Why PE and M&A Firms Should Ditch In-House AI for Sector-Specific Solutions

In the age of artificial intelligence (AI), businesses across industries are under pressure to adopt cutting-edge technologies that improve efficiency, optimize processes, and enhance decision-making. Private equity (PE) and mergers and acquisitions (M&A) firms, known for their focus on operational excellence and value creation, are no exception. However, as firms evaluate how best to leverage AI, they often face a crucial decision: Should they build their own in-house AI solution, or should they partner with a company that specializes in sector-specific AI solutions?

This article argues that building an in-house AI solution is an expensive, risky, and resource-intensive endeavor that rarely pays off. Instead, partnering with an AI provider that has a proven, sector-specific solution is the smarter path to realizing the transformative benefits of AI. Let's break down the rationale behind this recommendation.

The "It Looks Simple" Trap

AI development often appears deceptively simple. Many firms believe that creating an in-house solution involves little more than combining a large language model (LLM) with a vector database and connecting it to their existing systems. In reality, AI systems—especially those focused on retrieval-augmented generation (RAG) or sector-specific applications—are incredibly complex, requiring ongoing maintenance, optimization, and expertise.

Consider a scenario where a PE firm attempts to build its own RAG solution for deal analysis, due diligence, or portfolio management. What starts as a simple project quickly snowballs into a full-scale engineering and operational challenge:

What seemed like a manageable two-month project evolves into an ongoing operational burden with escalating costs and mounting frustration.

The Hidden Costs of Building In-House AI

One of the biggest misconceptions about building an in-house AI solution is the belief that leveraging open-source tools and internal talent will make the project cost-effective. On paper, it might look like a straightforward way to save money, but the reality is that the hidden costs of development, infrastructure, and maintenance quickly outweigh any perceived savings.

To begin with, there are significant infrastructure costs involved in building and running an AI system. Hosting large language models (LLMs) and vector databases requires a robust setup, including development, testing, and production environments. These systems must also be continuously monitored, with backup and disaster recovery mechanisms in place to ensure uptime and reliability. Each layer of infrastructure adds complexity, time, and expense to the project.

Then there's the human capital needed to make the system work. Developing a production-grade AI solution requires a specialized team of experts, including machine learning engineers and DevOps specialists. These roles don't come cheap, and when you add quality assurance teams and project managers into the mix, personnel costs quickly escalate. Hiring, onboarding, and retaining this kind of talent in today's competitive job market only adds to the financial burden.

The costs don't stop once the system is built. AI solutions require ongoing maintenance to remain effective and secure. Additionally, performance optimization and scalability improvements are necessary to handle growing user demands and evolving requirements. All of these tasks demand dedicated resources, both technical and financial, turning the AI system into a long-term operational expense.

The Security and Compliance Risks

PE and M&A firms handle vast amounts of sensitive data, from deal documents to operational insights about portfolio companies. Building an in-house AI solution introduces significant security and compliance risks:

The cost of a single security breach or compliance violation can be catastrophic—not just financially but also reputationally.

The Expertise Gap

Even with talented engineers, building a robust AI solution requires niche expertise in areas like LLM deployment, prompt engineering, anti-hallucination optimization, and vector database management. Recruiting, retaining, and upskilling the necessary talent is not only expensive but also highly competitive in today's labour market.

Additionally, the AI landscape evolves rapidly. Keeping up with the latest advancements, addressing new threats, and integrating emerging technologies is a full-time job. Sector-specific AI providers are better positioned to manage these challenges because their sole focus is developing and maintaining cutting-edge solutions tailored to your industry.

Time-to-Market Considerations

Speed is critical in the world of private equity and M&A. Whether it's identifying investment opportunities, conducting due diligence, or improving portfolio company performance, the faster you can act, the better your outcomes. Building an in-house AI solution delays your ability to capitalize on AI-driven insights, giving competitors a significant advantage.

By contrast, partnering with an AI provider allows you to deploy a ready-made solution in weeks, if not days, enabling immediate value creation.

The Advantages of Partnering with Sector-Specific AI Providers

Sector-specific AI providers bring several benefits that make them the clear choice over in-house development:

When In-House Development Might Make Sense

To be fair, there are specific scenarios where building an in-house AI solution could be justified:

• Unique Regulatory Requirements: If your firm operates under highly specific regulations that no vendor can meet, customization may be unavoidable.
• AI as a Core Product: If AI is integral to your business model and value proposition, investing in an in-house solution might align with your strategic goals.
• Unlimited Time and Resources: While rare, some firms may have the financial and human capital to support in-house development without impacting their operational focus.

For the vast majority of PE and M&A firms, however, these scenarios do not apply.

The Bottom Line

Building an in-house AI solution might seem like a way to save money and retain control, but in reality, it's a high-risk, high-cost undertaking that rarely delivers the expected value. From skyrocketing development costs to ongoing maintenance challenges, the burden of building your own solution can distract your team from its core mission: creating value for stakeholders.

In the fast-paced world of private equity and M&A, the choice is clear: Don't reinvent the wheel. Instead, leverage the expertise of AI providers who have already solved the problems you're facing. Your team, your budget, and your bottom line will thank you.

Conclusion: Why Roclo Is the Smart Choice for Private Equity and M&A Firms

As private equity and M&A firms navigate the complexities of deal-making and relationship management, the decision to adopt AI can be transformative—but only if approached wisely. Building an in-house AI solution may seem tempting, but the hidden costs, technical hurdles, and long time-to-market often outweigh the benefits. Instead, partnering with a sector-specific provider like Roclo offers a proven, efficient, and targeted path to AI-driven success.

Roclo is purpose-built for the unique demands of the M&A and private equity sectors. Unlike generic solutions, it's designed to seamlessly support the full deal lifecycle, from relationship-building with prospects to managing deal flow from start to finish. Its AI capabilities are enhanced by pre-configured ingestion of critical documents like IMs, pitch decks, due diligence material, and company reports. With all documents metatagged, Roclo ensures its AI engine delivers accurate, actionable insights tailored to your needs.

What sets Roclo apart is its integration with key industry tools. From accessing news sources and financial data to pulling shareholder information, it's already aligned with the resources your team relies on daily. It also includes a complete history of notes and interactions with prospects, enabling its AI engine to identify untapped opportunities and uncover hidden alpha in your deal flow.

Choosing Roclo means your firm can bypass the challenges of building from scratch and start leveraging a proven AI solution that's been developed with your industry's unique requirements in mind. The result? Faster time-to-market, reduced costs, and a sharper competitive edge in a sector where every advantage matters. For private equity and M&A firms looking to stay ahead, Roclo isn't just an option—it's the solution.