The Data Edit

Summary

In this episode of the Data Edit podcast, our host Olly is joined by guests Esoof Piperdi, Lead Consultant at Agile, and Steven Lin, Product Manager at Semarchy. The conversation delves into the complexities of AI adoption in businesses and the importance of understanding data foundations before implementing AI, the challenges of measuring ROI, and the necessity of data assessments to ensure effective technology integration. The episode highlights the need for collaboration between IT and business leaders to drive successful outcomes and foster innovation within organisations.

takeaways

1. AI is not always the right answer; understanding the business challenge is key.
2. Data foundations must be established before implementing AI solutions.
3. C-suite executives often have unrealistic expectations about AI capabilities.
4. Measuring ROI for AI initiatives is challenging but essential for justification.
5. Time is a critical resource that impacts business operations and forecasting.
6. Data quality and governance are foundational for successful AI implementation.
7. Innovation should be encouraged within organizations to stay competitive.
8. Collaboration between IT and business is crucial for successful technology adoption.
9. Data assessments help identify gaps and opportunities for improvement.
10. Understanding customer needs and preferences is vital for effective AI use.

The Data Edit Podcast | AI Ethics, Data Strategy, And Cloud Migration | Agile

Most companies overshoot their data centre capacity, wasting billions of dollars and energy building more when they could do far more with less. In this candid episode, Olly and Ben dig into the real state of data infrastructure, asking whether rapid expansion is driven by true demand or competitive hype.

You’ll hear why many data centres remain underused, the environmental cost of new builds, and what might change if companies paused to plan more strategically. They also explore how AI demand, cooling advances, and regulation are reshaping the industry, plus the hidden risks of excessive infrastructure growth.

The conversation rounds out with a look at the UK US nuclear power deal and whether nuclear innovation is a clean energy breakthrough or added complexity in a climate crisis. Ideal for tech leaders, sustainability advocates, and curious listeners, this episode challenges assumptions and explores smarter paths to a more sustainable digital future.

In this episode of the Data Edit podcast, Olly and Dione Rogers discuss the critical role of AI and data in modernising the NHS and addressing its challenges. Dione shares her extensive experience in healthcare and emphasizes the need for a robust data strategy, education, and collaboration to improve patient care. The conversation explores the importance of data sharing, patient-centric approaches, and the future of digital health, highlighting the potential of technology to empower patients and enhance healthcare delivery.

Key takeaways

1. AI and digital technology are essential for NHS survival.
2. Data strategy is lacking in the NHS compared to digital strategy.
3. Time and education are significant barriers to progress.
4. Funding is not the main constraint; effective use of resources is key.
5. Virtual wards can alleviate pressure on hospitals.
6. Patient data ownership is crucial for better health outcomes.

In this episode of the Data Edit Podcast, Olly and Ben are joined by Levent Ergin from informatica to discuss the differences and challenges of AI adoption in the public sector compared to the private sector. They explore investment disparities, the importance of structured thinking, regulatory challenges, communication gaps, and data sharing issues. They take a deep dive into the need for both sectors to learn from each other and highlights various use cases for AI in the public sector. In order to succeed, organisations need to start small and scale their AI initiatives effectively.

Key Takeaways:

1. Investment in AI is significantly higher in the private sector.
2. Public sector projects often face tighter budget constraints.
3. Structured thinking is essential for successful AI implementation.
4. Regulatory frameworks for AI are still developing.
5. Communication between business and technical teams is crucial.
6. Data sharing challenges hinder public sector efficiency.

For more information about how Agile can help accelerate your AI projects get in touch today agle.co.uk

As AI adoption accelerates across banking, financial services, and insurance, leaders face a pivotal challenge: how to deploy AI responsibly while maintaining trust, compliance, and operational integrity.

In this episode, hosts Olly and Ben sit down with Levent Ergin of Informatica to unpack what itakes to build AI systems that perform reliably in high-stakes environments. While the conversation touches on the mechanics of AI, it ultimately returns to one central theme: AI success depends on strong foundational design, disciplined governance, and trustworthy data.

Core insights:

1. Establish cross-functional governance to ensure AI initiatives meet compliance and risk standards.
2. Select use cases strategically, beginning with low-risk, high-volume applications.
3. Invest in training to help teams understand, use, and supervise AI responsibly.
4. Continuously monitor AI systems to ensure value, accuracy, and compliance.
5. Prioritise authoritative, validated data sources to reduce hallucination and enhance reliability.

