Impact of AI:Explored

In this episode of Impact of AI: Explored, Gerjon and James sit down with Matt Soltau,  Global Director of Strategy and Operations at IntelliIPaaS (https://www.linkedin.com/in/soltaumatt/)

They unpack AI’s hidden sustainability problem and what enterprises can actually do about it. They dig into “digital waste” in the form of zombie jobs, legacy servers, data silos and endless file duplication, and how this invisibly bloats cloud bills and energy usage. The conversation covers why AI data centers are so power-hungry, how ESG reporting is lagging behind real digital emissions, and why clean data, solid integration plumbing and clear ownership for ESG metrics are foundational for any serious AI strategy. Matt also shares real-world stories on failed AI agents, governance guardrails, and practical steps to make AI both productive and sustainable instead of just another source of greenwashed slideware

chapters
00:58 The Hidden Costs of AI Energy Consumption
04:12 Understanding Digital Waste in Enterprises
08:30 AI's Role in Sustainability: Problem or Solution?
16:34 Preparing Data for AI: The Importance of Readiness
20:54 The Challenges of AI Implementation
27:13 The Blind Spot in ESG Reporting
32:11 Bridging the Gap: Efficiency and AI
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Welcome to the Impact of AI: Explored podcast. In this episode 
James and Gerjon sit down with Seda Akdemir, AI strategist at Cisco IMEA (https://www.linkedin.com/in/sedaakdemir/), to talk about moving beyond AI as a “shiny toy” into secure AI factories, real business value, and why generic AI strategies don’t work.

We dive into how enterprises can build an AI-ready stack (networking, security, data center, GPU, observability), why ROI must go beyond “time saved”, and how to keep CISOs on board while still innovating. Seda also shares her perspective as a woman in a male‑dominated AI and engineering world, and the skills teams need to stay relevant in an AI‑first future.

01:00 – Intro: Impact of AI & today’s guest Seda Akdemir
​01:29 – “Everything will be different in a year”: AI disruption starts now
​03:29 – From Microsoft to Cisco: AI as a strategic asset, not a feature
​05:17 – What is a secure AI factory and why enterprises need it
​07:32 – AI‑ready data centers, Nvidia/Red Hat/OpenShift and the full stack
​10:54 – Why generic AI strategies fail and how to think ROI beyond “time saved”
​18:42 – Red flags in enterprise AI projects: business alignment, culture and observability
​25:17 – Security, CISOs, agent identity and task‑based authorization
​32:03 – Skills for AI teams, women in AI, and final advice for listeners
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Welcome to the Impact of AI: Explored podcast. In this episode 
James and Gerjon have a awesome guest: Bhaskar Barat Sawant - Lead Engineer | Solutions Architect | AI/ML & Cybersecurity | Azure Cloud | SaaS Platforms | Microsoft Certified (https://www.linkedin.com/in/bhaskar-bharat-sawant-533218122/)

In this episode, we discuss the complexities and challenges of AI adoption in enterprises, emphasizing the importance of responsible AI deployment. We explore the disconnect between vendor promises and real-world experiences, the risks associated with AI agents, and the emerging concept of federated learning. The discussion highlights the need for strong architecture, governance, and observability in AI systems to ensure security and effectiveness.

Takeaways
* Organizations prioritize speed over security in AI adoption.
* AI introduces new risks that traditional systems didn't have.
* Deploying AI requires proper architecture and governance.
* Vendors often oversimplify AI as a plug-and-play solution.
* AI is not a magic solution for every problem.
* Successful AI adoption requires careful planning and management.
* Start with a clear business problem when considering AI.
* Responsible AI involves continuous monitoring and transparency.
* AI agents need defined boundaries to operate safely.
* Federated learning allows for privacy-preserving AI training.
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Welcome to the Impact of AI: Explored podcast. In this episode James O'Regan and Gerjon Kunst explore the emergence of OpenClaw, a personal AI agent that has gained significant attention for its capabilities and potential risks. They discuss its features, the rapid rise in popularity, and the security concerns associated with its use. The conversation also touches on the evolution of AI agents, comparing OpenClaw with Claude Cowork, and the implications for both consumer and enterprise applications. The hosts emphasize the importance of understanding the security risks involved in using such technology and the potential for future developments in the AI landscape.
takeaways

OpenClaw is a personal AI agent that runs locally.
It can perform tasks like email scheduling and browser control.
Security concerns arise from giving AI full access to systems.
Many users are not taking necessary precautions when using OpenClaw.
The rise of OpenClaw reflects a shift towards consumer-driven AI solutions.
OpenAI's acquisition of the creator of OpenClaw highlights its potential.
AI agents can automate complex tasks, but risks must be managed.
The evolution of AI agents is leading to more autonomous systems.
Open source nature of AI agents allows for rapid development and exploitation.
Understanding security implications is crucial for users experimenting with AI.
Chapters

00:10 Introduction to OpenClaw and AI Agents
02:58 The Rise of OpenClaw: Features and Popularity
05:44 Security Concerns with OpenClaw
08:21 The Evolution of AI Agents
11:03 Comparing OpenClaw and Claude Cowork
13:45 The Future of AI in Business
16:43 The Impact of OpenAI's Acquisition
19:24 Consumer vs. Enterprise AI Solutions
22:15 The Open Source Nature of AI Agents
24:56 Conclusion and Final Thoughts
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Welcome to the Impact of AI: Explored podcast. In this episode 
James and Gerjon have a awesome guest: Dr. Nikita Golovko - Certified ISAQB CPSA-F Architect/Cybersecurity expert & PSSE (ISC2 CC) (https://www.linkedin.com/in/dr-nikita-golovko/)

In this conversation, we discuss the challenges and nuances of implementing AI in industrial settings. We look at the importance of bridging the gap between AI technology and practical application on the factory floor, highlighting the need for translators who can communicate between data science and operational teams. The discussion also covers the transition from lab-based AI to real-world applications, the significance of team structure in successful AI projects, and the responsibilities of engineers in ensuring AI systems are safe, transparent, and sustainable. We share insights on generative AI, technical debt, and the future of AI in industrial automation, advocating for a collaborative approach that includes human oversight in decision-making processes.

Takeaways
* Finding the right talent to bridge AI and operational gaps is crucial.
* AI solutions often fail when transitioning from lab to real-world environments.
* Technical debt can be identified by signals like fear of touching code.
* Generative AI is not yet suitable for industrial applications due to unpredictability.
* AI should enhance human decision-making, not replace it.
* Team structure significantly impacts the success of AI projects.
* Sustainability in AI means retraining models with new teams over time.
* Transparency and explainability are essential for AI systems.
* AI can assist in coding but should not replace foundational knowledge.
* Documenting decisions is key to managing technical debt.

Chapters
00:00 AI agents in industrial environments
01:33 introduction
02:18 Listener question: Bridging the Gap in Industrial AI Hiring
06:47 Challenges of Deploying AI in Real-World Environments
09:38 Technical Debt and Its Early Warning Signs
12:18 Generative AI vs. Classical Machine Learning in Industry
14:51 The Role of Predictability in AI Models
17:29 Local vs. Centralized AI Models in Production
20:14 Architectural Challenges in AI Implementation
22:55 Sustainability in AI System Design
25:52 The Future of AI in Industrial Applications
26:54 The Role of AI in Industrial Automation
32:00 AI as an Advisor: The Human Element
36:39 AI-Assisted Coding: Benefits and Risks
40:08 Responsibility and Ethics in AI Development
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