Tech Transformed

As AI technologies become more integrated into business operations, they bring opportunities and challenges. AI’s ability to process vast amounts of data can enhance decision-making but also raise concerns about data privacy, security, and regulatory compliance. 

Ensuring that AI-driven systems adhere to data protection laws, such as GDPR and CCPA, is critical to avoid breaches and penalties. Balancing innovation with strict compliance and robust data security measures is essential as organisations explore AI’s potential while protecting sensitive information.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Erin Nicholson, Global Head of Data Protection and AI Compliance at Thoughtworks, about the importance of compliance frameworks, best practices for transparency and accountability, and the need for collaboration among various teams to build trust in AI systems.

Key Takeaways:

• AI systems are powerful but require ethical and compliant design.
• Lack of standardisation in AI regulations poses significant challenges.
• AI models often need help with explainability and transparency.
• Compliance frameworks are essential for implementing AI in critical sectors.
• Documentation and audits are crucial for maintaining AI accountability.
• Baselining pre-AI processes helps build public trust in AI systems.
• Organisations should map regulations to the most stringent standards.
• Cross-functional collaboration is vital for effective AI compliance.

Chapters: 

00:00 - Introduction to AI, Data Protection, and Compliance

02:08 - Challenges in AI Implementation and Compliance

05:56 - The Role of Compliance Frameworks in Critical Sectors

10:31 - Best Practices for Transparency and Accountability in AI

14:48 - Navigating Regional Regulations for AI Compliance

17:43 - Collaboration for Trustworthiness in AI Systems

As organisations increasingly migrate to cloud environments, they face a critical challenge: ensuring the security and privacy of their data. 

Cloud technologies offer many benefits, including scalability, cost savings, and flexibility. However, they also introduce new risks, such as potential data breaches, unauthorised access, and compliance issues. 

With sensitive data stored and processed off-premises, maintaining control and visibility over that data becomes more complex. As cyber threats continue to evolve, robust data protection strategies are essential to safeguarding information in the cloud.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Sergei Serdyuk, VP of Product Management at NAKIVO, about the factors driving cloud adoption, the importance of having a robust disaster recovery plan, best practices for data protection, and the challenges of ensuring compliance with regulations.

Key Takeaways: 

• Cloud adoption is accelerated by low barriers to entry.
• Scalability in cloud environments is easier than on-premises.
• Data in the cloud is vulnerable and needs protection.
• The shared responsibility model places data protection on the user.
• A comprehensive disaster recovery plan is crucial for businesses.
• Regular testing of disaster recovery plans is essential.
• Data protection strategies must include regular reviews and updates.
• Compliance with data protection regulations is complex and varies by region.
• Balancing security and operational efficiency is a key challenge.

Chapters: 

00:00 - Introduction to Cloud Technologies and Data Protection

01:26 - Factors Accelerating Cloud Adoption

03:48 - The Importance of Data Protection in the Cloud

06:39 - Developing a Comprehensive Disaster Recovery Plan

10:05 - Best Practices for Data Protection

13:31 - Ensuring Compliance in Cloud Environments

15:56 - The Role of Continuous Monitoring in Data Protection

18:19 - Balancing Security and Operational Efficiency

Managed Service Providers (MSPs) are evolving beyond traditional IT support, becoming strategic partners in driving business growth. By embracing AI technologies, MSPs are improving operational efficiency, streamlining service delivery, and offering smarter solutions to meet modern challenges.

As businesses navigate digital transformation, MSPs are crucial in optimising IT infrastructure, enhancing security, and providing tailored solutions that fuel innovation. With AI-powered tools, MSPs meet today's demands and help businesses stay competitive. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Jason Kemsley, Co-founder and CRO of Uptime, about the proactive strategies that MSPs can adopt using AI, the challenges they face in implementation, and the ethical considerations surrounding AI solutions.

Key Takeaways:

• MSPs are transitioning from traditional IT support to strategic partners.
• AI is enhancing operational efficiency but not replacing human roles.
• Proactive support is often neglected due to resource constraints.
• AI can help MSPs predict and prevent IT issues.
• Challenges in adopting AI include unrealistic expectations and lack of accountability.
• There is potential for MSPs to develop their own AI tools.

Chapters:

00:00 - Introduction to Managed Service Providers (MSPs)

02:03 - The Evolving Role of MSPs in Business Growth

04:00 - AI's Impact on Service Delivery Models

07:22 - Proactive Support Strategies with AI

10:16 - Challenges in Adopting AI for MSPs

12:40 - Ethics and Accountability in AI Solutions

Trusted AI ensures that people, data, and AI systems work together transparently to create real value. This requires a focus on performance, innovation, and cost-effectiveness, all while maintaining transparency. However, challenges such as misaligned business strategies and data readiness can undermine trust in AI systems. 

