The AI Frontier Playbook

Most of us spend the day moving information between systems. AI can make that faster, but the real win is not needing to do it at all.

In this episode of The AI Frontier Playbook, I sit down with Bryan Goode, Corporate Vice President for Business Applications and Agents at Microsoft. Bryan was part of the team that launched Microsoft Teams, and today he leads Dynamics 365 and the AI agents being built around it. His argument is simple: making each person faster is the small win. Rebuilding the process is the real one.

Bryan explains why employees have become the "human middleware" between disconnected systems, and why the toggle tax of switching from app to app can cost the average information worker around four hours a week. He shares how L&G and Lifetime rethought their processes with AI, and why he believes AI makes business applications more important than ever, not less, as the value moves from the user interface down to the data, the intelligence layer, and governance.

We also get practical: when to use a pre-built Dynamics 365 agent versus building your own in Copilot Studio, how multi-agent orchestration works, where a human still needs to sign off, and how Agent 365 gives IT visibility across every agent in the organization. Bryan closes by sharing the agents he uses every day, what Microsoft Scout actually is, and why he believes AI will deepen the human connection at work.

You'll learn:

• Why making every individual faster with AI doesn't fix the underlying process
• What the "toggle tax" is and how much it costs the average information worker
• How L&G and Lifetime used AI to rethink customer service and order fulfillment
• Why the "SaaS apocalypse" gets it backwards, and how business applications are changing
• When to use pre-built Dynamics 365 agents and when to build your own in Copilot Studio
• How multi-agent orchestration connects agents into one business process
• How to decide where human approval is required as agents start taking action
• Why the best results combine bottom-up experimentation with top-down process change
• How to choose your first process, the right sponsor, and the metric to measure relentlessly
• Why the business, not IT, should own the outcome of an AI process
• The agents Bryan uses every day, and what Microsoft Scout is
• Why Bryan believes AI will make work more human, not less

Whether you're leading an AI transformation, running a business function, or trying to figure out where agents fit in your organization, this episode gives you a practical roadmap for rebuilding the way work gets done.

Resources & Links

Bryan LinkedIn: https://www.linkedin.com/in/bryangoode/

Our Sponsor Wepoint: https://www.wepoint.com/en

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Your AI agent may not need a sophisticated exploit to be hacked. Sometimes, one emoji is enough.

In this episode of The AI Frontier Playbook, I sit down with Richard Diver, Director of Story Design at Microsoft, author of Guardians of AI and Unlearn Security, and a security veteran with 30 years of experience. We explore why the biggest AI security problems aren't really technology problems — they're thinking problems.

Richard breaks down how threat modeling changes when AI can act autonomously, why every AI system should be evaluated through three simple questions — asset, access, and actor — and why you should think through the "what if?" before an agent ever reaches production. We get into prompt injection, indirect attacks hidden inside documents and images, AI memory as a new attack surface, and why giving one AI access to everything may be one of the biggest mistakes organizations can make.

We also explore Richard's "green team" concept — why security can't remain the responsibility of a small security department, and why the people actually using AI every day need to be part of the security process. He shares six things companies should check before turning on their first AI agent, how to limit an agent's blast radius, and why the worst attacks aren't the ones that cause immediate damage — they're the ones nobody notices for years.

You'll learn:

• Why major security failures are usually caused by how we think, not a lack of security tools
• Richard's Asset, Access, Actor framework for threat modeling AI systems
• How a single emoji or special character can become an attack vector against AI
• Why prompt injection can hide inside documents, emails, images, and other content
• Why giving one AI access to everything creates unnecessary risk
• Why Richard would rather have 1,000 agents doing one job each than one AI with access to everything
• Why "read-only" access isn't necessarily safe when an AI can read and share information
• How AI memory creates a new long-term attack surface
• What the "green team" is and why security needs to involve everyone
• The six things to check before turning on your first AI agent
• Why reducing the blast radius may be more realistic than trying to prevent every attack
• Why the most dangerous AI attacks are the ones that go undetected

Whether you're building AI agents, deploying AI across an organization, or simply using AI tools in your everyday work, this episode gives you a practical way to think about security before something goes wrong.

