The Truth Score: a new approach to measuring AI readiness

Every AI proposal assumes your data is fine. Nobody has checked. The usual ways of checking don’t help much: traditional data quality metrics were designed for operational systems and reporting, not for AI. They measure averages that mask distributions, single fields rather than the combinations a model uses, and one system at a time rather than the gaps between them.

The result is organisations with green data quality dashboards that still cannot build working AI systems. The metrics say the data is good, but the AI fails anyway. Something is wrong with how we measure.

The Truth Score™ was built to address these limitations. It is a score out of 100 for how far your data can be trusted, measured on the records themselves rather than on a survey or a self-assessment. Paired with a Leverability read (which AI ideas you can use now, after fixes, or not at all), it tells a CEO or investor whether the data under an AI proposal can carry it, before the money is committed.

How the Truth Score works

The Truth Score measures data across six pillars, each scored from 0 to 100. The pillars carry fixed weights and the same rules apply everywhere, so a score is built the same way at each company. Where a pillar is not assessed, the memo says so. It is rule-based, not AI-generated: the same data, scored on the same date, gives the same score. Every pillar shows what was measured and what was not, and a pillar with no signal is marked not assessed rather than guessed. The full method is set out on our approach page.

Accuracy (22%)

Accuracy measures whether data correctly represents reality. It looks for duplicate records, invalid emails, phone numbers and dates, statistical outliers and inconsistent formats: the errors that make one customer look like three.

A high accuracy score indicates that data reflects reality well enough for models to learn true patterns. A low score indicates that models would learn from erroneous data and reproduce errors in predictions.

Completeness (18%)

Completeness measures whether the data an AI application needs is actually there. It looks at fill rates for required and key fields, and at record-level completeness: what share of records have every required field, not just each field on its own.

A high completeness score indicates that the data volume and coverage are adequate for model training. A low score indicates gaps that would limit training data or leave blind spots in model coverage.

Governance (18%)

Governance measures whether anyone is looking after the data. It looks for records with no owner, stale records nobody has touched, whether lifecycle stages are tracked, and whether changes leave an audit trail.

A high governance score indicates that current quality will persist. A low score indicates that quality may degrade even if current measurements are acceptable.

Consistency (16%)

Consistency measures whether the same thing is recorded the same way. It looks at format standardisation, whether related fields agree with each other, whether dates run in a sensible order, and whether values fall in plausible ranges.

A high consistency score indicates that data can be reliably combined and that historical patterns reflect consistent definitions. A low score indicates definitional shifts that could confuse models.

Connectivity (14%)

Connectivity measures whether your systems agree about the same customer, contract or employee. It matches records between systems (CRM to billing or finance, for example), checks that contacts link to the right companies, and looks for shared identifiers. This is where the surprises usually sit: every system can look clean on its own while the gaps between them are wide.

A high connectivity score indicates that an AI tool drawing on more than one system will see one consistent picture. A low score indicates that it will see several conflicting ones.

Lineage (12%)

Lineage measures whether you can trace where data came from. It looks at whether sources are identified, whether transformations are documented, and whether changes are versioned.

A high lineage score indicates that an AI output can be traced back to the records behind it. A low score indicates that nobody can explain why the model said what it said.

Critical pillars and the cap

A weighted average can hide a fatal gap. If two systems disagree about who your customers are, strong scores elsewhere don’t make the data safe for AI. So three pillars are treated as critical: Accuracy, Completeness and Connectivity.

If the weakest of the three is below 80, the overall score is capped at 0.55 × that pillar + 35, rounded. The cap only ever lowers a score; it never raises one.

A worked example from our sample memo (real engine output on generated test data, not a client): Accuracy 88, Completeness 97, Governance 69, Consistency 88, Connectivity 44 and Lineage 50 give a weighted average of 75. Connectivity is the weakest critical pillar at 44, so the cap is 0.55 × 44 + 35 = 59.2, rounded to 59. The Truth Score is 59: Band C, significant work needed. Without the cap, the same data would have read as a comfortable Band B.

Pillars with too little signal in the exports are shown as “limited signal” or “not assessed”, never guessed.

Use cases: the Leverability read

Different AI applications have different requirements. A demand forecasting model leans on consistent history. A fraud detection model leans on accuracy. A recommendation engine might tolerate some inconsistency but needs high completeness.

The Truth Score keeps its weights fixed, so it stays comparable. Use-case fit is handled separately, by the Leverability read: each AI play or use case is marked Deploy now, Needs work or Blocked, with the reason found in your own records.

The same underlying data can therefore support one AI idea today and block another. That reflects the reality that data ready for one application may not be ready for another.

Interpreting Truth Scores

Truth Scores fall into four bands:

A, 80 and above: AI ready. The data can support AI for the plays assessed. Proceed with reasonable confidence. Minor fixes may improve results but are not blocking.

B, 60 to 79: Minor issues. AI work can proceed but may face challenges. Targeted fixes to the weakest pillars would improve outcomes. Weigh the cost of fixing against accepting some model limitations.

C, 40 to 59: Significant work needed. Gaps exist that would likely undermine AI. Fix the specific blockers before committing serious budget.

D, below 40: Foundation needed. Foundational data work should precede AI development. Investing in AI before addressing data gaps would likely waste resources.

The bands are guidance, not absolute rules. Context matters. A use case with enormous value potential might justify proceeding at a lower score with acceptance of higher risk. A use case with modest value might require a higher score to justify investment.

From score to action

Truth Scores are valuable not just for go/no-go decisions but for directing the fixes:

Prioritise the weakest pillars: Focus on pillars with the lowest scores, especially the heavily weighted ones and the ones your AI plays depend on. Because of the cap, lifting a weak critical pillar matters more than polishing a strong one.

Understand specific gaps: The Truth Score is backed by detailed findings that identify specific issues in specific records. Use these findings to plan targeted fixes rather than broad data quality initiatives.

Set improvement targets: Define the score, and the Leverability read, that would let you proceed with confidence. The remediation roadmap gives each fix an owner, an effort estimate and a “done when” test, written for your own team or any outside firm to execute. We never do the work.

Compare across use cases: If multiple AI use cases are being considered, the Leverability read puts them side by side. Pursue the ones marked Deploy now while fixing the data behind the ones that need work.

Tracking over time

Because the method and the weights are fixed, Truth Scores are comparable over time and across companies scored the same way. A portfolio can be read on one method, one company after another.

A re-read shows whether fixes have landed and whether quality is drifting back. That is the job of AI Value Tracking: a quarterly re-read of the Truth Score and Leverability read for each company, against the roadmap.

Conclusion

Traditional data quality metrics do not predict AI success. A dashboard can be green while the AI built on it fails, because it measures each system on its own. The problem is not measurement execution but measurement design.

The Truth Score addresses this gap by measuring the records themselves, across systems, on fixed pillars, fixed weights and a published cap, and by pairing the score with a Leverability read on the AI in question.

If an AI proposal is on the table, a Truth Score Read tests it against your own records before you sign.