AI: the new competitive edge in auto and equipment finance

Summary

The closing session of the Finance Connect Leaders’ Summit Europe 2026 brought a familiar but fast-evolving theme into sharp focus: artificial intelligence is no longer a side project. Moderated by Richard Huston, the panel – featuring Katrin Puettmann, Angelika Gemmer, and Wolfgang Köhne – offered a grounded, practitioner-led view of how AI is reshaping auto and equipment finance.

What emerged was not hype, but a clear message: 2026 is a defining year. The question is no longer whether organisations will adopt AI but whether they can do so fast enough, and in the right way, to remain competitive.

2026: the year AI separates winners from laggards

The shift is already underway. While many organisations have spent recent years experimenting with pilots and proofs of concept, AI is now moving decisively into core operational processes, from credit scoring and fraud detection to customer service. As this transition accelerates, competitive advantage is no longer defined purely by pricing or product margins, but by operational capability.

Crucially, AI does not follow traditional IT investment cycles. Instead of five- to seven-year roadmaps, organisations are facing a technology landscape that evolves in months. This creates a widening capability gap between those able to continuously adapt and those constrained by slower, legacy approaches.

From cost savings to capability building

A recurring theme throughout the discussion was the need to rethink how AI investments are justified.

Rising labour costs and workforce shortages make automation, particularly in contact centres, an attractive proposition. Even relatively simple use cases, such as automated email routing, can deliver immediate value.

However, as the panel emphasised, focusing solely on short-term ROI risks missing the bigger picture. AI is not just another efficiency tool; it is a capability-building exercise. Organisations must invest in data, infrastructure, and skills that enable continuous iteration, rather than one-off returns.

Puettmann challenged traditional financial thinking directly: applying conventional ROI models to AI may hold organisations back. The real question is not “what is the payback period?” but “are we building the capability to keep up with the pace of change?”

Use cases: from contact centres to risk intelligence

The panel highlighted a spectrum of AI applications across the auto and equipment finance landscape, reflecting differing business models.

In high-volume, customer-facing environments, AI adoption is accelerating rapidly. Chatbots and virtual assistants – once seen as ineffective – are now capable of resolving routine queries, supporting agents, and improving response times. The key differentiator is data maturity: organisations with integrated, high-quality data and omnichannel systems are pulling ahead.

In lower-volume, relationship-driven environments, human interaction remains central. However, AI is proving valuable in augmenting decision-making, particularly in risk assessment.

One compelling example involved supplier risk analysis. Traditional credit models, heavily reliant on historical financial data, can miss critical qualitative signals, such as legal issues or reputational risks reported in the media. AI-powered agents, capable of scanning and synthesising unstructured information, can significantly enhance underwriting decisions.

This points to a broader insight: AI is not just about doing existing tasks more efficiently – it is about expanding the scope of what organisations can analyse and act upon.

Legacy systems: barrier or opportunity?

Legacy infrastructure remains a widely cited challenge. According to Puettmann, the majority of organisations see it as a barrier to AI adoption.

Yet the panel pushed back on the idea that transformation requires wholesale system replacement. Instead, the emerging consensus is to “bridge, not rebuild.”

By layering API-driven, cloud-based interfaces over existing systems, organisations can unlock data and deploy AI use cases without disrupting core operations. This approach also provides flexibility in a rapidly evolving technology landscape, where today’s leading model may be obsolete within a year.

Köhne was particularly direct on this point, arguing that blaming legacy systems is often an excuse. In many cases, the data required for AI already exists, sometimes in vast quantities. The real challenge lies in extracting and utilising it effectively.

Small bets, fast cycles

If there was one operational takeaway from the session, it was the importance of speed through iteration.

Rather than large, monolithic AI programmes, the panel advocated for “small bets” – targeted use cases delivered in short, bounded cycles (for example, three initiatives in 90 days). This approach enables organisations to learn quickly, demonstrate value, and build momentum.

Supporting this requires structural change. Traditional governance models, characterised by lengthy approval processes, are ill-suited to AI. Instead, organisations need empowered teams, clear guardrails, and the ability to experiment without constant escalation.

The human factor: adoption over technology

While much of the discussion focused on technology and strategy, the human dimension proved equally critical.

Gemmer emphasised that successful AI adoption depends on engagement and trust, both internally and externally. Employees must be involved in shaping AI tools, with their feedback guiding development. Positioning AI as a “colleague” rather than a threat can accelerate acceptance and unlock productivity gains.

For customers, transparency is key. Clearly signalling when AI is being used, and providing easy access to human support, can mitigate concerns and improve overall experience.

Ultimately, speed of adoption is as much about culture and change management as it is about technology.

AI and the future of customer interaction

The session concluded with a debate on whether AI will replace human interaction in customer service.

The consensus: it’s not a binary choice. The balance between AI and human engagement will vary depending on scale, complexity, and customer expectations. High-volume environments will continue to push automation, while relationship-driven sectors will retain a stronger human element.

What matters is not choosing one over the other, but finding the optimal mix, where AI enhances efficiency and insight, and humans deliver value where it matters most.

Beyond the obvious: ai across the value chain

In closing, Puettmann urged organisations to look beyond frontline use cases. The real transformative potential of AI lies in end-to-end operational redesign – from software development and testing to project management and proposal generation.

In this sense, AI is not just a tool for customer-facing functions, but a catalyst for rethinking how work is done across the enterprise.

Final thought

The “AI in auto and equipment finance” session underscored a critical shift: AI is no longer experimental, it is foundational.

The organisations that will lead in the coming years are not necessarily those with the most advanced technology today, but those that can adapt continuously, move quickly, and embed AI into the fabric of their operations.

As the summit made clear, the train is already moving. The real risk is not getting on board, but waiting too long to try.