AI in Restructuring: Only as Good as Its Data

Early in my career working with restructuring professionals, I saw just how much of their time went into piecing together the full picture from scattered financial data. Back then, AI was still a sci-fi concept. Fast forward to today, and every restructuring conference is buzzing about algorithms and automation.

As someone who’s spent years developing tools and data solutions for the restructuring industry, I find this both exciting and a bit concerning. Can an AI really do the gritty work of untangling a distressed company’s problems?

Riding the AI hype wave in restructuring

Every few years, a new technology promises to “revolutionise” how restructuring teams work. From my perspective leading product and data strategy, I see AI’s potential every day – and its pitfalls.

Corporate restructuring is messy. I’ve seen how human judgment, negotiation skills, and contextual understanding drive outcomes that no algorithm can fully replicate.

But AI can speed up the legwork. It’s like having an ultra-fast research assistant that can scan decades of filings and crunch numbers far faster than my old spreadsheets ever could.

The potential efficiency gains are real. Some companies report major productivity boosts – one survey found about 69% of organisations saw faster work and improved efficiency after implementing AI.

Imagine cutting days of document review down to hours – that’s the kind of edge AI can provide. But restructuring professionals operate in high-stakes situations, and speed means nothing without accuracy.

If the inputs are flawed, even the fanciest model will spit out nonsense. And in a turnaround, bad information is a killer.

Data: The unsexy key to effective AI

Here’s the unglamorous truth: AI is only as good as the data behind it.

Working with financial and corporate data every day has made me a bit of a data skeptic. I’ve learned how easily incomplete or outdated information can derail even the most sophisticated analysis.

We’ve all seen what happens when clever models meet bad data. Think back to the financial crisis of 2008: complex models built on shaky mortgage data looked brilliant until they imploded and nearly tanked the global economy. That’s a stark reminder that fancy algorithms are no substitute for solid, clean information.

In day-to-day restructuring, data quality issues are everywhere.

In a corporate restructuring, incomplete or inaccurate records can easily delay or derail a deal – which is why at Company Watch, we obsess over getting one version of the truth before letting any AI loose on the numbers. We gather data from reliable sources, double-check it, and keep it updated.

There’s no glory in that grunt work, but without good data, any AI-driven analysis is built on sand. 

What about tools that turn data into insights?

Having good data is one thing; making sense of it quickly is another. That’s where the tools come in. Modern professionals now have an arsenal of software (AI-driven and others) for analysis, visualisation, and monitoring. The trick is picking the right tools – the ones that truly add value.

For example, my team at Company Watch built a platform that turns hundreds of thousands of unsearchable Companies House filings into a searchable database. Instead of slogging through PDFs one by one, I can instantly query the data and find the proverbial needle in the haystack.

That’s huge when time is short and information overload is the norm.

We also built an AI tool that automatically drafts a financial health summary for a company. It pulls in the key figures, risk scores, and recent filings, then produces a digestible report. Because it cross-checks everything against official sources, I can trust that it’s factual. This saves me hours I used to spend compiling and writing up reports. It’s like having a junior analyst who works at superhuman speed (and never needs coffee).

Overall, the best tools don’t replace our judgment – they turbocharge it. They highlight patterns or red flags I might miss and free me to focus on strategy instead of number-crunching.

A tool only delivers value when people actually use it. To succeed, it must align with existing workflows and integrate smoothly with current systems. If it feels clunky or disruptive, adoption will stall. That’s why it’s essential to introduce new technology carefully – starting with a low-pressure pilot, providing hands-on training, and ensuring the tool supports rather than slows down the team.

When implemented effectively, the right tool can be the difference between drowning in data and harnessing it for insight. With UK business failures at a 12-year high, organisations need solutions that help them move faster, make smarter decisions, and stay ahead of risk.

People still matter in the loop

With all these advances, it’s fair to ask: what’s left for us humans? The short answer is plenty.

The restructuring professionals I work with often remind me that while AI can automate analysis, it still takes human expertise to interpret results and make the tough calls. In fact, with grunt work sped up by machines, my role has evolved to focus even more on judgment. An AI might flag a trend or risk, but it won’t tell you if a business model is fundamentally broken or just hitting a rough patch – that’s on us.

From my side, I see AI as a powerful assistant rather than a replacement. The best outcomes come when human intuition pairs with data-driven insight. An AI model might flag a cashflow risk, but a practitioner knows to ask why – whether that’s seasonality, a short-term supply issue, or something deeper.

An algorithm might rank creditors by risk, but a human will pick up the phone and discover nuances no model can grasp.

One industry guide put it well: “It’s not about replacing people, it’s about enabling them to make faster, sharper decisions when the clock is ticking.” That rings true, because restructuring is often a race against time. AI can crunch data and spotlight what’s critical, but we humans still have to act on it. AI can also serve as a second set of eyes – sometimes it flags something I overlooked, giving me a chance to catch an issue early. That kind of collaboration makes our plans stronger.

Ultimately, I never take an AI’s output at face value without a sense-check. There’s zero room for errors or fabricated info in a creditor meeting or courtroom. I treat the AI’s analysis as a starting draft, not the final word.

“Trust, but verify” is the mantra. I like tools that show their work – if I can see which data points an algorithm relied on, I can trace and confirm them. In essence, we become editors of the AI’s work, using our experience to ensure the story holds together.

I’m optimistic about where this is heading. The AI tools we have now are only the beginning. I can imagine a near future where AI models multiple restructuring scenarios in parallel and helps pinpoint the best path forward – restructuring professionals are already seeing hints of that. But no matter how advanced AI becomes, I’ll keep hammering home the basics: without timely, accurate data and a clear understanding of the business, even the best AI will fall flat.

On the flip side, if you have those fundamentals in place – reliable data, the right tools, and a skilled team – you can tackle just about any corporate crisis that comes your way.

AI and data together are indispensable allies, helping us handle complexity that would have been impossible to manage a decade ago. My philosophy is to embrace these new tools, but keep my eyes open and my feet on the ground. It’s still a blend of old-school savvy and new-school analytics. If people in the restructuring industry get that balance right, I’m confident we’ll see more businesses make it through the storm – and that’s the outcome we’re all working for.