Digital commerce teams rarely lack ideas. Most understand how AI, data, and personalisation could improve customer experiences. The problem, as explored in this episode of Don’t Panic, It’s Just Data, is turning those ideas into something that works at scale, in real time, and without slowing the business down.

Hosted by Dana Gardner, Principal Analyst at Interarbor Solutions, the discussion brings together Jürgen Obermann, Senior GTM Leader EMEA and Piotr Kobziakowski, Senior Principal Solutions Architect from Vespa.ai. Rather than focusing on hype, the conversation centres on the everyday realities of modern e-commerce systems and why progress often feels harder than it should.

AI introduces new expectations around speed, relevance, and adaptability. As a result, many digital commerce platforms are built on foundations designed for a different era. Years of development have resulted in fragmented environments, often based on microservices that once provided flexibility but now introduce complexity.

As Jürgen explains, even small changes can trigger long delivery cycles. Engineering teams may need months to safely update systems, not because the ideas are difficult, but because the infrastructure has become fragile.

Search is where most e-commerce journeys begin, yet many platforms still rely on keyword-focused approaches that struggle to interpret intent. Customers expect results that reflect who they are, what they want, and why they’re searching. Delivering meaningful personalisation requires systems that combine signals, context, and ranking logic in real time. Without that, experiences remain generic even when data is available.

The conversation then turns to architecture. Traditional search stacks, particularly Lucene-based systems, often hit performance limits when vector operations and advanced ranking are introduced. These capabilities tend to be bolted on rather than designed into the core. Piotr highlights a deeper issue, which is fragmentation. Search, ranking, recommendation, feature stores, and inference engines often live in separate systems. Each integration adds latency, duplicates data, and slows innovation.

This episode of Don’t Panic, It’s Just Data offers a calm, practical view of AI in digital commerce. Progress comes not from adding more complexity, but from simplifying how systems work together. When search, personalisation, and recommendation are designed as part of a cohesive whole, digital commerce platforms become easier to evolve and better equipped to serve both customers and the business.

For more insights into modern search architectures and AI-native commerce platforms, visit Vespa.ai.

• Many teams see the potential...

In the recent episode of the Don’t Panic It’s Just Data podcast, Shubhangi Dua, Podcast Producer and B2B Tech journalist at EM360Tech, reports on the podcast shot live in London. Guest speakers, Pavel Dolezal, the CEO at Keboola, sit down with Vineta Bajaj, Group CFO, Holland & Barrett.

They get specific about how modern finance leaders move faster: start with one governed source of truth, then layer automation, and only then AI. They explore how the CFO role is evolving. From reporting numbers to also owning the non-financial “whys” behind them.

In the age of the AI boom, that shift turns every CFO into a product owner of data. But as Pavel Doležal puts it, without a clean, connected foundation, AI is just noise.

According to Vineta Bajaj, Group CFO of Holland & Barrett, the role of the CFO has fundamentally changed. Today’s CFO must act as a product owner for data, not just owning the numbers but also determining how data is defined, structured, and used throughout the business.

Drawing on her experience with Ocado Group, Rohlik Group (one of the fastest online grocery businesses in the world), and now Holland & Barrett, Bajaj points out that financial problems remain persistent across organisations.

Issues such as slow month-end closes, duplicated processes, delayed reporting, and limited decision-making speed are still common. These challenges are even greater in complex businesses that operate across multiple entities and countries. Differing charts of accounts, outsourced finance teams, and fragmented systems create added friction.

Bajaj stresses the answer isn’t "add another tool". CFOs should treat finance and data as a complete product, one that serves the business as its customer. This requires understanding finance processes, clearly defining financial and non-financial data, and prioritising what has the greatest impact on the business.

The Holland & Barrett CFO further emphasises that CFOs cannot pass this responsibility off to IT or BI teams. When data ownership is outside finance, it becomes someone else’s problem. However, when finance takes ownership of master data and its definitions while working closely with commercial and operational teams, it creates a single source of truth that the entire organisation can trust.

Also Watch: The Real Future of Data Isn’t AI — It’s Contextual Automation

Analytics, automation, and AI only work if the foundations are solid. Before adding AI assistants or real-time dashboards, CFOs must ensure that finance processes are clean, standardised, and automated. Poorly coded purchase orders, late journal entries, and inconsistent definitions can undermine even the most advanced technology.

At Holland & Barrett, this perspective led Bajaj to create a dedicated data function within finance. It ensures accountability for master data, definitions, and governance. The aim is not just to speed up reporting, but to gain deeper insights by linking financial outcomes with non-financial factors such as foot traffic, pricing, customer behaviour, and external influences like weather.

This integrated viewpoint allows finance teams to go beyond explaining variances and focus on the...

We live in a world where technology moves faster than most organisations can keep up. Every boardroom conversation, every team meeting, even casual watercooler chats now include discussions about AI. But here’s the truth: AI isn’t magic. Its promise is only as strong as the data that powers it. Without trust in your data, AI projects will be built on shaky ground.

In this episode of Don’t Panic, It’s Just Data podcast, Amy Horowitz, Group Vice President of Solution Specialist Sales and Business Development at Informatica, joins moderator Kevin Petrie, VP of Research at BARC, to tackle one of the most pressing topics in enterprise technology today: the role of trusted data in driving responsible AI. Their discussion goes beyond buzzwords to focus on actionable insights for organisations aiming to scale AI with confidence.

Amy opens the conversation with a simple but powerful observation: “No longer is it okay to just have okay data.” This sets the stage for understanding that AI’s potential is only as strong as the data that feeds it. Responsible AI isn’t just about implementing the latest algorithms; it’s about embedding ethical and governance principles into every stage of AI development, starting with data quality.

Kevin and Amy emphasise that organisations must look at data not as a byproduct, but as a foundational asset. Without reliable, well-governed data, even the most advanced AI initiatives risk delivering inaccurate, biased, or ineffective outcomes.

Responsible AI is more than compliance or policy checkboxes. As Amy explains, it is a framework of principles that guide the design, development, deployment, and use of AI. At its core, it is about building trust, ensuring AI systems empower organisations and stakeholders while minimising unintended consequences. Responsible data governance is the practical arm of responsible AI. It involves establishing policies, controls, and processes to ensure that data is accurate, complete, consistent, and auditable.

The takeaway from this episode is clear and that is responsible AI starts with responsible data. For organisations looking to harness AI effectively:

1. Invest in data quality and governance — it is the foundation of all AI initiatives.
2. Embed ethical and legal principles in every stage of AI development.
3. Enable collaboration across teams to ensure transparency, accountability, and usability.
4. Start small, prove value, and scale — responsible AI is built step by step.

Amy Horowitz’s insight resonates beyond the tech team: “Everyone’s ready for AI — except their data.” It’s a reminder that AI success begins not with the algorithms, but with the trustworthiness and governance of the data powering them.

For more insights, visit Informatica.

• AI is only as good as its data inputs.
• Data quality has become the number one obstacle to AI success.
• Organisations must start small and find use cases for data governance.
• Hallucinations in AI models highlight the need for vigilant