Most GenAI programs fail in the same way: pilot chaos. Teams start with a shiny demo, but they don’t have a clear use case, clean and accessible data, governance guardrails, security approvals, evaluation methods, or an adoption plan. The result is predictable—stalled pilots, blocked legal reviews, unpredictable costs, and tools that don’t survive beyond a small group of enthusiasts.
This guide gives you a practical AI readiness assessment you can run in a single meeting: a weighted AI readiness scorecard to measure LLM readiness across strategy, data, security, governance, evaluation, architecture, ownership, and adoption. You’ll also learn how to interpret your score, what to fix first, and how to move from “experiments” to enterprise-grade outcomes.
Quick Answer Box
- What “AI readiness” means for LLMs: Your ability to deploy LLM-powered workflows safely, reliably, and cost-effectively—beyond ad-hoc ChatGPT usage.
- What the score covers: use cases & ROI, data readiness, security/privacy, governance, evaluation, architecture/integration, operating model, adoption, cost control, and compliance/risk.
- How scoring works: 10 domains, each scored 0–10, weighted to a 0–100 total.
- What “ready” looks like: clear use-case ROI, governed data access, guardrails, evaluation framework, monitoring, and a named owner for day-to-day operations.
- Biggest readiness blockers: unclear use cases, poor data quality/access, missing governance, weak evaluation (hallucinations), security/privacy constraints, and no operating model.
What AI Readiness Means in 2026
ChatGPT usage vs enterprise LLM implementation
Using ChatGPT for drafting, summarizing, and brainstorming is useful—but it’s not enterprise LLM implementation. Enterprise work requires:
- Controlled access (who can see what)
- Audit logs and retention policies
- Safe handling of PII and sensitive data
- Consistent quality (evaluation, not vibes)
- Integration into real workflows and systems
- Monitoring, cost controls, and ongoing ownership
RAG vs fine-tuning vs agents (high-level)
- RAG (Retrieval-Augmented Generation): The model answers using your documents/data retrieved at query time. Often the fastest path to trustworthy enterprise value—if data is governed and retrieval quality is tested.
- Fine-tuning: You adjust a model’s behavior using training examples. Useful for consistent style or structured tasks, but it doesn’t “store your documents” the way many people assume.
- Agents: Systems that plan and take actions (create tickets, update CRM, trigger workflows). Powerful, but they raise the bar on safety, permissions, and monitoring.
When you’re NOT ready (and should pause)
You’re not ready for production LLM deployment if:
- You can’t identify 2–3 use cases with measurable ROI and owners.
- You can’t safely control data access, retention, and auditing.
- You can’t evaluate hallucinations and business accuracy.
- You don’t have a plan for post-launch operations (who runs it daily).
- You don’t have a cost control model (budgets, routing, caching, limits).
Common Mistake: Treating “LLM readiness” as a tech stack problem. Readiness is an operating model problem: data + security + governance + evaluation + ownership.
AI Readiness Scorecard
Scorecard weights (total = 100)
| Domain | Weight |
| A) Use-case clarity & ROI model | 15 |
| B) Data readiness (quality, access, governance) | 15 |
| C) Security & privacy (PII, access control, logging) | 12 |
| D) AI governance & policy | 10 |
| E) Evaluation & QA | 12 |
| F) Architecture & integration readiness | 10 |
| G) AI operating model & ownership | 8 |
| H) Change management & adoption | 8 |
| I) Vendor/model strategy & cost control | 6 |
| J) Compliance & risk management | 4 |
| Total | 100 |
How to score: Each domain is scored 0/3/5/8/10, then multiplied by weight ÷ 10.
Example: Domain A score 8/10 with weight 15 → contributes 12 points.
A) Use-case clarity & ROI model (Weight: 15)
What good looks like
- 2–5 prioritized use cases with a business owner each
- Clear success metrics (time saved, revenue lift, risk reduction)
- Baseline measurements and ROI assumptions
- A plan for workflow integration (not “a chatbot” floating alone)
Common failure patterns
- “We want GenAI” without a workflow target
- No owner after pilot
- Success defined as “people like it” instead of measurable outcomes
Checklist questions
- Do we have 2–5 LLM use cases mapped to workflows?
- Does each use case have a business owner and tech owner?
- Do we have baseline metrics today (time/cost/error rate)?
- Is ROI defined (value model + costs)?
- Do we know the risk level by use case (low/medium/high)?
