Meridian Point

Alex Brungardt sells automation into one of the most paperwork-heavy corners of technology: federal governance, risk and compliance. His advice to buyers is blunt. The fix isn't more AI. The fix is knowing where a human decision has to stay human. In this episode of The Meridian Point, Kumar Dattatreyan talks with Alex about automating the front end of compliance, building a culture of testing before you trust an agent and separating real AI progress from hype.

Alex is Director of Business Development at Radus Software, the company behind the Metronome delivery and compliance platform. He came up outside engineering, with a healthcare background, and he says that outsider's eye is his edge with customers.

WHAT YOU'LL LEARN

COMPLIANCE SHIFTS LEFT Most organizations treat governance, risk and compliance as an afterthought, then scramble before release. Alex describes building GRC into planning and development the way teams moved security to the front. Teams upload internal, federal or municipal requirements. Examples he gives include STIGs, the authority to operate process, HIPAA and FISMA. Metronome then pulls compliance features into a backlog that the team accepts or rejects.

AUTOMATE THE RESEARCH, KEEP THE DECISION Every feature or story that enters the backlog is accepted manually. The tool combines existing work with the GRC requirements. It doesn't invent random artifacts. Alex also points out that much of this logic runs on a plain rules engine, and AI adds speed when it scans large volumes of information.

TEST FIRST IS A CULTURE Alex ties trust in automation to testing. He cites a DevOps coach who taught him that a bug is a test not yet written. Once testing proves a system meets your accuracy threshold, you can automate that decision. He pairs that with observability and near real-time data so a bad decision surfaces fast.

START WITH THE TOOLS YOU ALREADY OWN His first move for AI beginners is to check the settings and new features in the productivity suite they already use, within company AI policy. He thinks program managers shouldn't hand-write status reports anymore. For deeper work he recommends R&D and new applications, where existing systems stay safe and customers face no learning curve.

THE GRC TEAM IN THREE YEARS Alex expects teams of similar size with different skills. Automation frees time for work leaders kept pushing down the list, such as auditing, reducing technical debt and trimming overlap between federal requirements and an organization's own stricter policies.

JOBS, SKILLS AND PROTOTYPES Alex calls himself an optimist. He likes the model where a product manager and an engineer pair with agents, because people keep their jobs and output grows. He wants young people to learn technical skills and also strong English language arts, including how to tell human text from machine text. He passes along a message from small business owners who work with the National Geospatial-Intelligence Agency: send prototypes, not proposals.

OVERHYPED AND UNDERHYPED Overhyped: calling generative AI "artificial intelligence." Alex says real intelligence arrives with AGI. Underhyped: the national race to reach AGI. Both hosts admit the idea makes them uneasy.

A CONCERN FROM THE HOST Kumar raises the risk of leaning on AI for decisions and asks what decision you'd have made without it. That question sets up a follow-up episode on how people keep learning, and keep their skills sharp, when no one is watching.

CONNECT WITH ALEX BRUNGARDT

Note: Radus's Metronome isn't the usage-based billing company that shares the name. Check the Radus site for the right product.

CONNECT WITH KUMAR DATTATREYAN

ABOUT THE MERIDIAN POINT The Meridian Point is a live bi-weekly broadcast for executives and transformation leaders working through disruption and organizational change. Join us live on LinkedIn, YouTube and Facebook.

#MeridianPoint #AIHype #GRC #GovernanceRiskCompliance #Compliance #FederalIT #Automation #LowCode #AIAgents #HumanInTheLoop #DigitalTransformation #DevOps #Agile #FutureOfWork #Leadership

Host: Kumar Dattatreyan

Episode Date: October 6, 2026 Watch on YouTube

A banker in 1903 told his client not to invest in Ford. The automobile was a fad. The horse was here to stay. We laugh now, but from inside that moment the call looked reasonable. That is the trap Glenn Marshall came back on the show to talk about. Exponential change is nearly invisible while you are standing in it, and by the time it is obvious the disruption has already won.

Glenn makes the case that energy is where this is playing out right now, faster than almost anyone is pricing in. The spark was the closing of the Strait of Hormuz and the overnight jump in oil prices. From there the conversation goes under the hood to Wright's Law, the rule that cost falls a fixed percentage every time production doubles. Solar has followed that curve for decades. It now supplies about 8.7% of global electricity, up from 1% in 2015. Texas is the proof on the ground, where 11 grid conservation appeals in 2023 became none the next summer after a solar and battery buildout.

Why the 1903 Banker Was Not a Fool The banker who called the car a fad was not stupid. In 1903 the horse still made sense. Glenn uses that moment to explain why exponential growth is so hard to see from the inside, and why the same blindness is hitting leaders today who plan in straight lines while their industry bends into a curve. The lesson is not about cars or solar. It is about the pattern itself.

The One Rule Behind Chips, Phones and Solar Wright's Law says that every time cumulative production doubles, cost drops a fixed percentage. For solar that drop is about 20% per doubling, and it has held for decades. Glenn traces the same curve through the computer chip, from a machine everyone lined up to use to the one in your pocket, then lands it on solar. Once you see the rule, you stop being surprised by what it does.

