WATCH: From AI experimentation to institutional advantage in mid-market private equity

Artificial intelligence is no longer a theoretical discussion in private equity. Foundational models are advancing quickly, AI capabilities are being embedded into core systems, and firms are under increasing pressure from LPs to demonstrate that they are building modern, technology-enabled origination capabilities.

At the same time, many mid-market firms are discovering that layering AI onto existing systems does not automatically translate into better deal flow, stronger prioritization, or faster decision-making.

The difference between firms seeing measurable impact and those still experimenting often has less to do with model sophistication and more to do with something less visible: whether their underlying data environment has the context to accurately reflect their investment strategy.

In two recent conversations (videos below), Alex Bajdechi and Phil Westcott shared what they are observing across the market and why the next phase of competitive advantage in private equity will be shaped more by data architecture than by model access alone.

The operational gap between AI ambition and reality

Deal Engine Global VP of Sales, Alex Bajdechi, talking about PE firms and what they need to win in such a tech-enabled world getting to grips with AI, tools, data, context integration and more. 

Most mid-market private equity firms already operate with a substantial technology stack. They license third-party datasets, maintain a CRM, track opportunities, and build target company lists. On paper, the components required for AI-enabled origination appear to be in place.

In practice, the operating reality inside deal teams is often more fragmented.

Alex Bajdechi, Global VP of Sales at Deal Engine, has spent years working closely with private equity firms navigating CRM strategy and origination workflows. A consistent pattern emerges: investment thinking and investment data are rarely tightly aligned.

Sector theses are often articulated in slide decks. Investment criteria live in narrative documents. Historical deal knowledge sits in free-text CRM notes. Market intelligence resides in external platforms that are not fully integrated. The firm’s edge exists, but it is distributed and inconsistently structured.

When AI is introduced into this environment, expectations are understandably high. However, if the underlying data is incomplete, inconsistent, or disconnected, AI outputs will inevitably reflect those constraints. Teams begin to question reliability. Results require manual validation. Momentum slows.

This dynamic explains why many firms remain stuck in reactive patterns. They respond to inbound opportunities rather than systematically hunting against defined criteria. They move across disconnected applications that do not meaningfully communicate. They build lists that loosely reference strategy but are not rigorously anchored to it. They experiment with AI tools without seeing durable change in workflow.

These outcomes are not a failure of AI technology. They are a reflection of the fact that AI systems can only reason over the structure and context they are given.

Firms beginning to see consistent impact are addressing the issue at a foundational level. Rather than prioritizing additional tools, they are translating their investment strategy into structured data. They are aligning CRM history, company tracking, market intelligence, and proprietary insights into a unified operating layer that mirrors how the firm evaluates opportunity.

For many mid-market funds, this does not require rebuilding from scratch. A significant proportion of the relevant data already exists internally. The shift lies in organizing and governing that data so it becomes coherent, auditable, and directly aligned to strategy.

When that alignment is in place, AI becomes less experimental and more operational.

Turning implicit judgment into structured institutional intelligence

Deal Engine Founder and CEO Phil Westcott, talking about how, a strategic level, the challenge extends beyond integration.

Every private equity firm believes it has differentiated insight. Partners develop pattern recognition through years of transactions. Teams form strong views on which business models scale, which management dynamics matter, and which signals are predictive within a given sector.

The challenge is that this knowledge is often implicit. It lives in individuals’ experience, fragmented notes, or loosely structured CRM entries. It influences decisions, but it is not consistently encoded.

Phil Westcott’s perspective is that if firms want AI to meaningfully enhance investment performance, this implicit knowledge must be structured and institutionalized.

That process involves converting narrative theses into defined, measurable criteria. It requires mapping historical deal outcomes to structured attributes so patterns can be analyzed systematically. It means integrating CRM history, documents, and external data into a governed environment that reflects how the firm actually evaluates investments.

When this work is done, AI is no longer reasoning over generic market information. It is operating within firm-specific context. Outputs become more aligned with how the partnership thinks. Insights can be traced back to defined inputs. Over time, institutional memory compounds rather than dissipates.

This is the logic behind building a dedicated data engine. Not as another point solution competing for attention, but as connective infrastructure that unifies proprietary and external data into a coherent foundation.

In that environment, AI becomes materially more useful because it is grounded in structured context rather than isolated datasets.

From reactive deal flow to systematic signal generation

One of the most practical consequences of strengthening the data foundation is a shift in how origination is executed.

Relationship-driven sourcing will always remain central to private equity. However, competition for differentiated opportunities has intensified, and LPs increasingly expect firms to demonstrate discipline in how they source and prioritize investments.

When investment criteria are codified and data is unified, firms can move beyond reactive workflows toward systematic signal generation. Rather than waiting for opportunities to surface, they can continuously monitor companies that match structured criteria. They can identify changes in performance, ownership, hiring patterns, or strategic direction that align with their thesis. They can prioritize outreach based on contextual triggers instead of static lists.

This approach does not replace human judgment. It sharpens it.

Analysts spend less time reconciling data across systems. Associates evaluate opportunities against clearly defined attributes. Partners gain visibility into how pipeline development aligns with stated strategy. AI outputs become more reliable because they are grounded in transparent inputs.

Over time, origination becomes more repeatable. Institutional knowledge becomes embedded in infrastructure rather than residing solely in individuals. The firm’s AI narrative with LPs is supported by demonstrable architecture rather than isolated experiments.

Building long-term AI advantage on durable foundations

It is tempting to assume that competitive advantage in AI will be determined by access to the most advanced models. In reality, foundational model capabilities are improving across the industry, and access is becoming increasingly standardized.

The more durable source of differentiation lies in proprietary context and disciplined data architecture.

Mid-market firms that layer AI onto fragmented systems may achieve incremental improvements, but they are unlikely to unlock sustained advantage. Firms that invest in structuring and governing their data environment are building infrastructure that compounds over time.

The practical question is not simply whether a firm is using AI. It is whether its data foundation accurately represents how it invests, how it sources, and how it creates value.

For firms willing to address that foundation directly, AI becomes more than an overlay. It becomes an embedded extension of the firm’s strategy, integrated into everyday workflows and capable of scaling institutional intelligence.

That is where enduring competitive advantage begins.

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