Episodes
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When it comes to the IT enterprise, the integration of AI is proving to be a transformative force. Modern IT departments face mounting challenges, from managing complex infrastructures and resolving system inefficiencies to ensuring robust cybersecurity and meeting escalating user demands.
AI offers a solution by automating routine tasks, streamlining processes, and enabling predictive analytics that anticipate issues before they arise. This not only enhances operational efficiency but also frees IT teams to focus on more strategic initiatives, driving innovation and value creation across the organisation.
However, adopting AI in enterprise IT systems comes with its own challenges, including ensuring data privacy, overcoming resistance to change, and maintaining the right balance between automation and human oversight. Effective AI integration requires a thoughtful approach—one that leverages AI’s capabilities while retaining human control to ensure accountability and ethical decision-making.
In this episode, Kevin Petrie, VP of Research at BARC, speaks to David Campbell, VP of Product at GoTo, about the challenges faced by modern IT departments, the role of practical AI in addressing these challenges, and the importance of balancing automation with human oversight.
Key Takeaways:
AI can modernise IT systems without disrupting operations.AI helps identify inefficiencies and provides actionable insights.Human oversight is crucial in AI-driven IT processes.AI can automate tasks and flag critical issues proactively.Security is a top priority for IT departments.Data quality is essential for effective AI strategies.AI can streamline knowledge sharing across IT teams.AI can enhance help desk workflows and reduce resolution times.Chapters:
00:00 - Introduction to AI in Enterprise IT
02:06 - Challenges in Modern IT Systems
04:33 - The Role of AI in IT Modernization
06:57 - Balancing AI Automation and Human Oversight
10:13 - AI's Impact on IT Security
12:34 - Ensuring Data Quality for AI
16:23 - Top AI Use Cases in IT Management
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Today, organisations that thrive are those that foster a culture of curiosity. Encouraging curiosity empowers teams to question the status quo, explore innovative solutions, and stay ahead of emerging trends. By promoting an environment where employees feel encouraged to ask "what's next?" and seek learning opportunities, companies can adapt more swiftly to technological advancements and market shifts. This mindset drives innovation and cultivates resilience, enabling organisations to turn challenges into opportunities.
Preparing for future technological trends requires more than just adopting the latest tools—it demands a workforce that is curious and agile. A culture of curiosity fuels engagement, cross-disciplinary collaboration, and creative problem-solving, all of which are critical for navigating the complexities of technologies like AI, blockchain, and quantum computing.
By investing in curiosity through training, open communication, and a safe space for experimentation, organisations can build a future-ready workforce that embraces change and drives sustainable growth.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Andrew Grill, author of Digitally Curious: Your Guide to Navigating the Future of AI and All Things Tech, about the future of work and the integration of AI.
Key Takeaways:
Digital curiosity empowers individuals to understand technology better.Leaders play a crucial role in fostering a culture of curiosity.Ethics in AI involves data privacy, transparency, and fairness.Curiosity leads to digital fluency, which is essential for the future.Organisations should encourage experimentation with new technologies.Understanding technology is vital for effective leadership.The future workforce will require continuous upskilling and adaptability.Chapters:
00:00 - Introduction to Digital Curiosity
03:00 - The Importance of Digital Curiosity in Technology
05:50 - Overcoming Barriers to Technology Engagement
09:13 - Ethics and Responsibility in AI
12:05 - Cultivating a Culture of Curiosity in Organizations
14:58 - Future Technological Shifts and AI Integration
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The world of IT Finance is shifting, driven by the transition from traditional capital expenditure (CapEx) models to operational expenditure (OpEx) in cloud-based environments. This evolution not only enables businesses to scale their resources dynamically while optimizing costs, but also introduces new complexities in forecasting, budgeting, and resource allocation.
As organizations adopt cloud technologies, the ability to manage financial resources with precision becomes a cornerstone of competitive advantage. Augmented FinOps, a groundbreaking approach that merges traditional financial operations with AI-driven insights, transforms how companies navigate these challenges, empowering decision-makers with unparalleled visibility and control.