Resources & Links

• Richard on LinkedIn: linkedin.com/in/rdiver
• Book: Guardians of AI
• Book: Unlearn Security
• Our sponsor Visionet: visionet.com

📬 Enjoyed this episode?

• Connect with me on LinkedIn: linkedin.com/in/samuelboulanger
• Visit the website: frontierplaybook.ai
• Newsletter: theaifrontierplaybook.substack.com

Customers almost never ask "can you build this" anymore. What they actually want to know is "can I trust it."

In this episode of The AI Frontier Playbook, I sit down with Sapna Grover, Senior Director at Microsoft leading the Cloud, Data & AI Center of Excellence. Before this, she spent close to three decades in software, as a scientist at India's Defense Research and Development Organization, in leadership roles at Pegasystems, and in engineering leadership at Microsoft across Windows and Search. Today she's helping the world's largest companies actually put AI into production.

Sapna breaks down why AI flips the traditional software path: you start with the business outcome and the data, not the code, and you have to define what a "good" answer even looks like since AI rarely gives the same response twice. Using a live airline customer-support example, she walks through what AI evaluation really takes: a golden data set, an LLM as judge, and tracing every decision so AI stops being a black box.

We also get into why the most dangerous AI failures are silent, quality drifting while every dashboard stays green, and the single most expensive mistake she's seen companies make: falling for the technology before the problem. She closes by sharing the AI agents she's built for her own life, including one that drafts her Friday team update and another that tracks her protein intake better than any app could.

You'll learn:

• Why the fundamentals of building great products haven't changed, even though AI has changed the engineering problems underneath them
• How AI flips the traditional software path, starting with business outcome and data instead of code
• Why "did it produce the expected answer" is the wrong question for AI, and what to ask instead
• How to build a golden data set and use an LLM as a judge to evaluate AI at scale
• Why tracing turns AI from a black box into a glass box you can actually debug
• Why the most dangerous AI failures are silent, with quality and trust eroding while every system metric stays green
• The most expensive mistake companies make with AI, and why it usually isn't a technical one
• Why AI needs the same financial discipline as cloud infrastructure, and how uncontrolled cost quietly kills ROI

Whether you're a product leader shipping your first AI feature or a technology leader responsible for what happens after launch, this episode gives you a practical framework for building AI your organization can actually trust.

Resources & Links

Guest LinkedIn:linkedin.com/in/sapna-grover-9170032

📬 Enjoyed this episode?

Subscribe to the AI Frontier Playbook:https://theaifrontierplaybook.substack.com/ Connect with me on LinkedIn: https://www.linkedin.com/in/samuelboulanger/

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#AI #FutureOfWork #Startups #MicrosoftCopilot #EnterpriseAI

AI in retail isn't the chatbot on the website — it's the frontline worker running the store floor, and that's where the real transformation is happening.

In this episode of The AI Frontier Playbook, I sit down with Ghazanfar Riaz, Senior Vice President of the Global Digital Solutions Group for the Microsoft Business Unit at Visionet, who has spent 20 years shipping enterprise technology and is now deploying AI into some of the biggest retailers in North America.

We're not talking about shopping assistants or chatbots — we're talking about the person restocking shelves, running the till, and checking if your size is in the back. Ghazanfar walks through what a frontline AI co-pilot actually does in practice: starting an associate's shift with a prioritized brief instead of a stack of emails, catching shelf exceptions before a customer notices a product missing, and guiding new hires through SOPs without pulling a manager off the floor. We get into why the biggest bottleneck in AI adoption isn't the technology or the data — it's people, and specifically whether frontline workers are trained and involved early enough to actually trust the tool. Ghazanfar also shares a real example of a retailer whose shelf exceptions were happening 100 times a week, taking 25 minutes to catch and fix, with only 70% resolved in the same shift — and how closing that gap became the model for a good AI use case. We close on what a retailer risks by waiting another year, and what the frontline retail job might look like a decade from now.

