AI for private equity
Forward Deployed helps private-equity firms and portfolio companies pick valuable workflows and build them into production AI systems, then transfers the operating capability to run them.
Start with
A specific operating workflow
Measure with
A production scorecard
Finish with
An owned, transferable capability
From diligence to deployment
The useful unit of AI value creation is a workflow: one with an accountable owner and usable data, where quality can be measured on the way into production.
Before close
Test where AI could change the operating plan and which workflows have usable data and owners. This stage also weighs the integration and security risks that could limit the thesis, along with how hard adoption will be.
Typical outputs
First 100 days
Turn a broad AI mandate into a ranked deployment backlog, where each item has an operating owner and a scorecard, with a decision gate before the build.
Typical outputs
Hold period
Build bounded agents and internal tools inside the systems where portfolio-company teams already work, and automate the steps around them.
Typical outputs
Across the portfolio
Convert what the deployment taught, from controls to evaluation patterns and implementation assets, into a playbook for the next portfolio company.
Typical outputs
Representative workflow patterns
These examples show the kinds of workflows the delivery model can support. They are patterns, and none of them is a client case study or a performance claim.
Bounded research and document-review workflows, with sources visible and a clear escalation path to investment-team judgment.
Account research and customer context brought into the operating systems teams already use, plus service triage and repeatable follow-up.
Internal tools that assemble context and surface exceptions, keeping source data visible for recurring analysis.
Retrieval and drafting for contracts and proposals, and for the policies and internal knowledge around them, with permissions and human review built in.
Agentic-coding rollout with conventions and governance that fit the portfolio company's codebase, and evaluation to show it is working.
Company-specific agents and tools for operational work, where quality comes down to context and judgment as much as integrations.
The full deployment system maps what a deployable PE workflow needs, from data and tools through human review and monitoring.
Security and governance
Production readiness depends on how a workflow handles access and failure, and on who reviews and owns it. Those decisions belong in technical discovery and implementation, long before anyone writes a policy document.
Map the minimum data and permissions a workflow needs before production, along with the systems it touches and the retention rules it must follow.
Define task-level tests and an operating scorecard before use scales, so quality gets judged on evidence.
Specify which decisions stay with operators and when the system escalates. Reviewers get to see the evidence behind an output.
Give every deployed workflow an accountable owner and a feedback path, then document how it operates and keep monitoring it.
Engagement model
Scope and timing depend on the workflow and the portfolio-company context around it. Each phase creates a decision point, so the team decides on evidence at every step.
Days 0–30
Map the operating process and audit the data and systems behind it. This phase surfaces the constraints and ends with a production scorecard.
Days 31–60
Build the first production workflow and connect the data and tools it needs. Evaluation and monitoring go in before anyone depends on the output, and a human reviews the cases that call for judgment.
Days 61–90
Roll the workflow out and make it reliable under real load. We train the operators, then hand the deployment over as a capability the team can repeat on its own.
Related resources
Common questions
Private-equity operating teams and deal teams assessing AI implications, along with portfolio-company leaders who have to deploy AI into real business or engineering workflows.
Both at once. The first phase combines technical discovery with workflow selection, and it ends with a ranked opportunity and a production scorecard at a clear decision gate.
Yes. A focused deployment establishes the workflow with its controls and evaluation approach, and the operating lessons carry into any broader portfolio playbook the firm takes on later.
The right system depends on the workflow and its constraints. One engagement might call for a production agent, another for an internal tool or a workflow automation. Evaluation systems and agentic-coding enablement come up as well.
Each deployment starts with a workflow-specific scorecard that sets a baseline and a target. Evidence might mean task quality and cycle time, or exception rates and operator adoption, with reliability tracked throughout. Each phase closes at a decision gate where that evidence says what happens next.
Next step
Tell us about the workflow and its portfolio context, plus the systems involved and the operating outcome you need to evaluate.
Discuss a portfolio workflow