StrategyBridgeAI at EACVA 2025: what company analyses must deliver today

Our Product Manager Manuel presented at the EACVA (European Association of Certified Valuators and Analysts) conference in Munich on a question that is genuinely on a lot of people's minds right now: what does a reliable company analysis need to deliver in 2025, and where does AI actually help?

The challenge teams are facing

Analysts today are dealing with more data than ever and more pressure to turn it around quickly. The tools available have changed significantly. But the core requirements for a good analysis have not: it needs to be precise, traceable, and defensible. Those three things are harder to deliver, not easier, when you add speed and scale into the mix.

What the workshop covered

Manuel walked through three areas where the tension between AI potential and analytical rigour shows up most clearly.

Data quality is still the foundation. AI does not fix bad data. If the inputs are incomplete, inconsistently sourced, or full of outliers, the outputs will be too. At StrategyBridgeAI, all core KPIs are calculated deterministically. Sources are checked, outliers are flagged, and gaps are filled in a traceable way before any pattern recognition happens.

AI belongs in the right part of the process. There is a meaningful difference between using AI for pattern recognition across large datasets and using it as a substitute for financial logic. The first is genuinely useful. The second creates analyses that look convincing but do not hold up under scrutiny. The workshop made the case for keeping those two things clearly separated.

Judgment cannot be automated. Speed is valuable, but the analyst still needs to understand what the numbers mean, where the model might be wrong, and how to explain the conclusion to a client or a court. That requires domain expertise, not just access to a good tool.

The StrategyBridgeAI approach in practice

The way the platform is built reflects these principles directly. Deterministic calculation of core KPIs, systematic source verification, outlier detection, and clean gap-filling all happen before AI is applied. The result is analysis that is fast, but also explainable, auditable, and robust enough to stand behind professionally.

The conclusion from the workshop: data quality and transparency are not obstacles to using AI well. They are the precondition for it.

The EACVA conference brings together certified valuators and analysts from across Europe. StrategyBridgeAI presented as part of the programme on data and AI in professional valuation practice.