If your organisation is exploring AI, now is the time to ensure your data, governance, and risk frameworks are ready. The Informatica and Agile partnership empowers financial institutions to:

• Build trusted, compliant, end-to-end data pipelines
• Reduce AI hallucinations with authoritative data and automated quality controls
• Implement governance frameworks aligned to regulatory expectations
• Modernise data architectures to support scalable, responsible AI
• Confidently operationalise AI use cases that deliver measurable impact

Investigate how Informatica and Agile can help your organisation deploy AI safely, accurately, and at scale.

Transform your data foundations and unlock AI you can trust.

In this episode, Olly and Ben discuss the Ryder Cup and the effective use of data in sports, particularly focusing on Eduardo Molinari's role in the European team. They explore how data can drive positive outcomes and compare it to the American team's approach, the concept of AI, and the importance of prompt engineering. During the episode Ben and Olly discuss how the quality of prompts can influence the effectiveness of AI responses and the need for validation in AI interactions.

Key Takeaways

• The Ryder Cup showcased the power of data in sports.
• Eduardo Molinari played a crucial role in data analysis for the European team.
• Data should be used for positive outcomes in decision making.
• The American team may not have utilized data as effectively as the Europeans.
• AI's effectiveness can be influenced by the quality of prompts.
• The concept of an 'ever decreasing circle' in AI training data is significant.
• A large percentage of stored data is not utilized effectively.
• Investing time in crafting prompts can lead to better AI responses.
• Prompt engineering is becoming a critical skill in AI interactions.
• Validation of AI responses is essential for accuracy.

There will be countless businesses trying to sell you their AI model, but which is the one to get you where you need to go? Or, do you need to create your own?

When you are on the journey, how do you know when it is time to ramp up and accelerate your strategy? Then, how do you monitor and maintain the AI, and do you have the skills in your team to do it?

Our experienced leaders in AI strategy and implementation will share insights on how enterprise organisations can approach questions of bespoke vs off-the-shelf AI, when to step up a gear with AI, and how to extend the life of the AI model that you invested in.

Takeaways

• The essentials of AI monitoring, maintenance, and oversight

• The advantages, disadvantages, and responsibilities with bespoke AI models

• How to assess your position and readiness relation to AI (and your competition)

Organisations are keen to make progress on their AI strategy and implementation, and don’t want to be left behind in the race. However, AI success isn’t about speed, but direction. Charge ahead too quickly, and you could find yourself making costly diversions that don’t get you where you need to go.

This webinar is about the clarity, purpose and structure that will keep your AI projects on the right road and avoiding the common pitfalls and potholes.

Our experienced leaders in AI strategy and implementation will share insights on how enterprise organisations can set a strong AI foundation, avoid common pitfalls, and move forward with confidence.

Takeaways

• choose AI solutions based on real organisational needs, not trends

• make your data the right fuel for AI

• integrate AI to enhance, not disrupt your workflows

• drive adoption with stakeholder trust and engagement

The AI race is on, but do the runners know where they’re going?

AI offers transformative potential — organisations are eager to act quickly to gain a competitive edge, but they know that moving too fast without a clear strategy can lead to costly missteps. This webinar is about stepping back, assessing where you are, and mapping the journey ahead to make sure your AI ambitions lead to real, sustainable results.

Our experienced leaders in AI strategy and implementation share insights on how enterprise organisations can set a strong AI foundation, avoid common pitfalls, and move forward with confidence.

Takeaways

• A clearer understanding of what it takes to ensure AI success in complex organisations

• Practical tools to assess, adjust, and enhance your AI strategy

• Insights from real-world expertise to align innovation with operational stability and compliance

In this conversation, Olly and Ben explore the positive applications of AI and language models, focusing on a case study that demonstrates how a small language model was used to analyse customer complaints, leading to significant improvements in customer service efficiency.

They also discuss the role of AI in optimising energy usage in utilities, emphasising sustainability and the need for innovative solutions to future energy challenges.

Takeaways

• AI can analyze complaints to improve customer service.
• Identifying common complaints can lead to quick fixes.
• Empowering staff can reduce customer wait times significantly.
• AI can enhance the efficiency of customer interactions.
• Sustainability in energy usage is a critical application of AI.
• AI helps utilities manage energy supply and demand effectively.
• Data cleansing is essential for accurate AI analysis.
• Future energy challenges require innovative AI solutions.

For more information visit agile.co.uk or contact [email protected]