Compare AI Due
Diligence Approaches
TL;DR
Standard due diligence misses the AI layer entirely. Tech-only DD catches the technology but ignores adoption readiness. AI-augmented DD from WorkWise evaluates capabilities, data quality, model risk, and whether the team can actually use what's been built.
Three approaches. Different coverage. Very different outcomes. Here is what each one actually examines, what it 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 due diligence is good at what it was designed for. Financials, legal exposure, market sizing, management backgrounds. These are known quantities with known methods. Your accountants and lawyers have done this a thousand times.
But most target companies now claim some form of AI capability. The CIM says "proprietary AI." The management presentation shows a slide about machine learning. The data room has a folder called "Technology" with a few architecture diagrams.
Standard DD teams look at this material and move on. They don't know what questions to ask. They can't tell the difference between a fine-tuned model and an API wrapper. They can't evaluate whether the training data is clean or whether 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"
This gap matters more every quarter. AI is no longer a line item in a tech budget. It is increasingly the thing that determines whether a portfolio company can scale, defend margins, or deliver on the growth story you paid for.
Side-by-Side Comparison
Not all due diligence is equal when AI is involved. Here is exactly what each approach 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 has $2M in annual tech spend. They won't tell you that $1.4M of that goes to an AI vendor whose contract renews in 90 days with a 40% price increase clause.
They'll note the company has "AI-driven pricing." They won't flag that the model was trained on 2023 data and hasn't been retrained since, meaning its predictions are quietly degrading every week.
Biggest gap: no ability to assess whether AI claims in the CIM are real, exaggerated, or completely fabricated. This happens more than you'd expect.
Tech-only DD blind spots
Tech DD firms evaluate code quality, infrastructure, and scalability. Good things to know. But they often treat AI as another software component. It isn't.
They'll confirm the model runs. They won't ask whether anyone on the team actually trusts it enough to use it in their daily workflow. A model with 95% accuracy that the operations team ignores is worth exactly zero.
They also miss the commercial context. A technically sound AI system that doesn't map to a revenue driver or cost reduction is an expensive hobby. Tech DD tells you what exists. It doesn't tell you what it's worth to the business.
What WorkWise AI DD adds
We designed our AI due diligence specifically for PE deal teams, family offices, private credit lenders, and independent sponsors. That means 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, and 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 our AI DD process for one audience: deal teams that need to make investment decisions under time pressure. Every step produces something your IC 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 actually running in production or sitting in a staging environment.
Data Quality Assessment
We audit the full data pipeline. Sources, cleaning processes, labeling accuracy, freshness, and bias exposure. Bad data in means bad predictions out, no matter how good the model architecture is.
Model Risk Scoring
Every model gets a risk score based on drift exposure, retraining frequency, vendor dependency, and failure mode analysis. You'll know exactly which models are stable and which are ticking clocks.
Adoption Readiness Review
We assess whether the team can actually use the AI that exists. Workflow integration, trust levels, skill gaps, and change resistance. This is where most post-acquisition AI plans fail, and where behavioral science expertise makes the difference.
Frequently Asked Questions
Can we run AI DD in parallel with our standard due diligence process?
Yes. That's how most of our engagements work. We integrate with your deal timeline and share findings with your existing DD providers so there's no duplication. Our 2-3 week timeline is designed to fit inside a standard PE deal process without slowing anything down.
What if the target company won't give us access to their code or models during DD?
This happens. We have a structured process for assessing AI capabilities through management interviews, output analysis, and indirect indicators. We can't get to the same depth as a full code review, but we can flag the major risks and give your IC a clear picture of what's verifiable and what isn't. The refusal itself is a data point.
How is this different from hiring a technical advisor for the deal team?
A technical advisor gives you opinions. We give you a structured assessment with risk scores, a value-creation roadmap, and an IC-ready memo. The difference is rigor and format. Your partners don't want to sit through 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 capabilities before going to market. This is increasingly important. Buyers are getting smarter about AI claims, and a pre-verified AI assessment can meaningfully accelerate the sales process and support your asking price.
What size deals does this apply to?
We work on deals from $50M to $2B+ enterprise value. The AI risk doesn't scale linearly with deal size. A $100M acquisition of a data-driven business can have more AI risk exposure than a $1B platform deal. The question is how central AI is to the investment thesis, not how large 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 fit your deal size and budget. Independent sponsors in particular benefit from this because you don't have an internal technology team to lean on during the deal process.
Don't Let AI Claims Go Unverified
Your next deal probably involves AI claims you can't verify with traditional DD. Let's talk about what a proper AI assessment would look like for your specific deal.