# Shefali Korke

Shefali Korke works on revenue analytics and AI for business decision-making. She built Inteliqx, a platform focused on finding revenue risks in private-equity due diligence, and writes about the data foundations needed to make AI useful in business.

- Profile: https://b2binfluencer.marketing/creators/shefali-korke/
- LinkedIn: https://www.linkedin.com/in/shefalikorke
- Topics: artificial-intelligence, revenue-operations, data-governance, ai-readiness
- Based on 12 dated posts, latest 2026-10-07

## Summary

Useful for B2B content on revenue analytics, AI risk detection, and private-equity due diligence data checks.

- Best for: FinTech brands running content or co-marketing on AI risk detection in private-equity diligence.
- Not for: Consumer fintech app install campaigns for retail budgeting, investing, or consumer acquisition.
- Check first: Confirm partnership formats, commercial terms, and recent LinkedIn audience data for FinTech, risk, or private equity.

### Takeaways

- An AI review found about $3M annual revenue leakage in a $550M software company's billing data.
- Five CRM red flags include stalled pipeline deals, recycled opportunities, inconsistent segment win rates.
- Korke argues AI projects fail on bad data, not bad models.

## Main ideas

- **Revenue leakage is a systems problem, not a fraud problem**: Shefali Korke argues that major revenue losses at software companies usually stem from ordinary misalignment between CRM, billing and ERP systems, not intentional misconduct. Traditional due diligence reviews high-level financials and samples invoices, leaving too little time to scan every contract term, pricing agreement and transaction. Patterns such as recurring revenue that stopped without a cancellation, expired contract terms still billed at old rates, unbilled usage overages, temporary discounts that became permanent, and services delivered but stuck in billing exceptions emerge only when the full dataset is analyzed. Before a deal closes, that analysis can change valuation and negotiation; afterward, it can guide recovery in the first 90 days and process repairs to prevent a repeat.
- **AI projects fail on bad data, not bad models**: Drawing on 15 years of building production BI systems for multi-billion-dollar units at Dell EMC and RSA Security, Shefali Korke argues that companies often hire data scientists and buy machine-learning platforms while their CRM data remains messy, team definitions differ, manual pipelines break each quarter and shadow reporting undermines trust. Their point: AI amplifies bad data rather than fixing it. A model trained on garbage produces garbage, and an LLM working from inconsistent taxonomies can hallucinate confidently. Governance, clear ownership, consistent definitions and automated quality checks are the foundation for AI, not pre-AI chores. With those in place, Korke says clients at Vizible Results moved from concept to production AI in 6 to 12 weeks instead of 6 to 12 months.
- **Diligence should read CRM data as a risk signal**: Shefali Korke turns CRM data into revenue red flags for private-equity diligence, tying each to a cause rather than dismissing it as ordinary sales-cycle noise. Stalled pipeline deals sitting six months suggest weak sales execution or poor qualification. Reopened opportunities recycled repeatedly point to a lack of real buyer interest or sales teams gaming the numbers under pressure. Inconsistent win rates by segment expose product-market fit issues or misaligned strategy. Low activity-to-close ratios signal inefficiency or a weak value proposition. Quarter-end discounting spikes indicate weak pricing power and predict future churn. CRM data is messy, siloed and nearly impossible to analyze at scale within compressed diligence timelines, so these signals often go overlooked.
- **The hard part is getting leaders to accept the numbers**: Shefali Korke argues that the real obstacle to revenue intelligence is not tooling but willingness. Companies say they want a single dashboard showing where to focus, which deals are real, who is sandbagging and where the leaks are. But many resist the answers when data challenges sacred cows, pet projects or favorite narratives. The post describes the room going quiet when the numbers land, territories being defended and the story shifting. The choice: let data challenge assumptions, even when it stings, or run a stage production instead of a revenue engine. Without that acceptance, the exercise is theater, not intelligence.

## Work to read

- [Finding $3M in lost revenue in a $550M software company's billing data](https://www.linkedin.com/posts/shefalikorke_inteliqx-revenue-intelligence-for-pe-secondaries-activity-7394382987561201664-lM5W)
- [Five revenue red flags that due diligence misses](https://www.linkedin.com/posts/shefalikorke_inteliqx-revenue-intelligence-for-pe-secondaries-activity-7395104769729605632-vTnY)
- [Data foundations come before AI](https://www.linkedin.com/posts/shefalikorke_decision-intelligence-vizible-results-inc-activity-7394734841067372545-GZHs)
- [Revenue intelligence or theater?](https://www.linkedin.com/posts/shefalikorke_revenueintelligence-revops-b2bsales-activity-7397317864900177921-f1fC)

## Brand relationships

- **Inteliqx** (owned_business)
- **Vizible Results** (employment)
- **Dell EMC** (employment)
- **RSA Security** (employment)

## Partnerships

The five posts are Shefali Korke’s own writing, promoting Inteliqx, the revenue-analysis platform they describe building, or Vizible Results, the data-foundation work they describe doing. Calls to action point to inteliqx.com and to direct messages or replies for private-beta access. The posts do not describe sponsored placements, guest posts, speaking slots, affiliate arrangements, rate cards or media-kit terms, and they link to no separate sponsor policy or partnership page. There is no published partnership product for buyers to plan around: the posts set out no deliverables, pricing, exclusivity, lead times or approval requirements. The only visible contact path is the beta inquiry on the Inteliqx product site, a sales route for that platform rather than a creator-partnership channel. Buyers interested in working with Shefali should approach directly and establish scope and terms in that conversation.

## Alternatives

- Michael Taylor: Choose Michael Taylor when a fintech campaign needs contract-to-invoice leakage and procurement recovery rather than CRM-pipeline risk. The posts cover contract compliance practices: a central contract repository, obligation tracking, standardized clauses, continuous performance monitoring, a defensible audit trail, and named ownership. One post describes PRGX Contract Insights reading contracts and checking terms against invoiced amounts. It cites a public case where a national retailer ran 900+ supplier agreements through the tool: 50%+ of suppliers were paid earlier than contract required, terms ran 30+ days shorter than peer benchmarks, and the result was $22M in working capital unlocked and $2.8M+ in interest savings. For a diligence audience, that is a concrete example of post-close recovery and working-capital analysis.
- Jordan Zamir: Choose Jordan Zamir when a campaign is about revenue-recognition controls, contract data as a source of truth, or practical AI in finance workflows. One post explains ASC 606: recognize revenue as delivered, split bundled performance obligations, and understand why ERPs built around the general ledger and invoices often force finance teams to retype contract data. It argues for a single contract and billing source of truth. The other describes building an automated morning briefing in Claude from Google Sheets, Excel, CRM, and Turnstile, with a live session on turning prompts into repeatable skills. This is useful for a fintech audience focused on post-close finance operations or automation adoption.
- Clive William Kreft: Choose Clive William Kreft when the buyer task is founder-side exit preparation, confidentiality, or matching a business to the right acquirer. One post explains why a deal can be explored quietly: employees, customers, and competitors can react before a transaction is real, so timing, audience, and message matter. The other argues that buyers value different things in the same company, including customer relationships, market position, delivery capability, intellectual property, and team, and that a sale process should start by understanding the buyer landscape. For an audience of SaaS founders, agency owners, MSPs, or PE-backed sellers, this is practical process content.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/