Full Tech Ahead

Amanda Razani

On this podcast, I sit down with business leaders, researchers and executives to explore innovative technology solutions and products, whether they’re transforming industries today or still in development. But we go far beyond the tech itself. From real-world use cases and business implementation journeys to cybersecurity challenges and future trends, we uncover what’s shaping the digital landscape.We also dive into topics that matter to every tech professional: Work/life balance, business communication, education and training. Think of it as your one-stop shop for meaningful technology discussions that inspire and inform.

  1. 3일 전

    Manage Your AI Security Debt

    In this episode of "Full Tech Ahead," host Amanda Razani interviews Nidhi Aggarwal, Chief Product Officer (CPO) of HackerOne. They discuss the paradigm shift in cybersecurity risks caused by AI-accelerated software development. Aggarwal introduces HackerOne’s new continuous threat exposure management platform, H1, designed to bridge the "find-to-fix" lifecycle gap.  She reveals that following the release of advanced AI models, vulnerability report volumes surged by over 90% in April 2026 alone. This influx has dramatically shortened the "zero-day clock", the time between vulnerability discovery and adversary exploitation, from an average of one month down to mere hours or minutes.  To combat the resulting 25X spike in critical vulnerability backlogs and build up "exposure debt," Aggarwal emphasizes that organizations must abandon seasonal compliance checks in favor of continuous, AI-driven adversarial pen testing combined with human discernment. Key Quotes "Remediation has not kept pace... most CISOs are not looking for more vulnerabilities. Everybody's inundated with vulnerabilities.""The zero day clock... has steadily gone down from it used to be about a month last year to a matter of a few hours now in this year with AI.""Defense has to operate at that AI offensive scale... We have a concept called exposure debt... you have to think of it like technical debt or something sitting on your balance sheet.""The big advice would be offense is defense. So you have to think offensively." Takeaways Automate Defense at Machine Scale: Since generative AI has driven the marginal cost of cyberattacks close to zero, adversaries can now launch massive, automated exploits in under ten minutes. Security defense can no longer operate at human speed; prioritization and remediation must scale up to match offensive AI.Manage Your "Exposure Debt": Unremediated high-risk vulnerabilities function like technical debt on an enterprise balance sheet. Organizations must treat this exposure as a board-level risk conversation and design a continuous drawing-down plan rather than letting critical backlogs accumulate.Filter out "AI Slop" via Bifurcation: The explosion of automated AI scanning has altered risk distribution. Security teams are experiencing a bifurcation: they are flooded either with informational "AI slop" (false positives that existing controls block) or hyper-critical zero-days. Rapid automated validation is mandatory to isolate true exposure.Shift to Continuous Risk-Based Pen Testing: Move away from compliance-driven, checkbox security architectures. True defensive resilience requires automated, 24/7 white-box and black-box pen testing, paired with the creative adversarial judgment of ethical human researchers using AI.Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    Manage Your AI Security Debt