- Have we defined what “done” means for the pilot?
Scoring rubric (0/3/5/8/10)
- 0: No defined use cases; exploratory only
- 3: Use cases listed, but no ROI/owners
- 5: 1–2 use cases have owners and rough ROI
- 8: 2–5 use cases with metrics, owners, ROI model, delivery plan
- 10: Portfolio governance exists; pipeline, prioritization, and outcome tracking in place
B) Data readiness (quality, access, governance) (Weight: 15)
What good looks like
- Data sources identified and accessible via governed pathways
- Clean, maintained knowledge bases (documents, tickets, policies, SOPs)
- Metadata, permissions, and ownership defined
- A plan for updates (freshness) and provenance (where answers come from)
Common failure patterns
- Data is scattered and outdated
- No permission model; retrieval risks exposure
- “We’ll use SharePoint/Drive” with no structure or versioning discipline
Checklist questions
- Do we know the systems of record for our target use cases?
- Is the data clean enough for retrieval (duplication, stale docs)?
- Do we have an access control model (who can see what)?
- Are documents tagged/structured for retrieval and relevance?
- Do we have data owners responsible for quality and updates?
- Can we trace answers back to sources (provenance)?
Scoring rubric
- 0: Data unknown/unavailable; high chaos
- 3: Data exists but ungoverned and messy
- 5: Data identified; partial access controls; inconsistent quality
- 8: Governed access, clear ownership, structured sources for retrieval
- 10: Strong governance, freshness workflows, lineage/provenance, quality KPIs
C) Security & privacy (PII, access control, logging) (Weight: 12)
What good looks like
- Clear PII/sensitive data rules for prompts and outputs
- Role-based access control (RBAC) + least privilege
- Audit logging, retention, and incident response
- Secure integrations and secrets management
Common failure patterns
- Legal blocks deployment because rules are unclear
- No audit trail; cannot prove compliance
- Over-permissive access to sensitive data
Checklist questions
- Do we classify data types (PII, PHI, confidential)?
- Do we have prompt/input and output filtering requirements?
- Are logs retained and auditable?
- Are secrets managed (keys, tokens) properly?
- Is RBAC implemented end-to-end?
- Is there an incident response plan for AI outputs?
Scoring rubric
- 0: No security model for AI usage
- 3: Guidelines exist but unenforced
- 5: Partial RBAC/logging; unclear retention
- 8: Strong RBAC, logging, retention, and policy enforcement
- 10: Security validated, audited, and integrated with enterprise controls
D) AI governance & policy (Weight: 10)
What good looks like
- Acceptable use policy, model usage policy, and review workflow
- Ownership for approvals (legal/security/product)
- Standards for human review for high-risk outputs
- A process for policy updates as models evolve
Common failure patterns
- Everyone uses tools differently
- No clear approval gates
- Policy created but ignored
Checklist questions
- Do we have an acceptable use policy for GenAI?
- Do we define which data can be used and where?
- Are there review requirements by risk level?
- Do we have a governance committee or decision forum?
- Do we log prompts/outputs where required?
Scoring rubric
- 0: No governance
- 3: Draft policy only
- 5: Policy exists; partial enforcement
- 8: Governance operating with clear gates
- 10: Governance mature, audited, continuously improved
E) Evaluation & QA (Weight: 12)
What good looks like
- A repeatable evaluation method for quality and safety
- Test sets, benchmarks, and acceptance thresholds
- Measurement of hallucinations, factuality, and task success
- Ongoing monitoring for drift and regressions
Common failure patterns
- “It seems good” becomes the standard
- No test set; cannot compare changes
- No measurement of failure modes (hallucinations, refusal, toxicity)
Checklist questions
- Do we have an evaluation test set per use case?
- Do we define accuracy/groundedness thresholds?
- Do we test safety and policy compliance?
- Can we reproduce results across versions?
- Do we monitor production quality and feedback?
Scoring rubric
- 0: No evaluation; subjective testing
- 3: Manual spot-checking only
- 5: Some test cases, inconsistent measurement
- 8: Formal test sets, thresholds, and regression testing
- 10: Continuous evaluation + monitoring with clear release gates
F) Architecture & integration readiness (Weight: 10)
What good looks like
- APIs, identity, and workflow integration patterns defined
- A secure architecture for RAG/agents where needed
- Monitoring, rate limiting, and failure handling built-in
- Clear environment strategy (dev/test/prod)
Common failure patterns
- Prototype built in isolation
- No identity integration; permissions break
- No monitoring; outages become mysteries
Checklist questions
- Can we integrate with identity (SSO, RBAC)?
