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
58episodes
Episodes
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#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.
Transcribe →#604 AI Can Generate Expertise. It Still Can’t Generate Judgment | Dan Pratl, Founder & CEO, Quadron
Jun 500:40:44Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Dan Pratl, Founder and CEO of Quadron. Dan is building infrastructure around trust, credibility, reputation, and human judgment in a world where AI can generate expert-looking work at near-zero cost.The conversation reframes one of the most common assumptions about AI. The scarcity is no longer knowledge creation. The scarcity is verification, judgment, and the ability to demonstrate that a person stands behind a claim. Rather than treating AI as a replacement for expertise, Dan argues that AI increases the value of trusted human judgment.If you are building, investing in, operating, or leading in AI, enterprise software, digital infrastructure, or knowledge-intensive businesses, this conversation provides a framework for thinking about trust, reputation, and value creation in an AI-driven economy.About the GuestDan Pratl is the Founder and CEO of Quadron, a company focused on creating infrastructure for trust, credibility, reputation, and programmable incentives in the AI era.His background spans regulation, open source software, crowdfunding, decentralized finance, and crypto. Through those experiences, he developed a thesis that human expertise, judgment, and credibility should become measurable, portable, and economically valuable assets.His work focuses on solving a problem that becomes increasingly important as AI-generated content becomes abundant: determining who stands behind information and why that credibility should matter.LinkedIn: https://www.linkedin.com/in/danpratl/Website:https://quadron.tech/Personal Site:https://pratl.meKey Takeaways• AI has made knowledge generation abundant, but trust remains scarce.• The value of expertise increasingly comes from judgment rather than content creation.• Traditional credentials and social proof systems are losing effectiveness.• Credibility needs to become portable rather than tied to individual platforms.• Verification must become a byproduct of human ambition and incentives.• Human expertise is an evolving asset that compounds over time.• AI agents can execute tasks, but humans still define what good looks like.• Organizations that capture and reward human judgment will outperform those that only optimize automation.What You Will Learn• Why AI-generated expertise does not eliminate the value of human judgment.• How credibility may evolve into a measurable and portable asset.• The limitations of resumes, endorsements, and traditional reputation systems.• How programmable incentives can encourage verification and trust.• What a credibility wallet could look like in practice.• Why AI agents still depend on humans to define outcomes and quality.• How organizations can preserve and scale expertise in an AI-first environment.Episode Highlights00:00 — AI Makes Trust More Valuable Than Knowledge05:00 — Knowledge Becomes Abundant, Verification Becomes Critical08:00 — Why Judgment Outlasts AI Generated Expertise11:00 — The Case for a Portable Credibility Wallet14:00 — Quantifying Reputation Beyond Social Proof16:00 — Expertise Compounds Through Iteration18:00 — Turning Judgment Into an Economic Asset21:00 — Investing in Yourself as a Market25:00 — Verification Must Reward Participation30:00 — AI Agents Need Humans To Define Good33:00 — Companies That Ignore Human Judgment Fall Behind35:00 — Building a New Category Around Trust InfrastructureResources Mentioned• MCP (Model Context Protocol)• Skills.md• Red Hat• SEC (U.S. Securities and Exchange Commission)• CFTC (Commodity Futures Trading Commission)Listen 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, venture capital, AI, cybersecurity, and enterprise technology.