  2. 7월 16일

    Control Your AI Spending

    In this episode of "Full Tech Ahead," host Amanda Razani interviews Matthew Shaxted, CEO of Parallel Works. The conversation centers on a major obstacle facing enterprises today: skyrocketing token consumption and the ballooning costs of using frontier AI models. Shaxted explains that opening up unrestricted API access to hundreds or thousands of users leads to rapid budget depletion, citing recent industry examples like Uber. Drawing a parallel to the high-performance computing (HPC) and cloud migration trends over the past decade, Shaxted predicts a cyclical shift: while firms currently rely heavily on public cloud endpoints, economic pressures and massive utilization rates will drive them to bring data workloads back on-premise using increasingly powerful open-weight models (like the recently released GLM 5.2). To combat initial adoption chaos, Parallel Works offers a computing control plane called Activate, providing a single pane of glass to enforce visibility, tracking, and strict "token budgets" that automatically deny requests once expenditure thresholds are met. Key Quotes "Unless [token usage] is thought about in the very beginning in terms of how are you going to control and monitor token usage... it really becomes a big problem. We've seen the Uber story recently, where you burn through the entire budget in a few months.""Having strong visibility in where the tokens are going... make sure from the very beginning you have visibility into who's doing what, because that's going to start growing very quickly.""As soon as it basically is out of budget, it will deny the request until you get more allotted. That's exactly what's happening.""As the open weights get better and better—which I think we're starting to see with GLM 5.2 coming out recently—you can start running open weight models for certain classes of things with much more predictable cost." Takeaways Establish Financial Guardrails Early: Unrestricted enterprise AI access creates a cash burn. Implement central governance and a "computing control plane" from day one to enforce hard spending limits (token budgets) at the user or team level, preventing unexpected tech invoicing.Prepare for the On-Premise AI Hybrid Shift: Much like cloud computing evolved, enterprise AI will hit a baseline utilization rate (e.g., 8k/80% load) where renting per-token public APIs becomes economically unsustainable. Companies should plan a hybrid stack that shifts routine tasks to dedicated on-premise infrastructure running open-weight models to cut costs up to 6X.Incentivize Token Awareness: End-users rarely optimize resource usage unless faced with explicit constraints. Providing visible token limits encourages developers and practitioners to delegate simpler, mundane prompts to lighter, less expensive internal models rather than burning resources on premium frontier labs.Architect for Agentic Workloads: With the industry shifting toward massive agentic systems where hundreds of autonomous agents execute tasks 24/7, compute demands are projected to scale up to 1,000X. Managing this scale without breaking corporate cost structures requires unified virtualization and gateway gateways across cloud and hardware assets.Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    Control Your AI Spending

  3. 7월 9일

    Scale AI Content Safely

    In this episode of "Full Tech Ahead," host Amanda Razani interviews Chris Yates, SVP of Product, Design and Engineering at Pantheon. They discuss the critical "trust factor" and governance bottlenecks emerging as organizations rapidly adopt AI tools.  Yates explains that while AI has massively accelerated the velocity of creating code and content, companies are hitting a wall because their existing review and security processes cannot keep pace. This friction often drives employees toward "Shadow IT" and side-door shortcuts.  To bridge this gap, Yates advocates for building next-generation scaffolding that treats content and code as a unified substrate. By embedding corporate guidelines, engineering rules, and design systems directly into custom AI skills, organizations can achieve high-fidelity prototyping, enforce uniform brand voice, eliminate "AI slop," and maintain essential human-in-the-loop oversight through staging and replica environments. Key Quotes "Pantheon is, as we say, where the web works. So we are focused on enabling organizations to build and ship on the web at scale.""We've kind of hit this point of like, well, now we have to go push this all through the processes that we put up for good reason to create governance... moving the velocity of creation into the missing pieces of governance.""I can ask Claude... to create me a new website... and it might be beautiful... but then when I need to go change it, if I don't have the exact domain expertise, it becomes really difficult.""Don't wait on the next step, which is how do we drive governance and how do we put these guardrails on how we're doing things?" Takeaways Bridge the Velocity-Governance Gap: The core bottleneck in enterprise AI adoption isn't generation speed, but approval speed. Organizations must build automated scaffolding and pipelines capable of auditing AI-driven code and content variations at the same rate they are generated.Combat Shadow IT with Soft Guardrails: Employees will naturally take shortcuts to offload mundane toil. Instead of issuing strict bans, leaders should provide "soft guardrails" by standardizing tools and seeding internal AI systems with custom skills, corporate rules, and style guides.Bake Brand Voice into Design Systems: To prevent dry, generic "AI slop" from degrading corporate messaging, integrate communication standards directly into your engineering and design infrastructure. This ensures automated code components and text elements automatically adapt to the brand's exact tone and uniformity.Mandate Out-of-Production Human Review: While AI is highly effective at highlighting drift or running static checks, human oversight remains irreplaceable. Enterprise applications require non-production staging environments (replicas of the production fleet) to visualize and verify the "before and after" of AI-driven changes before going live.Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    Scale AI Content Safely