Why the Forecasts Keep Being Wrong The International Energy Agency has underestimated solar growth for years. Glenn explains the distinction most people miss. The miss is about deployment, how many gigawatts get built, not the per-unit cost curve. Countries and companies make investment decisions on projections that have been provably low, and that gap is where bad capital allocation lives.

How Texas Flipped in a Single Year Texas had 11 grid shortfalls in 2023. The next summer it had none, then so much midday solar that wholesale prices went negative. Glenn tells the story of technology-neutral competitive procurement, the market design that lets the cheapest source win without anyone picking favorites. It is the clearest real-world proof that this is not theoretical.

Why Nuclear Runs the Curve Backward Solar and batteries get cheaper as we build more. Nuclear does the opposite. Glenn walks through the negative learning rate, the gas turbines back-ordered to 2030, and the data centers now bolting airplane engines to the side of buildings because they cannot get power any other way. The cost comparison is becoming impossible to ignore.

What Nearly Free Energy Unlocks Glenn closes on abundance. Once the marginal cost of power falls toward zero, things that were too expensive become viable. Large-scale desalination. Cheap fuel for EVs and heat pumps. The harder industrial problems like green steel and cement. The takeaway for leaders is simple and uncomfortable. Costs are heading down, and your five-year plan should assume it.

By just 2030, solar electricity will be absolutely and reliably free during the sunny parts of the day for much of the year, pretty much everywhere." (Jenny Chase, BloombergNEF, quoted by Glenn)

"May the best technology win. Whoever can provide the cheapest electricity, that is who we are going with."

"I just want free energy and I want it now. It is not a left or right thing."

"It is really hard to see from the outside. In five years we will look back and say it was obvious. Right now we are in the thick of it."

"If the sun doesn't come up, we're all dead. The sun's going to come up."

Connect with Glenn Marshall LinkedIn: [confirm Glenn's preferred LinkedIn URL before publishing]

Glenn is based in Toronto and digs deep into energy, climate and the economics of disruption. He is glad to keep the conversation going with listeners who want to dig into Wright's Law and the energy transition.

Take the Disruptor Method Quiz Are you disrupting or about to be disrupted? Find out in under five minutes: https://www.thedisruptormethod.com/quiz

Work with Kumar Book a 30-minute conversation: https://tidycal.com/coachkumar/30-minute-meeting

The Meridian Point with Kumar Dattatreyan

Tonya McCaulley-Slater runs agile transformations for a living. She just won't say the word "agile" out loud. Not at first. Not until the results earn it.

Sounds backwards. It works. She walks into an organization, runs the demos, tracks the impediments, prioritizes the portfolio, and never teaches a soul what a sprint is. People buy in. Then they ask what this thing is called. That's when she tells them.

Here's the part that stuck with me. She sits across from a leadership team that swears they tried agile and hated it. So she draws the whole framework on a blank piece of paper with every label stripped off. They look at it and say they love it. Same framework. Same everything. The only thing she took out was the word.

We get into why the jargon is the thing killing your transformation. We get into Fannie Mae, where new leadership decided agile was a bad word and cut the whole thing. And then we get into her other job, the one you won't see coming. She coaches pro golfers on mental performance. The tool she uses on the first tee is the same one she hands a burned-out leader between meetings. You'll want to steal it before the episode ends.

Tonya is a Certified SAFe Program Consultant, a mental performance coach and the founder of MindDrive Performance. She spent fourteen years at Nationwide, then walked away to coach transformation full time at Cisco, Eli Lilly, JB Hunt and Fannie Mae. She is running an enterprise agile transformation at St. Thomas University now, all of it on the down low. She has a psychology undergrad and is finishing a master's in industrial-organizational psychology. She ran college track, played semi-pro soccer into her forties and now golfs. She is a certified mindfulness coach who works with university and pro athletes as well as leaders.

"The very first value of the Agile Manifesto is about people. And there's a reason for that. People make everything else work." — Tonya McCaulley-Slater

"I draw the framework out on a piece of paper with no labels and explain it. And they say, oh yeah, we love that. It's the same exact framework they said they didn't want." — Tonya McCaulley-Slater

"Anytime leadership changes, you have to get them to buy into the why so they understand the how. Otherwise they will dismantle the how, because they don't understand it." — Tonya McCaulley-Slater

"I can go into an organization and snap a process around them in six months. The question is, will the people do it? Will the culture support it?" — Tonya McCaulley-Slater

"The first ceremony I go to is the retrospective, because I can tell if they're a team just by attending their first one." — Tonya McCaulley-Slater

"You get ten seconds to wallow in your grief. Then we reset and make an intentional action toward the next step." — Tonya McCaulley-Slater

New here? Subscribe to The Meridian Point on YouTube and follow on LinkedIn. We go live every two weeks with the people reshaping how work gets done, then post the audio to Apple Podcasts and Spotify. If you build teams, lead change or coach people, this is your room.

Tonya coaches leaders and elite athletes on mental performance through MindDrive Performance. Reach out and keep the conversation going on agile, transformation and performance psychology.

Air Date: September 8,2026

Kumar Dattatreyan and his longtime collaborator Glenn Marshall sit down for a fireside chat that starts with a finding Kumar didn't expect. He ran the same product management cohort he's run several times. Same content, same kind of audience. The one new variable was a set of AI agents guiding the asynchronous work with Socratic questions. The AI-enabled group shifted their mindset faster, engaged deeper and produced better artifacts. People admitted to a bot what they wouldn't admit to a human instructor.

