Podcast
The CTO Show with Mehmet Gonullu
Hosted by Mehmet Gonullu · EN
Broadcasting from Dubai, The CTO Show with Mehmet explores the latest trends in technology, startups, and venture funding. Host Mehmet Gonullu leads insightful discussions with thought leaders, innovators, and entrepreneurs from diverse industries. From emerging technologies to startup investment strategies, the show provides a balanced view on navigating the evolving landscape of business and tech, helping listeners understand their profound impact on our world.
mehmet@yassiventures.com
56episodes
Episodes
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#614 AI Can Replace Tasks, But Still Can’t Replace Judgment | Dilip Chetan
4w ago00:41:04Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Dilip Chetan, founder of DefensibleZone.ai. Dilip brings more than two decades of experience across Google, Meta, Oracle, Salesforce, and Intuit, spanning engineering, product strategy, human factors, and customer research.The conversation challenges the assumption that AI adoption is primarily a technology deployment or workforce reduction exercise. AI can automate tasks, write code, analyze data, and operate agents, but it still struggles with accountability, context switching, taste, and the human judgment hidden inside job descriptions.If you are leading enterprise AI adoption, restructuring technical teams, deploying autonomous agents, or investing in AI-enabled companies, this conversation provides a clearer way to separate useful automation from organizational risk.About the GuestDilip Chetan is the founder of DefensibleZone.ai, where he is developing a framework to help professionals and organizations identify capabilities that remain valuable as AI expands into more areas of work.He has more than 20 years of technology experience across Google, Meta, Oracle, Salesforce, and Intuit. His background includes engineering, product management, product strategy, user research, customer analysis, and human factors.His Defensible Zone framework focuses on the intersection of natural affinity, market demand, and the areas AI has not yet reached. The framework is designed to move the discussion beyond which tasks can be automated and toward which human qualities remain essential.LinkedIn: https://www.linkedin.com/in/dilipchetan/Website: https://defensiblezone.aiPersonal website: https://dilipchetan.comKey TakeawaysAI can replace tasks without replacing the judgment that makes those tasks valuable.Workforce reduction is the wrong starting point for enterprise AI adoption.Job descriptions must change before AI can genuinely free people for higher-value work.Human value extends beyond skills into context, accountability, taste, and judgment.The more accountability a decision carries, the less autonomy an AI agent should receive.Too little context makes AI invent answers, while too much context can reduce its effectiveness.Metrics become dangerous when companies measure activity without connecting it to business purpose.A defensible career depends on understanding natural affinity before evaluating market demand or AI exposure.Episode Highlights00:00 — Dilip Chetan’s path across major technology companies05:00 — AI adoption requires organizational redesign, not software deployment07:00 — Workforce replacement is the wrong AI objective09:30 — The Defensible Zone separates value from automation12:30 — Human qualities matter more than task inventories14:30 — Autonomous agents create value and accountability risk18:30 — Effective AI use depends on controlled context21:30 — Judgment can be measured only in parts25:30 — AI metrics must follow the company’s purpose31:00 — Leaders need vision beyond AI adoption35:30 — Natural affinity starts with serious self-examination39:00 — Where to find Defensible Zone resourcesListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#613 Innovation Is Not the Bottleneck. Commercialization Is | Roland Austrup
Jul 1000:45:04Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Roland Austrup, Chief Growth Officer at Innventure. Large companies produce valuable technologies, but they are rarely structured to build new businesses around them.The conversation reframes innovation as only the first stage of value creation. A technology can work, address a real market need, and still fail because productization, leadership, supply chains, financing, and market adoption were treated as secondary concerns. Roland explains why commercialization requires a repeatable operating process, not simply a stronger invention or a larger R&D budget.If you are leading corporate technology, building industrial companies, or investing in AI infrastructure and deep technology, this conversation clarifies where technical promise ends and company-building risk begins.About the GuestRoland Austrup is the Chief Growth Officer at Innventure, a public company that creates and operates businesses built around technologies developed by large multinational corporations.His background includes currency trading, founding an asset management company, helping finance PureCycle Technologies, and supporting its public listing in 2021. At Innventure, he works across company creation, capital strategy, industrial technology commercialization, and portfolio growth.His experience sits directly at the boundary between proven corporate R&D and the operating work required to turn it into an independent company.LinkedIn: https://www.linkedin.com/in/roland-austrup-0874825/Website: https://www.innventure.comKey TakeawaysLarge companies are built to improve existing businesses, not create new