What is AI agent governance?

AI agent governance is the set of policies, controls, and monitoring systems that ensure autonomous AI agents behave safely, comply with regulations, and remain auditable. It covers decision logging, policy enforcement, access controls, and incident response for AI systems that act on behalf of a business.

Does the EU AI Act apply to my company?

The EU AI Act applies to any organisation that develops, deploys, or uses AI systems in the EU, regardless of where the company is headquartered. High-risk AI systems face strict obligations starting 2 August 2026, including risk management, data governance, transparency, human oversight, and conformity assessments.

How do I test an AI agent for security vulnerabilities?

AI agent security testing evaluates agents for prompt injection, data exfiltration, policy bypass, jailbreaks, and compliance violations. Talan.tech's Talantir platform runs 500+ automated test scenarios across 11 categories and produces a certified security score with remediation guidance.

Where should I start with AI governance?

Start with a free AI Readiness Assessment to benchmark your current maturity across 10 dimensions (strategy, data, security, compliance, operations, and more). The assessment takes about 15 minutes and produces a prioritised roadmap you can act on immediately.

From AI Assessment to Action: Your 90-Day Implementation Roadmap

Why “Post-Assessment” Is Where AI Programs Succeed or Die

An AI assessment is supposed to clarify where you are and what to do next. In practice, many teams stop at the report: a maturity score, a list of use cases, a few risks, and a vague recommendation to “invest in data and governance.” The missing piece is the implementation bridge—a structured plan that converts findings into decisions, pilots, operating rhythms, and measurable business value.

This 90-day roadmap is designed for professionals who have already completed (or are about to complete) an AI assessment and need to move from analysis to execution—without spinning up a massive transformation program or losing momentum.

Before Day 1: Convert the Assessment into an Executable Backlog

Your assessment likely produced themes like “data quality issues,” “low AI literacy,” “high-value use cases,” and “governance gaps.” Turn these into a backlog you can manage.

Do this immediately:

Extract initiatives (not just observations). Example: “No model monitoring” becomes “Implement model monitoring baseline for pilot systems.”
Tag each initiative with:
- Business domain (Sales, Ops, Finance, HR, IT)
- Type (use case, data, governance, security, platform, skills)
- Effort (S/M/L) and dependency (none/low/high)
- Risk level (low/medium/high)
Define success metrics for each (outcome and leading indicators)

Output: a single prioritized list you can review weekly.

The 90-Day Roadmap (At a Glance)

Your first 90 days should deliver three things:

A working operating model (who decides, how work flows, how risk is managed)
1–2 pilots in production-like conditions (not just proofs of concept)
A repeatable path to scale (standards, templates, and funding logic)

Use the phases below to get there.

Days 1–15: Align, Decide, and Set the Rules of the Road

1) Establish a Clear AI “North Star” (One Page)

You need a crisp statement of intent so teams stop debating what AI is “for.”

Include:

Business outcomes (e.g., reduce cycle time, improve forecast accuracy, increase conversion)
Where AI will not be used (important for trust and focus)
Guiding principles (privacy-by-design, human oversight, measurable value)

Keep it short enough that leaders will actually repeat it.

2) Confirm Sponsorship and Decision Rights

AI work stalls when nobody can make tradeoffs across functions. Set decision rights early.

Define:

Executive sponsor (owns outcomes and funding)
Product owner(s) for each pilot (owns scope and adoption)
Data owner (owns access, quality, definitions)
Risk owner (privacy, security, compliance)
Technical owner (architecture, integration, reliability)

If decision rights aren’t clear, every meeting becomes a negotiation.

3) Stand Up a Lightweight AI Governance Rhythm

Avoid creating a bureaucracy. You need just enough structure to move fast safely.

Implement:

Weekly delivery standup (pilot teams, blockers, next milestones)
Biweekly steering review (scope changes, resourcing, risk escalations)
Risk checkpoint (model/data review gate before anything touches real users)

Key artifacts (templates you can reuse):

AI use case brief (problem, users, data, success metrics, constraints)
Data access request + approval record
Model risk checklist (privacy, bias, explainability needs, human review plan)
Launch readiness checklist (monitoring, rollback, documentation)

4) Select Your Pilot Use Cases Using a Balanced Scorecard

Assessments often output a long list of “high value” ideas. Choose pilots that are both valuable and executable.

