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The Intelligence Age

Mark Smith [nz365guy]

The Intelligence Age explores how intelligent technology is changing people, business, and work. Host Mark Smith [nz365guy] talks with practitioners, leaders, and innovators about putting AI to work: adoption that sticks, governance that holds, and the skills that matter next.


Podcasting since 2017, with more than 830 episodes. New episodes weekly.


https://theintelligenceagepodcast.com

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  • #847
    Yesterday · 36 min

    From Copilot Adoption to Copilot Fluency

    From Copilot Adoption to Copilot Fluency Full Show Notes: https://www.theintelligenceagepodcast.com/847 Mark Smith catches up with Cadie Kneip from Microsoft, a returning guest. Her focus has moved from getting people to adopt Copilot to building Copilot fluency, so using AI becomes automatic in daily work. They cover personal Copilot habits and resistance to AI. They also cover how agents are changing everyday workflows, which Microsoft tools to use for what, and what Cadie is looking forward to over the rest of 2026. Key topics and takeaways From adoption to fluency: Fluency means using Copilot naturally, without thinking, like reaching for your phone to check flights. The aim is to make Copilot a habit in your workflow. AI as the first stop: Mark rarely uses Google now. He goes to voice-mode AI, which knows his local context. He uses Copilot for personal topics because he controls his own tenant. Talking ideas out with AI: Voice conversations, asking the AI to play devil's advocate and asking it to "tell me why I'm wrong" help you probe your own thinking. AI tends to tell us what we want to hear. Why people resist AI: Mark cites a figure that 82% of Americans dislike AI or resist it. Cadie puts a lot of it down to loud, clickbait negativity and to fear about identity and jobs. Her answer is to focus on what's possible and on the human value of judgment and point of view. Agents beyond chatbots: Workshops on reimagining workflows start with the tasks people hate most. Cadie automated her weekly Credly badge process with Scout, saving hours each week. Scout vs. Cowork: Scout suits repeatable execution and an "executive assistant" style of work. Cowork suits work that needs strong context from Work IQ, which draws on your Microsoft Graph. Cowork has improved a lot and now offers model selection. Auto vs. picking models: Cadie starts on auto and switches models when results disappoint. Mark always picks his own. They also talk about Microsoft's own models and image generation. What builds fluency: Prompting skill matters less now. The tipping point is building a simple agent in Agent Builder and seeing how it works behind the scenes. People then move on to Copilot Studio and pro-code tools. Looking ahead: Cadie wants her team to each have at least one AI-improved workflow, and she's curious what people will do with the time they get back. They also joke about household robots. If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #846
    October 26 · 42 min

    AI, Indigenous Language Sovereignty, and Decolonizing Technology

    AI, Indigenous Language Sovereignty, and Decolonizing Technology Full Show Notes: https://www.theintelligenceagepodcast.com/846 In this conversation, Mark Smith speaks with Natasha MacDonald, an Inuit educator and Heritage Lab volunteer, about protecting Indigenous language and culture in the age of AI. Natasha explains how her community is using closed-loop technology, community vetting, and local control to preserve Inuktitut without handing data to big tech. We also discuss colonial language loss, intellectual property, youth-led revitalization, and why cultural data should stay under Indigenous sovereignty. Key topics Natasha shares her Inuit background, her family roots in Nunavik, and how food, travel, and cross-cultural experience shaped her perspective. She explains the history of colonization in her region, including missionaries, federal day schools, forced settlement, and the shift from nomadic life to permanent communities. Natasha describes how Inuktitut remains strong in Nunavik, with roughly 95% of the local Inuit population still speaking or using it, even as English pressure grows. We discuss Heritage Lab, where Natasha serves as vice president, and its mission to protect and preserve Indigenous languages through technology. She explains the nonprofit’s closed-loop model, including community-owned data, language committee approval, and hosting on servers located on Indigenous lands. Natasha outlines the risks of big tech and external institutions scraping language data, training AI models without consent, and turning Indigenous knowledge into profit. She describes a dispute involving a University of Laval partnership with Anthropic and why her community objected to Inuit data being used to train AI on low-resource languages. We discuss the difference between data sovereignty and intellectual property, and how Indigenous knowledge is treated as community-held rather than individually owned. Natasha emphasizes that language revitalization must include youth, who are building tools, games, and models while learning pride and agency in the process. She also talks about the role of art as cultural memory, economic support, and a point of resistance against AI-generated imitation. Resources: Heritage Lab: https://heritagelab.ca/en/story Mila: https://mila.quebec/en/news/mila-and-cifar-launch-the-indigenous-ai-advisory-circle If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #845
    October 19 · 46 min