- Do we have stable APIs and system access paths?
- Do we have an integration plan for the target workflow?
- Are monitoring and rate limits designed?
- Do we have a deployment pipeline and environment separation?
Scoring rubric
- 0: No architecture; scattered prototypes
- 3: Prototype architecture exists but not enterprise-ready
- 5: Integration possible; limited monitoring/governance
- 8: Clear architecture with integration and reliability patterns
- 10: Mature platform approach with repeatable deployment and controls
G) AI operating model & ownership (Weight: 8)
What good looks like
- Named owners for: product, data, security, MLOps/LLMOps, support
- Support processes and SLAs
- Release management for prompts, retrieval sources, model changes
- Clear “who runs this on Monday morning”
Common failure patterns
- No one owns it after launch
- Fixes happen ad-hoc
- No process for changes; quality drifts
Checklist questions
- Do we have a product owner for the AI solution?
- Who owns data sources and updates?
- Who owns evaluation and release gates?
- Who handles user support and incidents?
- Do we have a change/release process?
Scoring rubric
- 0: No ownership model
- 3: Informal ownership
- 5: Roles exist but unclear responsibilities
- 8: Clear operating model and support process
- 10: Mature LLMOps model with SLAs, releases, and accountability
H) Change management & adoption (Weight: 8)
What good looks like
- Users trained on workflows, not features
- Adoption metrics tracked (usage, success rate, time saved)
- Feedback loops and continuous improvement
- Clear communication and stakeholder alignment
Common failure patterns
- Tool is built but not adopted
- Users don’t trust outputs
- No measurement of impact
Checklist questions
- Do we have workflow-specific training materials?
- Are adoption and impact metrics defined?
- Do we have feedback and iteration cycles?
- Are managers reinforcing usage in daily work?
- Do we have a communications plan?
Scoring rubric
- 0: No adoption plan
- 3: Training planned, not executed
- 5: Training executed; little measurement
- 8: Strong adoption plan with metrics and iteration
- 10: Adoption is measured, improved, and tied to outcomes
I) Vendor/model strategy & cost control (Weight: 6)
What good looks like
- Model selection criteria (quality, latency, cost, privacy)
- Routing and fallback strategy (smaller models for simpler tasks)
- Budgeting, rate limits, caching, and monitoring
- Awareness of vendor risk and portability concerns
Common failure patterns
- Costs spike unexpectedly
- One model used for everything
- No governance of usage
Checklist questions
- Do we track cost per use case and per workflow?
- Do we have rate limits and budgets?
- Do we route tasks to appropriate models?
- Do we use caching where appropriate?
- Do we have vendor risk mitigation?
Scoring rubric
- 0: No cost strategy
- 3: Rough cost awareness only
- 5: Some controls; limited routing/monitoring
- 8: Strong routing + budgets + monitoring
- 10: Mature cost governance with optimization and portability planning
J) Compliance & risk management (Weight: 4)
What good looks like
- Risk classification of use cases
- Compliance checks integrated into delivery
- Auditability and documentation standards
- Vendor/legal review processes defined
Common failure patterns
- Compliance is discovered too late
- No audit trail for decisions and outputs
- High-risk use cases launched without safeguards
Checklist questions
- Have we classified use cases by risk level?
- Do we know compliance requirements by industry?
- Do we have audit and documentation standards?
- Do legal/security approvals have a path and timeline?
Scoring rubric
- 0: No risk/compliance planning
- 3: Informal review only
- 5: Some checks, inconsistent execution
- 8: Clear risk management and auditability
- 10: Mature compliance integrated into delivery and operations
Copy/Paste Scorecard
Score each domain 0/3/5/8/10, then multiply by (Weight ÷ 10):
- A Use-case clarity & ROI (15): __/10
- B Data readiness (15): __/10
- C Security & privacy (12): __/10
- D Governance & policy (10): __/10
- E Evaluation & QA (12): __/10
- F Architecture & integration (10): __/10
- G AI operating model (8): __/10
- H Adoption & change (8): __/10
- I Model strategy & cost control (6): __/10
- J Compliance & risk (4): __/10
- Total score (0–100): ____
Pro Tip: Run the scorecard with business + IT + security in the same room. The gaps you surface are usually misalignment gaps—not “missing tech.”