The conversation works through why that happens. Kumar's working theory is psychological safety: the bot behaves like a peer with a narrow base of knowledge, not a positional expert, so people engage with the fundamentals they're embarrassed to ask about. Glenn's first reaction was closer to terror, and he talks through how he reframed it as the ideal learning partner rather than a threat. The second half turns to the collapse of the iron triangle when one person can vibe-code an application in an afternoon, the rise of the two-slice team of one human and several agents, and the ROI and token-cost questions nobody can answer yet. The recording cut off on a technical glitch before Glenn and Kumar reached two threads they teased at the top: organizational ecosystems and the Strait of Hormuz. Those are queued for a future conversation.

The AI-enabled cohort experiment Kumar's team won work to train product managers, product owners, business analysts and product-adjacent roles. They convinced the client that a six-to-ten week cohort beats a two-day certification class. They built AI agents into the asynchronous portions to guide learners through ideation, the why behind the product, customer empathy, journey mapping, OKRs, their COBI variant (Customer Outcomes, Business Impact), and writing epics and stories. The AI didn't do the work. It interrogated, Socratic style, and pushed people to think.

Same course, one variable Kumar ran the identical course several times in 2025 without AI. The story maps and journey maps came back rudimentary. With the agents added, the same content produced deeper engagement and real artifacts. Within three weeks, the AI-enabled group's mindset had shifted toward discovery, iteration and hypothesis testing far more than the group that used the agents less.

Why psychological safety changes learning The bot doesn't judge, doesn't carry a title and doesn't hold a positional imbalance over the learner. Kumar's read is that people engage with a bot the way they'd engage with a trusted peer who happens to know a little more about one narrow thing. Glenn connected it to the ideal one-on-one teacher you could never afford to staff twenty times over.

Gamification on steroids, or something more Kumar pushed back on calling it gamification. No badges, no quizzes, no points. The payoff is the artifact appearing through collaboration with the agent. Glenn granted the label "gamification on steroids" while Kumar argued it goes past that into personalized coaching.

The listener bot and feedback at scale Part of the course used a listener bot that reads a submitted assignment, identifies the module and lesson, and returns feedback automatically. Some feedback came from Kumar and his colleagues, most from the agent. Learners couldn't tell the difference and took the feedback well either way. Because AI runs at a flat monthly rate rather than by the hour, that kind of one-on-one assessment scales in a way a human team can't match.

The iron triangle collapse and the two-slice team Anyone can vibe-code an application in an afternoon with very little budget. Cost, scope and budget stop behaving like real constraints. Kumar floated the shift from the two-pizza team to the two-slice team: one human as the master brain, augmented by five or six agents. The open questions are ROI, token cost and where humans still belong. He noted Microsoft limiting Claude Code use over token costs and a client moving cautiously toward AI for fear of runaway spend.

The five-year horizon Glenn split his outlook. The next couple of years look amazing and very useful. The next five look terrifying if the technology isn't managed well, and in his view it isn't being managed well so far. Kumar agreed the shape of future work is unclear, including which new jobs it creates.

Kumar: "The bot was better at getting these people to engage with the basics, the fundamentals that maybe they were embarrassed to admit that they didn't know."

Kumar: "It takes many sessions before the coachee will trust me enough to tell me things I think they might tell a bot in visit one."

Glenn: "Emotions by definition don't make sense." (on why people feel safer with a bot that records everything)

Kumar: "Instead of a two-pizza team, it's going to be a two-slice team. One human with several agents helping them do their work."

Kumar: "The human is still going to be the master brain, aided and augmented by agents around them in whatever they do."

Glenn: "It's amazing and terrifying. In five years, if we're not managing this properly, and so far we're not, this could very easily go to a very bad place."

Peter Merel (quoted by Glenn): "AI is going to do all jobs, all work, except for the stuff that we want to do ourselves."

Kumar's Work:

Referenced in the Episode:

Glenn Marshall is an XSCALE Business Agility coach and trainer based in Toronto. Reach him here:

Glenn Marshall is an agile coach, Certified Scrum Master and XSCALE Business Agility coach and trainer with a software architecture and development background. He's certified across Kanban, Disciplined Agile, LeSS, SAFe and Management 3.0, and trained as a professional coach. He's the creator of Next Level Co-Creation, a bottom-up transformation method, and a frequent Meridian Point collaborator on XSCALE, permaculture and ecosystem thinking.

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The Meridian Point with Kumar Dattatreyan

Gary Cohen spent twenty years building a delivery machine that shipped software on schedule almost every time. Customers liked the features. They showed up to every sprint review. Then a private equity board member asked one question that reframed his career: what additional revenue do we expect from all these features? The honest answer exposed a hard truth. The team had optimized for delivery instead of impact.

In this episode Gary and Kumar get into why building the right thing is harder than building the thing right. They cover embracing uncertainty as a strategy, continuous discovery, why red, yellow, green status meetings turn into theater, and where product management goes once AI makes the build cheap.

Gary Cohen is Principal Consultant at Practical Agility. He is an engineering and product leader who ran product and engineering groups in software product companies for two decades. He led an agile transformation for one of Google's customer support groups. Today he helps teams shift from optimizing delivery to practicing real product discovery, drawing on continuous discovery work and a hard-won view of how organizational change actually sticks.