Score each candidate on:

Business value (cost, revenue, risk reduction)
Time-to-impact (can you show results in 90 days?)
Data readiness (availability, quality, permission)
Workflow integration (can it fit into real work?)
Risk complexity (regulated decisions, sensitive data, explainability needs)

Pick 1–2 pilots and explicitly defer the rest.

Deliverables by Day 15:

One-page AI North Star
Named owners and decision rights
Governance cadence + templates
1–2 pilots selected with success metrics

Days 16–45: Design and Build in “Production-Like” Conditions

1) Translate Each Pilot into a Minimum Viable Product (MVP) Scope

Many AI pilots fail because they try to solve the entire problem end-to-end.

Define:

Primary user (who will use it weekly?)
Single workflow moment (where it shows up)
Decision boundary (what the AI suggests vs what humans decide)
What “good” looks like (quantitative metric + qualitative acceptance)

Examples of MVP boundaries:

Start with recommendations, not automation
Start with one region, one product line, or one team
Start with read-only outputs before write-back to systems

2) Lock Data Access and Data Definitions

AI assessments often identify “data problems” broadly. For a pilot, be specific.

Do:

Create a data dictionary for the pilot (fields, meaning, owner, refresh rate)
Implement data quality checks (missing values, outliers, drift)
Document permissions and retention rules

The goal is to avoid late surprises like “we can’t use that field” or “it updates monthly.”

3) Build the System, Not Just the Model

A model without integration rarely changes outcomes. Treat your pilot like a small product.

Minimum components:

Data pipeline (even if simple)
Model/service (or rules + model hybrid)
User interface or delivery mechanism (dashboard, embedded view, messaging)
Logging and monitoring (inputs, outputs, user actions)
Feedback loop (thumbs up/down, correction capture, outcome labeling)

4) Define Safety and Quality Gates

Your first deployment should be designed to protect users and the business.

Add:

Human-in-the-loop review where needed
Fallback behavior if confidence is low
Audit trail for decisions and data used
Adversarial testing (edge cases, prompt injection risks if applicable)
Red-team mindset: “How could this fail in the real world?”

Deliverables by Day 45:

MVP scope and acceptance criteria
Data dictionary + access approvals
Working pilot in a controlled environment
Monitoring and safety gates defined

Days 46–75: Pilot, Measure, and Drive Adoption

1) Run a Real Pilot with a Defined Cohort

A pilot is not a demo. Choose a cohort where you can measure impact.

Specify:

Who uses it (roles, count)
When they use it (workflow step)
How long the pilot runs (typically 2–4 weeks)
What baseline you compare against

2) Track Three Layers of Metrics

To avoid “cool but useless,” measure adoption and outcomes.

Track:

Adoption metrics: active users, frequency, completion rate
Quality metrics: accuracy, error rates, escalation rates, confidence distribution
Business metrics: cycle time, cost per case, conversion, leakage reduction

Pair metrics with qualitative feedback:

What do users trust?
Where does it slow them down?
What would make it indispensable?

3) Train Users with Micro-Enablement

AI adoption fails when training is an afterthought.

Use:

A 30-minute onboarding for the cohort
A one-page “How to use this safely” guide
Office hours twice a week during the pilot
A visible channel for bug reports and questions

4) Iterate Quickly—but Don’t Move the Goalposts

You should improve the experience while keeping the success criteria stable.

Common high-impact iterations:

Clarify outputs (“why” behind recommendations)
Reduce friction (fewer clicks, better defaults)
Improve data features or refresh frequency
Add confidence indicators and escalation paths

Deliverables by Day 75:

Pilot results with metric readout
User feedback summary
Prioritized iteration list
Decision recommendation: scale, extend, or stop

Days 76–90: Scale Decisions and Institutionalize What Works

1) Make the Scale/Stop Decision with Evidence

Avoid “pilot purgatory.” Decide based on pre-agreed criteria.

Use a simple decision matrix:

Did we hit the minimum business impact threshold?
Is adoption strong without constant pushing?
Are risks controlled with documented mitigations?
Can we operate it reliably with available skills?

If the answer is “no,” stop or re-scope—then move to the next use case.

2) Productize the Operating Model

Turn what you learned into repeatable standards.

Institutionalize:

A reusable intake process for AI ideas
Standard documentation (data, model, risks, monitoring)
A release process and rollback plan
Ownership model: who maintains, who approves changes, who monitors drift

3) Build Your “Next 2 Quarters” AI Portfolio

Use your assessment backlog plus pilot learnings to create a realistic plan.