TL;DR
Key facts every business leader should know before investing in AI:
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- Only 7% of enterprises have AI-ready data when they start building — poor data readiness, not technology, is the leading cause of failure
- Up to 50% of AI projects are abandoned after the PoC stage; only 13% ever achieve enterprise-wide impact
- AI initiatives exceed budgets by 30–70% on average; data engineering alone accounts for 40–60% of total project cost
- Minimum data requirement: ≥12 months historical data for predictive models; ≥85% completeness for structured datasets
- A score below 60 on the AI Readiness Model = do not proceed — resolve blockers before committing budget
- Annual maintenance costs 15–25% of build cost every year — this must be in the business case from day one
Introduction:
Most AI projects don’t fail because the technology is wrong.
They fail because nobody checked whether the conditions were right — before the first line of code was written.
Poor data quality. Undefined ROI. Regulatory obligations discovered halfway through a build. These aren’t edge cases. They are the rule. Up to 50% of AI projects are abandoned after the PoC stage, and only 13% ever achieve enterprise-wide impact — and in almost every case, a structured feasibility check would have caught the problem early.
This guide gives you the exact framework Emvigo uses with clients across financial services, healthcare, retail and manufacturing: a 0–100 scoring model, real benchmarks, interactive checklists, and a clear decision framework you can take straight to your board.
What Is an AI Readiness Assessment?
An AI readiness assessment is a structured evaluation that scores your organisation across four areas — data quality, ROI clarity, risk and compliance, and organisational capability — to produce a Go, Conditional Go, or No-Go recommendation before development begins. A score of 80–100 means proceed. 60–79 means fix specific gaps first. Below 60 means stop: investing now will cost more to unwind than to resolve upfront
You may also see this referred to as an AI feasibility assessment, AI feasibility checklist or AI implementation checklist. These terms are used interchangeably across the industry — this guide covers all of them.
AI Readiness Assessment vs PoC vs AI Strategy
Many organisations jump straight to a Proof of Concept — or conflate readiness with strategy. These are three distinct stages and they are not interchangeable:
| Stage | Purpose | Key Question | Output |
|---|---|---|---|
| Readiness Assessment | Validate feasibility before investing | Should we build this? | Go / No-Go Decision |
| Proof of Concept | Test technical viability | Can the model be built? | Working Prototype |
| AI Strategy | Plan for scale and long-term deployment | How do we scale across the business? | Roadmap + Investment Plan |
A PoC that skips readiness typically stalls because it relies on cleaned or cherry-picked datasets, ignores integration realities, and proves technical possibility without confirming business viability. If you want to understand the structural reasons AI projects fail before they reach production, the specific failure patterns around data, integration, and sponsor alignment are documented in detail at Why AI Projects Fail.
When Should You Run an AI Readiness Assessment?
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- You are exploring your first AI use case and need to build a credible business case
- You want to move from experimentation to production-ready AI
- A previous AI initiative stalled or underperformed and you need to understand why
- You are under pressure to justify AI spend to a board or executive team
- You operate in a regulated environment — financial services, healthcare or insurance — where compliance risk is high
Why AI Feasibility Matters More Than Most Leaders Realise
The Numbers Behind the Risk
| Failure Mode | Verified Data / Impact | Source | How it Connects |
|---|---|---|---|
| AI projects abandoned after PoC | At least 30% of GenAI projects abandoned by 2025 | Gartner report | Many projects never reach production due to unclear ROI, cost, and data issues |
| AI project failure rate (broader view) | Up to 50% abandoned after PoC | Gartner analysis | Confirms high failure even after initial validation |
| Data readiness gap | Only 7% of enterprises have AI-ready data | Business Insider | Root cause of failure — most projects start without usable data |
| Data as a failure driver | 60% of AI projects may be abandoned due to poor data readiness | Business Insider | Explains why failure rates are so high |
| Scaling gap (pilot → impact) | Only around 13% achieve enterprise-wide impact | TechRadar | Even “successful” pilots rarely scale |
| Value realisation gap | Only a small fraction deliver measurable ROI | TechRadar | Links to business dissatisfaction and abandonment |
Cost Overruns Are Structural, Not Exceptional
AI initiatives exceed initial budgets by 30–70% on average, with data engineering alone accounting for 40–60% of total project cost. In UK mid-market organisations (£10M–£100M revenue), these overruns typically escalate into six-figure problems within the first build cycle. The most common culprits are underestimating data preparation effort, discovering integration complexity too late, and rebuilding models due to poor initial assumptions. Understanding the hidden costs of AI implementation before committing a budget is the difference between a credible business case and an expensive surprise.