Transcribe →#603 Startups Scale Too Early. The Basics Are Still Broken | Raphael Peyret
Jun 100:59:43Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Raphael Peyret, Founder and Principal Advisor at SHA/RP. Raphael brings experience across product, cybersecurity, Google, and startup execution from MVP to acquisition. The main tension is clear: companies keep chasing scale before the basics are working.The conversation reframes AI security, startup growth, product management, and GTM as the same sequencing problem. AI-native threats matter, but unpatched systems, weak credentials, poor MFA adoption, unclear positioning, premature sales hiring, and feature overload still break companies first. Raphael argues that founders need defensible security, repeatable sales, and product discipline before they scale people, spend, or complexity.If you are building, investing in, or leading early-stage enterprise technology, cybersecurity, AI, or SaaS companies, this conversation gives a practical way to separate progress from motion.About the GuestRaphael Peyret is the Founder and Principal Advisor at SHA/RP, where he works with startups as an independent advisor and fractional executive across product management and cybersecurity.His background includes Google and a VP of Product role at Harangi Cybersecurity, a Singapore-based cybersecurity startup that moved from MVP through fundraising, acquisition, and integration into Bitdefender.Raphael frames startup execution through the lens of risk, product discipline, and sequencing, which makes him well placed to discuss where founders and security leaders usually move too early.LinkedIn: https://www.linkedin.com/in/rpeyret/Website: https://sha-rp.comKey TakeawaysAI threats get attention, but basic security failures still cause most breaches.Startups need defensible security, not enterprise-grade security theatre.Cybersecurity should help startups move faster without creating reckless exposure.Founders often hire sales before they understand how their product sells.A salesperson cannot fix unclear positioning or unfinished customer pain.Product teams fail when they add features before solving the core problem.Founder bottlenecks appear when decisions stay personal instead of becoming systems.Motion becomes progress only when each step proves a specific assumption.What You Will LearnThe difference between AI security headlines and the breach risks most companies actually face.How startups can define good enough security without copying enterprise playbooks.Why basic hygiene such as MFA, SSO, and credential management still matters most.When hiring sales too early creates more confusion than revenue.How product management helps founders stop becoming the bottleneck.Why feature expansion can hide weak product-market understanding.What separates motion from progress in founder execution.Episode Highlights00:00 — Raphael Peyret connects cybersecurity with startup execution02:00 — AI threats distract from basic security failures05:00 — Security teams still struggle to speak business language09:00 — Startups need defensible security, not overbuilt controls15:30 — Security diagnostics expose the risks founders miss18:00 — MFA and SSO still form the security base20:30 — Good enough security helps startups keep moving24:30 — AI can reduce friction before attacks begin27:00 — Startups hire sales before sales is repeatable31:00 — Marketing cannot fix unclear positioning35:00 — Product teams add features before solving pain40:30 — Founders need systems before they can scale46:30 — Fractional leadership bridges the early expertise gap49:30 — Motion and progress are not the same thing56:30 — Founders need sequencing across every functionListen NowAvailable on all major podcast platforms and YouTube.Follow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.
Transcribe →#602 AI Can Set Meetings. It Still Can’t Build Trust | Alex Grant
May 2900:54:04Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Alex Grant, SVP of Sales at North. Alex brings more than 16 years of experience building sales teams across fintech, payments technology, and SaaS. The conversation centers on a hard tension: AI can create more opportunities, but it still cannot create trust by itself.The conversation reframes AI in sales and fintech as an execution problem rather than a technology topic. Alex explains why companies are using AI to move faster, secure payment systems, and generate more qualified opportunities, while also showing why complex software still needs a human seller who can translate risk, value, and trust for the buyer.If you are leading revenue, building fintech products, investing in AI-enabled software, or selling complex enterprise technology, this conversation shows where AI can accelerate the system and where human judgment still carries the deal.About the GuestAlex Grant is the SVP of Sales at North, where he is building the company’s first coast-to-coast W-2 outside sales channel. Before joining North, Alex spent more than 16 years building sales teams in fintech, payments technology, software, and SaaS, including time at a Fortune 500 company.At North, Alex