A Strategic Framework for Independent Sponsors Navigating AI Implementation in Portfolio Companies
The question comes up in nearly every conversation I have with independent sponsors today: 'Where do I start with AI for my specific company?' It's a fair question that reflects a broader challenge facing mid-market portfolio companies across industries.
Unlike enterprise organizations with dedicated innovation teams and broader room to experiment, mid-market companies need to be more selective. The real challenge is rarely interest in AI, it's knowing where to start, which process to target first, and how to build a case for measurable value. This creates a strategic opportunity: the companies that develop clear evaluation frameworks for AI implementation can move faster and more confidently than their enterprise competitors who often get stuck in lengthy pilot phases.
Having worked with a wide range of independent sponsors and their portfolio companies on technology transformation initiatives, I've developed a framework that addresses the core questions every portfolio company faces: where should they begin with AI, what should they evaluate first, and which kinds of AI initiatives are most likely to create measurable value early in the process.
I hear this question a lot: 'We want to implement ChatGPT in our operations' or 'We need machine learning for our business.' The enthusiasm makes sense. AI promises are compelling, and no one wants to fall behind.
But here's what I've noticed after working through dozens of implementations: companies that see real impact (40% efficiency gains, measurable cost reductions) start somewhere else entirely. They map where their people spend time on work that's both repetitive and standardized. Think about prior authorization requests that follow identical steps every time, invoice processing that never varies, or customer service inquiries where the same questions come up repeatedly.
The companies that face challenges usually haven't had the chance to document these patterns yet. Without that foundation, even the best AI tools struggle to deliver meaningful results.
Looking across AI implementations in industries like healthcare and logistics, we’ve seen what tends to create value early and what usually slows adoption down. That experience has helped shape a practical framework that helps independent sponsors evaluate where AI can create the most immediate value in their portfolio companies.
Here's how each step works in practice:
Step 1: Map Your Time Drains
Start by identifying the 3-5 business processes that consume the most employee hours each week. These are typically found in customer service, data entry, reporting, scheduling, or compliance documentation. The key is to focus on processes that are both high-volume and highly standardized.
In our work with a healthcare insurance company, manual data entry, file processing, and administrative tasks were consuming significant productivity. These repetitive workflows followed identical patterns but carried high error risk. When mapped and automated, the company eliminated human error and freed up teams to focus on client service and other initiatives.
Step 2: Quantify the Opportunity
Once you've mapped time drains, calculate both the direct cost savings and the opportunity cost of redeploying that time. If your team spends 20 hours per week on manual data entry, that's not just 20 hours of labor cost, it's also 20 hours that could be spent on higher-value activities like customer relationship building or strategic analysis.
The companies that build the strongest business cases for AI investment think beyond simple labor arbitrage. In one legal services engagement, automating workflow processes led to a 40% increase in efficiency and ultimately a 10% rise in enterprise valuation. The real value wasn't the automation itself, but what the team could accomplish when they weren't buried in administrative tasks.
Step 3: Start with Proof of Concept
Pick one process for a focused 15-day pilot. The goal isn't to achieve perfect automation immediately - it's to demonstrate measurable improvement and build organizational confidence in AI-driven solutions.
Successful proof of concepts are usually narrower than teams expect. A legal services client needed remote deposition capability. The solution combined real-time transcription, exhibit sharing, and encrypted video conferencing to enable 100% remote depositions.
This focused approach proves that augmenting human capabilities with specific improvements delivers better results than attempting to transform entire workflows at once.
Through our work with portfolio companies, we've identified several warning signs that indicate an AI initiative is likely to fail:
Technology-First Thinking: Starting with 'we need machine learning' instead of 'we need to solve X business problem.' The most successful AI implementations begin with process optimization, not technology selection.
Lack of Data Readiness: Attempting AI implementation when basic data collection and storage processes are inconsistent. AI requires clean, structured data to generate reliable outputs.
Unrealistic Timeline Expectations: Expecting transformational results within 30 days. Meaningful AI implementation requires time for employee training, process refinement, and performance optimization.
Resistance to Process Change: Implementing AI while insisting that existing workflows remain unchanged. AI delivers the greatest value when it enables new ways of working, not when it's forced to replicate outdated processes.
Lack of AI Governance: Implementing AI without clear governance around data access, decision-making authority, and performance measurement. Successful AI initiatives require defined roles for who approves investments, monitors progress, and ensures compliance—especially critical for companies preparing for exit or reporting to multiple stakeholders.
The AI conversation in mid-market companies is dominated by a few expensive distractions.
Beyond the technology-first thinking we discussed earlier, there's the complexity fetish. Companies assume sophisticated machine learning models will outperform simple automation tools. In practice, well-designed workflow automation with human in the loop often works perfectly well for mid-market operational challenges. The goal is business impact, not technical elegance.
There's also a transformation theater. Everyone talks about "AI transformation," but the companies seeing results focus on specific operational improvements first. Transform one process well, then expand. Companies that try to revolutionize everything simultaneously usually revolutionize nothing, and most critically, leaving people behind in the adoption
While the framework above applies across most companies, certain industries have natural starting points that consistently deliver results.
In legal services, we see strong wins with contract review and document discovery. There's something about the systematic nature of legal work that makes AI implementation almost obvious once you map the operations.
Healthcare portfolio companies often get the fastest results with administrative bottlenecks like the authorization example I mentioned earlier. Most healthcare operations have similar repetitive tasks that follow identical steps every time.
Fintech companies usually start with fraud detection or loan processing automation. The data is already structured, the decision trees are clear, and the ROI is easy to calculate.
What I see across AgTech, Security, Animal Health, and Retail is similar: start with the operational areas where human expertise matters most, but the repetitive groundwork can run on autopilot.
The goal of any initial AI implementation should extend beyond immediate process improvement. Successful portfolio companies use their first AI project to build organizational capabilities that enable future innovations.
This means investing in employee training, establishing data governance practices, and creating feedback loops that help the organization learn from both successes and failures. Companies that view AI as a one-time technology deployment miss the opportunity to develop competitive advantages that compound over time.
The portfolio companies that see the greatest long-term value from AI are those that treat their initial implementation as the first step in a broader digital transformation journey. They use early wins to build momentum for larger initiatives and develop the internal expertise needed to identify new opportunities for AI-driven innovation.
For independent sponsors evaluating AI opportunities in their portfolio companies, the path forward starts with honest assessment of current-state operations. Before exploring technology solutions, invest time in understanding where your teams spend their hours and which processes create the most operational friction.
The companies that successfully implement AI share a common characteristic:
Starting with AI doesn't require massive upfront investment or complex technical infrastructure. It requires honest assessment, focused execution, and patience for results that compound over time.
For additional perspective on AI implementation frameworks specifically designed for PE-backed companies, read our detailed guide: Where to Start with AI in PE Companies.