Regulatory Exposure Is Real and Growing
In the UK and EU, AI feasibility must account for GDPR, the EU AI Act (phased enforcement from August 2026) and FCA guidance on model explainability in financial services. The EU AI Act classifies credit decisioning, employment screening and clinical decision support as high-risk AI — each carrying explicit transparency and human oversight obligations. Failing to address these before development begins can result in legal and reputational damage that far outweighs any efficiency gain.
AI Readiness Scoring Model (0–100)
Use this model before working through each domain. Score your organisation in each area (0–25), apply the weighting, and total your result. The output gives you a single defensible number for board conversations.
| Pillar | Weight | What Good Looks Like (Score 20–25) | Your Score (0–25) |
|---|---|---|---|
| Data Readiness | 30% | Completeness ≥85%, error rate ≤5%, depth ≥12 months, label accuracy ≥90% | |
| ROI Clarity | 25% | Measurable outcome defined, full lifecycle costs mapped, KPIs agreed before build | |
| Risk & Compliance | 20% | GDPR assessed, FCA/EU AI Act reviewed, model risk identified, security reviewed | |
| Organisational Readiness | 25% | Skills assessed, all stakeholders aligned, infrastructure validated, change plan in place | |
| TOTAL | 100% |
| Score | Decision | What It Means |
|---|---|---|
| 80–100 | GO | Strong foundations. Proceed to MVP scoping. Define sprint structure and KPIs before build begins. |
| 60–79 | CONDITIONAL GO | Viable — but resolve flagged gaps before development starts. Document prerequisites and assign ownership. |
| Below 60 | NO-GO | Significant blockers present. Investment now costs more to unwind than to address upfront. Pause and remediate first. |
This scoring model forms the backbone of Emvigo’s AI consulting and feasibility work
AI Project Kill Signals — Stop If You See Any of These
Before working through the full checklist, check for absolute blockers. If any of the following are true, pause the project immediately. These are not theoretical thresholds — they are the conditions that cause AI initiatives to stall post-build, after significant budget has already been committed.
Stop the project if:
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- Data access takes more than 8 weeks to secure from internal or third-party sources
- No measurable KPI can be defined within 2 weeks of discovery kickoff
- Projected model accuracy improvement over baseline is less than 10%
- Integration requires rebuilding more than 3 core systems
- ROI payback period exceeds 24 months without board-level strategic rationale
- GDPR consent documentation is absent or incomplete for the required dataset
- No named internal owner for model performance and retraining post-launch
Data Readiness Assessment
Data is the foundation of every AI initiative. If data feasibility fails, everything else collapses around it.
Production-Ready Data Benchmarks
Vague assessments of ‘good data quality’ are not sufficient for a credible feasibility exercise. Use these specific thresholds:
| Metric | Minimum Threshold | Why It Matters |
|---|---|---|
| Completeness | ≥85% for structured datasets | Below this, models learn from gaps rather than patterns |
| Error rate | ≤5% for operational AI use | Higher rates compound at inference scale |
| Historical depth — predictive models | ≥12 months; 24+ months preferred | Less history = higher overfitting risk |
| Historical depth — pattern detection | ≥6 months minimum | Insufficient for reliable seasonal pattern recognition |
| Label accuracy (supervised ML) | ≥90% | Poor labels are the leading cause of model underperformance |
| Pipeline latency (real-time AI) | ≤500ms for customer-facing inference | Higher latency renders real-time systems unusable in production |
If your data does not meet these thresholds, that is not automatically a blocker — but the time and cost to close the gap (typically 3–9 months for operational datasets) must be factored into your business case before development starts.
