Insights

What your data can support, before anyone signs.

Every AI proposal assumes your data is fine. Nobody has checked. Notes for CEOs, CFOs and investors on where AI plans break, why every system looks clean on its own, and what to check before the budget moves.

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Start here AI readiness6 min read

Why enterprise AI projects fail: the data was never checked

AI projects rarely fail on the model. They fail on data nobody tested before the money moved, in the gaps between systems that look clean on their own.

8 July 2025Read the article

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Private equity5 min read

One method, twelve companies, one baseline: getting ahead of the AI readiness gap

A single read answers one company’s question. A portfolio baseline on one method shows where AI can run across the fund, and where to fix first.

16 June 2026Read

Data quality5 min read

Vendor scores tell you one system is happy with itself. That is not readiness.

A vendor’s readiness score tells you one system is happy with itself. Why the gaps between systems decide AI readiness, and who is placed to measure them.

16 June 2026Read

Data quality5 min read

Where AI projects actually break: the cross-system gap PE firms keep funding through

AI projects rarely break because the model is wrong. They break in the gaps between CRM, finance and HR, where no single-system check ever looks.

10 June 2026Read

AI readiness5 min read

Agentic AI in portfolio companies: why teams deploy before the data is ready

AI agents act on data without a human in the loop, so each gap between systems becomes an action. Why teams deploy first, and what to check first.

3 June 2026Read

Private equity5 min read

Clean data, verifiable KPIs, and the multiple you leave on the table at exit

A buyer’s diligence team will test whether your KPIs trace back to the systems. Where that breaks, the multiple suffers. Find the gaps before they do.

19 May 2026Read

AI readiness5 min read

The Amplification Effect: why AI multiplies the data errors already in your portfolio

AI doesn’t fix a shaky data foundation, it amplifies it. Garbage in, garbage amplified: where the errors sit, and how to find them before the model does.

12 May 2026Read

Private equity4 min read

The hidden cost of bad CRM data in buy-and-build roll-ups

Every acquisition brings its own CRM. Where roll-up data breaks between entities, why AI amplifies it, and what it means at exit.

5 May 2026Read

Data quality5 min read

Data governance is not a compliance cost. It is a value creation lever.

Governance is usually filed as a compliance cost. With AI acting on the data, it decides whether the AI plan works and whether the KPIs survive diligence.

28 April 2026Read

Private equity5 min read

AI due diligence in PE deals: why data readiness belongs in the investment committee

AI sits in the value creation plan, yet the IC rarely sees an independent read on whether the data can carry it. Why that check belongs before the vote.

14 April 2026Read

Private equity6 min read

The complete pre-AI checklist for portfolio companies

Five sections to clear before an AI project starts: use case, data availability, data quality, integration and risk. Each gap left open is a risk.

13 January 2026Read

AI readiness5 min read

Why data readiness is now a strategic imperative

AI is now a board topic, and data readiness decides what AI can do. Why it needs an executive owner, real measurement and a place in the budget.

6 January 2026Read

Guides and frameworks5 min read

Planning your 2026 AI initiative: a data-first approach

Five steps to plan an AI initiative from the data up: audit the current state, match use cases to data strengths, find the gaps, build the roadmap, budget.

16 December 2025Read

Research and reports5 min read

AI data quality trends to watch in 2026

Six shifts for 2026: proof of data readiness before budgets, integration, AI-specific governance, synthetic data and more realistic expectations.

9 December 2025Read

Research and reports4 min read

2025 in review: the year AI hit the data quality wall

AI pilots in 2025 met the data they would have to run on. The lesson for 2026: check the data first, use case by use case, before the budget moves.

2 December 2025Read

AI readiness7 min read

The Truth Score: a new approach to measuring AI readiness

A score out of 100 for how far your data can be trusted: six weighted pillars, four bands and a published cap, with a read on what the data can run.

25 November 2025Read

Private equity5 min read

AI value creation in private equity: beyond the hype

Where AI creates value in a portfolio company, the failure patterns that waste the budget, and a five-step framework that starts with the data.

