We lost a deal once because our diligence took too long.
Not the formal diligence. The weeks of work between "this looks interesting" and "we're ready to submit an LOI."
The target was a multi-site physician group in a market where we needed density. Good payer mix. Founder approaching retirement. No banker involved. Our team was running three other active processes simultaneously. The lead who would normally own the pre-LOI work was buried in a closing. I was the one pulling market data, building the preliminary model, cross-referencing the competitive landscape, and validating payer mix against internal data. All while managing two other targets.
By the time we had a package ready for our IC, another group had submitted an LOI with a 30-day exclusivity window. They did enough homework to move with conviction. We hadn't.
That bottleneck is the same one I hear about in almost every conversation with corp dev teams today.
The Layer Nobody Measures
Most deal teams track days-to-close. Almost none measure the hours per target between "first look" and "LOI submission."
Pre-LOI diligence in healthcare M&A is a stack of analytical work: market sizing the target's geography, validating seller financials against benchmarks, building a preliminary model, mapping the competitive landscape, assessing operational fit against the platform thesis.
Each layer is a 10-20 hour exercise when done manually. For a team running 3-5 active targets, that's 50-100 hours of pre-LOI work in flight. Spread across 2-3 people also managing live closings and board reporting.
LBMC's analysis of the current healthcare transaction landscape states it directly: "The traditional approach of signing an LOI and then performing diligence has given way to a new model: conducting targeted, high-impact diligence before exclusivity." They found that buyers who do this "consistently separate successful acquirers from those who struggle to close."
The problem is typically not awareness. The problem is that the work required exceeds the bandwidth of most lean corp dev teams. When bandwidth is the constraint, you move too slow and lose the deal, move too fast and miss something, or your senior people burn out doing analyst-level research because there's nobody else.
What AI Compresses
Bain's 2026 M&A report, surveying 300+ executives, found AI adoption in M&A more than doubled in 2025. 45% of practitioners now use AI tools. One-third are systematically deploying AI or redesigning processes around it. But the telling detail: sourcing and screening remain the primary use cases. Pre-LOI analytical diligence, the middle of the funnel, is still largely manual.
From what I've seen, AI has real leverage at three specific layers of pre-LOI work:
Market intelligence assembly. Census ACS data, CMS Provider of Services files, state licensing databases, payer mix proxies. AI can query these sources, structure the output, and produce a first-draft market assessment in hours instead of weeks. The publicly available data is better than most teams realize.
Competitive landscape mapping. Identifying every PE-backed platform in a target market, their acquisition history, their footprint, and their likely appetite. A week of PitchBook research and Google searches compressed to a day.
Preliminary financial modeling. Not QofE-level analysis. The first-pass model that gets you from "this looks interesting" to "here's the indicative valuation range." AI takes a CIM or teaser, extracts financials, builds a comp table against current market multiples, and generates a working model with sensitivity analysis.
PwC's Deals team has been public about using Harvey AI across the deal lifecycle. Their Deals AI Lead described using Harvey Vault to analyze customer contracts for price pass-through clauses at scale, something "previously impractical within compressed timelines." The result: "Instead of spending all of the time working through a small sub-set of contracts, we were able to focus the diligence time on the risks and upsides." In a follow-up case study, PwC's team noted: "Where a team member might previously have spent a day pulling key facts and context on a target, they can now get to an informed perspective in an hour."
Same principle, applied to the pre-LOI layer. The tools handle assembly. Practitioners handle judgment.
What AI Cannot Do Here
Seller motivation. Whether a founder is truly ready to sell or testing the market. Whether their stated reason matches reality. That intelligence comes from conversations.
Physician retention risk at the human level. AI can flag key-person dependency from provider count and revenue concentration. It cannot tell you whether the 58-year-old founder will stay for the earn-out or is mentally checked out.
Referral relationship durability. CMS shared patient data shows billing patterns that imply referral networks. It won't tell you that Dr. Smith sends patients to the target because they trained together, and that relationship won't transfer to a new owner. In healthcare M&A, the referral pathway is often the business.
IC politics. Your committee doesn't just want to know if a target is attractive. They want to know if it fits the exit narrative. AI can't answer that.
The Framework
Here's how I'd structure the pre-LOI analytical stack for a physician practice acquisition today:
Layer 1: Market Validation (AI-led, 4-6 hours)
Demographics, payer mix proxies, provider supply mapping, demand indicators. All queryable from free public data: Census ACS, CMS POS files, SEER, state licensing databases.
Layer 2: Competitive Intelligence (AI-assisted, 6-8 hours)
PE-backed platform inventory by market, recent transaction activity, white space identification, consolidation stage assessment.
Layer 3: Preliminary Valuation and Fit (Human-led, AI-assisted, 8-12 hours)
Financial model from CIM or teaser data, comp table with current multiples, strategic fit scoring, risk flag summary.
Total: 18-26 hours for a structured pre-LOI package. Compared to 40-60 hours manually, that's a 50-60% compression. The time saved reallocates capacity from data gathering to decision-making. Teams that systematize this evaluate 2-3x the targets with the same headcount.
If you're building a healthcare platform and the pre-LOI bottleneck is real for your team right now, reply to this email or book a call at the link below. I'd be happy to talk through what a systematized version of this looks like.
-Shawn