Delivery is not the same as impact. You can ship efficiently and still fail to move revenue or usage. Language matters. Until someone asks about business outcomes, teams optimize for the schedule they are measured against. Uncertainty is an advantage. When everyone can execute, execution becomes a commodity. The edge goes to whoever reduces uncertainty fastest. Kill solutions early. The faster you invalidate a weak idea, the sooner you reach the one worth building. Status meetings can be redesigned. Swap "why are we not green" for "are we still convinced this is the right opportunity." Cheaper building raises the stakes on discovery, not lowers them. More competition means finding the valuable thing matters more than ever.

"We can ship stuff people don't want a lot faster." — Gary Cohen

"This is the beginning, when you just start working in an area, and it is when you know the least about it. So you are making a decision way too early." — Gary Cohen

"You get these watermelon projects, which are green on the outside but really red inside. It is essentially theater." — Gary Cohen

"It is that presence of uncertainty that creates the opportunity for differentiation in the marketplace." — Gary Cohen

"When something gets cheaper, people do more of it. So product discovery becomes more important, not less." — Gary Cohen

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#AIStrategy #CommunityBuilding #ProductManagement #Leadership #DigitalTransformation

Air Date: August 11, 2026

Episode Number: 177

Circle just went fully AI-native with a launch called Eclipse. Kumar Dattatreyan sits down with Rene Rosendahl, Head of Product for Email Hub and CRM at Circle, to unpack a decision most AI-native product teams don't make: pointing the AI at the admins and deliberately keeping it out of the member experience.

Rene walks through why content alone stopped being enough once AI made it cheap and abundant, and why community and human connection are what actually drive transformation. He explains where Circle applies AI (setup, moderation, retention, the operational drag that eats a community admin's day) and where it draws a hard line (member to member conversation, the part people showed up for). The conversation closes with how Rene runs product and engineering as a single seat instead of two, and how Circle keeps shipping fast without turning into a Frankenstein of one-off features.

From Consulting to Building Rene left ERP systems, project management, and agile coaching because consulting felt mercenary: teach a team something and move on, with no long-term stake in the outcome. Product management gave him ownership of a problem over time instead of a framework handed off and walked away from.

Content Versus Transformation Content is cheap now and getting cheaper, with AI generating or influencing much of it. Rene's argument: people don't want content, they want transformation, and reading a book or taking a solo course doesn't move anyone's life. A cohort going through the same journey together, with accountability built in, does.

Where Circle Draws the AI Line Circle's Eclipse launch is AI-native, but Rene aimed that AI at the admin side: community setup, pricing and membership structure, content moderation, onboarding, and the repetitive questions that eat an admin's day. The member side stays human, with AI limited to narrow, obvious assists like summarizing long threads.

Why "AI Slop" Kills Communities People come to a community for authenticity and connection. Rene's read: when AI starts running the conversation instead of assisting behind the scenes, members sense it and disengage. The line isn't "AI everywhere," it's AI where it removes drag and nowhere near the reason people joined.

One Seat, Not Two Rene fuses product and engineering into a single line of ownership at Circle. His take: agreement without alignment is surface level, it doesn't surface the conflict underneath. Owning both roles forces him to hold user value, technical feasibility, and design usability at the same time instead of negotiating them after the fact.

Shipping Fast Without Building a Frankenstein Circle's speed comes from a specific combination: leadership that blends technical, product, and design judgment, a shared architecture and design system across teams, a refusal to build one-off features for a single customer, and macro product cycles (Circle 3.0, Circle 4.0) that act as reset points before drift sets in.

Measuring Product Velocity as Impact Circle runs quarterly planning built around cross-team priorities, customer feedback, and usage data, not a rigid framework. For efforts that cut across the whole engineering org, like Eclipse, the quarter's plan gets built so every team is pointed at the same outcome at the same time.

Where a Community Leader Should Start with AI Rene's advice: use AI first to reduce admin: structure, pricing, and membership setup, plus the operational drag of moderation and onboarding. Then use it as a consultative thought partner for patterns and next moves. The human side of the community, the reason people join and stay, is not the place to automate.

Quotes pulled directly from the recorded interview. Flag for a final check against the aired recording before reuse in any other content.

"Content alone is not sufficient. Imagine you're reading a book, and you're walking away from that book, not applying anything. It's not necessarily making you or your life any better."

"What people are looking for is authenticity and connection, not more AI slop."

"If the robots were to take over a community, a place, I think people ultimately sense that and they will find less value or be so annoyed that they disengage from it."

"You can agree on any number of things, but if you're not aligned, the agreement is just surface level because it doesn't surface the conflict that may be underneath."

"We're not building one-off features just for one customer. That's probably the quickest road to a Frankenstein."

"It's almost like you're asking a high performing basketball team to describe the chemistry that they have in order to make it repeatable for other teams."

Circle:

Connect with Rene:

Rene Rosendahl is Head of Product for Email Hub and CRM at Circle, the all-in-one platform for community-led businesses. He came up through ERP systems development, project management, and agile coaching before moving into product leadership roles at Kajabi and SamCart, and now leads a role at Circle that fuses product and engineering into a single line of ownership. Based in Irvine, California.

