At this year’s ACG Rocky Mountain Corporate Growth Conference, Grata’s VP of Sales, Phil Coughlin, helped spotlight a challenge many dealmakers feel daily but rarely tackle holistically: data. Not one big “project,” but a series of frictions - from incomplete visibility in sourcing to second‑round diligence questions that expose gaps, to post‑close reporting that takes longer than it should. The panel, moderated by Kyle Millar-Corliss – Pandoblox; featuring Phil Coughlin – VP of Sales, Grata, Henry Park – Founder/CEO, Candleblocks, and Pat Turpin - former Costco exec, Popchips founder; CPG board member, cut through hype to get practical on data readiness and AI in BI across the deal lifecycle.

Sourcing: From Rolodex to “Living” Market Intelligence

Phil framed the core sourcing problem succinctly: relationships still win - but only if you know who to build them with. Many firms still see just a fraction of the market and rely on static CRMs, missing emerging targets and context. The modern motion blends:

1. Full market visibility: combining proprietary relationship data with trusted, diligence‑grade external data in one living universe of companies.
2. AI‑enabled workflows: using AI to maintain taxonomies, enrich contacts, and prioritize outreach so associates spend time with people, not spreadsheets.
3. Metric‑based accountability: defining precise origination KPIs (e.g., conf‑to‑LOI conversion, broker/GP meeting counts by theme) and inspect them weekly.

“Your relationships remain paramount, but without a living dataset and AI‑enabled workflows, you’ll miss the hidden middle market. Hold teams accountable to clear sourcing metrics.” – Phil Coughlin, VP Sales, Grata

A simple conference example: ingest the ACG attendance list, map to your CRM history and recent deal activity, then let AI stack‑rank who to meet and draft context‑aware emails - turning 8 hours of prep into 1 and increasing the odds you’re first to field.

Diligence: When Data Shifts from Signal to Proof

In diligence, data stops being “interesting” and becomes existential. Pat described the moment confidence cracks: after first‑round requests, the second‑round questions hit with more granularity. If a team can’t reconcile story to system‑of‑record data - by SKU, cohort, channel, region - the buyer’s trust erodes.

A familiar pattern: top‑line growth masks churn in smaller accounts, or hidden costs sit undetected because ERP/BI wasn’t modeled at the right dimensionality. The credibility killers? Off‑the‑cuff answers that don’t tie to underlying data, or “we don’t track that” when asked for unit‑level economics.

“Buyers aren’t purchasing a revenue line, they’re buying a repeatable earnings engine. Granular, decision‑grade data is what proves that.” – Pat Turpin, Board Member & Advisor

Why BI/ERP Alone Don’t Create Readiness

Henry drew a hard line: BI is a visualization layer, not a readiness solution; ERP is transactional, not analytical by default. Both fail when companies skip the hard work:

• Common taxonomy & normalization across ERP, CRM, payroll, and ops data
• Cross‑functional agreement on definitions (e.g., margin, churn, cohort)
• Process readiness and change management (people using the system the new way)

“Business intelligence isn’t a product you buy; it’s an organizational journey. If you visualize bad data, you just get prettier bad data.” — Henry Park, CEO

Budget reality: ERP quotes often understate the true cost. Expect change‑management, data modeling, and customization to double the estimate - and more if you retrofit later.

AI in BI: Where It Helps and Where It Hurts

The panel’s consensus: AI meaningfully reduces “tedious” time, such as CRM hygiene, market research, enrichment, and conference prep. But only when it sits on clean, governed, private data with human oversight. Where it fails: trying to use AI to paper over missing definitions, poor taxonomy, or departmental disagreement about “the truth.”

“Use AI on top of proprietary data you trust and set guardrails. Hallucinations in comps, fake entities, or fabricated legal facts are how you lose deals.” –  Phil Coughlin

Common misconceptions the panel flagged:

• “AI will find our margin” → Only if the cost model is integrated (ERP + CRM + payroll, etc.) and normalized.
• “AI replaces analysts” → It augments analysts who’ve got clear SOPs; without them, automation just accelerates chaos.
• “Shadow AI is harmless” → It isn’t. Without an AI policy, employees paste sensitive data into public tools, creating compliance and IP risk.
  

Exit Readiness: A Practical Definition

“Exit ready” means you can produce investor‑grade reporting quickly, accurately, and at the right level of detail - and show that management uses those insights to run the business. It’s not a binder; it’s a repeatable system:

• Single source of truth across systems
• Drillable reporting (SKU/customer/channel/cohort) with audit trails
• Defined origination & pipeline KPIs linked to outcomes
• Governed AI that accelerates, not invents, insight

“The organizations with real data granularity earn trust faster, find hidden margin, and command premium exits.” – Pat Turpin

Rapid‑Fire from the Panel: What to Fix First

• Sourcing: “Install accountability on origination KPIs, own the numbers, weekly.” – Phil
• Readiness: “Get the data right first, taxonomy, normalization, clean joins, before ERP, BI, or AI.” – Henry
• Premium vs. Discounted Exits: “Granularity separates them, credibility, margin visibility, and repeatability.” – Pat

Across sourcing, diligence, and exit, data is no longer a side input - it shapes the outcome. AI doesn’t change that; it amplifies whatever foundation you’ve built. With a living market dataset, agreed definitions, and governed AI on private data, teams move faster, answer tougher questions with confidence, and earn the right to premium valuations.

See how Datasite, Grata, and Blueflame AI help dealmakers build living market visibility and accelerate diligence with AI that’s built for the deal process. Talk to us about operationalizing data readiness - before your next process moves to second‑round questions.