If you care about where robotics and AI are actually heading, this podcast is a must-listen.

Rise Robotics set a Guinness World Record for the strongest robotic arm ever built, lifting 3,181 kilograms, smashing the previous 2,300 kg record held by Fanuc Corporation. That alone should grab your attention. What follows is even more interesting.

This episodes offers a rare operator-level view of robotics. George Mathew sits down with Hiten Sonpal, CEO of Rise Robotics, an MIT-affiliated company with over $5M in US Air Force contracts, 20+ granted patents, and $24M+ raised from top-tier investors.

Hiten agrees that AI is moving out of the screen and into the real world through robots. The challenge is not intelligence, it is action. Turning language model outputs into real-world motion still breaks down at manipulation, tactile sensing, and power delivery for large humanoid systems.

Small humanoids can do backflips. They cannot lift meaningful weight for long. Rise Robotics invented “beldraulics”, a fluid-free linear actuation system that is:

• 3x faster

• 3x more efficient

• 3x more durable than hydraulics

Unlike hydraulics, their systems are inherently digital, providing position, force, health monitoring, and safety data by default. That is what makes heavy machinery AI-capable.
From Air Force munitions handling and airfield operations to electric truck lift gates, this is deployed technology.

One lift gate customer saves around 30 minutes per route per day, enough for an extra delivery or less overtime. Installation drops from 30 person-hours to 5. Maintenance falls sharply. No hydraulic oil, no spills, no cleanup. Customers see payback in roughly six months, driven by both cost savings and higher revenue.

Rise Robotics is scaling across oil and gas, marine, and food and agriculture, with interest in Europe and active openness to Australian partners. They are also experimenting with an uncommon funding model, opening institutional rounds to the public via regulated crowdfunding.

If you want a grounded, engineer-led discussion on the future of robotics, AI-enabled machines, and why hydraulics are on the way out, this episode delivers.

Highly recommended listening.

In this episode of The Complete AI Guide Podcast, George Mathew sits down with Christopher Carter, CEO of Approyo, and a leading SAP and enterprise technology experts, to explore what it really takes to drive meaningful AI adoption in small and medium-sized businesses. Drawing on decades of experience across more than 50 industries, Chris unpacks why strong operating models, clean and secure data, and disciplined pilots matter more than shiny tools. Together, they dive into practical strategies for unlocking real ROI from AI, avoiding common mistakes, protecting business-critical data, and building scalable foundations that turn experimentation into lasting business value. Most importantly he shares insights about working with legacy tech like SAP and protecting small & medium business data.

Happy New Year and Welcome to Season 2.

Over the past two years, Generative AI has reshaped how we work. Models can write, code, design, and summarise at the speed of a prompt. Yet despite their power, they share a core limitation. They assist, but they do not act.

In this episode, we explore why that is changing.

We are entering a new phase of the AI revolution, moving from Generative AI to Agentic AI. This shift is not about better answers or faster content creation. It is about execution. Agentic systems reason, plan, use tools, retain memory, and carry out multi step workflows to achieve outcomes. AI is moving from a passive tool to an active participant in work.

This transition forces a rethink of how organisations operate.

We introduce the Agentic Enterprise, where digital agents are built into the operating model. Humans are no longer required in every step. The model shifts from human in the loop to human on the loop. People set direction, define constraints, and supervise digital labour at scale, while autonomous systems handle execution.

We outline the four pillars that enable this shift: reasoning and planning, tool use, persistent memory, and true autonomy. Without all four, agency collapses into basic automation.

We also explore Frontier Firms. These organisations are not using AI for incremental gains. They are redesigning their businesses around autonomous execution. Growth is decoupled from headcount, allowing small teams to orchestrate large fleets of specialised agents.

This creates a new economics of work. As inference costs fall, organisations can expand scope and speed without adding bureaucracy. Decision making accelerates, execution becomes continuous, and the gap between agentic and non agentic firms widens.

Autonomy brings risk.

We examine challenges such as cascading errors, unintended optimisation, and legal liability. We discuss why agentic systems require new governance models, including an Agentic AI Mesh, strong observability, and controls to prevent agentic drift, grounded in frameworks like the NIST AI Risk Management Framework.

Finally, we focus on what this means for you.

For leaders, agency is now a strategic priority. Investing in governance, observability, and organisational readiness is essential to remain competitive.