Kumar Dattatreyan is co-founder of Agile Meridian and co-creator of The Disruptor Method™. The Meridian Point explores disruption and innovation through conversations with transformation leaders and entrepreneurs.

Connect: linkedin.com/in/coachkdat | Book a conversation: tidycal.com/coachkumar/30-minute-meeting

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Guest: Jeff Giesen
Air Date: July 28, 2026

Most disruption stories get told after the person makes it out the other side. Jeff Giesen is still in the middle of his, and that's what makes this conversation different.

Jeff spent his career as an engineering leader and agile transformation practitioner. He built and led teams of Release Train Engineers, taught SAFe courses, and drove enterprise-scale change at a company in the travel reservation space. Then COVID flattened airline travel, the layoffs started coming every six months to a year, and eventually his group got hit. The scaled agile transformation that had helped the company deliver started to look like overhead, and the roles went with it.

Instead of waiting for the next job to appear, Jeff went bigger on a real estate business he'd been building since 2011. He moved from small multifamily investing into ground-up townhome development, and he's now got eight units finishing in late August. Then the second disruption arrived: the Denver market softened, rates climbed, and units that were built to sell had to be converted to rentals while he waits for the numbers to work.

So Jeff is carrying two disruptions at once, a contracted tech job market and a softened real estate market, both tying up his capital and his direction. Kumar calls this space the trough of disruption, the gap between what was and what's next. This episode is an honest look at what that gap feels like from the inside, and what it teaches you that you can't see when everything's going smoothly.

Why Agile Didn't Save the Company Jeff's company ran a full scaled agile implementation, and it helped, but not enough to change the outcome. In a slow-moving business tied to the airlines, the transformation delivered improvements that weren't large or fast enough to justify the overhead once economic pressure hit. Kumar and Jeff both name the real lesson: transformation without clear outcomes measured along the way starts to look like cost, not value.

Transformation Is a Journey, Not an End State The two agree that transformation has no finish line. It's constant improvement, constant attention to the market and the product, deeply iterative. The failure mode isn't the framework. It's treating the framework as the goal.

From Side Hustle to Main Hustle Jeff bought his first rental in 2011 and managed it in his spare time. Watching scrape-and-build projects go up around his Denver duplex pulled at him for years. He couldn't act on it with a full-time job until he found a one-stop development partner who could source land, entitle it, design, and build. That relationship is what let a non-GC get into ground-up construction.

When Two Markets Shift at Once Denver softened. Rates shocked upward. Rate buy-downs on completed units didn't move them. Jeff's read is that it isn't only rates: prices are high, everything costs more, and the political and economic backdrop adds friction. The pivot has been to convert unsold builds into rentals to cover expenses while waiting for sale numbers to make sense.

Life in the Trough Jeff doesn't feel like the sky is falling, but he feels his hands are tied. His capital is locked in projects that haven't returned, so the commercial multifamily moves he wants to make are on hold. What the limbo has taught him is less tactical and more personal: trust, confidence, and managing fear before fear runs the show.

Applying What He Taught to His Own Situation The transformation principles don't transfer to construction management, since Jeff is intentionally hands-off there and defers to operators who know permitting, cities, and counties. What does transfer is relationship-building. He vetted his development partner through his real estate network, and those relationships have carried him through a stretch where builders are struggling.

The Job Search Reality Getting back into technology has been hard. AI-driven applicant tracking systems screen him out fast, and he hasn't cracked what they're looking for despite reworking resumes and cover letters with AI. His honest take: networking is still how experienced people actually find work, and he's lost roles to internal candidates the interviewers already knew.

Where He's Headed No polished answer. Jeff stays open: technology roles, a real estate tangent that uses his program management skills, maybe something he starts. He describes the discomfort of feeling like he's wandering rather than pointed in one direction, and he's holding the tension between focus and optionality on purpose.

Note: this was a live broadcast. Verify each quote against the final recording before reuse in blog or promo copy.

"I didn't quit the company. They quit me."

"I absolutely feel like I'm in limbo."

"What I liked about it is the creation."

"The investments you make sometimes don't return when you expect."

"I think there's more to it. I think the prices are high."

"These AI, ATS systems really pull you out real quickly, and I haven't mastered what they're looking for."

"I've mainly found jobs networking, and I think the networking is still huge."

"You can just really get yourself going if you'll allow it to."

Real estate and development work: giesenhouse.org

YouTube: Jeff shares content on real estate and his development journey (search "Jeff Giesen" on YouTube)

LinkedIn: Connect with Jeff Giesen on LinkedIn 

Jeff is open to hearing from anyone with questions on real estate, or from people who think he might be a fit for a technology or transformation role. If you're navigating your own trough, this is a good person to reach out to.

Kumar Dattatreyan is an ICF PCC executive coach, co-founder of Agile Meridian, and creator of The Disruptor Method™. The Meridian Point explores disruption and innovation through conversations with transformation leaders and entrepreneurs living through change.

Connect: LinkedIn | agilemeridian.com | disruptormethod.com

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Most organizations hired to scale Agile are solving the wrong problem. They add teams, stack committees, multiply hand-offs and approval gates, then wonder why delivery is slower than it was three years ago. Nawaz Butt has spent years inside some of Canada's largest organizations, including Canada Life, and his diagnosis runs counter to almost everything the transformation industry sells: the goal isn't more teams. It's fewer, right-sized, autonomous teams aligned to a purpose they actually own.