    Microsoft AI, model routing, and the move toward portable workflows

    Microsoft AI, model routing, and the move toward portable workflows Full Show Notes: https://www.theintelligenceagepodcast.com/845 Mark Smith and Joris de Gruyter, who has spent four years deep in the Microsoft AI ecosystem, discuss where AI has become useful recently and where the rough edges remain. The conversation centers on model choice, vendor lock-in, local versus cloud inference, and how Markdown and open standards are becoming a portability layer for notes, skills, and AI workflows. Key topics Joris says one of the biggest shifts has been the move from coding agents like Codex and Claude Code into non-coding work for information workers. He explains that tools like Microsoft Co-work and Copilot can be powerful, but users need enough technical savvy to get the most out of them. Mark and Joris compare cloud-first AI workflows with local, device-based workflows, especially for browser work, Playwright, and tasks requiring local access. They discuss Microsoft’s effort to make Co-work feel more like a first-class Microsoft product, with broader model support. Both argue that model choice matters less than before; skills, prompts, and workflow design are becoming the real intellectual property. They explore Markdown as a portable format for AI-era data, and why Microsoft supporting Markdown in SharePoint matters. Joris explains how OneNote, Obsidian, and Loop each solve part of the note-taking problem, but none fully combine syncing, portability, and API access. Mark describes using AI to clean up old data, emails, and archives rather than preserving everything indefinitely. They discuss vendor lock-in and the need for open standards that let people switch models and services. Joris makes the case for local inference as open-weight models improve and consumer hardware gets better. They close with a critique of major technology companies, arguing that strong hardware has not yet been matched by software and AI integration that meets user expectations. If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #844
    October 12 · 42 min

    Te Reo Māori, translation, and using AI without losing human meaning

    Te Reo Māori, translation, and using AI without losing human meaning Full Show Notes: https://www.theintelligenceagepodcast.com/844 Mark Smith speaks with Tane Karamaina, a senior translator working across Te Reo Māori and English, about how language, culture, and AI intersect in real-world translation work. The conversation explores why translation is never just word-for-word substitution, how AI can support but not replace human judgment, and the risk of homogenizing dialects as language technology improves. In this episode, Tane shares practical examples from translation work, including punctuation, implicit meaning, and culturally specific phrases like “O for awesome.” The discussion also covers pronunciation, regional dialects, ancestral intelligence, and how AI in New Zealand could be used responsibly as an assistive tool rather than a substitute for human expertise. Key topics Tane Karamaina introduces himself through a pepeha and explains the importance of naming whakapapa, place, and identity. He describes his work as a senior translator with more than 10 years of experience translating between English and Te Reo Māori. Translation is framed as cultural mediation, not word-for-word conversion. AI is presented as a useful tool for translators, but not a replacement for human understanding, interpretation, and responsibility. Tane explains how he uses AI for simple support tasks like punctuation and breaking down source text structure. The episode highlights how meaning is often implicit, culturally bound, and not directly transferable across languages. A deeper discussion compares Te Reo Māori and English through examples like “language” and “moana,” showing how words carry different worldviews. Mark and Tane discuss parallels between Te Reo Māori and Japanese, especially respect, vocal tone, and open syllable structure. Tane raises concerns about AI-driven language standardization flattening regional dialects and local variation. He argues the best protection against homogenization is for people to keep speaking and transmitting their ancestral dialects. The conversation closes on the idea that AI in New Zealand can be positive if it supports, not replaces, human beings and their communities. Resources Anyone looking for a translator can source one from here: https://en.tetaurawhiri.govt.nz/translators For those wanting to up their te reo game can find some really cool resources from here: https://www.reomaori.co.nz/resources Books Ancestral Intelligence Tikanga Whakaaro: Key conepts in Māori culture by Cleve Barlow Ngā Pēpeha a Ngā Tīpuna by Hirini Moko Mead and Neil GroveHe Whiriwhiringa Selected Readings in Māori by Bruce Biggs Te ReoTaking Care of Business: Business Terms in Aotearoa - Second Edition for te reo speakers wanting to learn more modern terminology that spans the legal, business and technology domainsHe Kohinga Kīwaha - Te Taura Whiri i te Reo Māori If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #843
    October 5 · 37 min

    AI, context, and New Zealand’s data center future

    AI, context, and New Zealand’s data center future Full Show Notes: https://www.theintelligenceagepodcast.com/843 Mark Smith speaks with Oliver Hartwich about how AI has changed since January, why agentic tools have become the real shift, and what that means for workflows, context, and cross-model collaboration. They also dig into New Zealand’s position in the global AI and infrastructure landscape, especially around data centers, energy, solar, batteries, and water use. Key topics In this episode: Oliver says the biggest change in 2026 so far has not just been better models, but the rise of agentic AI tools like Claude Code, ChatGPT work modes, and Perplexity Computer. He explains why AI now feels less like a novelty and more like a practical, increasingly capable working layer, especially when multiple models are used together and checked against each other. Mark raises the concern that New Zealand may be vulnerable because AI compute and data may not stay in-country across the major hyperscalers operating locally. Oliver discusses the post Fable moment, when access uncertainty highlighted how dependent users have become on frontier tools and how quickly expectations shift across providers. The conversation turns to context management across platforms, including when to preserve long-term memory and when to use a fresh AI for independent review. Oliver describes building a personal Claude Skill from his own writing archive, compressing books, reports, articles, and newsletters into markdown so the system can reflect his style and past thinking. They discuss using one AI to peer review another AI’s work, including ping ponging a skill between Claude and ChatGPT to improve quality and completeness. Oliver shares how Codex can be used for computer control and debugging by going directly into system settings and config files instead of relying on manual UI hunting. The discussion moves to MCP-style integrations, including using Site CITE AI to connect large academic literature databases into AI workflows for faster research and self peer review. Mark and Oliver compare the old PhD research process with the newer AI-assisted version, where initial literature collation can be compressed from roughly a year to a few weeks. They reflect on the tradeoff between speed and thinking time, arguing that the slower, manual research process also created space for reflection, sleep, and deeper synthesis. The final section focuses on New Zealand data centers, geothermal energy, grid stability, solar adoption, battery storage, and the challenge of building infrastructure with social license. Resources: Oliver Hartwich's essay on AI in education: https://oliverhartwich.com/2026/06/25/bildung-and-the-machine/ If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #842
    September 28 · 41 min