You'll learn:

1. Why the real AI transformation in retail is happening on the store floor, not in the customer-facing chatbot
2. How a co-pilot morning briefing turns an associate's shift into a prioritized to-do list instead of guesswork
3. Why shelf-exception detection and auto-generated replenishment tasks are closing an expensive, invisible retail gap
4. Why the biggest bottleneck in retail AI adoption is people, not technology or data
5. What separates a retailer that's actually ready for AI from one whose project stalls in the first few weeks
6. How assisted selling and cross-store inventory visibility turn a lost sale into a substitute purchase
7. Why returns are one of the highest-impact, lowest-effort use cases for frontline AI
8. Why waiting a year to deploy AI costs a retailer more in lost organizational learning than in outdated technology

Whether you're a retail operator trying to figure out where to start with AI, or a technology leader building the next generation of frontline tools, this episode gives you a practical framework for turning daily store friction into your first AI use case.

Resources & Links

• Ghazanfar Riaz on LinkedIn: linkedin.com/in/gsheikh-01

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#AI #FutureOfWork #Startups #MicrosoftCopilot #enterpriseai

AI gets the attention, but trust gets the PO. The coolest demo in the world means nothing if an enterprise does not believe you are safe, compliant, and serious about data.

In this episode of The AI Frontier Playbook, I sit down with Tom Davis, Partner at Microsoft for Startups, who founded Solair in 2011, built it on Azure, and sold it to Microsoft in 2016.

Tom has spent the last several years helping thousands of founders turn AI and cloud innovation into real business outcomes, and he brings a rare perspective: he has built and sold a company, and now he sits on the other side helping the next generation of startups scale. We talk about why speed to a working product stopped being the advantage it used to be, why plugging into something like Microsoft Copilot can matter more than the product itself, when a founder should build a tool in house instead of buying one off the shelf, and the one mistake that quietly kills startups that looked incredible in the demo. Tom also shares how his own son is building a two person startup powered by AI agents, and why he believes trust, not technology, is what closes enterprise deals.

You'll learn:

1. Why time to MVP has collapsed and what that means for founders competing on speed alone
2. How to decide whether to build your own tool or buy something off the shelf
3. Why distribution through platforms like Microsoft Copilot can matter as much as the product itself
4. What enterprises actually check before they trust a startup with their data
5. Why technical founders often overbuild and lose sight of what customers actually want
6. How AI agents are changing the size and structure of a modern startup team
7. What founders should build into their architecture from day one to avoid costly rework later
8. How to test an idea with a proof of concept before committing months of runway to it

Whether you are a founder building your first AI powered product or an operator trying to understand how enterprise buying decisions really get made, this episode gives you a practical framework for the next 90 days.

Quick thanks to our sponsor OS4 Techno Services, a Quebec based IT firm helping organizations navigate infrastructure, cybersecurity, cloud, and AI with strong Microsoft expertise. Learn more about OS4 Techno Services: https://www.os4techno.com/

📌 Resources & Links

Tom Davis on LinkedIn: https://www.linkedin.com/in/tom-davis-startups/

📬 Enjoyed this episode?

Subscribe to the AI Frontier Playbook: https://theaifrontierplaybook.substack.com/

Connect with me on LinkedIn: https://www.linkedin.com/in/samuelboulanger/

Visit the website: https://frontierplaybook.ai

AI projects stall when nobody names the real business bottlenecks first. In this episode of The AI Frontier Playbook, I sit down with Ryan Cunningham, Microsoft CVP of Copilot Studio and Power Platform, to talk about why, and what actually separates a good AI use case from a wasted one.

Ryan leads agents, apps, and automation across the entire Microsoft stack, and in this conversation we get into why most AI projects stall before they even start, whether apps and low code still matter now that you can generate almost anything from a chat window, how to actually choose between pro code, low code, and vibe coding, and how Copilot Studio's credit based pricing is reshaping the way teams think about cost. Ryan shares the real story behind a customer who turned a 36 hour quote generation process into a 2 hour one, and why that is the model for a good AI use case. Quick thanks to Kezber, a Microsoft partner, for sponsoring this episode.

