Compare AI Due
Diligence Approaches
TL;DR
Standard DD misses AI entirely. Tech-only DD catches the technology but ignores whether people will use it. WorkWise AI DD checks the models, the data, the risks, and whether the team can actually run what's been built.
Three approaches. Different coverage. Very different outcomes. Here's what each one checks, what each one misses, and how to pick the right one for your next deal.
By Dr. Leigh Coney, Founder of WorkWise Solutions
Why Standard DD Falls Short
Traditional DD is good at what it was built for. Financials. Legal exposure. Market size. Management backgrounds. Your accountants and lawyers have done this a thousand times.
But most targets now claim some kind of AI capability. The CIM says "proprietary AI." The management deck has a slide about machine learning. The data room has a "Technology" folder with a few architecture diagrams.
Standard DD teams skim it and move on. They don't know what to ask. They can't tell a fine-tuned model from an API wrapper. They can't tell if the training data is clean or if the model will break when the market shifts.
What traditional DD covers well
Financial statements and projections
Legal and regulatory compliance
Market size and competitive position
Management team evaluation
What it misses about AI
Data quality and pipeline integrity
Model risk and drift exposure
Adoption readiness of the team
Technical debt in the AI stack
Vendor lock-in and API dependency
True defensibility of "proprietary AI"
The gap gets bigger every quarter. AI isn't a tech budget line anymore. It's often the thing that decides whether a portfolio company can scale, defend margins, or deliver the growth story you paid for.
Side-by-Side Comparison
Not all DD is equal when AI is involved. Here's exactly what each one covers.
| Dimension | Traditional DD | Tech-Only DD | WorkWise AI DD |
|---|---|---|---|
| Scope | Financials, legal, market, ops | Tech stack, code quality, infrastructure | AI capabilities + data + models + team readiness + financials integration |
| AI Expertise | None. Relies on management claims. | Software engineering focus. May lack ML/AI depth. | PhD-level AI + behavioral science. Built for PE deal context. |
| Data Quality Audit | Not assessed | Database structure review only | Full pipeline audit: sources, cleaning, labeling, freshness, bias detection |
| Model Risk Assessment | Not assessed | Basic model inventory | Drift analysis, failure modes, accuracy under distribution shift, vendor dependency |
| Behavioral / Adoption Assessment | Management interviews (general) | Not assessed | Team capability mapping, workflow integration, change readiness scoring |
| Timeline | 4-8 weeks (part of broader DD) | 2-4 weeks | 2-3 weeks (standalone) or integrated with deal timeline |
| Deliverable Format | Standard DD report section | Technical assessment PDF | IC-ready memo with risk scoring, value-creation roadmap, and 100-day plan |
| Cost Range | $50K-$200K (included in broader DD fees) | $30K-$80K | $40K-$100K (standalone AI DD engagement) |
What Each Approach Misses
Traditional DD blind spots
A traditional DD provider will tell you the target spends $2M a year on tech. They won't tell you $1.4M of that goes to an AI vendor whose contract renews in 90 days with a 40% price hike baked in.
They'll note "AI-driven pricing." They won't flag that the model was trained on 2023 data and hasn't been retrained since. Its predictions are quietly getting worse every week.
Biggest gap: they can't tell you whether the CIM's AI claims are real, exaggerated, or made up. This happens more often than you'd expect.
Tech-only DD blind spots
Tech DD firms check code quality, infrastructure, and whether it scales. Good things to know. But they treat AI like regular software. It isn't.
They'll confirm the model runs. They won't ask whether anyone trusts it enough to use it. A 95% accurate model that the ops team ignores is worth zero.
They also miss the commercial angle. An AI system that doesn't tie to a revenue driver or a cost cut is an expensive hobby. Tech DD tells you what exists. It doesn't tell you what it's worth.
What WorkWise AI DD adds
We built our AI DD for PE deal teams, family offices, private credit lenders, and independent sponsors. Every finding maps to a decision you need to make before close.
The output is an IC-ready memo, not a technical document your partners won't read. Risk scores. Value-creation opportunities. A 100-day post-close roadmap. The kind of thing that changes how you price a deal or structure an earn-out.