Structured vs Unstructured Data
| Data Type | Examples | Preprocessing Effort vs Structured Baseline |
|---|---|---|
| Structured | Databases, transaction logs, CRM records | Baseline |
| Semi-structured | JSON APIs, XML feeds, log files | +30–50% effort |
| Unstructured | Documents, emails, images, audio | +100–200% effort |
Integration Readiness
Integration challenges consistently account for 15–30% of total build cost and are among the most overlooked feasibility blockers. Before committing to a build, confirm which systems will feed data to your AI, which will consume its outputs, whether stable APIs exist, and whether latency constraints could limit real-world usability. Emvigo’s analytics and business intelligence services help organisations establish data foundations that make AI deployment faster and more defensible.
Data Readiness Checklist
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- Required data sources identified and accessible
- Completeness ≥85% for all structured datasets
- Error rate ≤5% for operational use
- Historical data depth ≥12 months for predictive models
- Label accuracy ≥90% for supervised ML
- Governance, security and ownership clearly defined
- Structured vs unstructured data requirements understood
- Integration pathways to and from AI confirmed with API documentation
ROI and Business Value Assessment
Typical AI Project Cost Breakdown
| Component | % of Total Project Budget | Underestimation Risk |
|---|---|---|
| Data preparation and engineering | 30–50% | High — often treated as a given, not a cost line |
| Model development | 15–25% | Medium |
| Integration | 15–30% | High — APIs and latency usually discovered late |
| Testing and deployment | 10–15% | Low–Medium |
| Ongoing maintenance (annual) | 15–25% of build cost per year | Very High — almost universally excluded from initial business cases |
A model that costs £100,000 to build typically requires £15,000–£25,000 per year in retraining, monitoring and support. That recurring cost must appear in the business case from day one, or the CFO will surface it at year-end review instead.
AI Use Case ROI Benchmarks (UK Market)
These ranges reflect real-world outcomes from published case studies and industry research. Use them to pressure-test your own projections, not as guarantees:
| AI Use Case | Typical Build Cost (£) | Time to Value | Typical Year-1 ROI | Failure Risk |
|---|---|---|---|---|
| Invoice processing automation | £25,000–£80,000 | 2–4 months | 150–300% | Low (10–20%) |
| Automated document processing | £20,000–£70,000 | 2–4 months | 200–400% | Low (10–20%) |
| Customer churn prediction | £60,000–£150,000 | 6–9 months | 120–250% | Medium (25–40%) |
| Demand forecasting (retail) | £50,000–£120,000 | 4–8 months | 100–250% | Medium (20–35%) |
| Predictive maintenance | £80,000–£200,000 | 6–12 months | 150–400% | Medium (20–35%) |
| Dynamic pricing optimisation | £120,000–£300,000 | 9–18 months | 200–500% | High (40–60%) |
| AI-assisted credit risk scoring | £150,000–£500,000 | 9–15 months | 100–300% | High (compliance-driven) |
| Clinical decision support | £200,000–£600,000 | 12–18 months | Hard to quantify year 1 | High (regulatory) |
KPIs for AI Success
Define success criteria before a single line of code is written. Without agreed KPIs, AI outcomes become impossible to judge objectively. Emvigo’s guide on how to set KPIs for AI and MVP projects covers a practical framework for this.