ones from zero.A working technology is not a business until someone can productize, finance, and distribute it.The first test of a corporate technology is the size and urgency of the unmet market need.Technical validation reduces invention risk but leaves scaling, adoption, and execution risks intact.A strong economic value proposition matters more than whether a product is merely desirable.Capital strategy is part of company building, not an administrative step after product development.AI infrastructure may create more defensible value than easily replicated software applications.What You Will LearnThe four evaluation gates Innventure uses before creating a company around corporate technology.Why entrepreneurial company creation requires a different skill set from corporate R&D.How market need, technical readiness, operating costs, and margins shape commercialization decisions.The reasons capital planning must begin before a new company enters the market.How multinational corporations can serve as technology sources, customers, and distribution channels.Why AI growth creates opportunities in cooling, power, grid infrastructure, and industrial systems.Episode Highlights00:00 — Corporate invention requires a separate company-building capability04:00 — Large companies rarely start effectively from zero06:00 — Market need is the first commercialization gate08:00 — Technical validation does not eliminate scaling risk13:00 — Company creation fails when one capability is missing16:00 — Capital strategy is an operating requirement17:00 — AI infrastructure supplies the picks and shovels22:00 — Real AI exposure differs from borrowed language25:00 — Proven technology makes execution more predictable27:00 — Company creation can become a repeatable process30:00 — CTOs need external commercialization pathways35:00 — AI demand creates downstream industrial opportunities41:00 — The enabling layer may hold greater value42:00 — Operators create businesses, not investors aloneListen NowAvailable on all major podcast platforms and YouTubeConnect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#612 The Next EV Race Is on the Water | Alexandre Mongeon
Jul 300:33:36Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Alexandre Mongeon, CEO and Co-Founder of Vision Marine Technologies. Alex is building electric marine propulsion systems for family boats, commercial use cases, and future autonomous vessels. The conversation frames marine electrification as the next EV market after cars.The episode reframes electric boats as a commercialization problem, not only a battery or engineering problem. Alex explains why range, charging, customer behavior, third-party validation, rental data, and dealer distribution all decide whether hard tech becomes a real market. The signal is clear: the technology can work years before the market is ready to buy it.If you are building, investing in, or operating in EV infrastructure, hard tech, mobility, or climate-related industrial markets, this conversation shows what adoption looks like when the product is physical, expensive, regulated, and unfamiliar to buyers.About the GuestAlexandre Mongeon is the CEO and Co-Founder of Vision Marine Technologies, a company focused on electric marine propulsion and electric boat systems. He has spent more than a decade working on maritime electrification, including high-performance electric boats, OEM integrations, rental operations, and dealer distribution.Alex brings an operator’s view of how hard tech moves from prototype to commercial demand, with lessons from boat racing, McLaren Engineering validation, customer rentals, manufacturer integrations, and public-market investor conversations.LinkedIn: https://www.linkedin.com/in/alexandre-mongeon-a57354114/Website: https://visionmarinetechnologies.com/Retail and rentals: https://www.nauticalventures.com/Key TakeawaysElectric boats are not waiting for invention, they are waiting for market education.Boat range became measurable only after electric systems forced better data.Hard tech credibility depends on third-party validation, not founder conviction.Rental operations gave Vision Marine real customer behavior data before scale.The European marine EV market is more mature than the US market.Distribution can matter more than OEM adoption when large manufacturers move slowly.Investors understand physical technology faster when they experience the product directly.Autonomous electric vessels may become a larger commercial market than recreational boats.What You Will LearnThe reason marine electrification is following the EV car market with a delay.How customer education becomes the main constraint after the technology works.Why range anxiety in boats is different from range anxiety in cars.How rental data helped Vision Marine understand real boating behavior.The role third-party engineering validation played in building market credibility.Why Europe may adopt electric boats faster than the US.What commercial and government use cases could change the electric marine market.Episode Highlights00:00 - Electric boats move beyond a niche category01:30 - Boat racing exposed the cost of combustion05:30 - Performance stopped being the right target07:30 - Customer education becomes the main constraint11:00 - Family boats become the core market13:30 - OEM integrations reduce adoption friction16:30 - AI starts with range and usage data20:30 - Credibility comes from validation and rentals24:00 - Europe is ahead in marine electrification29:30 - Autonomous vessels open commercial demandListen NowAvailable on all major podcast platforms and YouTube.
