Archives: Agenda

AI investment is accelerating—but according to recent Harvard Business Review research, most financial services firms are still struggling to translate that investment into meaningful business impact. Why? Because AI success isn’t a technology challenge—it’s an organisational one.

Join Seismic to unpack the findings from Harvard Business Review and explore how leading firms are closing the gap between AI ambition and execution—by rethinking roles, redefining skills, and embedding AI into the fabric of their client-facing teams.

This session will show how AI-powered enablement is transforming distribution into an intelligent, connected ecosystem—where marketing, sales, and client experience are aligned through data, and every interaction is guided, personalised, and measurable at scale.

Walk away with a practical blueprint for building an AI-ready organisation—one that moves beyond experimentation to operationalise AI for growth, efficiency, and trust in a rapidly evolving financial landscape.

What you’ll take away:

• Key insights from Harvard Business Review on why AI initiatives stall—and how to overcome it
• How to evolve skills, roles, and operating models to embed AI across the business
• What it takes to turn enablement into a predictive, AI-powered growth engine
• How to scale personalisation, productivity, and compliance—without losing the human touch

AI will impact how we work in the coming years, the agentic way of working. All we see are the success stories on LinkedIn, during sales calls and fellow peers in your network. This session discuss the rarely discussed challenges of agentic  and how to mitigate some of the associated risks.

1. What are the strategic implications of AI to the financial industry and what do we expect to see in the future?

A forward-looking discussion on how AI is reshaping financial services strategy, from data governance and organisational design to customer propositions and regulatory expectations.

Nivedh Iyer, Head of Group Data Management, Group Data & Analytics, Danske Bank  

2. Integrating AI Solutions into Contact Centre systems: Preparing people processes, and data for automated customer interactions

A practical exploration of how AI is embedded into existing contact centre platforms, aligning data, workflows and operating models so automation delivers real efficiency and experience gains without undermining quality, governance or control.

Farhad Fatemi, Sales Engineer. Puzzel 

3. Strategic adoption of Agentic AI: Automating customer-facing communications across voice, chat and email

An honest discussion on moving agentic AI from pilots to production across channels, examining where automation genuinely adds value and what is required to scale it safely while maintaining oversight, consistency and trust.

Jesper Jønsson, Director of AI Products, Puzzel 

4. Can Nordic financial institutions escape the pilot graveyard? 

The average Nordic bank is running 15-30 AI pilots simultaneously. Most will never reach production – not because of bad technology but because of a broken strategy. The pattern is always the same: a proof of concept wins a budget, a pilot runs for six months, stakeholders celebrate the demo. But nothing scales, the P&L doesn’t move, the board asks where the ROI is and, inevitably, another cycle begins. We’ve been through this ourselves, and we built our way out of it. Let’s discuss what really works for aligning people, technology, and value engineering to capture the full potential of AI.         

Genady Chybranov, Head, BFSI Centre of Excellence Sigma Software

5. From blueprint to production: Modern banking at speed with AI, automation and compliance intelligence

This roundtable explores how financial institutions can move from strategy to execution, putting AI solutions quickly and safely into the hands of customers and employees. As firms face growing pressure to scale AI in highly regulated environments, delivery speed, governance and compliance intelligence are becoming critical to success. Join this discussion to examine how financial institutions can remove barriers to deployment and accelerate the delivery of real business value.

Christer, Hedström, Regional Sales Director Nordics, OutSystems

6. How to set the right level of ambition for AI

How ambitious should organisations really be with AI, and how do leaders set the right targets to turn ambition into real impact? How should organisations prioritize and follow up to ensure progress in the areas that matter most?

Karianne Sundahl, Head of data and analytics, Storebrand

Financial institutions are generating more data than ever, yet over 68% of it goes unused, and most AI projects fail not because of the models, but because of the data foundation beneath them. Drawing on real-world case studies across fraud detection, AML investigations, and ATM operations, this session explores how organisations can deliver quantifiable business value by unifying and acting on structured and unstructured data in real time.

1. What are the strategic implications of AI to the financial industry and what do we expect to see in the future?

A forward-looking discussion on how AI is reshaping financial services strategy, from data governance and organisational design to customer propositions and regulatory expectations.