The applicationof AI in FinOps promises to amplify its potential by automating routine tasks, uncovering patterns in vast data sets, and providing predictive analytics to enhance strategic planning.
In this episode, Jon Arnold, Principal of J Arnold Associates, speaks with Kyle Campos, CTPO at CloudBolt, about Augmented FinOps and the role of AI and ML in automating cost management processes.
Key Takeaways:
FinOps is a community-driven approach to managing cloud spend.The shift from CapEx to OpEx has transformed financial operations.Augmented FinOps combines AI with traditional FinOps practices.Organizations must measure the “insight to action” gap in their processes. Machine learning can automate and optimize cloud spending decisions.C-suite expectations often misalign with operational realities.Tracking metrics is vital for improving FinOps maturity.Investing in FinOps is about optimizing resources, not just cutting costs.Chapters:
00:00 - Introduction to FinOps and Cloud Management
02:52 - The Shift from CapEx to OpEx in Cloud Spending
06:13 - The Role of AI in Financial Operations
08:56 - Understanding Augmented FinOps
12:13 - Best Practices for FinOps in the Age of AI
15:00 - Leveraging Machine Learning for Cost Optimization
18:13 - Insight to Action: Measuring FinOps Effectiveness
21:04 - Conclusion and Key Takeaways
CloudBolt is The Cloud ROI Company™. It is singularly focused on solving the most pressing problem with cloud today: increasing return on investment (ROI). With the introduction of Augmented FinOps capabilities, CloudBolt is leveraging AI/ML-informed insights and applying intelligent automation and orchestration proactively and retrospectively to make complete cloud lifecycle optimization a reality. CloudBolt enables organizations to realize the full potential of any cloud fabric by closing the “insight to action” gap. By streamlining, clarifying, and optimizing spend and control, CloudBolt helps organizations place value at the center of every cloud decision. For more information, visit www.cloudbolt.io.
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As artificial intelligence continues to shape industries and society, the need for robust AI governance has never been more critical. At the forefront of this governance are privacy-enhancing technologies (PETs), which play a key role in ensuring that AI systems operate in a way that respects and protects individuals' data.
The European Union’s AI Act, one of the most ambitious regulatory frameworks for AI, sets clear standards for transparency, accountability, and risk management. Understanding the implications of this legislation is crucial for businesses looking to innovate responsibly while avoiding potential legal and ethical pitfalls.
Countries around the world are taking varied approaches to AI governance, with some prioritising privacy and ethical considerations while others focus on fostering technological innovation. This diversity presents challenges and opportunities for organisations striving to implement AI responsibly.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Dr Ellison Anne Williams CEO and founder of Enveil, about the need for model-centric security and the potential of PETs to mitigate risks associated with sensitive data in AI applications.
Key Takeaways:
Privacy-enhancing technologies are crucial for data protection.The EU AI Act sets a precedent for global AI regulation.Organisations must start with the problems they aim to solve.Data sensitivity must be considered in AI model training.Privacy-enhancing technologies can facilitate cross-border data sharing.AI is a neutral tool that requires responsible governance.The implementation of privacy technologies is still evolving.Global standards for AI governance are necessary for ethical use.Chapters:
00:00 - Introduction to AI Governance and Privacy
01:07 - Understanding AI Governance
03:51 - Privacy Enhancing Technologies Explained
08:28 - The Role of the EU AI Act
12:42 - Implementing Privacy Enhancing Technologies
17:20 - Harmonizing AI Governance with Privacy Technologies
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In this episode, Paulina Rios Maya, Head of Industry Relations at EM360, speaks with Caroline Hicks, Senior Event Director of the AI Summit Global Series, about the upcoming AI Summit New York, which will take place December 11-12, 2024.
They discuss the event's focus on AI's practical applications across various sectors, the importance of ethical considerations in AI, and the collaborative efforts among different industries to harness AI technology responsibly.