When to Use Each Approach
Use traditional DD alone when...
- 1. The target has no meaningful AI or ML components in its operations or product.
- 2. AI is mentioned in the CIM but only as a future roadmap item, not a current capability.
- 3. Your investment thesis doesn't depend on the target's technology differentiation.
Add tech-only DD when...
- 1. The target is a software company and you need to assess code quality, tech debt, and infrastructure scalability.
- 2. AI is a small component of a larger software product and the investment thesis is about the product, not the AI.
- 3. You have internal AI expertise on the deal team who can supplement the tech DD findings.
You need AI-augmented DD when...
- 1. The target's valuation is partially justified by AI capabilities or "proprietary technology."
- 2. Your value-creation plan includes deploying AI across portfolio companies post-close.
- 3. The CIM makes specific claims about AI-driven revenue, cost savings, or competitive moats.
- 4. You're evaluating a data-heavy business where model accuracy directly affects unit economics.
- 5. Your IC needs a clear picture of AI risk before approving the deal.
"I've reviewed CIMs that claim 'proprietary AI' when the entire system is a GPT-4 API call with a custom prompt. I've seen 'machine learning models' that are really just Excel regression lines someone packaged into a dashboard. The gap between what CIMs claim and what actually exists is the most underpriced risk in PE right now."
Dr. Leigh Coney, Founder of WorkWise Solutions
"The biggest mistake organizations make with AI is assuming it works because it demos well. A demo is a controlled environment. Production is chaos. The gap between those two things is where most AI projects go to die."
Ethan Mollick, Professor at Wharton, Author of Co-Intelligence
How WorkWise AI Due Diligence Works
We built this process for one audience: deal teams making decisions under time pressure. Every step gives your IC something it can act on.
AI Capability Mapping
We separate real AI from marketing AI. What models exist, what they do, how they were trained, and whether they're running in production or still sitting in staging.
Data Quality Assessment
We audit the full data pipeline. Sources, cleaning, labeling, freshness, and bias. Bad data in means bad predictions out, no matter how good the model is.
Model Risk Scoring
Every model gets a risk score based on drift, retraining frequency, vendor dependency, and how it fails. You'll know which models are stable and which are ticking clocks.
Adoption Readiness Review
We check whether the team can actually use the AI that exists. Workflow fit, trust levels, skill gaps, and whether people will resist it. This is where most post-acquisition AI plans fail, and where behavioral science changes the outcome.
Frequently Asked Questions
Can we run AI DD in parallel with our standard due diligence?
Yes. That's how most of our work goes. We plug into your deal timeline and share findings with your other DD providers so nothing is duplicated. The 2-3 week window fits inside a standard PE deal process without slowing it down.
What if the target won't give us access to code or models during DD?
This happens. We assess AI capability through management interviews, output analysis, and indirect signals. It's not as deep as a full code review, but we can flag the big risks and show your IC what's verifiable and what isn't. The refusal itself is a data point.
How is this different from hiring a technical advisor?
A technical advisor gives you opinions. We give you a structured assessment with risk scores, a value-creation roadmap, and an IC-ready memo. Your partners don't want a 90-minute technical briefing. They want a document that tells them the risk level and what it means for the deal.
Do you only work on buy-side DD, or sell-side too?
Both. On the sell side, we help portfolio companies document and validate their AI before going to market. Buyers are getting smarter about AI claims. A pre-verified AI assessment speeds up the sale and supports your asking price.
What size deals does this apply to?
We work on deals from $50M to $2B+ enterprise value. AI risk doesn't scale with deal size. A $100M data-driven business can carry more AI risk than a $1B platform deal. What matters is how central AI is to your thesis, not how big the check is.
Can family offices and independent sponsors use this, or is it only for large PE firms?
We work with all four. PE firms, family offices, private credit lenders, and independent sponsors. The engagement scales to your deal size and budget. Independent sponsors benefit the most since you don't have an internal tech team to lean on during the deal.
Don't Let AI Claims Go Unverified
Your next deal probably has AI claims your traditional DD can't verify. Let's talk about what a proper AI assessment would look like for your deal.