For individuals, roles are shifting from task execution to supervision and orchestration. New archetypes such as Agent Managers and Workflow Orchestrators are emerging, and advantage will belong to those who can scale digital labour.

This episode offers a clear framework for navigating a world where intelligence is abundant and the passive tool era is over.

In this episode, we are looking ahead to 2026, a year identified as a monumental pivot point for global AI adoption. We are moving past the era of the "co-pilot" - where AI acts as a friendly assistant drafting emails, writing code or debugging code - and entering the era of autonomous execution, where AI takes the wheel to perform entire functional processes end-to-end. This episode unpacks 10 major trends that define this new landscape, grouped into critical areas of impact: Operational Shifts: We discuss the transition from AI suggestions to autonomous decision-makers and the radical redesign of business operating models to accommodate fluid, agent-managed systems.• The New Economy: The rise of the "service as a software" economy, where businesses pay for outcomes rather than software licenses, and how agentic AI may cannibalize the traditional SaaS industry.• Value and Risk: Why the focus is shifting from 10% efficiency gains to 10x value creation, and the exponentially harder-to-manage security risks that arise when machines execute high-stakes decisions at high velocity.• Specialization vs. Generalisation: The emergence of "domain variants" - AI trained on specific proprietary workflows -which serve as a new competitive moat compared to generalist models.• Global and Physical Trends: The geopolitical imperative of sovereign AI and the integration of AI into the physical world via humanoid robots.• The Human Element: Why human change and organizational adoption have become the primary bottlenecks, outweighing technical constraints or compute power. For listeners, this episode serves as a strategic roadmap for surviving and thriving in an agents world. You will discover why operating models now matter more than language models and why the "differentiation at the pure tech layer is blurring" as AI becomes a utility like electricity or the cloud. The core takeaway is a shift in career and business value: the research suggests that "operators" who can implement change will outperform "innovators" who simply create it. By listening, you will understand how to transition your role from a tactical "instrument player" to a strategic "conductor" who manages AI objectives, guardrails, and ethical oversight. Ultimately, this episode challenges you to consider how much time your organisation should spend on changing human behaviour versus simply implementing new models, as this ratio may define the winners and losers of the next five years.

This episode explores one of the most important and least discussed factors in AI adoption inside small and medium businesses. It is not the tools or the models. It is the people. As AI moves into daily workflows, six distinct tribes are forming across organisations, each with its own mindset, motivations, and impact on progress.

Listeners will get a clear view of the Evangelists who push ahead with enthusiasm, the Natives who build clever shortcuts that sometimes outpace governance, the Migrants who want to learn but fear breaking things, the Agnostics who question the hype, the Rebels who challenge every assumption, and the Saboteurs who resist so strongly that performance is affected.

The episode breaks down how these tribes behave, how they influence each other, and what leaders can do to guide them toward productive, safe, and aligned use of AI. Expect a practical framework, simple strategies, and examples of conversations that help teams move forward with confidence instead of confusion.

This is designed for anyone responsible for people, productivity, or transformation. Listeners will walk away with a way to diagnose what is actually happening inside their organisation and a set of actions that can be applied immediately.

A concise, grounded look at the human patterns shaping AI adoption and a useful guide for turning mixed mindsets into collective momentum.

Artificial intelligence is reshaping how Australian organisations operate, innovate, and serve their customers. The opportunity is enormous. Responsible AI gives you stronger trust, sharper performance, and a real competitive edge.

But the speed and complexity of AI also create new risks. Opaque models, hidden decision logic, and unpredictable behaviour can introduce bias and undermine trust. Once trust is broken, research shows it is incredibly hard to win back. If you work in governance, if you are a technical specialist, or if you lead a team that depends on AI, you need the capability to manage commercial, reputational, and regulatory risk.

This commute-friendly episode breaks down the Guidance for AI Adoption, the Australian government’s national benchmark for safe and sustainable AI use. Developed by the National AI Centre, it brings together earlier guardrails and distils them into six core practices designed to protect people, organisations, and broader society.

In this episode, you will hear how to establish meaningful human control, how to test and monitor systems throughout their lifecycle, and how to make supply chain responsibility clear and enforceable. These practices close the gap between knowing what good looks like and actually putting it into action.

By mastering these six practices, you build confidence, trust, and long term value. You position your organisation to innovate safely. And you give yourself the skills to lead in an AI powered world.