focuses on building full-time sales teams that can sell payment technology, AI-supported security, and software solutions with a structured career path, training model, and field-led culture. His perspective is grounded in the operational reality of selling fintech and payments technology to businesses of different sizes.LinkedIn: https://www.linkedin.com/in/ralexgrant/Website: https://north.comKey TakeawaysAI can generate meetings, but it cannot replace trust in complex technology sales.Buyers want AI, but many still struggle to define what they actually need.Payment security is one of the clearest practical use cases for AI in fintech.Smaller businesses often underestimate security risk until they become easier targets.AI lowers the cost of testing new software ideas before committing years of development.Automated SDR workflows will pressure traditional appointment-setting models.Human sellers still matter when buyers need confidence before signing large contracts.Sales teams perform better when leadership gives field teams real access and voice.What You Will LearnHow AI is changing prospecting and appointment setting in fintech sales.Why payment security is becoming a stronger AI use case than generic productivity.The reason smaller merchants often misunderstand their exposure to fraud and breaches.How buyers talk about AI when they know they need it but cannot define the purchase.Why complex software still needs human interpretation during the sales process.When W-2 sales teams create more control than 1099 agent-led distribution.What sales leaders can do to keep field teams engaged, heard, and useful.Episode Highlights00:00: AI sales needs a human interpreter05:00: Payment security becomes the practical AI case07:30: Small businesses misread their breach exposure11:30: Buyers want AI before defining the need14:00: AI lowers software development risk17:00: Automated SDRs pressure old appointment models19:00: Trust still decides large software purchases28:30: North shifts toward a W-2 sales model36:00: Salespeople need access, not just incentives45:30: Door-to-door selling still creates human trust50:00: AI turns ideas into working prototypes fasterResources MentionedNorth: https://north.comW-2 sales model: referenced as North’s full-time sales channel structure1099 agent model: referenced as the traditional distribution model in payments technologyListen 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 →#601 The AI Bottleneck Is No Longer GPUs. It’s Energy and Memory | Eugene Cheah
May 2500:45:37Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Eugene Cheah, CEO of Featherless AI. The AI bottleneck is no longer just GPU access. Power, memory, inference cost, and model reliability are becoming the real constraints.Eugene reframes the AI infrastructure debate away from a simple race for bigger models and more chips. The conversation connects energy capacity, HBM shortages, open source model adoption, linear attention architectures, and the enterprise need for predictable AI systems. It also challenges the assumption that the best AI strategy is always to use the largest available model.If you are building, investing in, or operating AI infrastructure, this conversation gives a clearer view of where AI economics, hardware constraints, and production reliability are heading.About the GuestEugene Cheah is the CEO of Featherless AI, an AI startup making open source AI models accessible through a single platform.Featherless AI started from AI research and optimization work around RWKV architecture, with a focus on reducing inference cost and making AI models more accessible. Eugene’s work sits at the intersection of open source AI, model efficiency, GPU infrastructure, HBM constraints, and inference optimization.He is well positioned to frame this shift because Featherless AI works directly on the infrastructure layer between developers, open models, and production inference.LinkedIn: https://www.linkedin.com/in/eugene-cheah-a47791126/Website: https://featherless.aiKey TakeawaysAI infrastructure constraints are shifting from GPU access to power, memory, and inference efficiency.HBM scarcity becomes more serious as models and context windows continue to grow.Bigger models do not solve the enterprise problem of reliable execution.Open source models are becoming strong enough to replace many closed model use cases.Fine-tuned smaller models can outperform frontier models on narrow enterprise tasks.Nvidia’s moat weakens when developers can move workloads across more hardware choices.Linear attention architectures matter because quadratic memory scaling is economically unsustainable.Enterprises value model control when closed providers change, deprecate, or restrict models too often.What You Will LearnThe real infrastructure bottlenecks behind AI deployment beyond GPU availability.How HBM pressure affects model size, context length, and inference economics.Why energy capacity can delay AI infrastructure even when chips are already available.How open source models are changing enterprise AI adoption and deployment control.Why smaller fine-tuned models can beat larger models