Transcribe →#611 You Are Probably the Bottleneck And AI Won’t Change That | Jordan Solender
Jun 2900:40:36Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Jordan Solender, founder of Jordan Solender Coaching and IT Select. The central tension is simple: if every decision still depends on the founder, AI will not fix the business.Jordan argues that most founders are not resource constrained, they are clarity constrained. The conversation reframes AI from a shortcut into a leverage layer that only works when outcomes, SOPs, KPIs, and ownership are clear. Instead of chasing tools, models, and agents, Jordan makes the case for removing the founder from one repeatable process at a time.If you are building, investing in, or operating a founder-led company, this conversation gives you a practical lens for spotting bottlenecks before they become the operating model.About the GuestJordan Solender is the founder of Jordan Solender Coaching and IT Select. He is an investor, entrepreneur, operator, and founder coach focused on helping business owners remove themselves as the bottleneck in their own companies.His work sits at the intersection of AI, delegation, systems, SOPs, and founder operating models. He is the creator of the 10/80/10 Rule, a framework for maintaining accountability without micromanagement.LinkedIn: https://www.linkedin.com/in/jordansolender/Website: https://jordansolender.comCoaching: https://jordansolendercoaching.comKey TakeawaysAI will not fix a company that depends on the founder for every decision.The behaviors that help founders start companies often limit them later.Delegation starts with documentation, not hiring.Most founders are clarity constrained before they are resource constrained.A delegated task usually fails because the system is unclear, not because the person failed.Founders need visibility into execution, not control over every step.AI agents amplify documented systems and accelerate messy ones.Persistence commits to the outcome, while stubbornness commits to the method.What You Will LearnThe early signs that a founder has become the company bottleneck.How to identify one repeatable task that should no longer depend on you.Why SOPs make delegation possible before headcount increases.How the 10/80/10 Rule creates accountability without micromanagement.What AI can handle inside documented business processes.Why small teams can operate with more leverage when systems are clear.When founder persistence becomes ego and blocks company growth.Episode Highlights00:00 — Founders often become the hidden constraint02:30 — Scale starts when founders stop deciding everything05:00 — Every approval path reveals the bottleneck08:30 — Delegation starts before the first hire10:00 — Clarity determines what can be delegated12:00 — Good delegation is measured by outcomes13:30 — The 10/80/10 Rule reduces micromanagement16:30 — AI works best inside documented systems18:30 — Chaos cannot be automated by agents21:00 — Tool choice follows the business bottleneck25:00 — AI can remove inbox and coordination drag27:30 — Flatter companies still need stronger leadership31:00 — Uncoachable founders blame everything outside themselves34:00 — Persistence and stubbornness are not the same36:30 — AI yes-men can amplify founder ego38:30 — Jordan shares where listeners can find himListen NowAvailable on all major podcast platforms and YouTubeConnect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#610 Your POS Should Run the Business. Not Just Take Payments | Ahmed Sameh
Jun 2600:41:02Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Ahmed Sameh, CMO at Fortis. Ahmed brings a fintech and B2B marketing view on how SMEs are changing the way they run daily operations.The conversation reframes POS as more than a payment terminal. For small businesses, the real constraint is not accepting cards, it is connecting payments, inventory, customer data, loyalty, invoicing, reporting, and AI into one operational system. Ahmed explains why adding more tools often creates more manual work, more blind spots, and weaker decisions.If you are building, investing in, or operating in fintech, SME software, retail technology, or AI-enabled business operations, this conversation shows why the transaction layer is becoming the control point for business intelligence.About the GuestAhmed Sameh is the CMO at Fortis, a software company focused on helping SMEs manage payments and day-to-day operations.Ahmed has more than 14 years of marketing experience, mainly across B2B and fintech. His background includes work connected to Tap Payments, Mastercard, FAB, and startup launches in the region.He is the right person to frame this topic because Fortis sits at the point where payments, merchant operations, customer data, and AI reporting meet.LinkedIn: https://www.linkedin.com/in/ahmed-samehfa/Fortis: https://wefortis.com/Key TakeawaysPOS is becoming the operating layer for SMEs, not just a payment device.Small businesses lose margin when transactions and inventory are tracked manually.More software does not create efficiency when systems remain disconnected.Customer data becomes useful