What Your Score Means
Score bands table
| Score band | Readiness level | What it means |
| 0–30 | Not Ready | High risk of pilot chaos; foundations missing |
| 31–55 | Early | Some building blocks exist; needs structure |
| 56–75 | Building | Ready for a controlled pilot with guardrails |
| 76–90 | Ready | Ready for production deployment in selected workflows |
| 91–100 | Advanced | Scaled operating model; continuous improvement |
0–30: Not Ready
- Characteristics: No clear use-case ROI; Data access and governance unclear; Security and compliance not defined; No evaluation method.
- Next actions: Identify top 10 use cases, narrow to 2–3; Classify data and define access controls; Draft governance and evaluation basics.
- First 2–3 wins: Internal policy + safe usage framework; Use-case prioritization workshop; Data source inventory + permission model.
31–55: Early
- Characteristics: A few ideas and partial data access; Some security awareness; Evaluation is ad-hoc.
- Next actions: Define success metrics and owners; Build a test set and acceptance thresholds; Establish basic operating model roles.
- First 2–3 wins: One controlled pilot with evaluation gates; Governance starter policy; Cost tracking for pilot usage.
56–75: Building
- Characteristics: Use cases defined and data identified; Some governance and security controls; Architecture supports integration.
- Next actions: Build a production-grade pilot with monitoring; Implement LLMOps (release gates, regression testing); Formalize hypercare and adoption plan.
- First 2–3 wins: RAG assistant for a high-impact knowledge workflow (illustrative); Automated drafting + review workflow for a team process (illustrative); Support triage + knowledge retrieval pilot (illustrative).
76–90: Ready
- Characteristics: Strong foundations and clear ownership; Evaluation and monitoring exist; Security and governance are operational.
- Next actions: Expand to additional workflows with a portfolio approach; Optimize cost via routing/caching; Improve adoption metrics and feedback loops.
- First 2–3 wins: Multi-workflow rollout with shared platform controls; Automated QA and regression for model changes; Cost optimization program tied to usage.
91–100: Advanced
- Characteristics: Repeatable deployment model; Enterprise governance and auditability; Continuous measurement and improvement.
- Next actions: Scale globally; strengthen portability and vendor risk mitigation; Expand agentic workflows with strict permissions; Build advanced evaluation and safety tooling.
- First 2–3 wins: Enterprise-wide LLM platform maturity; Strong guardrails for agent actions; Continuous compliance + audit automation.
LLM Readiness Implementation Checklist
Strategy
- Define top 10 use cases; prioritize 2–3
- Assign business owner + technical owner per use case
- Define success metrics and baseline
- Define ROI model (value + costs + risk)
- Define risk level per use case
Data
- Identify systems of record
- Inventory documents/knowledge sources
- Clean and deduplicate critical sources
- Define metadata and ownership
- Implement permissioning for retrieval
- Define freshness/update workflow
- Enable provenance (traceable sources)
Security
- Classify data (PII/PHI/confidential)
- Define prompt and output handling rules
- Implement RBAC/SSO alignment
- Enable audit logging and retention
- Secrets management for API keys
- Incident response for AI output issues
Governance
- Acceptable use policy
- Review gates by risk level
- Documentation standards
- Model/tool approval process
- Human-in-the-loop requirements for high risk
Build & integration
- Architecture defined (RAG/agent patterns as needed)
- API integration plan
- Environment separation (dev/test/prod)
- Rate limiting and fallback behavior
- Observability (logs/metrics/traces)
Evaluation
- Create test set per use case
- Define acceptance thresholds
- Hallucination and groundedness tests
- Regression tests for prompt/model changes
- Production feedback loop
Deployment & monitoring
- Release gates and change management
- Cost monitoring per workflow
- Usage monitoring and alerting
- Drift monitoring (quality over time)
- Support and escalation process
Adoption
- Workflow-based training
- QRGs and playbooks
- Super users and champions
- Adoption metrics defined
- Iteration cadence (weekly/biweekly improvements)
Common AI Readiness Gaps
“We don’t have clean data”
- Symptoms: irrelevant answers, missing docs, users stop trusting it.
- Root cause: no ownership, no structure, no freshness process.
- Fix plan: start with 1–2 high-value sources; clean, tag, permission them; implement updates and provenance.
“We don’t know which use cases matter”
- Symptoms: many pilots, no outcomes.