on specific production tasks.When linear attention architectures reduce memory demand compared with transformer attention.What hardware choice, model portability, and local inference mean for AI infrastructure strategy.Episode Highlights00:00 — AI infrastructure moves beyond the GPU race03:30 — Nvidia, AMD, and Huawei follow different hardware strategies07:30 — Power becomes the first AI infrastructure bottleneck08:30 — HBM pressure exposes the memory constraint12:00 — AI follows the same pluralism as databases15:00 — Developers start with big models, then specialize18:30 — Transformer memory scaling becomes an economic problem23:30 — Hardware choice starts weakening platform lock-in29:30 — Reliability matters more than raw intelligence36:00 — Open source gives enterprises model control41:30 — Small models can now build real applicationsResources MentionedFeatherless AI: https://featherless.aiRWKV architecture: AI architecture referenced by Eugene as part of Featherless AI’s research backgroundListen 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 →#600 AI Reliability Is a Business Risk. Not Just an Engineering Problem | Helen Gu
May 2200:37:26Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Helen Gu, Founder and CEO of InsightFinder AI. Helen brings decades of research in distributed system reliability, anomaly detection, and AI-driven operations. The conversation focuses on why AI reliability is becoming a business risk, not just an engineering issue.The conversation reframes AI observability as a production control layer for enterprises deploying AI agents. Helen explains why traditional DevOps and SRE practices are not enough when systems are probabilistic, model behavior changes, data shifts, prompts evolve, and agents begin taking actions across workflows.If you are building, investing in, operating, or leading AI systems inside enterprise environments, this conversation gives you a practical frame for reliability, drift, runtime monitoring, and accountability.About the GuestHelen Gu is the Founder and CEO of InsightFinder AI, and a professor at North Carolina State University. InsightFinder AI was founded from her research in distributed system reliability using AI technology.Helen has worked on anomaly detection, prediction, diagnosis, and system reliability since the late 1990s. She also spent a sabbatical year at Google evaluating anomaly detection algorithms, which later helped shape the foundation for InsightFinder AI.LinkedIn: https://www.linkedin.com/in/helen-gu-b1aa42b6/Website: https://insightfinder.com/Key TakeawaysAI systems can fail silently while still returning confident answers.AI reliability is becoming a business risk, not only an engineering concern.Multi-agent systems can spread upstream mistakes across business workflows quickly.Traditional SRE practices do not fully cover model behavior, prompts, and data drift.Runtime monitoring matters more once AI moves from sandbox testing to production.Observability alone is not enough without diagnosis, recommendations, and remediation.Model drift can change business outcomes even when infrastructure appears healthy.Human review shifts from doing work to supervising AI decisions and guardrails.What You Will LearnWhy probabilistic AI systems require different reliability practices than software systems.How model drift and data drift change production behavior over time.What silent AI failure looks like inside enterprise workflows.The reason sandbox testing misses real production AI failure cases.How runtime monitoring helps detect hallucinations, bias, leakage, and accuracy issues.Why AI observability must connect infrastructure, data, prompts, models, and business outcomes.What leadership teams need to consider before AI agents begin taking actions.Episode Highlights00:00 — Helen Gu frames AI reliability from research02:30 — AI systems answer confidently even when wrong04:30 — SRE lessons do not fully transfer to AI07:00 — AI reliability needs fine-grained runtime metrics08:30 — Silent failure creates hidden business damage10:00 — Multi-agent mistakes propagate faster than humans12:00 — Model drift changes outcomes without warning15:00 — Sandboxes miss production AI behavior18:00 — Observability must become actionable control21:30 — AI reliability becomes a leadership responsibility24:30 — AI Labs test prompts, models, and datasets28:30 — AI agents become part of enterprise workflows31:30 — Responsible AI starts with accepting failure riskListen 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 →#599 AI Agents Are the New Attack Surface. Security Teams Are Already Behind | Jason Remillard