To build trusted AI, it’s crucial to first trust the data. A robust data platform is essential for creating reliable and sustainable AI systems. Tools like Teradata’s ClearScape Analytics help address concerns about AI, including issues like generative AI hallucinations, by providing a solid foundation of trusted data and an open, connected architecture.

In this episode, Doug Laney, Analytics Strategy Innovation Fellow with West Monroe Partners, speaks to Vedat Akgun, VP of Data Science & AI and Steve Anderson, Senior Director of Data Science & AI at Teradata, about trusted AI. 

Key Takeaways:

• Value creation, performance, innovation, and cost-effectiveness are crucial for achieving trusted AI.
• Trusting data is essential before building AI capabilities to avoid biases, inaccuracies, and ethical violations.
• A robust data platform is a foundation for creating trusted and sustainable AI systems.
• Generative AI raises concerns about hallucinations and fictitious data, highlighting the need for transparency and accountability.
• Teradata offers features and capabilities, such as ClearScape Analytics and an open and connected architecture, to address trust issues in AI.

Chapters:

00:00 - Introduction and Defining Trusted AI

01:33 - Value Creation and the Importance of Driving Business Value

03:27 - Transparency as a Principle of Trusted AI

09:00 - Trusting Data Before Building AI Capabilities

14:51 - The Role of a Robust Data Platform in Trusted AI

21:09 - Concerns about Trust in Generative AI

23:03 - Addressing Trust Issues with Teradata's Features and Capabilities

25:01 - Conclusion

Balancing transparency in AI systems with the need to protect sensitive data is crucial. Transparency helps build trust, ensures fairness, and meets regulatory requirements. However, it also poses challenges, such as the risk of exposing sensitive information, increasing security vulnerabilities, and navigating privacy concerns.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Juan Jose Lopez Murphy, Head of Data Science and Artificial Intelligence at Globant, to discuss the ethical implications of AI and the necessity of building trust with users. 

Key Takeaways:

• Companies often prioritise speed over transparency, leading to ethical concerns.
• The balance between transparency and protecting competitive data is complex.
• AI misuse by malicious actors is a growing concern.
• Organisations must educate users on digital literacy to combat misinformation.
• Confidently wrong information is often more trusted than qualified uncertainty.

Chapters

00:00 - Introduction to AI Transparency

03:03 - Balancing Transparency and Data Protection

05:57 - Navigating AI Misuse and Security

09:05 - Building Trust Through Transparency

12:03 - Strategies for Effective AI Governance

As organisations adopt AI, data literacy has become more critical than ever. Understanding data—how it's collected, analysed, and used—is the foundation for leveraging AI effectively. Without strong data literacy, businesses risk making misguided decisions, misinterpreting AI outputs, and missing out on AI’s transformative benefits. By fostering a data-driven culture, teams can confidently navigate AI tools, interpret results, and drive smarter, more informed strategies.

Ready to boost your data literacy and embrace the future of AI?

Key Takeaways: 

• Companies are optimistic about ROI from AI investments.
• Data literacy is crucial for effective AI implementation.
• Technical debt poses challenges for AI infrastructure.
• Data quality and governance are essential for AI success.
• Trust in AI systems is a growing concern.
• Organizations must start with clear business priorities.
• The Data Festival will provide practical insights for AI adoption.

DATA festival is where theory meets practice to create real, actionable knowledge. This event brings together #DATApeople eager to drive the realistic applications of AI in their fields.

Leaving the hype behind, we look at the actual progress made in applying (Gen)AI to real-world problems and delve into the foundations to understand what it takes to make AI work for you. We’ll discuss when, where and how AI is best applied, and explore how we can use data & AI to shape ourfuture.

Sign up now to secure your spot

Data labelling is a critical step in developing AI models, providing the foundation for accurate predictions and smart decision-making. Labelled data helps machine learning algorithms understand input data by assigning meaningful tags to raw data—such as images, text, or audio—ensuring that AI models can recognise patterns and make informed decisions. 

AI models struggle to learn and perform tasks effectively without high-quality labelled data. Proper data labelling enhances model accuracy, reduces errors, and accelerates the time it takes to train AI systems. Whether you're working with natural language processing, image recognition, or predictive analytics, the success of your AI project hinges on the quality of your labelled data.

In this episode, Henry Chen, Co-founder and COO of Sapien, speaks to Paulina Rios Maya about the importance of data labelling in training AI models. 

Key Takeaways:

• Data labelling converts raw data into structured data that machine learning models can recognise.
• Reducing bias and ensuring data quality are critical challenges in data labelling.
• Expert human feedback plays a crucial role in improving the accuracy of AI training data and refining AI models.

Chapters:

00:00 - Introduction and Background

01:07 - Data Labeling: Converting Raw Data into Useful Data

03:02 - Challenges in Data Labeling: Bias and Data Quality

07:46 - The Role of Expert Human Feedback

09:41 - Ethical Considerations and Compliance

11:09 - The Evolving Nature of AI Models and Continuous Improvement

14:50 - Strategies for Updating and Improving Training Data

17:12 - Conclusion

Traditional KYC processes are inadequate against modern fraud tactics. While KYC helps with initial identity checks, it doesn't cover evolving threats like AI-generated deepfakes or ongoing account takeovers. 