Nivedh Iyer, Head of Group Data Management, Group Data & Analytics, Danske Bank  

2. Integrating AI Solutions into Contact Centre systems: Preparing people processes, and data for automated customer interactions

A practical exploration of how AI is embedded into existing contact centre platforms, aligning data, workflows and operating models so automation delivers real efficiency and experience gains without undermining quality, governance or control.

Farhad Fatemi, Sales Engineer. Puzzel 

3. Strategic adoption of Agentic AI: Automating customer-facing communications across voice, chat and email

An honest discussion on moving agentic AI from pilots to production across channels, examining where automation genuinely adds value and what is required to scale it safely while maintaining oversight, consistency and trust.

Jesper Jønsson, Director of AI Products, Puzzel 

4. Can Nordic financial institutions escape the pilot graveyard? 

The average Nordic bank is running 15-30 AI pilots simultaneously. Most will never reach production – not because of bad technology but because of a broken strategy. The pattern is always the same: a proof of concept wins a budget, a pilot runs for six months, stakeholders celebrate the demo. But nothing scales, the P&L doesn’t move, the board asks where the ROI is and, inevitably, another cycle begins. We’ve been through this ourselves, and we built our way out of it. Let’s discuss what really works for aligning people, technology, and value engineering to capture the full potential of AI.         

Genady Chybranov, Head, BFSI Centre of Excellence Sigma Software

5. From blueprint to production: Modern banking at speed with AI, automation and compliance intelligence

This roundtable explores how financial institutions can move from strategy to execution, putting AI solutions quickly and safely into the hands of customers and employees. As firms face growing pressure to scale AI in highly regulated environments, delivery speed, governance and compliance intelligence are becoming critical to success. Join this discussion to examine how financial institutions can remove barriers to deployment and accelerate the delivery of real business value.

Christer, Hedström, Regional Sales Director Nordics, OutSystems

6. How to set the right level of ambition for AI

How ambitious should organisations really be with AI, and how do leaders set the right targets to turn ambition into real impact? How should organisations prioritize and follow up to ensure progress in the areas that matter most?

Karianne Sundahl, Head of data and analytics, Storebrand

NOBA Bank Group’s AI journey at was never just about technology. With a small, dedicated team and strong executive support, they developed internal and customer-facing AI solutions by focusing on adoption, change management, and practical impact. Rolling out licenses or generic “off-the-shelf” tooling quickly proved insufficient; instead, they prioritised self-service creation, and focused on understanding, categorising, and embedding internal knowledge in a structured manner. By leveraging Azure OpenAI and internal APIs, they achieved fast and secure results. Key learnings from their internal rollout directly shaped their customer chatbot, proving that true success comes from understanding user needs and organisational context. Importantly, this approach allowed them to connect AI development to clear business goals and deliver tangible customer value. Join us for a deep dive into their approach, adoption metrics, and why rolling out AI in a financial institution is “not a tech project”- it’s about people, process, and purpose.

This session explores the real-world challenges teams face when implementing AI; the late nights, unexpected setbacks, and the hard-earned lessons that shape success. Through candid stories and reflective insights, participants will unpack what went wrong, why it happened, and how resilience and learning turned breakdowns into breakthroughs.

For financial institutions, the “gold rush” of AI experimentation is meeting the “brick wall” of the EU AI Act. As organisations move from internal pilots to customer-facing production, the critical question is no longer just about model accuracy – it’s about governance. How do you track every prompt, redact PII in real-time, and manage skyrocketing token costs without slowing down your developers? In this session, SEB and Kong share a blueprint for leveraging a dedicated AI Gateway – a centralized “control plane” that decouples AI innovation from regulatory complexity.

Moderator: Steve Roberts, Global Strategic Advisory Services Lead, Kong Inc

AI ambition is high across financial services, but measurable impact remains uneven. This session centres on a detailed real world case study, illustrating how an outcome-led framework helped a firm move beyond pilots to deliver tangible operational and commercial value through embedded, workflow-driven AI.

• The hard truth about stalled AI initiatives
• Defining business outcomes before selecting technology
• How the framework was applied in practice
• Measurable results and lessons learned