May 1800:45:43Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Jason Remillard, Founder of Data443. Jason brings more than 30 years of cybersecurity, data security, infrastructure, and enterprise risk experience. The conversation focuses on the gap between AI adoption speed and the security operating models still built for slower systems.The episode reframes AI security as an execution and visibility problem, not only a model risk problem. Jason argues that security teams lose when they only block users, rely on slow approval workflows, or assume old SOC models can handle AI agents, MCPs, SaaS sprawl, and machine-speed data movement.If you are leading cybersecurity, enterprise IT, AI adoption, or digital infrastructure strategy, this conversation gives you a practical lens for where the real exposure is forming.About the GuestJason Remillard is the Founder of Data443, a data security company focused on securing data across systems, users, and enterprise workflows. His career spans more than 30 years, from early systems operations and ISP infrastructure to enterprise security and regulated environments.Jason has worked across cybersecurity, data protection, ransomware recovery, threat intelligence, DLP, attack surface management, and AI-related security challenges. His perspective is grounded in the operational reality of how users, security teams, and business units behave when controls create friction.LinkedIn: https://www.linkedin.com/in/jremillard/Website: https://data443.com/Key TakeawaysAI agents expand the attack surface faster than security teams can govern with manual workflows.End users bypass controls when security becomes a blocker to legitimate business execution.DLP cannot solve data loss when users can photograph, move, and re-enter information elsewhere.Security teams need to enable safer decisions, not only enforce binary allow-or-deny rules.Inference can reduce AI security costs when models are trained for specific enterprise use cases.Threat intelligence must track agents, connectors, APIs, and machine actions as risk-bearing actors.Post-quantum risk matters because encrypted data can be stored now and decrypted later.Cyber resilience starts with assuming breach, not assuming the perimeter still holds.What You Will LearnThe reason cultural failure still sits behind many enterprise security failures.How AI agents change visibility across SaaS, APIs, Shadow IT, and enterprise data flows.Why traditional exception management breaks when AI decisions happen in milliseconds.How inference can help security teams operate faster without relying only on GPUs.What MCP and agent-to-agent workflows mean for API governance and connector risk.Why post-quantum security is already relevant for long-lived sensitive data.The practical starting point for cyber resilience when attacks cannot be fully prevented.Episode Highlights00:00 — Jason Remillard frames three decades in cybersecurity04:30 — Security failure starts with not-my-job thinking08:30 — DLP breaks when users bypass friction12:00 — AI agents change enterprise visibility13:30 — Approval workflows cannot match AI speed17:30 — Non-human actors create identity risk20:30 — AI defense depends on trained inference27:00 — Multimodal input changes user behavior28:30 — MCP turns APIs into hidden risk31:00 — Attackers gain the same AI velocity35:00 — Quantum risk makes stored data vulnerable39:00 — Resilience starts by assuming breachListen 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 →#598 AI Pilots Don’t Fail. Enterprise Systems Do | Omid Pakseresht, CEO of Goodfolio
May 1500:39:40Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Omid Pakseresht, CEO of Goodfolio. Omid works on enterprise AI systems that move beyond pilots and into real business workflows.The conversation reframes enterprise AI failure as a systems problem, not a model problem. Omid argues that most AI initiatives break because the workflow, ownership model, governance layer, audit trail, and adoption path were never designed properly. The model may work, but the enterprise system around it often does not.If you are building, investing in, or leading enterprise AI adoption, this conversation gives you a clearer way to judge whether an AI initiative is ready for production or stuck as another pilot.About the GuestOmid Pakseresht is the CEO of Goodfolio, a company focused on helping enterprises build and scale AI systems inside real workflows.His background is in product and technology, with a particular focus on finance. He has spent around 10 years building and scaling AI solutions in enterprise environments.Omid is well placed to frame this signal because his work sits at the point where AI models meet workflow design, governance, compliance, and business outcomes.LinkedIn: https://www.linkedin.com/in/omidpakseresht/Website: https://goodfolio.comKey TakeawaysMost enterprise AI fails because the system around the model was never built.A working AI pilot is not proof that the business is ready for production.AI adoption fails when it is treated as a data science project.Workflow owners must be part of the AI design process from the beginning.Human-in-the-loop fails when humans become late-stage QA gates.AI can create new bottlenecks when upstream productivity increases faster than downstream capacity.Regulated AI needs audit trails, governance layers, risk monitoring, and clear decision rights.AI ROI must be tied to business outcomes, not seat counts or software usage.What You Will LearnThe difference between an AI tool and an AI system inside an enterprise workflow.How AI pilots fail after the proof of concept looks successful.Why model quality is rarely