Insights by Ilie Ghiciuc

Document extraction accuracy at scale is a sequence of failure modes, not a single problem. Fine-tuning an open-weight visual-language model on domain-specific data closes most of the distance from a general-purpose baseline, but rarely reaches the threshold a business case actually requires. Pushing past that ceiling depends on three engineering techniques applied in sequence, each addressing a failure mode the others cannot. There is a question that comes up early in almost every AI conversation we have with founders and product leaders: "Is our process a good candidate for this?" It sounds like a simple question. It is not. A recent MIT study reports that 95% of enterprise generative AI pilots fail to deliver measurable business impact, and that the primary cause is not the technology itself but the absence of workflow integration and a defined outcome before the build begins. Most teams answer the question by focusing on the technology first, evaluating what a particular model or agent framework can do, and then searching for a process to apply it. That sequence produces many promising pilots but leaves production systems in short supply.

Document extraction accuracy is not a single problem but a sequence of failure modes resolved in order. Fine-tuning an open-weight visual-language model closes most of the gap from a general-purpose baseline, but rarely the gap that matters: the one between early performance and the threshold a business case requires. Closing that distance is a separate engineering effort, and the techniques that get there compound on each other rather than substitute for each other.

At some point in almost every AI project we work on with a regulated client, someone on the team says some version of the same thing: "We can't send that data outside." It usually lands like a problem. The obvious implementation path, connect your data to a capable hosted model, get results back, iterate, suddenly has a wall across it. SOC 2 compliance, data residency requirements, security perimeters built around credentials and sensitive client information: any of these can make the default approach to AI architecture a compliance violation rather than a technical decision.

Technology due diligence is often treated as a risk-screening exercise, yet many post-acquisition execution failures stem from issues it fails to surface. Traditional diligence evaluates technology in its current state, but rarely assesses how systems, teams, and decisions behave under pressure. In regulated and fintech environments, this gap shows up quickly through slowed delivery, integration friction, and rising compliance overhead. Architecture, decision-making structure, regulatory design, and knowledge concentration ultimately determine how fast and safely a business can evolve post-close. When technology due diligence is used to inform value creation planning rather than just deal approval, execution risk becomes clearer and early momentum is easier to protect.

Across private equity-owned companies, AI-driven efficiency is shaped less by tool choice than by operating discipline. Structural complexity, architectural clarity, and ownership determine whether AI reduces friction or adds to it. Initiatives that succeed are narrowly scoped, validated early, and embedded in existing workflows with clear accountability. Those who fail tend to scale ambition before proving impact. In this context, AI is most effective when treated as a lever for execution quality rather than a standalone transformation.

Private equity value creation doesn't end after the first ninety days. In our last article, we examined the post-buy tech sprint, a period where execution speed and alignment are crucial for building early momentum. But once that initial groundwork is laid, the focus shifts. Speed is still important, but now it’s about sustainability. And that’s where the conversation moves from value capture to value protection.

For private equity sponsors, the deal close used to signal the start of a new chapter. Now, it marks the beginning of a sprint. The pace of activity in 2025 has intensified: U.S. deal value topped $838.5 billion last year, and global figures are on track to hit $1 trillion. European PE deal value also rose strongly in 2024 and shows cautious but positive momentum into 2025. But the path to value has become anything but straightforward.

Gartner names it 2025’s top AI trend, but only teams with scope, governance, and value metrics will succeed. Picture an invoice-scanning bot that doesn’t just flag anomalies—it renegotiates payment terms, logs the change in SAP and emails the supplier before finance gets its first coffee. That leap from “copilot” to fully autonomous colleague is what Gartner dubs Agentic AI, the top strategic technology trend for 2025.

In the early 2000s, achieving “unicorn” status—a $1 billion valuation—was often the result of a strategic blend of growth, innovation, and venture capital. However, the tech landscape has shifted dramatically in the last two decades. By 2020 and 2021, the scene was further transformed by the COVID-19 pandemic and the influx of a $2 trillion economic stimulus, sparking a surge in venture funding and soaring tech valuations.

The financial services industry is undergoing a revolution lead by new, digital-centric neobanks and fintechs. Built from the ground up with user-friendliness and flexible functionality as their guiding principles, these new players are rapidly gaining traction. A report by AppRadar shows that neobanks are taking the lead in user growth with 18 million more downloads than legacy banks in 2023.