Curious about how to protect your business from the latest threats like fake IDs, account takeovers, and AI-generated deep fakes? Tune in to our latest episode, where we dive into the essentials of full-cycle verification and real-time transaction monitoring. Find out how AI and machine learning can revolutionise your fraud detection efforts and why staying updated with regulatory changes is crucial for maintaining top-notch security. 

In this episode of Tech Transformed, Alvaro Garcia, Transaction Monitoring Technical Manager at Sumsub, speaks to Paulina Rios Maya, Head of Industry, about the manifestations of identity fraud during the user journey stages and the need for comprehensive fraud prevention measures.

Key Takeaways:

• Identity fraud manifests at different user journey stages, including onboarding and transaction monitoring.
• Businesses must implement full-cycle verification and transaction monitoring solutions to detect and prevent fraud in real time.
• AI and machine learning are crucial in analyzing suspicious user behaviour and spotting complex fraud patterns.
• SumSub offers platform solutions that include KYC, business verification, transaction monitoring, and payment fraud protection.

00:00 - Introduction and Overview

00:35 - Identity Fraud in the User Journey

02:01 - Types of Fraud and Fraud Prevention

04:20 - Real-Time Monitoring and Enhancing Systems

05:46 - Common Types of Fraud Faced by Financial Institutions

08:40 - The Challenge of AI-Generated Deepfakes

10:04 - Beyond KYC: Additional Measures for Fraud Prevention

12:29 - Prevention Measures and Synthetic Identity Fraud

15:21 - Effective Fraud Prevention Solutions

17:45 - Assessing the Effectiveness of Fraud Prevention Strategies

19:08 - Staying Up to Date with Regulatory Requirements

21:31 - Conclusion

AI is revolutionising contact centres by automating routine tasks, reducing response times, and enhancing customer experience. AI is built to handle simple inquiries efficiently and at scale. 

It helps contact centres close the gap between customer expectations and conventional customer service by enabling engagement through digital channels. AI-driven analytics improve decision-making by capturing and analysing data from customer interactions. Organisations can overcome challenges by starting small and gradually building trust in AI's capabilities. 

In this episode, Paulina Rios Maya, Head of Industry Relations at EM360 speaks to Jon Arnold, Principal at J Arnold & Associates about the use of AI in contact centres. 

Key Takeaways:

• AI is a transformative technology that rethinks customer engagement and addresses customer problems in contact centres.
• AI enables engagement through digital channels and helps contact centres close the gap between customer expectations and conventional customer service.
• AI-driven analytics improve decision-making by capturing and analysing data from customer interactions.
• Organisations can overcome challenges by starting small and gradually building trust in AI's capabilities.
• AI helps protect privacy and mitigate fraud in contact centres.

Chapters:

00:00 - Introduction and Overview

01:06 - The Transformational Power of AI in Contact Centers

03:00 - Automating Routine Tasks and Enhancing Customer Experience

06:24 - Engaging Customers through Digital Channels

11:09 - Improving Decision-Making with AI-Driven Analytics

15:28 - Overcoming Challenges and Building Trust in AI

17:23 - Protecting Privacy and Mitigating Fraud in Contact Centers

Join us in this exciting episode of Tech Transformed, where we talk to Kelly Vero, a pioneering game developer, digital leader, and visionary in the metaverse. With a career spanning 30 years and a resume that includes contributions to legendary franchises like Tomb Raider and Halo 3, Kelly brings a wealth of knowledge and experience to the table.  

Kelly’s unique journey in the tech world is nothing short of extraordinary. From joining the military to learn about ballistics for Halo 3 to founding the award-winning startup NAK3D, she has always pushed the boundaries of what’s possible. 

Kelly Vero speaks to Paulina Rios Maya about the hurdles of being a woman in the tech industry, the principles of gamification, and the overhyped trends in AI and NFTs. They discuss what’s genuinely beneficial versus what’s just noise.

Key Takeaways: 

• The gaming industry offers opportunities for problem-solving and creativity.
• Role models can come from everyday people who inspire and support others.
• Gamification is about creating an engaging user experience that encourages return and contribution.
• The tech industry has seen beneficial changes in globalized platforms and a focus on quantum solutions.

Chapters: 

00:00 - Introduction and Background

02:28 - The Gaming Industry and Problem Solving

07:36 - Challenges and Role Models in the Tech Industry

11:54 - The Principles and Ethical Considerations of Gamification

18:30 - Beneficial Changes and Overhype in the Tech Industry

20:25 - Creating Digital Objects and the NFT Standard

23:45 - Introducing NAK3D: Bringing Non-Designers into Design