18 November 2025Read

AI readiness2 min read

The AI vendor landscape is broken

Capability claims run ahead of reality, data requirements are understated and lock-in is engineered. How to read an AI vendor pitch before you sign.

28 October 2025Read

Private equity4 min read

Data governance for AI: the foundation PE firms overlook

Ownership, quality monitoring and change management decide whether AI keeps working after launch. How to assess governance in a portfolio company.

14 October 2025Read

Research and reports7 min read

AI readiness by industry: where portfolio companies stand

Software, manufacturing, healthcare, retail and financial services: where each sector’s data tends to be strong, where it breaks, and what to check first.

16 September 2025Read

Guides and frameworks8 min read

Building a data quality scorecard: metrics that matter

Green dashboards can hide data that AI can’t use. The metrics that matter for AI: sufficiency, feature completeness, label quality and entity resolution.

9 September 2025Read

Research and reports8 min read

The hidden cost of bad data on AI initiatives

Direct spend is the smallest part. Personnel, opportunity cost, credibility and delay add up. How to frame the business case for checking data first.

2 September 2025Read

Data quality8 min read

Why your CRM data is not ready for AI

Inconsistent entry, missing fields, stale contacts and duplicates: why CRM data breaks AI models, and the order in which to fix it before you build.

26 August 2025Read

Data quality8 min read

Data quality vs data readiness: why the distinction matters

A dashboard can be green while a churn model fails. Why data quality and AI readiness are different questions, and how to test for the second one.

12 August 2025Read

AI readiness9 min read

Anatomy of a failed AI project: an illustrative churn model story

An illustrative churn model story: the promise, the warning signs, the production failure, and what a data check before the build would have changed.

5 August 2025Read

Data quality10 min read

The six dimensions of data quality that determine AI success

Completeness, consistency, accuracy, timeliness, validity and governance: what each means for AI, how to measure it, and how to weight it per use case.

29 July 2025Read

AI readiness6 min read

Why enterprise AI projects fail: the data was never checked

AI projects rarely fail on the model. They fail on data nobody tested before the money moved, in the gaps between systems that look clean on their own.

8 July 2025Read

Every AI proposal assumes your data is fine. Nobody has checked.

Exports from two or more systems, compared record by record. The gaps between them, and a verdict, in a signed memo within 5 working days of complete exports. From a firm with nothing else to sell you.

Get your Truth Score See a real sample memo

Starts with a 30-minute call. Commits you to nothing.

What your data can support, before anyone signs.

Start here

Why enterprise AI projects fail: the data was never checked

Read by topic.

One method, twelve companies, one baseline: getting ahead of the AI readiness gap

Vendor scores tell you one system is happy with itself. That is not readiness.

Where AI projects actually break: the cross-system gap PE firms keep funding through

Agentic AI in portfolio companies: why teams deploy before the data is ready

Clean data, verifiable KPIs, and the multiple you leave on the table at exit

The Amplification Effect: why AI multiplies the data errors already in your portfolio

The hidden cost of bad CRM data in buy-and-build roll-ups

Data governance is not a compliance cost. It is a value creation lever.

AI due diligence in PE deals: why data readiness belongs in the investment committee

The complete pre-AI checklist for portfolio companies

Why data readiness is now a strategic imperative

Planning your 2026 AI initiative: a data-first approach

AI data quality trends to watch in 2026

2025 in review: the year AI hit the data quality wall

The Truth Score: a new approach to measuring AI readiness

AI value creation in private equity: beyond the hype

The AI vendor landscape is broken

Data governance for AI: the foundation PE firms overlook

AI readiness by industry: where portfolio companies stand

Building a data quality scorecard: metrics that matter

The hidden cost of bad data on AI initiatives

Why your CRM data is not ready for AI

Data quality vs data readiness: why the distinction matters

Anatomy of a failed AI project: an illustrative churn model story

The six dimensions of data quality that determine AI success

Why enterprise AI projects fail: the data was never checked

New articles, when they’re published. Nothing else.

Every AI proposal assumes your data is fine. Nobody has checked.