    Data sovereignty, governance debt, and AI for Māori startups

    Data sovereignty, governance debt, and AI for Māori startups Full Show Notes https://www.theintelligenceagepodcast.com/842 Amber Taylor joins Mark Smith to unpack Māori data sovereignty at the point of AI system design, with a sharp focus on what startups need to think about before they build. The conversation gets practical fast, moving from governance debt and investor pressure to the risks of using third party AI platforms with sensitive indigenous knowledge. We discuss why governance has to be designed in upfront, how sovereignty changes when data touches external AI systems, and what an indigenous layer for AI could look like in practice. Key topics Amber shares why the paper started with a startup lens, not just a sovereignty lens, because small teams do not have the same governance or architecture resources as large organisations. She introduces the idea of governance debt, arguing that AI governance added after a system is built creates the same kind of accumulated risk as technical debt. Amber explains why many AI governance frameworks miss the realities of founders and small teams who need workable, early-stage decision support. The discussion covers the limits of simply keeping data on Aotearoa soil if it still passes through external AI systems that the organisation does not control. Amber and Mark dig into the misconception that platforms are not training on user data, and why ticking a consent box is not the same as governance. They explore how investors can pressure startups to monetise data in ways that conflict with the original agreements made with communities and data holders. Amber shares how her own work with indigenous storytelling set clear protocols upfront, including ownership, attribution, and what happens if a company is sold. The conversation uses 23andMe as a cautionary example of what happens when DNA data becomes an acquired asset under new ownership. Amber outlines a future direction for an indigenous AI layer that could sit on top of any model, govern knowledge use, improve accuracy, and return reciprocity to source communities. The episode closes on the idea that New Zealand often builds first and regulation catches up later, creating opportunity for smaller AI companies to move quickly and responsibly. Resources: Native Sentient Website: https://www.nativesentient.com/ Link to paper we talked about: https://zenodo.org/records/21219022 Marae TV interview; https://www.tvnz.co.nz/tvseries/marae If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #841
    September 21 · 46 min

    AI, Shipping Faster, and the Future of Distribution

    AI, Shipping Faster, and the Future of Distribution Full Show Notes https://www.theintelligenceagepodcast.com/841 Mark Smith talks with Steve Mordue about how AI has changed the pace of building, shipping, and distributing software over the last six months. Steve shares what it looks like to move from being a seller and product visionary to building almost everything himself, why the last 5 percent is often the hardest part, and why distribution is now as important as the product itself. They also get into Microsoft’s role in AI, the rise of open weight models, model routing, enterprise trust, and how sovereign data concerns are reshaping what companies can safely adopt. Key topics In this episode, Steve Mordue describes the last six months in AI as a mix of speed, exhaustion, and concentration He says work is moving so fast that things built just months ago already feel outdated AI has not reduced the workload so much as multiplied the number of possible ideas Steve reflects on building Rapid Start CRM and what that taught him about scale He says the platform reached about 170,000 users Looking back, he feels it should have reached far more if marketing had been stronger We discuss the “last 5 percent” problem Mark raises the challenge of turning a near-finished product into something shippable Steve admits he is deeply affected by perfectionism and often obsesses over details others do not care about Steve explains why he now launches before perfection He says he recently forced himself to ship an AI app at 95 percent complete His view is that waiting for 100 percent means never launching We talk about how AI has changed his role from seller to builder Steve says his core strength has always been selling He now uses AI to write code, prototype, and build without depending on a large dev team He says the old dependency on teams and contractors has largely disappeared Every future Rapid Start product, he says, will be built by him alone His main limitation is no longer engineering access, but time They explore the value of domain expertise in the AI era Steve argues that people with deep knowledge in a narrow business area are best positioned to apply AI well He believes AI works best when paired with years of real industry experience Distribution becomes the central strategic problem Mark and Steve discuss how social platforms, email, and newsletters are losing effectiveness Steve says LinkedIn followers are usually peers, not buyers, which limits direct revenue impact They compare Microsoft’s strengths and weaknesses in AI Steve acknowledges Microsoft’s global distribution, infrastructure, and governance strengths If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #840
    September 14 · 34 min