In this conversation, Nawaz and Kumar Dattatreyan cover what de-scaling actually means in practice, why Conway's Law predicts most of the complexity practitioners are hired to untangle, how psychological safety (not talent) is the real differentiator between high and low performing teams, and why the word "team" gets thrown around to describe groups of people who have never truly pulled for each other.

They also get into the future of coaching, the limits of AI in human-centered practice, and what ikigai has to do with leaving a mark on the world.

Conway's Law as a Diagnostic Tool Organizations design systems that mirror their communication structures. As companies grow, they add hierarchy, committees and approval gates. The result is a compounding dependency problem that no amount of scaling Agile will fix. De-scaling starts with simplifying decision-making, not multiplying teams.

What "Team" Actually Means People throw the word team around to describe groups sharing a Jira board. A real team is small, knows each member's skills and abilities, backs each other up without being asked, and pulls toward the same goal. Most large organizations have groups, not teams. That distinction matters for delivery.

The Formula One Model for Alignment Ferrari and Red Bull each have 1,200 to 1,500 people on their Formula One teams. The question isn't how many people, it's whether leadership has done enough work to define the goal so clearly that everyone, from aerodynamics to pit crew, knows their role in winning. Common purpose is the only thing that makes scale work.

De-scaling in Practice: Delegation Poker Moving teams from a "telling" stance to an "owning" stance is not a single move. Delegation poker maps the spectrum from directive to fully autonomous. The aim is always to move right, toward autonomy, but most teams start somewhere in the middle. The key is making the conversation about trust explicit.

Bring the Work to the Team Too often, people are moved to the work rather than the work coming to the team. Long-lived teams with fixed capacity and clear commitments need ownership over what they build. When that ownership is missing, virtual teams get stood up by borrowing capacity from existing teams, initiatives clash, branches can't merge, and nobody is tracking the true cost of the fragmentation.

Psychological Safety as the Prerequisite Google's Project Aristotle identified psychological safety as the single biggest predictor of high-performing teams, above talent, above technical skill, above everything else. Nawaz opens every coaching engagement by asking how much safety exists before asking anything about process.

When a Team Doesn't Need a Coach Nawaz walked into an engagement, spent two weeks with a self-organized, nimble team that had been without a manager for over a year and was thriving. He went back to leadership and told them the team didn't need him. His coaching hours are limited. The job is to create conditions for self-sufficiency, not to preserve the coaching role.

AI in Coaching: What the Machine Misses Kumar packaged his own coaching knowledge into an AI agent named E. Kumar for a client to use between sessions. Nawaz asked organizations what the AI got wrong and got surface-level answers about abbreviations and misspelled names. His deeper point: AI can't feel the tension in a room, see the rolling eyes, or sense when someone is shutting down. Those are the signals that coaching actually runs on.

The Future of Coaching Coaching as a title may fade. The function matters more than the label. The real shift Nawaz wants to see is coaching and mentoring baked into leadership competency models so that every leader develops people as a core part of the job, not as a nice-to-have.

"The aim has to be not to have more teams, but to have fewer teams in the organization."

"I don't see people as Agile or non-Agile. I meet the team where they are."

"This team doesn't need me. I went back to leadership and told them that. My job as a coach is not to preserve my role. It's to create conditions where people become self-organized and self-sufficient."

"If somebody says to person one in the morning that you are an a-hole, and to person two in the afternoon, and to person three in the evening, you know who the a-hole is. It's your reflection you see."

"AI doesn't have any feeling. It won't feel the tension in the room. It won't see the visual cues. At least not yet."

"Do what you like to do. Or at least do more of it if you can't do only that."

LinkedIn: https://www.linkedin.com/in/nawazbutt/ Nawaz runs an Agile and leadership meetup group in Toronto. Connect with him on LinkedIn to follow his thinking on organizational design, de-scaling and people-first coaching.

LinkedIn: https://www.linkedin.com/in/coachkdat/

Book a strategy call: https://tidycal.com/coachkumar/30-minute-meeting

Learn more about Agile Meridian: https://www.agilemeridian.com

Learn more about The Disruptor Method™: https://thedisruptormethod.com/quiz

Nawaz Butt is an Agile coach and transformation practitioner based in Toronto, Canada. He works inside large organizations helping leadership teams navigate complexity without losing their people in the process. He currently serves as Program Steward at Canada Life, one of Canada's largest insurance providers. He runs a long-standing Agile and leadership meetup group with over 5,000 followers and brings a people-first, framework-agnostic approach to every coaching engagement.

Kumar Dattatreyan is an ICF PCC executive coach and co-founder of Agile Meridian. He is co-creator of The Disruptor Method™, a framework that helps leadership teams disrupt themselves to drive organizational transformation. The Meridian Point explores disruption, innovation and the leadership it takes to navigate both.

Spotify | Apple Podcasts | YouTube | LinkedIn New episodes broadcast live every other Tuesday at 12:30 PM Eastern. Leave a review if this episode added value.