  4. 7월 2일

    Grow Agency Revenue with AI

    In this episode of "Full Tech Ahead," host Amanda Razani interviews Sarah Edwards, CPSO at Kantata. They discuss the strategic implementation of AI in the services industry (consulting firms, agencies, and B2B IT service teams).  Edwards argues that measuring AI success solely through the lens of traditional productivity and speed is a massive mistake for services firms, as charging by hours while simply doing tasks faster inevitably leads to a financial "race to the bottom."  Instead, she advocates for shifting toward an AI-native operating model that powers the "expertise economy."  Key Quotes "Measuring productivity [is] the wrong way to measure AI success... if I'm just delivering things faster and faster, traditionally billing my time based on hours or days, well, how am I growing my revenue? That just becomes a race to the bottom.""Traditionally, expertise has been reliant on tribal knowledge... AI is disrupting all of that. For the first time, we can really compound that expertise across your business.""AI for me is not just about getting faster. It's how do I get better? Because unless I get better... I'm not going to win.""In services, quality has been something we've always struggled to measure... Now with the help of AI, we can truly start to gather and measure [sentiment] during project delivery." Takeaways Ditch the Speed Metric for Quality: In professional services, utilizing AI to execute work faster shrinks billable hours without adding value. Firms must stop treating AI as a siloed efficiency tool and start measuring leading indicators of revenue growth, margin improvement, and transformed service delivery.Capitalize on Compounded Expertise: Historically, consulting firms were constrained by "heroics" and individual expertise, which created an operational ceiling. An AI-native model unlocks years of hidden project context and conversation logs, instantly upskilling every consultant to the level of the firm's best performer.Automate the Sales-to-Delivery Handover: One of the largest operational friction points is when sales teams "throw a project over the fence" to the delivery team. AI agents can eliminate this silo by parsing entire sales-cycle call data into comprehensive briefs, ensuring scope, stakeholder concerns, and project requirements are perfectly aligned.Transition to Outcome-Based Metrics: Instead of tracking static, trailing metrics like "on time" and "on budget" at the end of a lifecycle, firms can use AI to track real-time qualitative health indicators, such as client sentiment, delivery team mood, and continuous project drift, while the work is actively in flight.Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    Grow Agency Revenue with AI

  5. 6월 18일

    Delivering High Quality Software with AI

    In this episode of "Full Tech Ahead," host Amanda Razani interviews Max Reele, VP of Delivery at Rise8. They discuss outcome-driven software delivery in high-compliance sectors, specifically focusing on defense tech and gov tech.  Reele outlines that while AI and agentic assistance allow engineering teams to deliver a much higher quantity of code, the ultimate focus must remain heavily on quality and mission outcomes.  Drawing from his 20 years of government experience, he warns against common tech project failure modes, such as the "Big Bang" release theory—attempting a hard cutover to completely replace a massive legacy system all at once.  To combat this and prevent deepening organizational silos, Rise8 advocates for rigorous corporate upskilling, working backward from strict mission metrics, and conducting biweekly demos of working software.  Furthermore, Reele champions "Extreme Programming" and engineering pairing to safely ground AI agents and prevent codebase hallucinations. Key Quotes "At Rise8, we're defense tech and gov tech focused... we build mission unique software for any mission... specifically in high compliance industries." "Whether it was all hands on keyboard developing the code, or whether it was assisted with Agentic development, the outcome still needs to be the outcome." "Everybody can become builders with agentic assistance in your development effort, but not everybody's really great builders. And it takes the seasoned software engineers to understand how to interact with the AI agents." "Please just stay focused on the mission you're trying to improve and let the business operations follow." Takeaways Implement "Extreme Programming" with AI: AI agents are flooding codebases with volume, but they can hallucinate or even falsify data to artificially pass test cases. Organizations must pair seasoned, senior engineers with junior developers to continuously audit, test, and safely prompt AI agents, keeping code reliable. Reject the "Big Bang" Release Trap: Attempting a sudden, full-scale replacement of a massive legacy operating system of record causes immense friction, timeline overruns, and project cancellations. Instead, break modernization efforts down into small, digestible bites and integrate users gradually throughout the journey. Enforce Biweekly Software Demos: The ease of localized AI tooling risks driving engineers into deeper silos. To force collaboration and structural alignment, teams must pull their features, security hygiene, and technical debt together into a coherent, working software demo presented to primary stakeholders every two weeks. Encourage Engineering Enablement: Business leaders must shift their mindsets regarding workforce upskilling. When an engineer raises their hand to ask for deeper training on how to handle AI agents safely in mundane functions, it should be viewed as a professional strength, not an operational flaw. Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    Delivering High Quality Software with AI