You'll learn:

1. Why most AI projects fail because nobody defined the outcome before picking a tool
2. How to turn a vague business problem into a measurable AI use case with a clear ROI
3. Why apps and structured interfaces are not going away even as chat based AI expands
4. What actually happens to custom software and SaaS as vibe coding becomes mainstream
5. How to decide between pro code, low code, and vibe coding for a given problem
6. Why credit based consumption pricing is replacing flat per user licensing for AI
7. How Power Apps turning into MCP servers changes what agents can do with your data
8. What governance and oversight look like once agents start operating with less human involvement

Whether you are a business leader trying to justify your first AI investment or a developer deciding which tool fits the job in front of you, this episode gives you a practical framework for making that call.

Thanks to Kezber for sponsoring this episode. Learn more about Kezber: https://kezber.com/

📌 Resources & Links

Ryan Cunningham on LinkedIn: https://www.linkedin.com/in/rycu

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Visit my website: https://frontierplaybook.ai

Learn how to automate sales and back-office processes with AI for business, without overwhelming your team. In this episode of The AI Frontier Playbook, I sit down with Eric Murray, Director of Innovation at Kezber, a Microsoft partner in Quebec, to break down AI implementation and business workflow automation with Microsoft Copilot, Power Platform, and Copilot Studio.

Many companies fail when they try to automate an entire department at once. Eric's approach starts instead with repetitive tasks that are low risk for your business, owned by a champion who knows every exception. We dig into how to identify the right processes to automate first, why most AI projects stall before delivering anything, and what it really takes to move from a working prototype to a secure production system.

Eric brings a practitioner's view shaped by real client deployments. We walk through a full accounts payable automation scenario, from the invoice landing in a shared mailbox to the data reaching the ERP, and break down which steps should be plain deterministic automation with Power Automate and which ones genuinely need AI reasoning in the loop. We also cover human-in-the-loop design, exception handling, data residency under Quebec's Law 25, governance with tools like Microsoft Purview and Agent 365, and why a single model swap took one of his AP solutions from 50 dollars a month to 5 dollars a month.

You'll learn:

1. Why deploying Copilot licenses is the starting line, not the finish line
2. How to spot a strong first automation candidate: repetitive, low risk, and owned by a champion
3. What red flags signal a process is not ready to automate
4. How to slice a process like accounts payable into small chunks that deliver value in weeks, not months
5. When to use straight automation versus AI reasoning inside the same workflow
6. How to design human-in-the-loop escalation so people trust the system instead of working around it
7. Why security, access control, and data residency are the real work behind any AI agent
8. How to measure ROI after launch and diagnose whether the problem is the tool or adoption

Whether you are a business leader wondering where to start with AI automation or a Power Platform and Copilot Studio builder taking prototypes into production, this episode gives you a concrete framework for picking the right first project and scaling it without losing trust.

📌 Resources & Links

• Eric Murray on LinkedIn: linkedin.com/in/eric-murray-ing-18851a6
• Kezber: https://kezber.com/en/

📬 Enjoyed this episode?

• Subscribe to The AI Frontier Playbook Newsletter: samuelboulangerai.substack.com
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#AI #FutureOfWork #MicrosoftCopilot #PowerPlatform #Automation

Why does AI adoption fail in business? In this episode of The AI Frontier Playbook, an AI podcast for enterprise leaders, I sit down with Stacey Mulcahy, Senior Director of the Microsoft Garage, to break down the real AI adoption challenges companies face and how Microsoft solves them with curiosity, hackathons, and hands-on AI skilling.

Stacey leads the team behind the Microsoft Global Hackathon and the Skill Up AI program, engaging over 70,000 employees in grassroots AI learning and employee enablement across the company.

We go deep on why formal learning paths are not enough, why curiosity and exploration drive real behavior change, and what Stacey calls the grok moment: the point where AI stops being a search engine and starts changing how you work. She also shares why leadership modeling makes or breaks adoption, how she runs learning in the open by streaming her own experiments, and why the GitHub Copilot CLI has completely changed her daily workflow.