The conversation highlights key discussions and innovations that will be showcased at the summit, emphasizing the transformative impact of AI on businesses and society
Register now and enjoy a 15% discount on Delegate Passes with code EM36015OFF via https://newyork.theaisummit.com/em360tech-reg.
Key Takeaways:
AI is showcasing its practical impact across diverse sectors.The AI Summit New York focuses on ethical and responsible AI.Collaboration is key in the AI community.AI is transforming industries like healthcare and retail.The summit will address AI's challenges and responsibilities.Innovative applications of AI will be highlighted at the event.AI can foster inclusivity and equal opportunities.Real-world implementation stories will be shared by companies.
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As AI reshapes industries and drives global innovation, the UK must urgently address its AI skills gap to remain competitive. Nations investing in AI education and training are gaining a clear advantage, leaving others at risk of falling behind. By equipping the workforce with essential AI expertise, the UK can strengthen its position as a leader in innovation and secure its economic future.
Developing AI skills isn’t just about maintaining a competitive edge—it’s about creating opportunities. This dual approach ensures that experts can drive technological advancements while a broad understanding of AI empowers diverse sectors to integrate its potential. Investing in education, upskilling, and industry partnerships will ensure the UK workforce is ready to meet the demands of an AI-driven world.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to James Kuht, CEO and Founder of Inversity, about integrating AI into education and the collaborative effort required from government and society to achieve this goal.
Key Takeaways:
The UK has a strong AI talent pipeline concentrated in key areas.Specialist and generalist AI skills are both important.AI can significantly boost productivity in knowledge-based tasks.Teachers need training to effectively integrate AI into education.AI skills will be a baseline requirement for future jobs.Government and society must collaborate on AI education initiatives.AI has the potential to reduce economic inequality.Most jobs will evolve rather than disappear due to AI.Chapters:
00:00 - The Importance of AI Skills for the UK
03:01 - James Kuht’s Journey in AI
05:57 - Building a Competitive AI Workforce
08:45 - Integrating AI into Education
12:07 - The Role of Government and Society in AI Education
15:01 - Addressing Inequality in AI Access
17:58 - Future-Proofing the Workforce with AI Skills
21:10 - The Impact of AI on Global Industries
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AI operates in two primary environments: on-device and cloud-based. On-device AI processes data locally, ensuring privacy and speed by eliminating the need for internet connectivity. Cloud-based AI, on the other hand, leverages powerful remote servers to handle complex computations and large-scale data analysis, enabling more robust capabilities but often at the cost of latency and potential privacy concerns.
Apple Intelligence exemplifies the strengths of on-device AI, with innovations like Siri, Face ID, and real-time photo enhancements all designed to prioritise user privacy while delivering seamless, responsive experiences. Unlike cloud-based AI, which may send sensitive data to external servers for processing, Apple’s approach ensures that personal information stays on the user’s device and is protected by advanced encryption. This difference builds trust and empowers users with faster, more reliable interactions.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Karel Callens, CEO at Luzmo, about best practices for developers integrating AI into their products.
Key Takeaways:
Apple Intelligence operates on-device but can access cloud resources.Developers must implement robust security measures for user data.Clear data policies enhance user trust in AI solutions.Opt-in and opt-out options empower users regarding their data.Education on AI usage is crucial for consumer confidence.Shared security standards can mitigate AI misuse.Regulation is necessary to keep pace with AI advancements.Big tech companies have a responsibility to ensure ethical AI use.Chapters:
00:00 - Introduction to AI: On-Device vs Cloud-Based
02:54 - Understanding Apple Intelligence and Its Benefits
05:47 - Security Measures in AI Integration
09:03 - Building Trust Through Transparency and Regulation
11:50 - Best Practices for Developers in AI Implementation
15:04 - The Role of Education in AI Trust and Security
17:47 - The Future of AI: Regulation and Responsibility
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AI Personas are the cornerstone of how these systems interact with users, delivering tailored and engaging experiences. These personas—crafted from user research, behavioural insights, and cultural contexts—help define an AI's tone, style, and decision-making approach. Whether it’s a friendly virtual assistant or a professional customer service bot, personas ensure that AI systems resonate with their audiences while maintaining a consistent identity.