the biggest barrier to enterprise AI adoption.How compliance, governance, and auditability shape production AI.What changes when AI becomes embedded into regulated workflows.Why AI can move bottlenecks rather than remove them.How leaders should evaluate AI ROI through outcomes instead of software spend.Episode Highlights00:00 — Enterprise AI failure starts beyond the model02:00 — Proofs of concept became the easy part04:00 — Workflow fit beats model quality in adoption05:30 — AI cannot remain a data science project08:30 — Production AI needs more than a model12:00 — Compliance workflows expose AI bottlenecks17:30 — Human-in-the-loop needs a better framing20:00 — Governance becomes table stakes for enterprise AI24:00 — AI ROI must connect to business outcomes28:00 — AI exposes process gaps before scalingResources MentionedGoodfolio: https://goodfolio.comInspector: Goodfolio tool for compliance review of marketing assets in regulated industriesAI agents: discussed in the context of compliance workflowsModel governance: discussed as a production requirementEvaluation pipelines: discussed as part of production AI systemsPrompt engineering versioning: discussed as part of AI system managementRisk monitoring: discussed as part of regulated AI adoptionData lakes: discussed as a comparison point for large enterprise technology projectsListen 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 →#597 Dashboards Are Dead. AI Agents Replace the Forecast Call | Laura Fu, GTM Architect at DevRev
May 1100:44:37Tap to summarizeIn this episode of The CTO Show with Mehmet, Mehmet sits down with Laura Fu, GTM Architect at DevRev. Laura brings a RevOps and sales enablement lens to a question many GTM leaders are now facing: AI does not fix sales by sitting on top of old workflows.The conversation reframes AI in go-to-market as an operating model problem, not a tooling problem. Laura argues that AI-native execution requires new feedback loops, better data capture, agent-readable systems, and a different view of enablement. The strongest claim is that dashboards and forecast calls become less central when agents can surface the signal directly.If you are leading, building, or investing in enterprise sales organizations, this conversation gives you a sharper way to think about AI-native GTM, CRM architecture, RevOps, sales enablement, and pipeline execution.About the GuestLaura Fu is the GTM Architect at DevRev, focused on improving go-to-market efficiency and how revenue organizations operate with AI.She is the author of Designing for Excellence: Sales Enablement in the AI Native World, a book about using AI to make sales enablement and GTM engines more fluid and operational.Laura is the right person to frame this signal because she connects sales enablement, RevOps, CRM systems, data quality, and AI agents into one operating model.LinkedIn: https://www.linkedin.com/in/laurazfu/Key TakeawaysAI does not make broken sales processes better, it exposes where the process was weak.Sales teams still move at human speed, but expectations now move at AI speed.AI-native GTM requires workflow redesign, not summaries copied into old systems.Traditional enablement fails when training is disconnected from the moment of need.CRM becomes more valuable as memory and context, not as a manual reporting database.Dashboards lose power when agents can detect revenue signals directly.Poor data quality breaks trust in AI faster than poor user adoption.RevOps teams will shift from analysts to GTM engineers who build and orchestrate systems.What You Will LearnThe difference between AI adoption and AI-native sales execution.How AI changes sales enablement from a training function into an operating system.Why dashboards become less useful when agents can scan signals directly.The CRM requirements that matter when agents need read and write access.How real-time feedback loops can reshape sales messaging, pricing, and positioning.Why data quality and change management decide whether AI tools get trusted.What an AI-first revenue organization could look like from day one.Episode Highlights00:00 — Laura Fu frames AI-native sales enablement02:30 — Sales teams face AI-speed expectations06:00 — AI adoption does not change execution09:30 — Traditional enablement was already broken12:00 — Enablement becomes a system, not function15:30 — The AI enablement flywheel takes shape20:30 — Change management breaks AI adoption first25:00 — Feedback loops separate messaging from delivery28:00 — Pipeline creation remains the strongest signal30:00 — Dashboards are dead in agent-led RevOps36:30 — AI finds pipeline signals faster39:00 — GTM engineers replace analyst-heavy RevOps42:30 — Laura shares the book and podcastResources MentionedDesigning for Excellence: Sales Enablement in the AI Native World by Laura Fu: Available on Amazon, Barnes & Noble, and bookstores: https://www.amazon.com/dp/B0FVBKGK4ZState of the AI Union: Laura Fu’s podcast on Apple Podcasts: https://podcasts.apple.com/gb/podcast/state-of-the-ai-union/id1851548376 Listen 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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