#DescalingAgile #OrganizationalDesign #AgileCoaching #LeadershipDevelopment #TeamDynamics #ConwaysLaw #BusinessAgility #DeliveryExcellence #ExecutiveCoaching #TheMeridianPoint #Disruption #Innovation #FutureOfWork #PsychologicalSafety #AgileTransformation

Guest: Ashwini Kumar, Product Manager & AI/ML Practitioner

Everyone thinks they understand AI now. Ashwini Kumar has been doing the actual work for six years, long before ChatGPT turned every executive into a self-proclaimed AI strategist. From manufacturing and energy to healthcare consulting and major telecom, he's been in the room when enterprises placed their biggest AI bets and watched those bets play out in real time.

In this conversation, Ashwini unpacks the patterns he keeps seeing: executives who treat AI as a pedestal project for their resume, organizations that automate broken human workflows instead of rethinking them, and the persistent gap between what companies hope AI will do and what it actually delivers. He also walks through his team's hands-on experience building an agentic RAG system for proposal generation that collapsed a two-week drafting cycle into two days with a four-person team. The conversation covers why machine learning was solving real problems long before LLMs arrived, where change management breaks AI adoption, and what a C-suite leader should actually ask before building an AI strategy.

Machine Learning Before the Hype Ashwini describes ChatGPT as "the front end of AI," something anyone can talk to. But machine learning was already powering Netflix recommendations, optimizing roof tile manufacturing recipes, and running CFD wing design simulations. The work was happening. It just wasn't visible to the public.

The Executive Pedestal Problem AI becomes a pet project for some executives. They push implementation so hard that they stop asking whether it's solving actual problems. The investment gets so large that the project "cannot fail," and when it does underperform, the numbers get fudged to protect the narrative.

Automating Broken Workflows Companies make the mistake of layering AI on top of existing human processes without examining whether those processes make sense. A human might jump between six systems to gather information. An automation doesn't need those same steps. Rethinking the workflow before applying AI is where most organizations skip the most critical step.

Agentic RAG vs. Standard LLM Prompting Ashwini's team built a proposal-generation tool using agentic RAG that produces a first draft in one to two days. In a standard LLM like Claude, you'd still need to prompt section by section. With properly designed agents handling the work, the system generates a complete proposal in one run with minimal follow-up prompting. For consulting companies where proposals typically take two weeks (or a month for government work), the time savings translate directly to more proposals submitted and more potential revenue.

The 20/60/20 Adoption Rule When you implement AI in an organization, roughly 20 percent of people are enthusiastic and jump in immediately. The middle 60 percent are indifferent, using it occasionally. The bottom 20 percent want nothing to do with it. Without engagement from that broader population, implementation goes flat and ROI disappears.

Start with Vision, Not Strategy When a C-suite executive asks for an AI strategy, Ashwini's first move is product thinking: what is your AI vision? What do you see it doing? Some executives are lofty. Some are spot on. Some don't have an answer at all. You can't build a strategy until you know where they actually stand.

The Hope-Reality Gap Ashwini's closing observation: there's still a gap between the reality of AI and the hope of AI. The reality will keep moving forward. But there will always be something on the horizon that it doesn't do yet.

"ChatGPT is sort of the front end of AI. Something that anybody can talk to."

"It becomes sort of a pet thing for some executives. Like, oh, it becomes sort of a thing that they can put on their pedestal. They push it sometimes so hard that they don't think about what problems it's solving."

"You shouldn't apply an AI process as a blanket way to solve what you're doing as a human workflow."

"A human might be jumping around in different systems to get information. An automation doesn't need to jump around. Well, it does jump around, but much faster."

"When we put the agents in and we did the agents correctly, it made a huge difference. We're able to generate the proposal in one run."

"There's a top twenty percent of people that are just all about it. And then you have the middle which is indifferent. And the bottom that doesn't want anything to do with it."

"The reality of AI and the hope of AI, there's still a gap there."

"Everybody believes AI is a magic bullet, but it's not."

Connect with Ashwini Kumar:

LinkedIn: linkedin.com/in/shawnakumar

Technologies & Concepts Referenced:

Ashwini Kumar is a product manager and AI/ML practitioner with six years of experience implementing machine learning and AI solutions across manufacturing, energy, healthcare consulting, and telecommunications. He specializes in identifying where AI can genuinely improve enterprise workflows versus where it's being applied as a superficial fix. His current work focuses on agentic RAG systems for knowledge-intensive business processes like proposal generation.

Kumar Dattatreyan is co-founder of Agile Meridian and co-creator of The Disruptor Method. The Meridian Point explores disruption and transformation through conversations with leaders, practitioners, and entrepreneurs who are shaping how organizations think and operate.

Connect: LinkedIn | agilemeridian.com | thedisruptormethod.com

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#EnterpriseAI #AIStrategy #MachineLearning #AgenticRAG #ChangeManagement

Guest: James Taylor, Founder and Executive Partner of Blue Polaris (formerly Decision Management Solutions)

Host: Kumar Dattatreyan

Episode Date: June 16, 2026 Watch on YouTube

Why You Need to Listen to This Episode If you have sat through one more AI strategy meeting that ended with "we just need better data," this episode is for you.