    Build AI That Works

    Build AI That Works Full Show Notes https://www.theintelligenceagepodcast.com/840 Mark Smth talks with James Diekman about how Accelerate Tech has scaled, where AI is actually creating value in government workflows, and why most successful projects are targeted workflow changes rather than broad rollout tools. They also dig into Australia’s growing caution around AI governance, data residency, and the practical push toward local models and sovereign infrastructure. We discuss how AI is being used inside engineering and marketing, why many Copilot-style deployments lose momentum, and how project management itself may evolve as agents take on governance, reporting, and coordination tasks. James shares what’s working in production, what keeps failing before launch, and where the next 6 to 12 months of AI adoption is heading. Key topics James Diekman shares that Accelerate Tech has grown to about 32 staff in the last six months, driven by demand in government projects and AI-enabled solutions. The conversation contrasts AI use in engineering and marketing versus more passive consumption in functions like HR and finance. James explains that the strongest results come from embedding AI into specific workflows, rather than treating it as a standalone chatbot or a broad deployment. They discuss why many Copilot rollouts see mixed adoption and usage drop-off when the tool sits outside the daily workflow. James describes the most successful approach as identifying a business process, pulling apart a sub-workflow, and then applying AI, automation, or judgment-based reasoning to that narrow area first. He notes that many AI projects never reach production, and says that out of 30-plus AI projects delivered, only five or six have made it fully into production. The discussion turns to local government systems, with James outlining how his team often acts as the integration layer, or “the plumbers,” between older council platforms and newer software. They cover Australia’s increasing AI governance maturity, including state frameworks, federal requirements, and the rise of dedicated AI roles inside agencies. Mark and James debate public concerns around data centers, water use, and energy, with James emphasizing the need for better policy and the practical constraints of local compute. They explore the move toward local models, onshore hosting, and reserving compute capacity as organizations seek more control, lower risk, and better throughput. James makes the case that teams do not always need frontier models like Opus for every task and that model choice should match the job, cost, and risk. The episode closes on project management, where James outlines a “project brain” concept using agents, shared knowledge, registers, ticketing, and workflow automation to support or partially replace manual PM effort. If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #839
    September 7 · 38 min

    Why the Death of UI Could Change Everything for Businesses

    Full Show Notes https://www.theintelligenceagepodcast.com/839 Mark Smth speaks with Ashish Bhatia about how his move from Microsoft to Audible changed the way he thinks about customers, AI, and product design. The conversation centers on personal AI, running OpenClaw locally, and why owning your memory, workflows, and data matters more as AI systems become more capable. They also explore Audible’s role in learning, the future of interactive audio, and how agents are beginning to replace clunky app-based workflows with more direct, personalized experiences. Key topics Ashish explains the shift from B2B at Microsoft to B2C at Audible, and how that changes the way you learn from customers He shares why personal AI matters to him more than ever, especially when it involves health data, finances, and memory Mark describes building a large OpenClaw setup with 26 agents, including a nine-agent DevOps team and automated bug fixing They discuss why running AI locally teaches real systems thinking through failure, debugging, and repeated iteration Ashish talks about using OpenClaw to manage daily life, including lunch ordering through Grubhub as a mission-critical workflow The conversation highlights the importance of owning your memory and being able to move your AI system across machines and platforms They explore the idea of the death of UI, where agents increasingly bypass app interfaces and handle tasks directly Mark raises concerns about AI companies ingesting books and content, leading to a discussion of copyright, hypocrisy, and cultural attitudes toward books Ashish shares how Audible supports different learning styles, especially for commuters, slow readers, and people who learn better through audio plus text He outlines Audible’s direction toward interactive, multilingual audiobooks with AI-powered recall, discovery, and personalization Resource Recommendations: The Infinity Machine - Demis Hassabis - Amazon.com: The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence: 9780593831847: Mallaby, Sebastian: Books The Thinking Machine - Jensen Huang - The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip: Witt, Stephen: 9780593832691: Amazon.com: Books Sapiens - Yual Noal Harari - Sapiens: A Brief History of Humankind eBook : Harari, Yuval Noah: Amazon.ca: Kindle Store The Apple Podcast that I have curated - https://podcast.ashish-bhatia.com/feed.xml Ashish Bhatia's website - ashish-bhatia.com If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #838
    August 31 · 38 min

    Why AI Is Becoming a Business Transformation, Not a Technology Upgrade

    Full Show Notes https://www.theintelligenceagepodcast.com/838 AI is about to replace bloated SaaS, and most businesses are still only playing with chatbots Sean G Muller says the next wave of AI is not about better prompts or prettier copilots. It is about rebuilding business around context, agents, and what actually creates value - before your software stack becomes the expensive middleman. Mark Smth and Sean unpack why the last six months have been a genuine shift: agentic loops are now good enough to handle real business work, not just experiments. Sean explains how he moved from traditional technical architecture into building full application pipelines, MCP servers, and background agents that review email, track social signals, draft responses, and keep business moving without adding more human overhead. You'll discover why context is the missing ingredient in almost every failed AI project, how Sean uses a simple meal-planning example to explain it, and why companies that scatter knowledge across laptops, SharePoint, Google Cloud, and people's heads are sitting on hidden risk. Sean also breaks down the difference between AI as a feature and AI as a business transformation engine, including the mistake many firms make when they bolt chat onto old workflows and call it progress. We also get into the coming SaaS pocalypse - the idea that tools like HubSpot, Salesforce, Xero, Slack, and Atlassian may face a serious reckoning as businesses realize they can build leaner, custom, agent-first systems for less than the cost of endless licenses and modules. Sean shares how he built a headless, agent-driven CRM and why he thinks greenfield builds will replace expensive transformation projects much sooner than most executives expect. This conversation matters if you lead a business, run operations, own a small or mid-sized company, or simply suspect your current software is forcing you to work the wrong way. If you want to understand where AI is actually delivering leverage right now - and how to avoid wasting money on shallow pilots - this episode is essential listening. Mark Smth hosts the conversation and brings the enterprise and product lens, pushing Sean to get specific about what success looks like for real businesses in New Zealand. Sean G Muller is an AI and enterprise architecture specialist based in New Zealand, known for helping organizations build practical AI systems, implement agentic workflows, and rethink business process from the ground up. Resources 1. The Cuckoo's Egg: Tracking a Spy Through the Maze of Computer Espionage - https://www.amazon.com.au/dp/0385249462?ref_=mr_referred_us_au_nz 2. Gemini: A Family of Highly Capable Multimodal Models — 2312.11805.pdf ⁠https://arxiv.org/abs/2312.11805 3. On the Measure of Intelligence — 1911.01547.pdf - https://arxiv.org/pdf/1911.01547 If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #837
    May 27 · 25 min

