put It Forward’s AI Control Plane enforces policy at execution time in finance and compliance workflows, logs every AI decision and escalation, and produces a structured audit trail aligned to regulatory expectations. Finance and risk teams can prove how AI-supported processes operate, who approved them, and how exceptions were handled - without manual log stitching.
Outcome metrics:
- Stronger audit readiness for AI‑enabled finance and compliance processes
- Lower exposure from AI actions that touch financial records and policies
- Higher confidence moving AI beyond pilots into core financial workflows
Pattern used: Deterministic governance for regulated workflows, with runtime controls on AI‑assisted decisions.
IT & Operations - From sprawl to control
Bring every AI workflow, agent, and automation under one governed layer.
The problem:
IT and operations teams are first to adopt AI for incident routing, runbook automation, monitoring, and service operations, but they are also the ones left managing a growing tangle of scripts, bots, and agents built on different tools. Without a control plane, AI quickly turns into operational debt: no central inventory, inconsistent permissions, and no single place to see where risk and cost are accumulating.
What Put It Forward does:
Put It Forward’s AI Control Plane gives IT and operations a single governance layer over all AI‑driven workflows. It tracks deployed agents and automations, applies identity and policy controls before actions run, manages approval and escalation paths, and surfaces spend and behavior across the entire AI estate - regardless of which team or tool created the workflow.
Outcome metrics:
- Reduced operational risk from unmanaged and shadow AI deployments
- Faster response to policy violations, failures, and cross‑system exceptions
- Consistent governance coverage across integrations, workflows, and automations
Pattern used: Centralized runtime governance across autonomous agents, agent‑assisted workflows, and operational automations.
Governance Beats Everything
Reason to Invest:No other governance approach comes close to runtime control for keeping enterprise AI safe, compliant, and production-ready.
Business Benefits:
- Governance enforced at execution, not after an incident
- Risk posture improves as AI deployment scales
- Security and compliance teams operate with confidence, not guesswork
Governance Saves Projects
Reason to Invest: The difference between a canceled pilot and a scaled production deployment is almost always a governance layer that can withstand internal and external scrutiny.
Business Benefits:
- More AI projects reach and stay in production
- Internal risk review becomes a faster, less contentious process
- Compliance evidence is generated automatically, not assembled manually
Accountability Drives Maturity
Reason to Invest: Ownership and accountability are not soft governance concepts - they are measurable operational inputs that determine whether enterprise AI creates sustained value.
Business Benefits:
- AI investments return tied to clear owners and measurable outcomes
- Cost per agent control and per workflow visible and actionable
- ROI tracked in the platform, not reconstructed in a spreadsheet
What Governed AI Looks Like on a Tuesday Morning
Not in a governance document. In production, when the systems are running and the business depends on them.
89% effective - the AI control plane outperforms every other governance approach
At 8:00am, the architecture team is not guessing which agents are running across the business or what systems they connect to. Every deployment - agent, workflow, automation - is registered in the control plane with an owner, a policy profile, a data access record, and a governance status.
When a new team wants to deploy an agent into a production workflow, the identity, guardrails, approvals, and audit path are part of the standard operating model. The decision to scale is based on evidence, not assumption. The board question - "what AI do we have running and who is accountable?" - has an answer.
32% of data security incidents now involve generative AI
A high-privilege agent attempts an action that exceeds its approved scope. The control plane blocks the action, logs the policy event, and routes the escalation for review - before any data is accessed inappropriately. Security does not learn about this in a month-end report.
They see the context in real time: what data the agent touched, what rule it violated, and whether the pattern is emerging elsewhere. When the EU AI Act audit arrives, the evidence trail is already built. Compliance is not a clean-up exercise - it is an operational output that runs continuously alongside the business.
42% of AI projects show zero ROI without governance accountability
One workflow is generating measurable business value. Another is burning tokens on a process that has drifted from its original purpose. A third is escalating too often to human review - signaling a policy threshold that needs adjustment.
Operations does not find out three weeks later in a reporting cycle. Cost, throughput, exception rate, intervention rate, and business outcome are visible together in the control plane that morning. Decisions about what to scale, tune, or retire are made while the system is still running - not after the budget has already been spent.
| Capability | Put It Forward AI Control Plane | Governance Overlays / Point Tools |
|---|---|---|
|
Agent and workflow inventory
|
Native registry across the full platform
|
External catalog - gaps exist
|
|
Runtime policy enforcement
|
Enforced in the execution layer
|
Post-hoc alerts or static rules
|
|
Cost visibility
|
By agent, workflow, team, and use case
|
Token usage dashboards only
|
|
Audit trails
|
Automatic, workflow-linked, business-context
|
Technical logs, manually assembled
|
|
Human oversight by design
|
Approval and escalation paths built into workflows
|
External review queue
|
|
Compliance evidence
|
Pre-built, continuous, audit-ready
|
Point-in-time snapshot reports
|
|
Coverage
|
Autonomous agents, agent-assisted workflows, deterministic processes
|
Usually one agent class only
|
|
Integration to enterprise systems
|
Native - same platform that runs integration and automation
|
Requires separate orchestration layer
|
|
Time to governance coverage
|
Days - built into the first deployment
|
Weeks to months of configuration
|
Governance and Regulation
By 2025, 50% of enterprises will have devised artificial intelligence (AI) orchestration platforms to operationalize AI, up from fewer than 10% in 2020. Gartner
Governance is not a project with an end date. It is a maturity trajectory. Put It Forward is designed to move enterprises from visibility to full runtime governance faster than any comparable approach.
Stage 1 - Establish Visibility (Weeks 1-2)
Most enterprises do not know what AI they have running. The first step is creating a system of record: agent inventory, workflow registry, owner assignment, data access mapping, and policy status for every active AI deployment.
Outcome: From "we think we know" to "we know."
Stage 2 - Enforce Policy at Runtime (Weeks 3-6)
Replace document-based governance with enforceable runtime controls. Define role-based access, data policies, action limits, escalation thresholds, and approval gates - and enforce them in the platform where execution happens.
Outcome: From "we approved it" to "we govern it continuously."
Stage 3 - Operationalize Cost and Accountability (Weeks 6-12)
Connect AI spend to business outcomes. Track cost and performance by use case so that AI can be managed as an operating portfolio - decisions about what to scale, tune, or retire are based on evidence, not intuition.
Outcome: From "we spend on AI" to "AI earns its place in the budget."
The Analysts Say Put It Forward Is Way Ahead
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