only when it is tied to actual transactions.AI reporting depends on clean business data before it can support decisions.WhatsApp commerce creates operational blind spots when orders are not captured properly.E-invoicing will push SMEs toward more structured digital operations.SMEs need simplification before they need more tools.What You Will LearnHow POS systems are evolving from card machines into business operating platforms.Why traditional payment terminals leave major gaps in customer and inventory data.The operational cost of running SMEs through spreadsheets, paper, WhatsApp, and separate tools.How customer transaction data can support loyalty, offers, and repeat business.Why AI for SMEs starts with structured payments, inventory, and customer records.What e-invoicing means for SME digitization in the UAE.When a small business should choose simplification over another software subscription.Episode Highlights00:00 — Ahmed Sameh frames Fortis and SME operations02:00 — SMEs still run critical work manually06:00 — Traditional POS leaves operational gaps10:00 — One terminal can shorten service workflows13:30 — UAE digitization is forcing SME readiness18:00 — WhatsApp commerce creates hidden operational risk22:30 — More tools often create less efficiency26:00 — Mobile-first SMEs need connected systems29:30 — AI needs transaction data before prompts33:30 — Agents move into reporting and inventory36:30 — SME growth depends on usable operational data39:00 — Fortis focuses first on the UAE marketListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#609 AI Can Assess Leaders. It Shouldn’t Replace Judgment | Logan Yonavjak
Jun 2200:51:31Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Logan Yonavjak, Co-Founder and CEO of Founder Readiness Engine. Logan brings an investor and operator view into how founders and senior leaders can be assessed beyond resumes, charisma, and gut feel.The conversation reframes leadership assessment as a decision system, not a personality test. Logan explains how transcript data, developmental psychology, quantitative linguistics, and AI can surface signals such as coachability, identity flexibility, strategic complexity, relational intelligence, and resilience. The key tension is clear: AI can improve how leaders are assessed, but humans should not hand over agency to the machine.If you are investing in founders, hiring senior leaders, building leadership teams, or evaluating startup risk, this conversation gives you a sharper way to think about people analytics, founder readiness, and AI-assisted decision-making.About the GuestLogan Yonavjak is the Co-Founder and CEO of Founder Readiness Engine. She is an impact investor turned entrepreneur with experience across private equity, university endowments, farmland investing platforms, sustainable investing, and early-stage technology.She teamed up with a data scientist and psychologist to build a platform that analyzes transcript data and identifies leadership readiness markers. Her work focuses on how founders, senior leaders, investors, and organizations can make better decisions about people under pressure and complexity.LinkedIn: https://www.linkedin.com/in/loganyonavjak/Website: https://www.readinessengine.io/Key TakeawaysAI can assess leadership readiness, but it should not replace human judgment.Founder evaluation still depends too heavily on gut feel, charisma, and warm references.Coachability and identity flexibility are critical signals for founder growth.Traditional assessments often miss how leaders develop under pressure and complexity.Strategic complexity shows up in how leaders hold multiple perspectives at once.Resilience is not a trait alone, it is a system leaders build around themselves.Relational intelligence can offset blind spots in highly technical or visionary founders.People analytics may become a stronger diligence layer for investors and operators.What You Will LearnHow AI can analyze transcript data to identify leadership readiness signals.Why coachability may matter more than credentials in founder evaluation.The limits of traditional assessments such as MBTI, DiSC, StrengthsFinder, and Predictive Index.How strategic complexity appears in the way leaders explain systems and tradeoffs.Why human agency must remain central when AI supports hiring or promotion decisions.What investors often miss when they rely on pattern matching and warm references.How leadership assessment could become part of due diligence, hiring, and lending decisions.Episode Highlights00:00 — Founder readiness becomes the central question05:00 — Traditional assessments miss developmental trajectory09:00 — Negative space reveals what leaders avoid13:00 — AI should augment, not replace judgment20:00 — Coachability becomes the strongest founder signal23:00 — Relational intelligence offsets leadership blind spots29:00 — Strategic complexity appears in language patterns31:00 — Resilience depends on systems under stress35:00 — Leadership data could reshape lending decisions39:00 — VC still relies heavily on gut checks43:00 — AI can model a stronger second brain46:00 — Technology can uncover human blind spotsListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#608 AI Won’t Replace Therapists. It May Replace Guesswork | Dr. Steve Rondeau