    Why AI Adoption Fails And How to Fix It

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM AI adoption fails when it stops at access to tools. This episode introduces a practical model that bridges the gap between AI tools like Copilot and measurable business outcomes. The focus shifts from technology to people, addressing key blockers such as time, confidence, and relevance. By grounding AI in real workflows, tailoring role-specific use cases, and measuring outcomes, organisations can move from low usage to meaningful impact fast. 🎙 Full Show Notes https://www.microsoftinnovationpodcast.com/837 👉 What you’ll learn Identify the real blockers stopping AI adoption in teams Shift from tool access to measurable business outcomes Design role-specific AI use cases that drive daily value Build AI training that fits real work, not theory Measure AI adoption with practical KPIs and usage data ✅ Highlights "These two things are very, very different" "Some things really need to happen in between those two places" "This is not about the tech. This is about the people" "They didn't know how to use the tool" "They didn't know what they were allowed to do" "When would they sit and just learn a link" "To actually tailor use cases that are relevant" "We have to measure how they are moving" "We moved a department from 15% to 83%" "It shifts how you are working" 🧰 Mentioned Microsoft Copilot (https://copilot.microsoft.com/) Copilot Notebooks (https://support.microsoft.com/en-us/topic/get-started-with-microsoft-365-copilot-notebooks-0775e693-11c6-4d80-8aba-fcc81a737a06) ChatGPT (https://chatgpt.com/) AI Boost Academy (https://hannabergstrom.com/) [ Vattenfall (https://group.vattenfall.com/) Microsoft MVP YouTube Series - How to Become a Microsoft MVP - https://www.youtube.com/playlist?list=PLzf0yupPbVkqdRJDPVE4PtTlm6quDhiu7 ✅ Keywords ai adoption, copilot, generative ai, ai training, business transformation, ai use cases, digital productivity, ai skills, organisational change, ai strategy, workplace efficiency, ai literacy If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #836
    May 25 · 38 min

    Stop Buying Software, Start Buying Outcomes

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM This episode dives into why large CRM and ERP projects keep failing and how AI is reshaping consulting, software delivery, and platform decisions. The core insight is simple. Organisations fail when they buy software instead of outcomes. With AI, experienced teams can move faster, strip away legacy complexity, and build only what the business actually needs. The conversation explores outcome-based thinking, flawed RFP processes, and why AI is accelerating the gap between great and average practitioners. 👉 Full Show Notes https://www.microsoftinnovationpodcast.com/836 🎙️ What you’ll learn How to frame technology decisions around outcomes, not vendors or features Why traditional RFP processes drive cost blowouts and failed projects The four core business problems most systems are really trying to solve How AI tools are changing the speed and shape of consulting work When building custom solutions now beats buying off-the-shelf software ✅ Highlights “So many people are still buying software like it’s a software decision rather than a business decision.” “You’re really only trying to solve one of four business problems.” “Most organizations aren’t even thinking in terms of what is the root of what I’m trying to solve.” “Your highest qualified people are not even in the game because you’ve already excluded quality.” “AI is now to the developer what Visual Studio was in the 90s.” “It made the good developers really good and the bad developers a lot faster.” “Low code was developed because we couldn’t get enough developers.” “There’s a third option now, and that is vibe code your own add-on.” “I think we’re less than 18 months away from this changing enterprise software.” 🧰 Mentioned Microsoft Power Platform: https://learn.microsoft.com/power-platform Microsoft Dynamics CRM (Dynamics 365 CRM): https://www.microsoft.com/dynamics-365/solutions/crm Microsoft Business Central: https://www.microsoft.com/dynamics-365/products/business-central Microsoft Finance and Operations (Dynamics 365 Finance): https://www.microsoft.com/dynamics-365/products/finance Notebook LM: https://notebooklm.google LinkedIn Sales Navigator: https://business.linkedin.com/sales-solutions/sales-navigator DynamicConsultantsGroup.com: https://dynamicconsultantsgroup.com ✅Keywords ai in consulting, outcome-based software, crm failures, erp projects, power platform, low code vs pro code, vibe coding, rfp process, enterprise software, applied ai skills, digital transformation, consulting strategy If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #835
    May 24 · 37 min