However, developing personas for AI isn’t without its challenges. Ensuring that AI responses remain appropriate, ethical, and unbiased while preserving a unique persona requires careful consideration. From avoiding stereotypes to addressing edge cases, the process demands robust testing and a clear understanding of how diverse user interactions can unfold.
When personas fail to account for the complexity of real-world scenarios, the risk of inappropriate or harmful responses increases. By combining creative storytelling with ethical AI design principles, organisations can navigate these challenges and build AI systems that are engaging and responsible in their behaviour.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Cobus Greyling, Chief Evangelist at Kore.ai, about the influence of cultural norms and value systems on AI and strategies for maintaining control over AI behaviour.
Key Takeaways:
Personas in AI shape user interactions and trust.Cultural norms influence AI decision-making processes.Balancing control and agency is crucial for effective AI.Adversarial attacks can undermine AI reliability.Transparency is essential for user confidence in AI.Organisations should not offload too much responsibility to AI.AI should enhance human creativity, not replace it.Proof of value is necessary for AI technology implementation.Chapters:
00:00 - Introduction to AI Personas and Their Impact
02:34 - The Role of Personas in AI Behavior
05:51 - Challenges in Ensuring Appropriate AI Responses
09:07 - Cultural Norms and Value Systems in AI
10:30 - Balancing Control and Agency in AI
14:14 - Strategies for Maintaining Control Over AI Behavior
21:24 - The Importance of Responsibility in AI Usage
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Low-code and no-code platforms are revolutionising application development by empowering technical and non-technical users to quickly and efficiently build powerful applications. These platforms provide intuitive visual interfaces and pre-built templates that enable users to create complex workflows, automate processes, and deploy applications without writing extensive lines of code.
By simplifying development, low-code and no-code tools open up software creation to a wider range of contributors, from professional developers looking to accelerate delivery times to business users aiming to solve specific problems independently. This democratisation of development reduces the demand for IT resources and fosters a culture of innovation and agility within organisations.
The impact of low-code and no-code technology extends beyond just speed and accessibility; it’s transforming how businesses adapt to change and scale their digital solutions. These platforms allow companies to quickly respond to evolving customer needs, regulatory requirements, and competitive pressures without the lengthy timelines associated with traditional development cycles.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Michael West, Analyst at Lionfish Tech Advisors about LCNC platforms and their benefits.
Key Takeaways:
Low-code and no-code platforms enable business solutions without coding.These platforms broaden the developer base to include non-technical users.Choosing the right platform involves considering functionality, standards, and vendor viability.Low-code platforms can handle enterprise-level applications effectively.AI integration is transforming how applications are developed.Democratisation of development addresses the shortage of professional developers.The market for low-code and no-code platforms is rapidly evolving.Future trends will focus on AI capabilities and user experience.Chapters:
00:00 Introduction to Low-Code and No-Code Platforms
02:59 The Evolution of Development Roles
05:49 Key Considerations for Adopting LCNC Tools
09:04 Democratizing Development and Innovation
11:59 Future Trends in Low-Code and No-Code Markets
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The intersection of cryptography and GPU programming has changed the face of secure data processing, making methods for encryption and decryption much faster and more efficient than ever imagined. Cryptography is the science of encrypting data with intricate algorithms initially designed to operate on very intensive computational powers. GPU programming provides the ability to utilise parallel processing of graphics processing units in cryptographic processes so they perform with unmatched speed.
While continuously evolving, GPUs are furnishing the computational muscle to execute ever-higher-level cryptographic algorithms without performance penalties. Developers now fully avail of the power of GPU parallelism to perform several thousand encryption tasks simultaneously, which is difficult for traditional CPUs to keep up with. This efficiency is critical in this growing data and rising cyber threat era, where organisations need rapid encryption and robust security.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Agnes Leroy, Senior Software Engineer at Zama, about the significance of encryption in high-stakes industries, the role of women in tech and the importance of mentorship in overcoming barriers in the industry.
