If you’re a PE firm researching how to deploy AI across your portfolio companies, you’re likely looking for ways to reduce operating costs, improve productivity, and support your portfolio value creation plans. But you may be facing a few challenges:
- You’re unsure how far each portfolio company has progressed with AI, which tools it’s already using, or which technical and governance risks need attention first.
- You’re struggling to get teams across your portfolio companies to adopt AI consistently and measure its impact on operating costs and productivity.
- You need AI solutions that work with each company’s systems and workflows, but aren’t sure what it takes to develop and maintain them.
Addressing these challenges requires a structured approach to AI deployment across your portfolio. That means deciding which companies to start with, how AI can support each company’s value creation plan, and what results to demonstrate ahead of exit. To help you understand what this involves, we’ll cover:
- Where AI Creates Value for Portfolio Companies
- How to Deploy AI to Create Portfolio-Wide Value Creation
- How Neurons Lab Helps PE Firms Deploy AI Across Their Portcos
- FAQs
Want to deploy AI across your portfolio companies? Neurons Lab can help. Let’s talk.
Where AI Creates Value For Portfolio Companies
For PE firms, the clearest opportunities to enhance portfolio performance lie in improving operational efficiency and reducing costs.
Operational efficiency improvements can apply across almost any sector in areas involving repetitive, time-consuming work, such as:
- Customer service: Agentic AI can retrieve information from company knowledge bases and prepare responses, freeing support teams to handle more complex inquiries. For example, an energy software provider developed an AI assistant that helps teams retrieve ticket information and relevant documentation to respond to customers faster.
- Marketing: Generative AI (Gen AI) can help teams produce and adapt campaign materials for different audiences and channels. A leading global financial services provider built an AI-powered content system that supports content creation in 20+ languages across 8+ marketing channels, with built-in compliance checks.
Cost reduction opportunities, on the other hand, can vary depending on a company’s industry and operations. For example:
- Finance: Teams can use agentic systems to automate parts of accounts payable processing and month-end close, reducing manual checks and reconciliation work.
- Predictive analytics: In industrial businesses, AI can help predict equipment failures to reduce downtime and maintenance costs. For example, a power plant developed a predictive maintenance system that reduced breakdown-related downtime by 20% and maintenance service time by 30%.
- Manufacturing: AI can help companies improve gas-consumption analysis. In one project, steelmaking companies reduced the time required from hours to minutes while improving accuracy by 15%
These examples show that AI can create value in different ways across portfolio companies, depending on their operations and business priorities. For PE firms, the challenge is finding an approach they can repeat across the portfolio while accounting for each company’s individual needs.
How to Deploy AI to Create Portfolio-Wide Value Creation
Deploying AI across PE portfolios can be challenging because each business has different systems, workflows, resources, and levels of AI adoption. However, you don’t have to start from scratch with every company.
The approach that works is to separate what can be standardized across the portfolio, such as assessment criteria, minimum governance standards, and engineering methods, from what needs to be tailored, like workflows, KPIs, and controls. Here’s what that entails:
1. Assess AI Readiness and Identify Opportunities Using a Shared Framework
Every portfolio company sits at a different stage of AI adoption. Without understanding existing capabilities and gaps, PE firms risk investing in AI initiatives that the business isn’t ready to support.
Start by assessing each portfolio company against the same set of criteria, such as existing AI capabilities, adoption levels, data readiness, governance risks, and technical gaps. While the assessment criteria remain consistent, the findings will differ. One company may need to address data quality issues before adopting AI, while another may already have the data access and budget to support more advanced applications.
These assessments give you a consistent baseline for measuring readiness and estimating the work each company requires. This makes it easier to compare opportunities across your portfolio and decide where to invest first, based on each business’s unique situation.
2. Align Your Firm and Each Portfolio Company’s Leadership on AI Priorities and Responsibilities
PE firms often set AI priorities across their portfolio in line with their investment strategy. But implementation depends on each company’s leadership committing the right resources, budget, and support. So this next step is for PE firms and each portfolio company’s leadership to agree on what AI should achieve, which opportunities to pursue, and who will oversee implementation.
Establish a common approach to setting priorities, approving budgets, and assigning responsibilities across your portfolio so every company follows a clear decision-making process. However, goals and budgets should be set for each portfolio company, while the level of PE involvement should depend on how independently each business operates.
A company with a strong leadership team and its own IT department may only need agreed targets and regular progress reports, while one with limited in-house technical expertise may need closer involvement from operating partners.
Involving portfolio company leadership in these decisions helps secure management support and gives each initiative a named owner responsible for delivery and results. Without this alignment, projects may stall or remain isolated experiments.
3. Map Workflows and Provide Role-Specific AI Training to Support Adoption
AI only improves operating performance once teams use it in their daily work. This step starts by mapping current workflows and recording baselines, such as time or cost per task, so you have KPIs to measure improvements against later. Teams then receive role-specific training to help them put AI into practice.