    AI as Your Operating System for Work and Life

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM AI is shifting from a tool to an operating system, enabling individuals to move faster, build bespoke solutions, and dramatically increase productivity. The biggest advantage now comes from curiosity, not technical depth. Those who experiment, learn a few tools deeply, and treat AI as a collaborator can compress hours into minutes and unlock new business models. Meanwhile, large organisations risk falling behind due to governance, inertia, and slow decision-making. 👉 Full Show Notes https://www.microsoftinnovationpodcast.com/835 🎙️ What you’ll learn Use AI as an operating system to replace and create software Move from idea to working solution in minutes, not hours Build capability by mastering a few tools, not chasing every new model Apply curiosity-driven workflows to unlock better AI outcomes Avoid common enterprise pitfalls around governance and tool sprawl ✅ Highlights "This software rewards the patient and the curious." "Learn two or three of these tools really well." "Are you using these tools to improve your quality of life or aren’t you?" "This is the last software that I’ll ever train." "We can create bespoke applications through any of these tools." "It’s eating other software." "People who choose to be left behind." "From idea to iteration in 30 minutes." "I feel like I’m becoming a bionic person." "The barriers to entry are just gone." 🧰 Mentioned ChatGPT – https://chatgpt.com/ Bing (Copilot Search) – https://www.microsoft.com/en-us/bing/copilot-search Google – https://www.google.com Gemini – https://gemini.google.com/ Claude – https://claude.ai/ Copilot – https://copilot.microsoft.com/ Perplexity – https://www.perplexity.ai/ Stability AI (Stable Diffusion) – https://stability.ai/ Ethan Mollick (Wharton profile) – https://mgmt.wharton.upenn.edu/profile/emollick/ Co-Intelligence (book) – https://www.penguinrandomhouse.com/books/741805/co-intelligence-by-ethan-mollick/ Suno (AI music generator) – https://suno.com/ ✅Keywords ai tools, generative ai, chatgpt, ai productivity, solopreneur ai, ai governance, llm workflows, ai operating system, automation, business ai, ai adoption, digital transformation If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #834
    May 20 · 24 min

    Make Copilot Safe: Fix Data Governance First

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM This episode explores why data governance must come before enabling Microsoft 365 Copilot, with insights from Khurram Hafeez. It breaks down how sensitivity labels, data loss prevention, and Microsoft Purview reduce the risk of unintended data exposure. You will hear practical guidance on preparing your environment, protecting sensitive information, and managing AI use across Microsoft tools and third‑party AI sites. The focus is on real‑world decisions organisations must make to safely adopt Copilot at scale. 🎙 Full Show Notes https://www.microsoftinnovationpodcast.com/834 👉 What you’ll learn Why Copilot increases the impact of poor data governance How sensitivity labels affect what Copilot can access and summarise When to use DLP versus inline DLP for AI risk management How organisations can limit data sharing with third‑party AI tools What licensing choices matter for Copilot and Purview governance ✅ Highlights “With the intro of the AI, the importance of the data governance compliance has much increased.” “The first defense layer that you should be apply, that is the sensitivity label.” “If a user doesn’t have permission, Copilot can’t actually extract the data.” “Standard policy actually is not sufficient these days.” “Users copy and paste or upload a sensitive file to third‑party AI sites.” “Inline DLP policy is one step ahead of endpoint DLP policy.” “Microsoft focus not to block something, to protect something.” “Before enabling the copilot, they ensure that their environment is ready.” “Copilot certainly honors the permissions the user have.” 🧰 Mentioned Microsoft 365 Copilot: https://learn.microsoft.com/microsoft-365/copilot/ Microsoft Entra Suite: https://www.microsoft.com/security/business/microsoft-entra-suite E5 licence: https://www.microsoft.com/microsoft-365/enterprise/e5 Pay‑as‑you‑go Purview licensing: https://learn.microsoft.com/purview/purview-billing-models ChatGPT: https://chatgpt.com/ Gemini: https://gemini.google.com/ DeepSeek: https://www.deepseek.com/ Microsoft MVP YouTube Series - How to Become a Microsoft MVP: https://www.youtube.com/playlist?list=PLzf0yupPbVkqdRJDPVE4PtTlm6quDhiu7 ✅ Keywords microsoft copilot, microsoft purview, data governance, sensitivity labels, data loss prevention, inline dlp, endpoint dlp, ai security, m365 e5, third party ai, information protection, enterprise ai If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #833
    May 18 · 28 min

    Adopt vs Adapt: Cut Power Platform Technical Debt

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM A practical rethink of how teams use Power Platform and Dynamics 365 to reduce technical debt, improve governance, and build sustainable solutions. The discussion centres on shifting from tech-first thinking to business outcomes, balancing low-code and pro-code principles, and preparing for AI and Copilot. The key insight is that long-term success comes from disciplined governance, reuse, and choosing when to adopt versus adapt. 👉 Full Show Notes https://www.microsoftinnovationpodcast.com/833 🎙️ What you’ll learn Shift from technology-first to outcome-first solution design Decide when to adopt vs adapt to reduce complexity Apply coding principles inside low-code environments Manage technical debt through governance and architecture Evaluate where AI and Copilot add real value today ✅ Highlights "taking a step back and looking at business need and use a process rather than technology first." "a term we're using in the department more and more is kind of adopt versus adapt." "the more you’re restricting yourself off from these possibilities." "it's treating it as a tool, a set of tools we can use rather than the default." "governance to me means more preventative." "it's really difficult to then stay on top of things like the Wave releases." "it’s more a matter of time than anything." "it's not the same thing as code, but it's not meant to be." "there's still a level of maturity that we need to get to." "what does Power Platform mean really and what is it there for?" 🧰 Mentioned Power Virtual Agents (now part of Copilot Studio): https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/microsoft-power-virtual-agents-now-part-of-microsoft-copilot-studio/ AI Builder: https://learn.microsoft.com/en-us/ai-builder/overview Copilot: https://copilot.microsoft.com/ If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #832
    May 17 · 30 min