Key Takeaways:
GPUs have evolved from graphics rendering to critical roles in data security.Fully homomorphic encryption allows computations on encrypted data.Quantum-resistant methods are crucial for future-proofing encryption.High-stakes industries require robust encryption to protect sensitive data.Diverse environments in tech foster innovation and collaboration.The future of encryption technology is exciting and unpredictable.Chapters:
00:00 - Introduction to Cryptography and GPU Programming
01:08 - The Evolution of GPUs in Data Security
03:33 - Challenges in Traditional vs Modern Encryption
05:50 - Quantum Resistance in Encryption Techniques
07:40 - The Future of GPUs in Data Privacy
08:38 - Importance of Encryption in High-Stakes Industries
10:00 - Potential Applications of Fully Homomorphic Encryption
11:42 - Women in Tech: Overcoming Barriers
15:33 - Conclusion and Resources
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LLMs and AI have increasingly become major contributors to transforming content creation today. Understanding and using prompt skills appropriately can help organisations optimise AI to generate high-quality content efficiently.
While AI offers multiple benefits, it's important to acknowledge the potential risks associated with its implementation. Organisations are advised to carefully consider factors such as data privacy, bias, and the ethical implications of AI-generated content.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Prof. Yash Shreshta, Assistant Professor at the University of Laussane, about prompt engineering and its benefits.
Key Takeaways
Prompts are commands given to AI to perform tasks.Prompt engineering allows users to communicate effectively with AI.Iterative processes improve the quality of AI outputs.Understanding LLM limitations is crucial for effective use.Collaboration can enhance the creative process with AI.The role of prompt engineering is rapidly evolving with AI advancements.Data privacy is a significant risk when using LLMs.Over-reliance on AI can lead to skill degradation.Organisations should integrate human creativity with AI.Regular training on prompt engineering is essential for maximising LLM benefits.Chapters
00:00 Introduction to Prompt Engineering and AI
01:30 Understanding Prompt Engineering
04:15 The Importance of Prompt Engineering Skills
06:37 Best Practices for Effective Prompts
08:31 The Evolving Role of Prompt Engineering
11:20 Risks and Challenges of AI in Organizations
13:15 The Future of Creativity with AI
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The fraud division has witnessed a dramatic transformation in the age of artificial intelligence (AI). As technology advances, so do the methods employed by fraudsters. Modern criminals use sophisticated techniques, such as deep learning and natural language processing, to deceive individuals and organisations alike. Such techniques allow them to mimic human behaviour, manipulate data, and exploit vulnerabilities in security systems.
That’s why organisations are embracing AI's strengths to combat these evolving threats. AI-driven solutions can provide real-time detection of fraudulent activities, analyse vast amounts of data to identify patterns and anomalies, and automate response processes.
In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Xavier Sheikrojan, Senior Risk Intelligence Manager at Signifyd, about AI fraud.
Key Takeaways:
Fraudsters have evolved from individuals to organised criminal enterprises.AI enables fraudsters to scale their attacks rapidly and strategically.Phishing attacks have become more sophisticated with AI-generated content.Synthetic identities can be created easily, complicating fraud prevention.Opportunistic fraud is impulsive, while proactive fraud is well-planned.Businesses often fail to act post-breach due to resource constraints.Inaction after a breach can lead to repeated attacks by fraudsters.AI must be used to combat AI-driven fraud effectively.Balancing fraud detection with customer experience is crucial for businesses.Chapters:
00:00 Introduction to AI and Fraud
01:32 The Evolution of Cybercrime
05:43 AI's Role in Modern Fraud Techniques
09:55 Opportunistic vs. Proactive Fraud
12:44 Business Inaction and Its Consequences
15:59 Combating AI-Driven Fraud with AI
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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
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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
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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
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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
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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
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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
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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
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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.
Chapters: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
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