Jun 1900:54:54Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Dr. Steve Rondeau of AxonEG Solutions. Dr. Steve brings more than two decades of work across developmental medicine, EEG brain scans, biomarkers, and mental health diagnostics. The core tension is clear: mental health has too often treated labels as answers, while the brain may be telling a different story.The conversation reframes AI in healthcare as a decision-support layer, not a replacement for clinicians. Dr. Steve explains why two people with the same diagnosis can respond completely differently to treatment, how a database of more than 50,000 brain scans changes the conversation, and why objective biological data can reduce trial and error in care. The episode also connects AI, explainability, human judgment, and empathy in a field where the cost of guessing can be very high.If you are building, investing in, or leading in AI, healthcare technology, digital health, or human performance, this conversation shows where data can improve decisions without removing the human from the loop.About the GuestDr. Steve Rondeau is with AxonEG Solutions, where his work focuses on EEG brain scans, biological markers, and objective data in mental health diagnostics. He is also the author of Think Like a Brain, a book focused on helping people understand brain patterns, treatment response, and why labels alone do not explain the full picture.His work is built around a database of more than 50,000 brain scans and a central question: why two people with the same mental health diagnosis can respond so differently to treatment.LinkedIn: https://www.linkedin.com/in/dr-steven-rondeau-148aa421/Website: https://thinklikeabrain.comKey TakeawaysMental health labels describe suffering, but they often fail to predict treatment outcomes.AI can support clinicians by narrowing options, not by replacing human judgment.A single diagnosis can hide thousands of possible biological patterns.Objective brain data can reveal treatment paths that symptom labels may miss.The DSM helps clinicians communicate, but it does not explain each patient’s biology.Human-in-the-loop AI matters most when decisions involve context, culture, and empathy.Personalized mental health requires testing the organ being treated.Psychedelic and neuromodulation treatments need better prediction before wider adoption.What You Will LearnThe reason symptom-based diagnosis can miss the biological drivers behind treatment response.How EEG brain scans can add objective data to mental health decisions.Why two patients with the same diagnosis may need completely different treatments.The role AI can play in connecting biomarkers, clinical data, and published research.How human judgment remains essential when algorithms recommend clinical paths.Why treatment prediction matters for psychedelics, ketamine, and neuromodulation.What personalized medicine looks like when the brain is measured directly.Episode Highlights00:00 — Why mental health needs better data02:30 — Diagnosis describes symptoms, not treatment outcomes07:30 — Building a 50,000 brain scan database12:30 — One diagnosis can hide thousands of patterns16:30 — A brain scan challenged the symptom label20:00 — Brain data can open harder conversations23:30 — Biology and environment shape the same brain28:30 — AI supports clinicians, not replaces them31:30 — Predicting who responds before treatment starts35:00 — Psychedelics need better patient selection40:00 — Mental health should test the organ it treats45:30 — Adoption depends on validation, funding, and trust52:30 — Where to find Dr. Steve RondeauListen NowAvailable on all major podcast platforms and YouTubeConnect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#607 More AI Won’t Help. Better Processes Will | Lara Hamilton
Jun 1500:37:23Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Lara Hamilton, a technology leader at HelpDesk Realty. The conversation focuses on why more AI will not help companies that have not fixed their processes first.Lara reframes AI adoption as an operations problem rather than a technology problem. The discussion moves from property management and paperless workflows to AI agents, security, documentation, and the practical friction that slows teams down. The core argument is clear: AI can save time, but only when the business knows how the work actually gets done.If you are leading IT, operating a growing business, investing in enterprise technology, or evaluating AI projects, this conversation gives a grounded view of where automation works and where it breaks.About the GuestLara Hamilton is a technology leader at HelpDesk Realty, where she works across IT operations, support, property technology, and compliance.Her background includes banking operations, process improvement, help desk services, property management systems, cybersecurity practices, and practical AI adoption.Lara brings an operator’s view of AI because she works with the systems, users, reports, tickets, and