| Use Case | Example KPI | Minimum Target Improvement |
|---|---|---|
| Invoice automation | Processing time per invoice | ≥80% reduction |
| Churn prediction | Customer retention rate vs. 12-month baseline | ≥15% improvement |
| Predictive maintenance | Unplanned downtime hours per quarter | ≥40% reduction |
| Credit risk AI | Default rate / false positive rate vs. current scorecard | ≥20% improvement |
| Document processing | Manual review hours per week | ≥60% reduction |
ROI Readiness Checklist
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- Clear business problem defined with measurable outcome
- Full lifecycle costs mapped, including 15–25% annual maintenance
- Value drivers identified and quantified against the benchmarks above
- KPIs agreed and baseline established before development starts
- Short-term and long-term ROI timeline documented with assumptions
- Build vs buy vs partner decision made with documented cost comparison
Risk and Compliance Assessment
UK and EU Regulatory Landscape
| Regulation | Applies To | Key Obligation | Status |
|---|---|---|---|
| UK GDPR | All orgs processing personal data | Consent, explainability, right to deletion | In force now |
| EU AI Act | High-risk AI deployments | Transparency, human oversight, risk classification | Phased from August 2026 |
| FCA AI Guidance | UK financial services firms | Model explainability, bias monitoring, audit trails | In force now |
| NHS Data Governance | Healthcare AI in England | Data sharing agreements, patient consent, IG Toolkit | In force now |
High-Risk AI Categories Under the EU AI Act
The following use cases carry explicit pre-deployment obligations from August 2026:
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- Credit scoring and lending decisions
- Employment screening and HR decision support
- Clinical decision support and patient triage
- Biometric identification systems
- Critical infrastructure management
Model Risk
Model risk includes inaccurate predictions, concept drift (performance degradation — typically measurable within 3–6 months in dynamic environments), lack of explainability, and overfitting to historical patterns. Your feasibility assessment should define monitoring frequency, who is responsible for identifying drift, and what the remediation process looks like when performance falls below the agreed threshold.
Vendor and Dependency Risk
| Risk Type | Question to Answer at Feasibility | Red Flag |
|---|---|---|
| Vendor lock-in | Can we migrate to another provider within 6 months if needed? | No exit clause in contract |
| Data ownership | Who owns training data and model outputs? | Vendor claims ownership of your data |
| Pricing volatility | Is API pricing fixed or usage-based with no cap? | No pricing guarantee beyond 12 months |
| Security posture | Has the vendor completed SOC 2 / ISO 27001 audit? | No third-party security certification |
Risk & Compliance Checklist
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- Model risks identified with monitoring and remediation plans
- Ethical considerations reviewed against affected demographic groups
- UK GDPR requirements assessed — consent, explainability, deletion rights
- FCA or NHS governance requirements reviewed (financial services / healthcare)
- EU AI Act risk classification confirmed for all use cases
- Security vulnerabilities and attack surfaces reviewed
- Vendor dependency, data ownership and exit strategy documented
Organisational Readiness Assessment
Skills and Talent
Ask honestly: do teams understand AI outputs well enough to act on them confidently? Is there in-house data or ML expertise, or will the organisation be entirely dependent on a vendor? Who will own models post-launch and be responsible for retraining? Gaps in internal capability lead to underuse of AI or over-reliance on a single supplier. Emvigo’s complete guide to AI implementation covers how to build internal capability alongside deployment.
Stakeholder Alignment
In Emvigo’s experience, the single most common cause of AI project stalls is a legal or compliance team encountering the initiative for the first time after the technical build is already underway. The most successful initiatives align all four functions from the very start:
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- Business or product leadership — define the outcome and success criteria
- Data and engineering teams — assess technical viability and data quality
- IT and infrastructure — evaluate systems readiness and integration constraints
- Legal, compliance and risk — review regulatory obligations before the first sprint
Change Management
AI changes how decisions are made, which roles are needed and how people work. Feasibility should account for the impact on existing responsibilities, training requirements, and the degree to which staff trust AI recommendations enough to act on them. Resistance is a business risk — and it must be planned for, not discovered after launch.