    AI Makes Breaches Faster: 30-Minute Data Theft

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM This episode explores how AI is accelerating both productivity and cyber risk. As attacks become faster and more automated, traditional perimeter security no longer holds. The conversation focuses on why organisations must assume compromise, adopt zero trust thinking, and build visibility before scaling AI. It highlights practical steps to govern AI use, reduce insider risk, and modernise access models beyond legacy VPNs. The core message is clear: embracing AI without security foundations can move your business backwards. 👉 Full Show Notes https://www.microsoftinnovationpodcast.com/832 🎙️ What you’ll learn Why AI has shortened breach timelines from days to minutes How to shift from perimeter defence to a zero trust mindset Practical ways to gain visibility over AI tools and data use Where unplanned AI adoption creates insider risk Why VPN-only security models struggle in cloud-first work ✅ Highlights “From the time of compromise to data exfiltration, we’re seeing 30 minutes.” “Cybercrime is doing the same thing with AI that businesses are.” “You can’t protect what you can’t see.” “You can’t govern what you don’t know about.” “We almost have to assume we’re going to get hit.” “Security by obscurity doesn’t work anymore.” “AI is introducing far more risk when there’s no planning.” “Once you’re in with a VPN, there’s zero control on the inside.” “The volume and scale and speed is what really concerns me.” 🧰 Mentioned Zero trust: https://learn.microsoft.com/security/zero-trust/zero-trust-overview VPN (virtual private network): https://www.cisco.com/learn/topics/security/what-is-a-virtual-private-network-vpn.html SASE (secure access service edge): https://www.microsoft.com/security/business/security-101/what-is-sase Booz Allen: https://www.boozallen.com/ Gartner: https://www.gartner.com/en Anthropic Claude: https://claude.ai/ ✅Keywords ai security, cybersecurity, zero trust, ai risk, data governance, cybercrime, vpn security, sase, insider risk, cloud security, ai adoption, threat landscape If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #831
    May 13 · 25 min

    Why Copilot Adoption Is Stuck at 3% And Fix It

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM This episode explores how business leaders are actually using AI inside organisations, and why adoption of tools like Microsoft 365 Copilot remains low. Chris shares practical insight from the frontline, covering Copilot strategy, Work IQ, agentic thinking, and how AI now enables teams to build internal tools faster than buying SaaS. The focus is not hype, but how to reimagine business models, reduce cost, and unlock new value with applied AI capability. 🎙️For full Show Notes https://www.microsoftinnovationpodcast.com/831 👉 What you’ll learn Why low Copilot adoption is an opportunity, not a failure How to think beyond productivity to AI-enabled business models When building internal AI tools beats buying SaaS What Work IQ enables that other AI systems cannot How agentic systems change OKRs, dashboards, and execution ✅ Highlights “AI is transformative, it is expensive, and it requires several years of exposure to capital budgets” “It’s not just about doing things better, but doing things you never could do before” “Microsoft is the largest commercial AI company on the planet” “Copilot can orchestrate over your information in a way that other AI can’t” “Work IQ would be a little bit better than the word graph” “I love the term vibe coding” “It would have been a couple of hundred thousand dollars two years ago” “I’ve taken over two thousand dollars of subscription costs out of my business” “They do exactly what I needed to do and no more” 🧰 Mentioned Microsoft 365 Copilot: https://www.microsoft.com/microsoft-365-copilot Host - Polaris podcast: https://polaris.synozur.com Co-Host Microsoft’s Intrazone podcast: https://aka.ms/TheIntrazone Microsoft MVP YouTube Series - How to Become a Microsoft MVP - https://www.youtube.com/playlist?list=PLzf0yupPbVkqdRJDPVE4PtTlm6quDhiu7 ✅ Keywords ai adoption, microsoft copilot, work iq, agentic ai, internal tools, ai strategy, okrs, vibe coding, business transformation, saas replacement, applied ai, enterprise ai If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #830
    May 11 · 27 min

