Artificial intelligence can affect an acquisition even when the target does not describe itself as an AI company. Employees may use generative assistants, customer platforms may contain predictive features, developers may rely on model APIs, and critical workflows may depend on automation that no one has documented. AI due diligence should reveal those dependencies before the investment committee accepts the valuation, risk, and post-close operating plan.
The objective is not to count licenses or collect a list of impressive demonstrations. A useful review connects each material use case to business value, source data, system access, contractual rights, human oversight, failure behavior, operating cost, security, and accountable ownership. It also separates proven production results from experiments, vendor promises, and employee workarounds.
ALLMSP helps private equity firms and portfolio operators in Lawrenceville, Suwanee, Gwinnett County, Metro Atlanta, and across Georgia investigate AI and technology conditions before and after a transaction. Our in-house team can assess the environment, validate controls, build the remediation roadmap, configure approved platforms, integrate systems, train users, and support ongoing operations.
A practical AI diligence process for an acquisition
- Define the investment questions: Identify which revenue, margin, customer, compliance, integration, and growth assumptions depend on AI, automation, data, or a specialized technology provider.
- Inventory real use: Find approved products, embedded features, model APIs, internally developed tools, browser extensions, employee accounts, automated decisions, and experimental workflows.
- Trace data and rights: Document source systems, sensitive information, permissions, retention, model training terms, intellectual property, customer commitments, and the target’s right to use every important input and output.
- Test operating controls: Review identity, administration, logging, human approval, monitoring, error handling, incident response, backup, continuity, and the ability to stop or replace the workflow.
- Validate economics: Reconcile claimed savings or revenue with baseline labor, adoption, quality, exception handling, subscription and usage costs, support effort, and measurable business outcomes.
- Convert findings into action: Assign each issue to a deal decision, closing condition, risk acceptance, purchase agreement consideration, insurance discussion, or funded post-close workstream.
Map every material AI use case and its business dependency
Begin with interviews that follow actual work. Ask leaders and frontline users where a model drafts, classifies, predicts, recommends, searches, scores, routes, or changes a record. Then confirm the answer through application inventories, expense records, identity logs, browser management, API keys, source repositories, automation platforms, vendor contracts, and support tickets. The gap between the approved inventory and observed use is itself a diligence finding.
For each material use case, prepare a concise operating card. State the problem, users, input, output, connected systems, human decision, customer impact, owner, cost, measured result, known limitations, and fallback. This makes it possible to distinguish a helpful assistant from a workflow that could interrupt billing, expose customer information, change a regulated record, or damage a service commitment.
- Production status: Separate ideas, demonstrations, limited pilots, employee-created shortcuts, approved production workflows, and features that are available but not enabled.
- Business reliance: Record what stops, slows, becomes more expensive, or changes quality when the model, integration, data feed, or vendor service is unavailable.
- Decision role: Identify whether AI creates a draft, recommends an action, approves a result, communicates externally, or changes a system without a person reviewing it.
- Customer and contract effect: Check whether statements, deliverables, service levels, pricing, confidentiality, ownership, or sector requirements depend on the workflow.
- Evidence of value: Compare management claims with adoption records, cycle time, correction rates, support demand, conversion, retention, quality, and financial results.
A complete inventory lets the deal team see where AI creates defensible capability, where it is ordinary software functionality, and where an undocumented dependency changes risk or valuation.
Examine data, security, contracts, and technical durability
AI performance depends on data that may be incomplete, duplicated, poorly labeled, inaccessible, or used beyond its original purpose. Trace representative inputs from their authoritative source through transformation, prompt or retrieval, model processing, output, review, and storage. Sample the records that matter most to the investment thesis, including difficult exceptions and historical periods that management may not use in a demonstration.
Technical review should also answer whether the target controls the accounts, code, prompts, models, integrations, and recovery path. A founder-owned subscription, former employee API key, undocumented open-source component, or provider-controlled administrator can turn an apparent asset into a post-close interruption. Validate the environment through settings, logs, tests, contracts, and direct evidence rather than screenshots prepared for diligence.
- Data provenance: Identify where important records came from, how they were changed, who approved their use, what quality checks run, and how errors are corrected.
- Sensitive information: Test whether prompts, files, logs, embeddings, exports, and support channels contain customer, employee, financial, health, legal, credential, or confidential deal data.
- Identity and administration: Review managed accounts, multifactor authentication, privileged roles, service identities, secret storage, emergency recovery, departures, and connected application permissions.
- Vendor and license terms: Confirm data use, model training, subprocessors, locations, retention, indemnity, service commitments, audit rights, ownership, portability, renewal, and termination behavior.
- Model and workflow quality: Use representative cases to measure unsupported output, inconsistent treatment, false confidence, drift, latency, unavailable sources, and the effectiveness of human review.
- Continuity: Verify configuration records, version control, dependency inventories, monitoring, fallback procedures, data exports, backup, restoration, and the effort required to replace a service.
The goal is a defensible view of what the buyer will own and control on day one, not a catalog of features that happen to contain the word AI.
Connect findings to valuation, deal terms, and the first 100 days
Summarize findings in business language. A weak model evaluation may affect a promised product capability, unreliable customer data may change a revenue assumption, and an expensive inference design may reduce expected margin. Tie each condition to the investment thesis, customer obligations, operating continuity, cost, scalability, cybersecurity, reputation, and the time needed to correct it.