Build vs Buy vs Partner
| Option | Best When | Typical Cost vs Partner | Timeline vs Partner |
|---|---|---|---|
| Build in-house | AI is a core differentiator; long-term IP is critical | 2–4× higher | 50–100% longer |
| Buy off-the-shelf | Standard use case; speed to market is the priority | Lowest upfront; highest lock-in risk long term | Fastest |
| Partner with a specialist | Speed, expertise and risk reduction matter more than full ownership | Baseline | Baseline |
For most UK mid-market organisations beginning their AI journey, the partner model delivers the fastest time to value with the lowest risk exposure — because specialist experience compresses the learning curve and reduces the probability of the failure modes that account for the 60% abandonment rate. Emvigo’s DevOps and cloud support services can help assess and bridge infrastructure gaps quickly.
Organisational Readiness Checklist
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- Skills, ownership and internal capability assessed honestly
- All stakeholders aligned including legal and compliance from day one
- Change management and adoption plan developed before build begins
- Infrastructure and cloud readiness validated against deployment requirements
- Post-launch model ownership, monitoring and retraining responsibility assigned
- Build vs buy vs partner decision made with documented rationale
When AI Is the Wrong Choice
AI is not always the right answer. Before committing to a build, check whether a simpler solution would deliver equal or better results at lower cost and risk:
| Scenario | Why AI Is Wrong Here | Better Alternative |
|---|---|---|
| Rule-based, deterministic decisions with zero variability | AI adds cost and complexity without improving outcomes | RPA or traditional workflow automation |
| Fewer than 6 months of historical data | Insufficient training data guarantees poor model generalisation | Collect data first; use business rules in the interim |
| Fewer than 1,000 transaction events per month | Volume too low to train a statistically reliable model | Manual workflow or spreadsheet automation |
| Full explainability legally required (e.g. credit refusal letters) | Complex models cannot produce human-readable explanations | Simpler statistical scorecard or logistic regression |
| Decision outcomes cannot be measured | No way to define success, validate the model or calculate ROI | Redesign the business process before adding AI |
This is one of the first questions addressed during the project discovery phase, and the answer shapes every decision that follows.
The AI Feasibility Process: 4 Stages
A structured feasibility assessment doesn’t need to be a long, drawn-out exercise. When done properly, it typically takes 2–6 weeks and results in a clear, board-ready recommendation.
1 — Discover
Start by clearly defining the AI use case.
What problem are you solving? What does success look like? Who are the key stakeholders involved?
Output: Use case brief and stakeholder map
2 — Assess
Evaluate feasibility across all critical domains — data, ROI, risk, and organisational readiness.
This stage involves working with relevant teams to identify gaps, constraints, and potential blockers.
Output: Gap analysis and red flag log
3 — Score
Assign scores to each domain using a 0–100 model.
Classify results into:
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- Green → Ready
- Amber → Conditional
- Red → Blockers
Output: AI readiness scorecard
4 — Decide
Based on the assessment, make a clear decision:
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- Go
- Conditional Go
- No-Go
If proceeding, define the prerequisites that must be addressed before development begins.
Output: Board-ready recommendation
What happens next?
If your initiative scores 60+ and receives a Conditional Go, the next step is to define an AI MVP — a focused, time-bound build designed to deliver measurable value while validating key assumptions in a real-world environment.
AI Feasibility in Practice: Real Client Examples
The three examples below are drawn from Emvigo’s direct client work. In each case, the feasibility assessment either saved the project from a foreseeable failure or fundamentally changed the approach before a single line of code was written.
Fintech: AI-Assisted Credit Risk Scoring
The problem: A lending platform wanted to use AI to improve credit decisioning for underserved customers. Initial enthusiasm was high — the business case looked straightforward on paper.
What feasibility uncovered: Historical approval data encoded significant demographic bias that would have amplified discriminatory lending patterns at scale. FCA explainability requirements were far more substantial than the team had anticipated — they shaped model architecture decisions that could not be retrofitted after build. Real-time API integration with credit bureaus introduced latency constraints that required a fundamentally different system design.
What the feasibility recommended: A phased approach — beginning with a risk-scoring model for existing customers where data quality was highest and the regulatory baseline was established, before expanding to new applicant segments. This reduced initial scope but made the timeline to a defensible, compliant production system significantly shorter.