Work with portfolio company teams to document how they complete tasks, what information they use, and where manual work creates delays. Ask teams to walk through the repetitive, time-consuming tasks you’ve already identified to understand how they currently handle them.
You can follow the same process for mapping workflows and setting baselines across your portfolio, but the actual workflows, AI opportunities, and KPIs themselves will differ by company.
Some materials like training content and reusable instructions that tell an AI tool or agent how to carry out a specific task (AI Skills) can be reused across businesses. This reduces the need to develop the same materials separately for each company.
However, teams still need role-specific training that reflects their daily tasks, the tools they’ll use, and how to check AI-generated results. This helps teams build the skills and confidence needed to use AI effectively, rather than relying on generic training that may not address their work.
4. Establish Governance and Evaluation Controls Before Scaling AI
Before AI use expands, each company needs controls that protect sensitive data, keep outputs reliable, and stand up to regulatory scrutiny. Setting common minimum standards across your portfolio gives you consistent oversight without imposing identical requirements on every business. For example:
- Data security and access controls: These protect sensitive information from unauthorized access or exposure. For example, a bank may restrict which teams can access customer financial records through its AI tools.
- Guardrails and human oversight: This prevents AI from making costly mistakes or taking actions without the necessary approval. For example, a retailer may require a manager to approve refunds above a certain amount before an AI agent processes them.
- Evaluation and monitoring: Companies can identify errors or declining accuracy before they affect operations. For example, a manufacturer may regularly test whether its AI system can still detect equipment faults as operating conditions change.
- Audit trails and traceability: These help teams investigate AI errors and demonstrate compliance by keeping a record of AI activity and supporting information. For example, an insurer may need to show which documents informed an AI-assisted claims decision.
While these standards provide a common foundation, individual controls may need to differ depending on each company’s data, industry regulations, and risk exposure. A regulated financial services business, for instance, will need stricter controls than one in a less heavily regulated sector.
5. Develop Custom AI Agents Where Existing Tools Fall Short
Off-the-shelf AI tools cover many everyday tasks, but they can’t always handle a company’s complex workflows, proprietary data, or compliance requirements. Custom AI agents, built around how a specific business operates, can automate that higher-value work.
For example, a fintech might need an AI agent that works with its proprietary databases, connects to older systems, and follows its internal decision rules to meet compliance requirements.
Although the solutions themselves will differ, you can standardize how you build them: defining requirements with the business, developing integrations within existing systems, testing performance before launch, and documenting the system. This gives each company an AI solution that fits its operations without having to develop an entirely new implementation process every time.
Another consideration is who will maintain, update, and extend these systems after deployment. Agents need updating as business rules, data sources, and integrations change, and an embedded engineering partner can provide that technical expertise and ongoing support as needed instead of hiring AI engineers at every company. This allows you to share engineering resources across your portfolio while working with a team that already understands your businesses and their requirements.
However, it’s important to agree on system ownership, documentation, and knowledge transfer from the outset. This helps portfolio companies retain control of their AI solutions and manage routine updates internally, with specialist support available for more complex changes.
6. Measure AI Performance and Business Impact to Guide Further Investment and Expansion
Measuring results tells you whether AI is paying off so you can decide where to invest further and which approaches to repeat at other portfolio companies. After deployment, track performance against the KPIs established earlier, including:
- Adoption: The share of teams using AI tools regularly.
- System performance: How accurately AI produces the expected results.
- Operational impact: Changes in time or cost per task compared with the baseline.
Use a common reporting format for portfolio monitoring so results are comparable across the portfolio, but set targets that reflect what each company’s AI initiatives intend to achieve, such as faster ticket resolution at one company and lower processing costs at another.
Coordinating these steps across several portfolio companies is hard to do without specialist support. You need a shared playbook that can be tailored to each business. Each company’s teams also need hands-on help to adopt AI. Using one partner for both keeps that support aligned rather than risking different approaches from separate providers.
How Neurons Lab Helps PE Firms Deploy AI Across Their Portcos
With Neurons Lab, you can move AI from isolated experiments to measurable operational improvements across your portfolio that support your value creation plan and strengthen each company ahead of exit.
As an AI enablement partner serving organizations across the US, Europe, and Asia, we combine executive training, AI adoption programs, and custom AI agent builds to support secure, practical deployment.
Through our single delivery model, clients build operational AI capability aligned with core workflows, governance, and business priorities. Trusted by 100+ clients, including HSBC, Visa, and AXA, we’ve accelerated AI integration in banking, wealth management, private equity, investment firms, fintechs, and other highly regulated industries.
Here’s how you’ll benefit from partnering with us:
Know Where To Deploy AI and What Each Company Needs to Move Forward
When portfolio companies are at different stages of AI adoption, it can be difficult to know which businesses to prioritize and where to start. With Neurons Lab, you can understand where each company stands with AI, decide what to do next with confidence, and get the support to put those plans into action.