James Taylor has been in the room for those meetings for twenty years. He helped create the decision management category at FICO. He co-submitted the DMN industry standard. He has watched companies pour money into predictive analytics, then machine learning, then generative AI, and watched most of those investments produce demos that impress executives and change almost nothing about how the business actually runs. He has a pretty clear idea of why.

It is not the technology. It is not the data. It is the question nobody is asking before the work starts: which decisions are we actually trying to improve?

In this conversation, James walks through the discipline he has spent his career building. Why decisions belong outside the process, not buried inside it. Why a language model alone cannot run a regulated business decision. Why "explainable AI" does not actually explain anything a regulator will accept. And the pattern he sees working in the wild, where AI handles the messy parts on either end and prescriptive logic handles the part that has to be auditable. By the end of it, you will probably start noticing missing decisions in your own organization that you have been walking past for years.

What You Will Learn in This Episode The Difference Between a Process and a Decision (And Why It Matters More Than You Think) Most teams build their systems as processes with decisions hidden inside as steps. James makes the case that this is the single biggest source of friction in enterprise software. Processes change slowly. They have lots of owners. They require retraining when they change. Decisions change all the time, often have one owner, and need to flex with the market. When you smear them together, every small change becomes a big project. When you separate them, the process gets dramatically simpler and the business gets dramatically more agile.

The California DMV Example That Settles the Whole Argument James drops one of those examples that you will use in your own conversations for years. The California DMV has had the same process for licensing vehicles since the 1950s. They send you a bill, you send them a check, they send you a sticker. Nothing about that has changed. What changes constantly is the math underneath, because politicians tinker with the fees every legislative session. Same process for seventy years. Different decision every year. This is what decision management is actually about.

The Four Conditions a Decision Has to Meet Before You Should Even Try to Automate It Not everything is a candidate for automation. James walks through the criteria he uses to figure out which decisions are worth the work: the timing has to be predictable, the decision has to be made often enough to justify the effort, the inputs have to be reliably available, and the outputs have to be bounded. If any of those are missing, the decision either stays with a human or needs to be redesigned before you can automate it. This is the filter most AI initiatives skip, and it is why so many of them end up in the slide-deck graveyard.

Why Explainable AI Does Not Actually Explain Anything This is the part of the conversation that should make every executive sit up. James was direct: explainable AI does not know how the model decided. It generates a plausible-sounding story about how the model might have decided. Regulators have figured this out. The CFPB has already said the rules do not change just because you used AI. Canada has legislation that requires you to be able to tell any affected customer exactly how a decision about them was made. A chatbot answer is not going to satisfy that. You need actual structure underneath.

The Architecture That Actually Works in Regulated Industries Here is where the episode flips the typical AI narrative on its head. James describes the pattern he sees succeeding at his clients. AI ingests the messy input — emails, attached documents, unstructured forms. A prescriptive decision engine runs the actual logic against that data. AI generates the explanation back to the customer or call center rep in plain language. To the user it feels like a chatbot. Under the covers, every step is logged, auditable, and defensible. You get the speed of AI and the accountability of a traditional system. This is not theoretical. This is what is shipping right now.

Why the Bottleneck Has Quietly Moved From Data to Decisions There is a moment in the conversation where James says something most AI buyers have not caught up to yet. The old constraint was that you had to digitize and clean your data before you could do anything useful with it. Large language models have largely solved that. They are good at reading documents. The new constraint is upstream of the data. If you do not know what you need to extract from those documents in the first place, the fact that AI can extract anything does not help you. The discipline that tells you what to look for is decision modeling. It used to feel optional. It is now the thing.

Why the Hardest Part of This Entire Field Is the Word "Decision" The closing exchange is the one that will stay with you. James explains that the single biggest obstacle to building this discipline inside organizations is that everyone already thinks they know what a decision is. Executives hear the word and picture strategic choices they make in boardrooms. James means the thousands of small operational decisions their systems make every day, often badly, often invisibly. The website that fails to recognize a returning user. The claim that goes to manual review when it did not need to. The loan that gets declined for a reason nobody can articulate. These are the decisions where the value is, and they are the ones nobody is paying attention to.

"It used to be for sure you had to digitize the data first before you could do anything else. And now large language models are really good at reading documents. So now what matters is do you know what data you need out of the documents?"

"Even if you look at explainable AI, mostly what it does is it comes up with a plausible explanation of how it might have decided. It doesn't really know how it decided."

"The people we see basically flailing around are the ones who are like, I'm going to throw the baby out with the bath water and start again with large language models. And we're like, why would you do that? You already know a lot."

"It's the D word, decision. On the one hand, everyone knows what decisions mean. The problem is that everyone knows what decisions mean. They think I mean the decisions they make. And generally I don't."

Connect with James Taylor LinkedIn: https://www.linkedin.com/in/jamestaylor/

Blog (JT on EDM): https://jtonedm.com

Blue Polaris: https://bluepolaris.com

Digital Decisioning: Using Decision Management to Deliver Business Impact from AI (MK Press, 2019)

Real-World Decision Modeling with DMN, with Jan Purchase (MK Press, 2016)

Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics

Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions, with Neil Raden

Take the Disruptor Method Quiz Are you disrupting or about to be disrupted? Find out in under five minutes: https://www.thedisruptormethod.com/quiz

Work with Kumar Book a 30-minute conversation: https://tidycal.com/coachkumar/30-minute-meeting