AI has been at the center of boardroom discussions, news headlines, government debates, and more over the last several years. But how much of the buzz is substantive and how much is just hype? Where is the technology actually headed and how will it affect our work and daily lives?
Grata co-founder and CEO Andrew Bocskocsky recently joined Peter A. Emmi, Marcus Höfer, Tim Cruttenden, and Gokce Akkahve at PEMACOM to discuss the current state of AI adoption in the M&A world, the ROI investors are seeing, and regional differences between Europe, the US, and Asia.
Here’s what you need to know.
1. The AI hype has cooled, but AI's potential is heating up.
There’s no question that AI is changing the way we work. It’s high-impact technology. It creates new businesses and opportunities. But it’s also unpredictable, and its progress is discontinuous, meaning AI experiences intervals of rapid innovation with slower periods in between.
In mid-market firms, AI enthusiasm appears to have cooled; however, the tech still has tremendous potential to automate heavy manual processes across the M&A industry.
The industry also has much to gain as it moves beyond the casual use of free, generalist tools like ChatGPT and closer to true AI enablement.
2. Firms are leaning into AI-enabled processes to reduce grunt work and drive efficiency.
AI adoption has become mainstream. As a result, AI-powered processes are reducing the amount of grunt work across the entire investment cycle, increasing both speed and accuracy.
But at this point, the majority of firms have mastered using AI to assist with individual tasks and they are now moving towards leveraging the technology to orchestrate end-to-end mandates.
In many venture capital and private equity firms, AI is 100% adopted across investment theses, from infrastructure and models to inference, compute, and application layers. This is fueled by visible revenue traction. For example:
- OpenAI generated $13B of revenue in 12 months
- Microsoft reported$13B in AI revenue
- Databricks saw revenue grow by 50% to $4B, indicating aggressive enterprise adoption alongside test-and-learn spending
Firms looking to integrate AI into more of their workflows should take a bottom-up approach. First, analyze the roles in the organization and break those roles down into big tasks to identify use cases for AI. From there, identify which use case should be implemented first. Once that process is complete, move on to the second use case, then the third, and so on.
Embedding AI-enthusiast employees (“AI champions”) across roles helps align technology with business needs and facilitates adoption, fostering innovation at the intersection of business and technology. Many consulting practices are applying this approach to their AI adoption plans.
3. Firms should measure ROI for AI by how much of the team is efficiently using the tech.
One study estimated that $3.7T worth of tasks can now be performed by AI. But ROI on AI investments isn’t always so easily measured. Some AI use cases show immediate efficiency gains and time savings; others improve work quality and speed without direct cost reductions right away.
A more appropriate way for firms to measure ROI on AI adoption might be how much of the team effectively leverages the technology. The adopters benefit the most, after all.
Europe has an opportunity to establish itself as a leader in robust, secure data frameworks.
Key regions across the globe are playing to their strengths in different ways. The current narrative is that the US invents, Asia scales, and Europe regulates.
Europe has to walk a fine line. Its regulatory-first approach can be a boon for the data realm, as it builds trust in accuracy and security. But applying this tactic to the tech realm gets tricky. If the approach is overly rigid, tedious, or slow, it can hinder progress.
4. Europe has an opportunity to position itself as a secure environment, which could prove advantageous if the region advances its own AI models.
Firms in the region should emphasize data sovereignty and monetization as core strengths. Building chips or full-stack AI may be too slow and capital-intensive given the current competitive landscape. But quality data is the foundation upon which the best AI technology is built.
Tools and technical components can be a collaborative effort, or they can be imported. Europe’s real opportunity for leadership is in building frameworks around rich, trusted data to enable workflows and scale.
For example, agentic AI will rely on contextual data as it gains momentum and becomes more integrated into M&A workflows. Strong European datasets can underpin future agentic capabilities.
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