- Root cause: no prioritization model, no ROI ownership.
- Fix plan: shortlist 10, score impact/feasibility/risk, pick 2–3 with measurable KPIs and owners.
“Legal/security is blocking everything”
- Symptoms: stalled approvals, unclear rules.
- Root cause: no policy, unclear data handling, no auditability.
- Fix plan: create a governance starter pack, classify data, implement RBAC/logging, define review gates by risk.
“We can’t evaluate hallucinations”
- Symptoms: unpredictable quality, no release confidence.
- Root cause: no test sets or thresholds.
- Fix plan: build test sets from real scenarios, define groundedness checks, add regression testing for changes.
“Costs are unpredictable”
- Symptoms: budget fear, usage throttling, leadership pushback.
- Root cause: no routing, no budgets, no usage governance.
- Fix plan: set budgets, rate limits, route tasks to smaller models, add caching and monitoring.
“No one owns it after launch”
- Symptoms: quality drifts, backlog grows, adoption stalls.
- Root cause: missing AI operating model.
- Fix plan: assign product owner + support lead + evaluation owner; establish release gates and SLAs.
The 90 Days AI Readiness Roadmap
| Weeks | Focus | Key deliverables | Owners |
| 1–2 | Alignment + scoring + shortlist | Scorecard completed, top 2–3 use cases, baseline metrics, risk classification | Sponsor, Head of Data, IT, Security |
| 3–6 | Foundations | Data source inventory + permissions, governance starter policy, evaluation plan, architecture design | Data lead, Security lead, Architect |
| 7–10 | Build pilot with guardrails | Working pilot integrated into workflow, test set + thresholds, monitoring + cost tracking | Product owner, Eng lead, QA |
| 11–13 | Deploy + monitor + adopt | Controlled rollout, training + comms, hypercare support, stabilization backlog | Change lead, Support lead, PM |
Weeks 1–2:
- Run the AI readiness assessment and agree on score
- Select 2–3 use cases with ROI and owners
- Define risk classification and review gates
Weeks 3–6:
- Prepare governed data sources
- Implement security controls, logging, retention
- Establish evaluation plan + test sets
- Confirm integration architecture
Weeks 7–10:
- Build a pilot with guardrails
- Run evaluation and regression tests
- Add monitoring and cost controls
Weeks 11–13:
- Deploy to a real team workflow
- Train users and measure adoption
- Stabilize and build the next-phase roadmap
Conclusion
LLMs can create real value—but only when you treat them as an enterprise capability, not a demo. The fastest path to outcomes is readiness first: clear ROI use cases, governed data, enforceable security and governance, objective evaluation, and an operating model that can sustain the system after launch. That’s how you avoid wasted pilots and reduce risk while scaling responsibly.
If you want help running a structured assessment and building a 90-day roadmap, Gigabit can deliver an AI Readiness Assessment and implementation support—from governed data foundations to evaluation and production deployment. Gigabit fuses world-class design, scalable engineering and AI to build software solutions that power digital transformation.
Frequent AI Readiness Questions
What is an AI readiness assessment?
An AI readiness assessment measures whether your organization can deploy AI/LLMs safely and effectively across real workflows—not just run experiments.
How do you measure LLM readiness?
Score readiness across use cases, data, security, governance, evaluation, architecture, operating model, adoption, cost control, and compliance.
What score means we’re ready?
Typically, 76–90 indicates you’re ready for production deployments in selected workflows. 56–75 means you’re building and should run controlled pilots with guardrails.
What’s the biggest blocker to GenAI?
Most often: unclear use cases with no ROI owner, and data that isn’t governed or accessible safely.
Do we need a data warehouse first?
Not always. You need governed access to the right data sources for your use case. A warehouse can help, but it’s not mandatory for early wins.
Is RAG safer than fine-tuning?
Often, yes—because RAG can ground answers in approved sources and can be permissioned and audited. But it still requires evaluation and governance.
How do we prevent hallucinations?
You reduce hallucinations through grounded retrieval (RAG where appropriate), strong prompts/guardrails, evaluation test sets, and human review for high-risk outputs.
How do we control LLM costs?
Use routing (smaller models for simpler tasks), caching, budgets, rate limits, monitoring, and cost-per-workflow accountability.
Who should own AI in an organization?
A named product owner for each solution, plus shared ownership across data, security, evaluation/LLMOps, and support.