Artificial Intelligence remains one of the most exciting capabilities in the enterprise, currently driving 40% of today’s stock market valuations. Yet, at the current state of practice, AI is often "over-promised and under-delivered". The harsh reality is that fewer than 15% of AI initiatives achieve their intended enterprise scale. This failure stems not from the quality of the models themselves, but from the inadequate operating model surrounding them. To make AI truly work, organisations must embed this capability within a structure that is rigorously aligned with their strategy, vision, and goals. Organisations that succeed don't just have great models - they have great operating models. Today, we break down the five essential pillars needed to transform AI from a standalone experiment into a core, value-compounding business enabler:1. Capability: Aligning the AI portfolio with organisational strategy, treating AI as a portfolio of reusable capabilities, not a one-off project.2. Engagement: Designing frictionless, human-centered processes and leveraging AI translators to drive specific outcomes for targeted personas.3. Reporting: Focusing on measuring strategic impact (like retention or growth) instead of just technical accuracy.4. Governance: Orchestrating for speed, strategy, and trust through aligned forums—like Ethics Boards—to ensure fairness and transparency.5. Structure: Building a foundation of leadership, alignment, and responsiveness, often by combining a central AI Center of Excellence (CoE) with embedded business unit translators.Join us as we explore how these five pillars provide the structure necessary for your organisation to build AI that compounds in value and impact over time.

Securing AI funding requires moving past technical hype and demonstrating concrete business value, as executives prioritise funding measurable outcomes like efficiency and strategic resilience, not technology alone. The core approach is a 3-step benefits realisation model that frames AI as a structured investment in workflow improvement. The first step involves getting the source of truth right, consolidating fragmented knowledge to improve workflow effectiveness, reduce errors, and deliver a clear return on investment through efficiency and lower operational costs. The second step leverages AI as a virtual teammate to boost workforce capacity and satisfaction by eliminating time spent on searching and rework, thereby unlocking human potential and scaling margin without adding headcount. Finally, the third step ensures the business can scale by using AI to dramatically reduce onboarding and up-skilling time, providing real-time guidance that makes the organisation flexible and resilient, connecting growth directly to increased productivity per employee. This integrated approach ensures the investment delivers faster time-to-productivity, improved accuracy, and trustworthy workflow data, ultimately allowing the organization to scale people, not payroll.

For small and medium-sized businesses, AI offers a massive promise: greater efficiency, smarter decisions, and a new level of personalised customer experiences. We all know that AI is not truly about replacing humans; instead, it is about amplifying what they already do best.But here is the million-dollar question: How do you deploy it successfully? The choice of your AI mode - whether it’s automation, AI-augmented workflows, or AI-driven agents - is what determines whether that investment succeeds or fails.Today, we are going to break down these three critical modes.We’ll look at Automation, the mode that excels at predictable, repetitive tasks like invoice processing or inventory reordering, providing efficiency but with little flexibility.Then, we shift to AI-Augmented Workflows, which keeps humans at the centre. This is where AI handles the heavy lifting - things like scanning medical images or triaging customer service emails - while human judgment and final decision-making remain essential.Finally, we explore AI-Driven Agents. These agents thrive in complex, fast-changing environments, handling tasks like dynamic pricing or supply chain optimisation by adapting in real time.Stay with us as we discuss real-world examples to help you understand where reliability is critical, where human judgment is non-negotiable, and where real-time decision-making is necessary. Getting this decision right is the key to enabling your business to respond faster, scale smarter, and make better decisions. Let’s dive in.

You've probably seen the headline that spread like wildfire: "95% of AI pilots fail". That staggering number comes from a recent MIT report and it's the kind of figure that commands attention. But is it the whole story?

Today, we're diving deep beyond that viral headline. While some research suggests a similarly sobering truth - that 88% of AI pilots never make it to production- we have to ask: what's really going on? Is the technology failing, or is something else at play? We'll discuss why this 95% figure, based on a limited number of interviews and surveys, might be misleading the entire market. The real gap might not be in the tech itself, but in how organizations are trying to scale it. While companies are quietly rolling back big AI ventures, 90% of employees are already using Large Language Models regularly. The problem isn't that AI doesn't work. The breakdown is organisational.

We'll explore the real reasons AI initiatives stall: from a lack of clear ROI and siloed operations to human resistance and the complex economics of scaling.So, stick with us as we unpack the "AI Adoption Chasm," challenge the narrative, and explore how organizations can finally break free from pilot purgatory.