  6. 6월 12일

    From AI Hype to Real Business Results

    In this episode of "Full Tech Ahead," host Amanda Razani interviews Mark Talbot, AVP Customer Success AI Incubation at Appian. They discuss transitioning enterprise AI from isolated experiments into governed production workflows, focusing on recent research conducted in collaboration with Harvard Business Review.  Talbot reveals a stark contrast in enterprise adoption: while 59% of organizations have AI in production, only 16% realize a high degree of measurable value. He attributes this gap to a failure to embed AI directly into core business workflows, as well as the mistake of applying AI to inefficient, broken legacy processes.  To scale successfully, Talbot advocates for the creation of AI Centers of Excellence (CoEs) to manage data fabric, fragmentation, and strict compliance (such as SOC 2 and FedRAMP).  Moving forward, he predicts a shift away from disconnected chatbot tools toward unified, automated platforms that offer full auditability, traceability and concrete business results. Key Quotes "My lens is always where does AI fit into real work in a way that's secure, measurable, and scalable?""Only sixteen percent realize a high degree of measurable value from those investments... because only eighteen percent said AI is primarily integrated into workflows.""If you have AI chat and you have ten thousand employees, you have ten thousand different ways of doing things. That's one of the reasons why AI needs to be embedded into existing workflows.""Prioritize sustainable implementation and the long term rather than chasing every AI trend." Takeaways Embed AI in Workflows for True ROI: Running isolated AI experiments or simple chat windows doesn't drive top-line business growth. Organizations that embed AI directly into automated, existing workflows report significantly higher value (70% reporting moderate to substantial success) because it systematically removes human toil.Empower AI Centers of Excellence (CoEs): Scaling AI requires organizational discipline. Establishing an AI CoE ensures that the company maps performance metrics before and after AI deployment, maintains strict data logging, and keeps the enterprise out of the headlines for data security failures.Demand Traceability and Auditability: In complex, regulated environments, governance is non-negotiable. Successful deployments rely on platforms (like Appian) that provide built-in compliance frameworks (SOC 2, ISO, FedRAMP) and offer clear explainability for every decision the AI makes.Move Beyond Chatbots and Model Hype: The era of comparing LLMs or relying on generic chat screens is fading. The future belongs to structured platforms where the technology is invisible, secure, and seamlessly integrated into day-to-day operations to deliver scalable efficiency.Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    From AI Hype to Real Business Results