workflows that determine whether technology succeeds or fails.LinkedIn: https://www.linkedin.com/in/larahamilton-multifamilyit/Website: https://www.teamtectonic.com/divisions/helpdesk-realtyKey TakeawaysAI does not fix broken processes, it depends on them being clear first.Undocumented work becomes a major risk when companies try to automate it.Operational friction is often where AI produces the clearest return.Small daily tasks can create large productivity losses when repeated across teams.AI agents can block support when they replace human escalation paths.Security controls fail when users experience them as constant friction.Multi-factor authentication remains unpopular, but it is still necessary.Human knowledge inside teams cannot be replaced by tools alone.What You Will LearnHow missing process documentation weakens AI adoption.Why AI projects fail when leaders start with tools instead of workflows.The specific types of operational friction that automation can remove.How ticketing data and reporting tasks can become practical AI use cases.Why AI agents still need human escalation paths.When security controls improve protection without hurting productivity.What property management can teach broader enterprise teams about digital adoption.Episode Highlights00:00: Lara Hamilton’s path from banking operations to IT02:00: Repetition creates the strongest case for automation04:00: Property management still carries manual process debt05:30: Paperless workflows expose resistance to operational change08:30: IT leaders must translate vision into execution09:30: Undocumented processes block better technology outcomes12:00: AI depends on the foundation beneath it16:30: Small AI use cases can return hours weekly22:00: AI agents can break support escalation paths25:30: Security must balance protection with user behavior28:00: Digital payments changed property operations after COVID31:00: Strong IT teams share knowledge across skill sets33:30: Learning compounds into institutional knowledgeListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#606 AI Can Generate Code. It Still Can’t Replace Engineering Judgment | Jason Li
Jun 1200:45:13Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Jason Li, CTO at Laurel. Jason brings experience from enterprise software, Salesforce, Ironclad, and AI-native product development.The conversation reframes AI adoption away from replacing work and toward understanding work. Faster code generation does not eliminate engineering bottlenecks. Quality, technical debt, review processes, and organizational design are becoming the limiting factors.If you are leading engineering teams, building AI products, or investing in enterprise software, this conversation provides a practical view of how AI is changing software development and technical leadership.About the GuestJason Li is the CTO at Laurel, an AI company focused on time intelligence and productivity. Previously, he worked in enterprise software and held roles at Salesforce and Ironclad.His work spans AI-native products, developer productivity, legal technology, and engineering leadership.His perspective comes from operating AI systems inside production environments while managing the realities of software quality, technical debt, and team structure.LinkedIn: https://www.linkedin.com/in/jasonhli/Laurel website: https://www.laurel.ai/Key TakeawaysAI shifts bottlenecks from code generation to code quality.Visibility into work creates more leverage than blindly automating tasks.Engineering productivity remains difficult to measure despite new AI tools.Agentic coding increases the speed at which technical debt accumulates.Existing code review processes were not designed for AI-generated code.Senior engineering judgment becomes more valuable in an agent-driven world.AI tools expose weaknesses in processes rather than eliminating them.Rewriting software may become cheaper and more common than in previous generations.What You Will LearnThe difference between replacing work and understanding work.How time intelligence creates operational visibility.Why measuring AI ROI remains difficult.How engineering teams are adapting to agentic coding.What skills remain valuable for engineers entering the profession.Why technical debt may increase faster in AI-assisted development.When software rewrites may become preferable to maintaining legacy architectures.Episode Highlights00:00 — Time intelligence extends beyond billing hours03:30 — Visibility matters before automation decisions05:00 — AI should amplify leverage, not replace people08:00 — Trust and reliability determine AI adoption12:00 — AI systems inherit organizational weaknesses15:00 — Measuring AI productivity remains difficult17:30 — Agentic coding changes software engineering20:00 — Engineering leadership becomes more hands-on25:00 — Judgment matters more than coding syntax30:00 — Technical debt grows faster with AI35:00 — Wrappers versus foundation model tools40:30 — Uncertainty creates new opportunitiesListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, AI infrastructure, cybersecurity, and venture capital.