Build a post-close plan before signing when possible. Immediate work may include securing administrative control, rotating secrets, restricting unapproved tools, preserving source code, validating critical data, correcting customer-facing claims, documenting human approval, and establishing incident reporting. Longer projects can improve architecture, data quality, evaluation, automation, adoption, and portfolio standards after urgent exposure is controlled.
- Decision record: State the evidence, business impact, confidence level, unresolved question, responsible executive, and how the finding affected the investment decision.
- Cost model: Include subscriptions, usage, cloud, integration, data preparation, security, testing, monitoring, support, training, legal review, migration, and replacement options.
- Closing readiness: List accounts, code, credentials, domains, contracts, repositories, documentation, vendor contacts, data exports, and approvals that must transfer to company control.
- First-week containment: Prioritize active data exposure, unknown privileged access, unsupported customer claims, unreliable automated actions, unowned services, and missing recovery capability.
- Verification: Define the exact test, evidence, owner, due date, and acceptance condition required to close each remediation item.
Diligence becomes valuable when it changes a decision, protects continuity, or gives the operating team an executable plan with evidence and ownership.
Private equity AI and technology diligence with ALLMSP
ALLMSP can perform technical discovery, application and account inventory, AI use-case mapping, data-flow review, cybersecurity testing, contract and ownership verification, cost analysis, risk prioritization, and first 100 days planning. We can continue directly into remediation and managed operations so findings do not disappear after the report is delivered.
Our Georgia team supports investment firms and portfolio companies in Lawrenceville, Suwanee, Gwinnett County, Metro Atlanta, and statewide. AI, cloud, cybersecurity, networks, endpoints, backup, software, marketing technology, documentation, training, and executive technology guidance are handled by one accountable in-house team.
- Pre-close assessment: Use cases, data, systems, accounts, contracts, security, intellectual property, costs, claims, dependencies, and deal-level findings.
- First 100 days roadmap: Immediate containment, ownership transfer, funded remediation, platform decisions, measurable pilots, standards, and acceptance evidence.
- Ongoing delivery: Implementation, integration, monitoring, managed IT, cybersecurity, user training, support, reporting, and continuous AI improvement.
Primary resources for AI and technology diligence
Use recognized risk frameworks to organize evidence, then apply them to the target’s actual systems, contracts, customers, data, and investment thesis.
- NIST AI Risk Management Framework. A voluntary structure for governing, mapping, measuring, and managing AI risk.
- NIST Generative AI Profile. Detailed considerations for risks that are distinctive to or increased by generative AI.
- NIST Cybersecurity Framework 2.0. A business-oriented way to assess governance, assets, protection, detection, response, and recovery.
- ALLMSP AI Services. AI readiness, workflow design, secure implementation, training, governance, and ongoing optimization.
Private equity AI due diligence FAQs
When should AI due diligence begin in a private equity transaction?
Start during technology and operational diligence, early enough for findings to affect valuation, scope, cost, deal terms, insurance, transition planning, and the investment committee decision. Waiting until after close removes options and can leave the buyer responsible for undocumented dependencies.
What counts as AI use at a target company?
Include standalone assistants, features embedded in business software, predictive models, recommendation systems, model APIs, custom applications, robotic and workflow automation, browser extensions, employee subscriptions, and vendor services that use AI to produce or change a business result.
How can a buyer find unapproved AI tools?
Compare interviews and approved inventories with expense data, single sign-on records, connected applications, browser management, API keys, source repositories, network and cloud logs, support tickets, procurement records, and realistic employee workflows.
Which AI diligence findings can affect valuation?
Material findings can include unsupported revenue claims, unreliable data, expensive operating design, weak intellectual property rights, customer contract conflicts, security exposure, low adoption, unmeasured quality, key-person dependence, vendor lock-in, and remediation that changes expected margin or timing.
What data questions belong in AI diligence?
Confirm authoritative sources, ownership, consent or contractual rights, sensitivity, quality, lineage, transformations, retention, geography, access, correction, deletion, model training terms, and whether output can be traced back to current approved records.
How should private equity firms evaluate AI business value?
Establish a baseline and measure completed business outcomes after labor for review and correction, platform and usage cost, support, errors, delay, adoption, customer impact, and risk. A faster draft is not value if people spend the saved time fixing unreliable output.
What should be secured immediately after an acquisition?
Gain company control of administrative accounts, code, repositories, API keys, domains, contracts, billing, data exports, recovery methods, documentation, and vendor contacts. Rotate sensitive credentials, restrict unsafe access, preserve evidence, and keep critical workflows operating.
Should every AI risk stop a deal?
No. Classify each issue by likelihood, impact, confidence, cost, correction time, investment-thesis effect, customer obligation, and available alternatives. The deal team can then accept, price, condition, insure, remediate, or avoid the risk with a documented decision.
Can ALLMSP handle remediation after the diligence review?
Yes. ALLMSP handles account control, security, data cleanup, platform configuration, integrations, workflow redesign, testing, documentation, employee training, support, and continuous improvement in house.
Where does ALLMSP provide private equity technology diligence?
ALLMSP serves Lawrenceville, Suwanee, Gwinnett County, Metro Atlanta, and organizations throughout Georgia. The scope can cover one target, a first 100 days program, or ongoing technology and AI oversight across a portfolio.