Outcome: 30% ROI and £1M in revenue in the first year — built on validated foundations.
Key lesson: Regulatory explainability requirements in fintech AI are not an afterthought. They shape model architecture decisions that must be made at feasibility — not at deployment.
Healthcare: Clinical Decision Support
The problem: A large healthcare organisation needed to replace a manual, error-prone patient management system with a digital platform for real-time clinical decision-making. At scale, fragmented records and manual processes were creating inaccuracies that directly impacted patient safety.
What feasibility uncovered: Patient data was distributed across multiple EMR systems with inconsistent formats, making integration a prerequisite before any intelligent decision support could be built. Real-time data processing requirements were far more demanding than anticipated — latency had to be treated as a patient safety risk, not a performance optimisation. The scale of operations, with thousands of beds and millions of daily data points, required an architecture capable of maintaining accuracy and uptime in a live clinical environment.
What the feasibility recommended: A phased, infrastructure-first approach — beginning with establishing a unified data layer across EMR systems and designing for real-time processing, before expanding into advanced analytics and decision support. This reduced initial scope but ensured the system could be deployed reliably in a high-risk clinical setting.
Outcome: The platform processed 40M+ data points daily across 2,500+ beds and reduced clinical errors by 75% — delivering measurable impact in a live healthcare environment
Key lesson: In healthcare AI, data consent and clinical accountability are feasibility questions, not deployment questions. The EU AI Act classifies clinical decision support as high-risk AI — regulatory obligations must be assessed before the first sprint.
These outcomes weren’t accidental. In each case, feasibility identified the blockers before budget was committed — and changed the entire approach before a single line of code was written. If you’re at a similar stage with an AI idea, book an AI feasibility assessment with Emvigo and get a scored recommendation.
Common Mistakes When Running an AI Feasibility Assessment
A feasibility assessment often fails not because of poor intent, but because critical factors are overlooked or addressed too late. The following mistakes are consistently seen across AI initiatives — and are often the reason projects fail before delivering value.
Treating feasibility as a purely technical exercise
Many teams approach feasibility as an engineering problem. In reality, most AI initiatives fail due to legal, organisational, and business validation gaps, not technical limitations.
What to do instead:
Include business, data, legal, and operational stakeholders from day one to ensure the solution is viable across all dimensions — not just technically.
Skipping ROI validation until late stages
When ROI is not validated early, scope is rarely tied to real business outcomes. As a result, value claims become difficult — or impossible — to defend after the system is built.
What to do instead:
Define measurable KPIs, expected ROI, and full lifecycle costs in parallel with the technical assessment, not after it.
Ignoring compliance until deployment
Regulatory requirements such as GDPR, FCA guidelines, and the EU AI Act are often treated as post-build concerns. In practice, these cannot be retrofitted without significant cost, delays, or even full redesign.
What to do instead:
Assess regulatory obligations during feasibility, as they directly shape model design, data usage, and system architecture decisions.
Using a PoC as a substitute for feasibility
A Proof of Concept (PoC) validates technical capability — not commercial viability. Treating a PoC as a replacement for feasibility leads to projects that can be built but should not have been.
What to do instead:
Run feasibility first to determine whether the initiative is worth building at all, before investing in a PoC.
Excluding ongoing maintenance from the business case
Ongoing costs are frequently underestimated. In reality, post-launch maintenance typically accounts for 15–25% of the initial build cost annually, and ignoring this can significantly distort ROI projections.
What to do instead:
Include annual maintenance, monitoring, and iteration costs in the business case from the outset to ensure realistic financial planning.
Emvigo’s guide to mistakes to avoid when building AI tools covers these patterns in detail with real examples.
How Emvigo Helps with AI Feasibility
Most teams don’t need more theory. They need a clear answer: will this work, what will it cost, and where are the risks?
Emvigo specialises in helping businesses make that call before a single line of code is written. Our feasibility work spans financial services, healthcare, retail and manufacturing — and it typically takes 4–6 weeks from first conversation to a scored, board-ready recommendation.