You'll learn:

1. Why AI adoption gets stuck even when the tools and programs are good
2. How to create the grok moment that turns skeptics into adopters
3. Why leadership modeling matters more than any champion program
4. What community skilling looks like and why learning with others beats learning alone
5. How creativity and play outperform productivity use cases as an entry point to AI
6. Why durable skills like adaptability, delegation, and taste matter more than tool knowledge
7. How Stacey uses the GitHub Copilot CLI, custom agents, and MCP integrations to run her day
8. What the future of work looks like when everyone manages a team of agents

Whether you are a leader driving AI transformation in your organization or an individual contributor trying to build AI literacy on your team, this episode gives you a practical playbook for making adoption actually stick.

📌 Resources & Links

Stacey Mulcahy on LinkedIn: https://www.linkedin.com/in/staceymakes/

📬 Enjoyed this episode?

Subscribe to Mastering AI for Productivity: https://samuelboulangerai.substack.com/

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#AI #FutureOfWork #AIAdoption #Microsoft #AILiteracy

How to use AI for content creation without losing your voice. Jack Rowbotham earned 7.5 million LinkedIn impressions in a year at Microsoft, and in this episode of The AI Frontier Playbook he breaks down his exact AI content workflow.

The real skill in the AI era is not creating more. It is having the judgment to ignore almost all of it. I sit down with Jack Rowbotham, Senior Product Marketing Manager for Copilot Studio at Microsoft, who built a personal brand of over 90,000 followers while working full-time and was named a Microsoft Frontier Trailblazer for FY26. We unpack the line between using AI tools to amplify your voice and letting them replace your thinking.

You'll learn:

• How to use AI tools for content creation without sounding like a robot or losing your voice
• Why leading with your own perspective before touching AI is what protects authenticity
• How Jack runs AI evals on his own LinkedIn posts to keep AI-generated content accurate
• What separates using AI to amplify your voice from outsourcing your thinking
• How the agentic AI agent boss model reshapes work for marketers, creators, and knowledge workers
• Why prioritization and judgment are the new edge once AI removes the execution barrier
• What a personal brand agent built in Microsoft Copilot Studio can actually do for you
• How to turn event photos, whiteboards, and slides into usable notes with AI in minutes

📌 Resources & Links
Jack Rowbotham on LinkedIn: https://www.linkedin.com/in/jackrowbotham/
Jack's LinkedIn Learning course, How to be an Agent Boss: https://www.linkedin.com/learning/how-to-be-an-agent-boss-leading-ai-agents-at-work

Enjoyed this episode? Subscribe to the newsletter: https://samuelboulangerai.substack.com/
Connect with me on LinkedIn: https://www.linkedin.com/in/samuelboulanger/
Visit my website: https://frontierplaybook.ai

AI strategy stuck in pilot purgatory? In this episode of The AI Frontier Playbook, I sit down with Pam Maynard, Microsoft's first ever Chief AI Transformation Officer, on artificial intelligence adoption, organizational change, leadership, automation, and the future of work.

Pam led Avanade as CEO across 60,000 employees before taking on this pioneering role at Microsoft. We unpack the four-layer metrics framework that prevents pilot purgatory, the Manulife $1 billion AI value example, why middle managers decide whether AI adoption sticks, and the McKinsey research showing companies with AI led from the top perform 3x higher than those without.

A big thank you to ITI for sponsoring this episode. ITI is a proudly Canadian company that has spent over 30 years helping organizations bring their IT projects to life by making technology accessible, meaningful, and human.

Whether you are a C-suite leader trying to scale AI across your enterprise or a manager closing the adoption gap on the ground, this episode delivers a practical framework you can put into action this quarter.

Learn more about ITI: https://iti.ca/en/microsoft-365-copilot/

📌 Resources & Links

Pam Maynard on LinkedIn: https://www.linkedin.com/in/pam-maynard/

ITI Microsoft 365 Copilot solutions: https://iti.ca/en/microsoft-365-copilot/

Episode with Meaghan Platt from ITI on AI, accessibility and inclusion: https://www.youtube.com/watch?v=v5ydmRrYXnQ

📬 Enjoyed this episode?

Subscribe to the AI Frontier Playbook Newsletter: https://samuelboulangerai.substack.com/

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#ArtificialIntelligence #FutureOfWork #Leadership #EnterpriseAI #DigitalTransformation