Transcribe →#605 AI Won’t Fix Broken Organizations. It Exposes Them | Jürgen Dauk
Jun 800:53:38Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Jürgen Dauk, advisor, consultant, and creator of the Leadership Operating System. AI is not the real bottleneck. Broken organizational design is.The conversation reframes AI adoption as a leadership and operating model problem rather than a software rollout. Jürgen argues that companies built around control, reporting, and top-down approval are too slow to capture real value from AI. The discussion moves from misaligned KPIs and forecast calls to distributed decision-making, experimentation, and why AI often amplifies the dysfunction already inside the company.If you are leading, investing in, or operating an enterprise technology company, this conversation clarifies why AI value depends less on tools and more on how decisions, teams, and accountability are designed.About the GuestJürgen Dauk is an advisor and consultant to companies and the creator of the Leadership Operating System. He is the author of The Leadership Operating System and has worked across technology, marketing, sales, customer support, customer success, and management roles.Jürgen’s background includes work with companies such as Oracle and OpenText, as well as transformation work across mid-sized and large organizations. His work focuses on helping companies move away from fear-based control and toward operating models where people, teams, and decision-making can support faster adaptation.LinkedIn: https://www.linkedin.com/in/juergendauk/Website: https://theleadership-os.com/Key TakeawaysAI does not fix broken organizations. It makes their weak points more visible.Company-wide AI rollouts fail when leaders mistake access for adoption.Control-based operating models create stability, but they also slow decision-making.Misaligned KPIs push sales, marketing, and customer success into internal conflict.AI should not automate bad processes before leaders question why those processes exist.Distributed decision-making becomes a survival issue when competitors move faster.Reporting calls and alignment meetings often create activity without real output.AI can multiply low-value work when organizations use it to produce more noise.What You Will LearnThe organizational patterns that prevent companies from benefiting from AI.Why Microsoft Copilot access alone does not create measurable productivity gains.How leaders can move from centralized AI rollouts to team-level problem solving.The role of distributed decision-making in faster AI adoption.Why experimentation culture matters more than formal AI training.How reporting calls, CRM inspection, and dashboards can create false control.What leadership teams must change before AI can create real operational value.Episode Highlights00:00 — AI exposes the organization behind the tooling05:00 — Misaligned KPIs turn teams against each other09:00 — Command and control was built for stability15:00 — Company-wide AI rollout can produce little value17:00 — AI works when teams rethink the process20:00 — Technical expertise belongs inside business teams22:00 — Experimentation turns failed pilots into useful learning25:00 — Reporting calls create alignment without real output29:30 — AI can multiply nonsense work38:30 — Slow decisions are now existential risk43:30 — The Leadership Operating System connects the pieces51:00 — Jürgen shares resources for organizational self-checksResources MentionedThe Leadership Operating System by Jürgen Dauk: https://www.amazon.com/Leadership-Operating-System-Accelerating-Dominating-ebook/dp/B0GX2TNS92Leadership Operating System website: https://theleadership-os.comDesign thinkingThe Innovator’s Dilemma by Clayton ChristensenListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
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