    How Agentic AI Is Rewriting ERP Workflows

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM This episode with Christian Segurado explores how agentic AI is reshaping ERP and enterprise delivery. The discussion covers deploying Copilot Studio agents in Teams, reducing ERP UI friction, accelerating fit gap and requirements work, and why multi agent orchestration matters. It focuses on where AI delivers real operational value today, the foundations required to unlock it, and what this shift means for people, process, and technology decisions in 2026. 👉 Full Show Notes https://www.microsoftinnovationpodcast.com/830 🎙️ What you’ll learn How agentic AI changes how users interact with ERP systems Where Copilot Studio and Azure Foundry fit in real implementations How AI accelerates requirements gathering and fit gap analysis Why clean data foundations are critical for agentic scenarios What multi agent, multi model AI means for enterprise teams ✅ Highlights “ERP being actually more the layer on the back where the agent inquires knowledge” “It is probabilistic, not deterministic” “We are deploying is on Teams” “It will fundamental change how we interact with the ERP” “You cannot start with the roof if you don’t have the foundation” “We have an agentic driven methodology” “Consistency creates predictability” “It does have a soul” “Multi agent, multi model will be the norm” 🧰 Mentioned Dynamics 365: https://www.microsoft.com/dynamics-365 Copilot: https://www.microsoft.com/microsoft-copilot Copilot Studio: https://www.microsoft.com/microsoft-365-copilot/microsoft-copilot-studio Azure Foundry (Microsoft Foundry): https://ai.azure.com Microsoft Fabric: https://www.microsoft.com/microsoft-fabric Medallion architecture: https://learn.microsoft.com/azure/databricks/lakehouse/medallion Azure DevOps: https://azure.microsoft.com/products/devops Microsoft Learn: https://learn.microsoft.com Microsoft Mesh: https://www.microsoft.com/microsoft-mesh ✅Keywords agentic ai, erp, dynamics 365, copilot studio, microsoft teams, multi agent systems, enterprise ai, ai governance, fit gap analysis, microsoft power platform, azure foundry, digital transformation If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #829
    May 10 · 31 min

    Why AI Alone Won’t Fix Innovation Culture

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM 👉 Full Show Notes https://www.microsoftinnovationpodcast.com/829 This episode explores why AI and innovation fail without culture change. Drawing on decades of innovation experience, the discussion breaks down common myths around productivity, frameworks, and lone heroes. The core insight is simple but hard to execute. Sustainable AI adoption starts with leadership alignment, psychological safety, and recognising that everyone is creative. Real change happens bottom‑up, reinforced by long‑term discipline, not hype or short‑term fixes. 🎙️ What you’ll learn Why AI does not automatically improve productivity or innovation How leadership behaviour shapes innovation culture What psychological safety enables in teams and organisations Why every employee is creative in practical, everyday ways How to build long‑term innovation habits that actually stick ✅ Highlights “If it’s that easy, everyone will be doing it.” “Sometimes I think AI makes me more unproductive.” “There is no lone hero that is going to save the company.” “Everyone knows at least one thing how they can improve something.” “Innovation is much more complex than one framework.” “Psychological safety is a property of a group, not an organisation.” “Everyone is creative, not just artistic people.” “Changing behaviour is one of the most difficult things.” “You cannot trick people, they will see if you walk the talk.” 🧰 Mentioned AI adoption: https://learn.microsoft.com/azure/cloud-adoption-framework/ai/ Innovation frameworks: https://www.deloittedigital.com/us/en/offerings/customer-led-marketing/customer-strategy-and-applied-design/applied-design-and-innovation/ten-types.html Design thinking: https://online.hbs.edu/blog/post/what-is-design-thinking Business Model Canvas: https://www.strategyzer.com/library/the-business-model-canvas ✅Keywords ai adoption, innovation culture, psychological safety, creativity at work, leadership, organisational change, productivity myths, design thinking, innovation frameworks, applied ai, business innovation, technology change If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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  • #828
    May 6 · 22 min

    How AI Is Taking Over Project Management Work

    Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM This episode explores how Microsoft Planner is evolving into a practical, AI-assisted project management tool for modern teams. Cindy Lewis explains how Planner fits into daily work through Microsoft Teams, how AI agents support planning, reporting, and risk assessment, and why small usability improvements matter. The discussion also covers Copilot, Dataverse, Power BI, and the shift from automation to agent-style AI that actively helps manage work. 🎙 Full Show Notes https://www.microsoftinnovationpodcast.com/828 👉 What you’ll learn How Microsoft Planner fits into modern, collaborative project management Where AI adds value in planning, reporting, and risk management Differences between Planner basic and premium plans and why they are merging How Copilot supports everyday productivity beyond automation Why data, feedback, and adoption timing matter for real-world AI impact ✅ Highlights “Everybody starts with a plan, whether or not you’re trained in project management.” “The future is collaboration and artificial intelligence, not desktop-only tools.” “We need to be able to ask a question of my plan and get the answer back.” “AI is doing a really nice status report that used to take hours of work.” “The average person doesn’t even know enough to know what it could do.” “They’ve made it so you can’t make the most common project management mistakes.” “Power BI is the winner if you want resource capacity across portfolios.” “Most companies are not ready, even though the future is already here.” “A lot of what people call AI is really automation.” “It’s the little annoying things that Microsoft is fixing that people don’t notice.” 🧰 Mentioned Microsoft Planner: https://www.microsoft.com/microsoft-365/planner Microsoft Teams: https://www.microsoft.com/microsoft-teams Microsoft Project: https://www.microsoft.com/microsoft-365/project/project-management Microsoft To Do: https://to-do.microsoft.com Microsoft Copilot: https://www.copilot.com Planner Agent: https://support.microsoft.com/office/access-planner-agent-86bf60a1-239d-4c37-b7b6-9a4111e1cc02 Microsoft MVP YouTube Series - How to Become a Microsoft MVP - https://www.youtube.com/playlist?list=PLzf0yupPbVkqdRJDPVE4PtTlm6quDhiu7 ✅ Keywords microsoft planner, project management, ai agents, copilot, microsoft teams, dataverse, power bi, task management, productivity, automation, collaboration, planning If you want to get in touch with me, you can message me here on Linkedin. Thanks for listening 🚀 - Mark Smith

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Showing 1–20 of 29 episodes