We conduct AI due diligence using a repeatable framework to assess each portfolio company against the same criteria. This covers data and IP ownership, operational and revenue opportunities, and investment required. This helps you compare findings across the portfolio, identify gaps, and decide where to invest first.
Then, through executive alignment, you and portfolio company leadership agree on which AI opportunities to prioritize, who is accountable for delivery, what data teams need access to, and the budgets required.
This gives each initiative a practical implementation plan, clear ownership, and leadership support.
Make AI Adoption Consistent and Measurable Across Your Portfolio Through Practical Enablement
Even after identifying AI opportunities, you may struggle to get your portfolio companies using AI consistently, safely and in ways that improve business performance. Neurons Lab helps your portfolio companies have the tools and hands-on support to make AI part of their daily operations.
Through workflow mapping and standardization, your companies can identify where AI can support daily tasks. They’ll receive guidance on selecting suitable AI tools, potentially using a common Anthropic or OpenAI platform, and develop AI Skills and plugins that reflect how teams work.
Some Skills, such as code review and cybersecurity checks, can be shared across portfolio companies, while customer-facing, design, and legal workflows are tailored to each company’s needs.
We also establish usage guardrails on LLM credits, so costs don’t climb unexpectedly as more teams start using the tools, and role-based permissions where senior teams get more automated workflows, and junior staff get review gates. So adoption sticks, teams get practical, role-specific training to develop the skills and confidence to use AI.
For example, Neurons Lab created a tailored training for more than 25 specialists at a leading Thai financial institution to use AI in investment, risk, and research workflows. After the training, 97% said they felt confident applying AI securely in their daily work.
With Neurons Lab, you’ll also get adoption tracking to help you measure operating performance and business outcomes, identify where additional support is needed, and understand what’s working and what isn’t. You can then use these findings to avoid repeating mistakes and apply successful approaches across other portfolio companies.
Build and Maintain Custom AI Agents Across Your Portfolio
It can be difficult to deploy the custom AI solutions your portfolio companies need when you lack the expertise to develop and maintain them. With Neurons Lab, you can build AI solutions tailored to each company’s needs, with specialist engineering support from initial development through deployment and optional ongoing maintenance.
Through custom AI development, you’ll have agents built around each company’s proprietary data, workflows, integrations, and business rules. That way, teams can automate complex, high-value workflows that off-the-shelf tools can’t handle.
We also apply aproven discovery, engineering, measurement and evaluation methods across your portfolio, reducing duplicated development work while adapting each solution to the business’s requirements. Systematic testing and ongoing evaluations also help teams identify errors and build trust in AI outputs.
Our Forward Deployed Engineers (FDEs) work directly within each company’s infrastructure to integrate existing systems, connect fragmented data sources and build in security controls, permissions, governance and audit trails from the start. These help portfolio companies monitor output quality and maintain oversight of how AI is used.
FDEs also document the solutions and transfer technical knowledge to internal teams, so your portfolio companies retain control of their AI systems and can make future changes without depending entirely on an external provider.
For optional ongoing maintenance, you can access FDE support on a retainer basis to update, improve, and extend AI systems as business requirements change. This gives you access to a technical team across your entire portfolio.
For example, Neurons Lab developed an AI document classification and knowledge management system for a fintech company in just four weeks. The solution helped teams find company information and process documents in under 60 seconds on average.
Work With An AI Partner That Can Support Deployment Across Your Portfolio
Deploying AI across multiple portfolio companies requires a consistent approach without overlooking each business’s individual needs. Neurons Lab brings together AI assessments, practical enablement, and custom AI agent development to support your portfolio companies at different stages of adoption.
That way, you have the expertise and guidance on hand to support operating performance, track the value of your AI investments, and demonstrate progress against each company’s value creation plan ahead of exit.
FAQs
How should private equity firms prioritize portfolio companies for AI deployment?
Private equity firms should prioritize portfolio companies for AI deployment by first assessing each business against the same criteria, such as existing AI capabilities, software solutions, adoption levels, data readiness, technical risks, and available resources. They can then identify which companies have the strongest opportunities to improve productivity or reduce operating costs, considering the investment and work required to implement AI.
Which parts of AI deployment can be shared across portfolio companies?
PE firms can share several parts of AI deployment across portfolio companies, including assessment frameworks, minimum governance standards, workflow mapping methods, training materials, and processes for developing and testing AI agents. Each PE-backed business, however, will need its own priorities, budgets, workflows, KPIs, and controls based on its operations and risks.
How can private equity firms measure AI ROI across their portfolios?
Private equity firms can measure AI ROI across their portfolios by comparing results against established baselines, such as time or cost per task. They should then weigh these improvements against implementation costs, AI usage fees, training, and ongoing maintenance.