We start by pressure-testing your use case in a structured discovery session — not to validate assumptions, but to challenge them. From there we audit your data against production benchmarks, model your ROI against real UK market outcomes, and review your regulatory exposure across GDPR, FCA guidance and the EU AI Act.
The output is a single AI Readiness Score from 0–100, a documented Go, Conditional Go or No-Go decision, and — if you’re proceeding — a scoped MVP plan with sprint structure and KPIs defined before development begins.
No optimistic projections. No surprises halfway through the build.
Ready to get your score? Book an AI Feasibility Assessment with Emvigo.
Frequently Asked Questions
What is an AI readiness assessment?
A structured evaluation that determines whether a specific organisation is prepared to implement AI effectively. It examines data quality against measurable thresholds, infrastructure, governance, team capability and commercial viability — and produces a clear Go, Conditional Go or No-Go recommendation before development begins.
How long does an AI feasibility assessment take?
Most AI feasibility assessments take 2–6 weeks. Data complexity typically adds 1–3 weeks; stakeholder alignment challenges add 1–2 weeks. This is substantially cheaper and faster than discovering the same problems mid-project — a lesson many organisations learn at significant cost.
What are the biggest red flags in an AI feasibility assessment?
The most reliable predictors of project failure: data completeness below 85% with no remediation plan; inability to define a measurable KPI within two weeks of discovery; ROI payback exceeding 24 months; no named internal owner for AI outcomes post-launch; and high regulatory risk without an explainability strategy. Any single one of these signals a need to pause before committing to a full build.
Can an AI project be technically feasible but commercially unviable?
Yes — and this is more common than most organisations expect. Many AI systems work technically but fail to deliver sufficient business value once full lifecycle costs (including 15–25% annual maintenance) and longer-than-planned adoption timelines are accounted for. Feasibility assessment exists specifically to catch this mismatch before investment is committed.
Is AI feasibility only relevant for large projects?
No. An AI feasibility checklist is equally important for generative AI applications, process automation, decision support tools and simpler integrations. The scale of the assessment should be proportionate to the investment — but skipping it entirely, even for sub-£50,000 initiatives, introduces avoidable risk that typically costs more to unwind than the assessment itself.
What happens after feasibility is confirmed?
Once feasibility scores 60+, the next step is defining an AI MVP — a scoped, time-boxed initial build that delivers measurable value and validates assumptions in production. Emvigo’s guide on scaling AI from PoC to production (emvigotech.com/blog/scaling-ai-poc-to-production/) covers what comes after feasibility, including sprint structures and KPI frameworks for the build phase.
Should we build, buy or partner once feasibility is confirmed?
Build in-house when AI is a core differentiator and long-term IP is critical — expect costs 2–4× higher and timelines 50–100% longer than the partner model. Buy off-the-shelf when the use case is standard and speed to market is the priority. Partner with a specialist when speed, expertise and risk reduction are more valuable than full ownership — which describes the majority of UK organisations beginning their AI journey.
Conclusion: Make AI a Business Decision, Not a Gamble
An AI readiness assessment is not about slowing down innovation. It is about making AI a disciplined, defensible business decision rather than an expensive experiment.
The numbers are unambiguous: 30–70% cost overruns are standard when feasibility is skipped; only 7% of enterprises have AI-ready data when they start building; up to 50% of AI projects are abandoned after the PoC stage and only 13% ever achieve enterprise-wide impact. The organisations that avoid these outcomes ask the right questions first — about data quality against real thresholds, about ROI against realistic benchmarks, about regulatory obligations before the first sprint.
If your AI Readiness Score is below 60, the most valuable thing you can do is address that now — not after budget has been committed. If your score is 60 or above, the blueprint for what happens next is clear.
Feasibility is not optional. It is what separates the organisations that build AI that works from the ones that are still trying to explain to their boards why the pilot never scaled.
Ready to get your AI Readiness Score? Schedule a free consultation or explore our AI consulting services.