  7. 6월 8일

    AI Bringing Care to Remote Areas

    In this episode of "Full Tech Ahead," host Amanda Razani interviews Dr. Jason Corso, Toyota Professor of AI at the University of Michigan and Co-Founder of Voxel51. They discuss Voxel51’s role as a developer tool software company for physical and visual AI, which has achieved over 4 million downloads.  The core of the conversation focuses on Vigil, an innovative healthcare AI project led by Dr. Corso and funded by ARPA-H’s Paradigm program. Vigil tackles the critical shortage of specialists and brick-and-mortar hospitals in rural America by equipping mobile medical units (clinics on wheels) with physically grounded AI.  Instead of replacing clinicians, Vigil acts as an advanced co-pilot, using computer vision and on-the-fly micro-guidance to upskill generalist healthcare workers (like registered nurses or EMTs) to perform complex procedures, such as cardiac ultrasound diagnostics, directly in remote communities. Key Quotes "Voxel51 is indeed a dev tool software company for AI that supports the developer... in the spaces of physical AI and visual AI.""I don't think AI is here to replace humans... I just believe that we are as technologists in AI, we are building tools that will augment humans.""We have this notion of a triangle of trust where the healthcare worker is trusting Vigil to help him or her, and the patient is trusting the healthcare worker, and then tacitly, the patient is trusting Vigil.""In the healthcare, in the visual domain, we can't hallucinate, first of all... We're really trying to get toward those guaranteeable guardrails." Takeaways Upskilling the Generalist Workforce: AI can dramatically expand healthcare access without needing to "clone specialists." By equipping existing local nurses or EMTs with AI-guided tools, they can perform specialized tasks—like capturing precise cardiac ultrasound imagery—that normally require years of dedicated training.The "Triangle of Trust": Successful AI deployment in healthcare relies heavily on the bedside manner and human connection. The patient trusts the clinician, the clinician trusts the AI, and the patient tacitly trusts the AI. Maintaining this human-centered relationship is crucial.Guaranteeable Model Guardrails: Unlike conversational LLMs that are prone to hallucination and rely on post-hoc prompt filters, critical visual AI systems in healthcare require deeply grounded, mathematical, and theoretical guardrails that prevent errors before they happen to ensure patient safety.Augmentation over Replacement: The future of advanced technology, including robotics (like actuated robotic arms in mobile clinics), is to augment human capabilities. AI provides an extra set of un-blinded eyes and precise micron-level assistance, allowing human workers to perform their jobs faster, better, and more equitably.Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    AI Bringing Care to Remote Areas

  8. 5월 29일

    The Role of AI in Healthcare

    In this episode of "Full Tech Ahead," host Amanda Razani interviews John Edwards, SVP of Citius Healthcare Consulting at CitiusTech. They discuss the rapid acceleration of AI in the healthcare sector, shifting from simple proof-of-concepts to full-scale, operationalized enterprise solutions.  Edwards highlights that the primary barriers to healthcare AI are not technical, but human and procedural. He notes that healthcare data is uniquely time-sensitive, and capturing the unwritten clinical context from a practitioner's head requires robust data quality and "human-in-the-loop" metrics.  To overcome generic AI limitations, CitiusTech developed Knewron, a specialized orchestration platform built with pre-embedded healthcare context. Ultimately, Edwards argues that the success of healthcare AI relies on strict governance to filter competing priorities, comprehensive change management to overcome clinician inertia, and a deep understanding of the human workflow—such as solving doctor burnout and "pajama time"—rather than just engineering prowess. Key Quotes ●       "While we do a lot of engineering work lately, a lot of data and AI work has been dominating what we're selling because that's what people are buying. We feel it with teams that know and understand the nuances of healthcare." ●       "The elusive return on investment only really occurs when you adopt AI... it requires you to think differently than just experimenting." ●       "The biggest mistake I see people making is automating a bad process." ●       "A perfect mousetrap that's never used won't catch any mice. You need to be able to get the human side of it engaged and excited." Takeaways ●       Overcome Clinician Inertia: Historically, adopting tools like the stethoscope took decades because doctors trusted their traditional methods. AI faces the exact same cultural resistance. Organizations must realize that driving adoption requires shifting budgets heavily toward change management—potentially spending two dollars on adoption for every one dollar spent on the technology itself. ●       Never Automate a Bad Process: Traditional healthcare processes were designed around human limitations and legacy software. True AI implementation requires pulling the actual decision-making and thinking into the system (via knowledge and context graphs), rather than just using AI to make an inefficient, outdated workflow run faster. ●       Use Healthcare-Specific AI Foundations: General AI tools lack clinical context and require rebuilding foundations from scratch every time. Utilizing industry-specific accelerators (like CitiusTech's Knewron platform) allows organizations to safely manage time-sensitive medical data and deploy agentic workflows much faster. ●       Solve Real Workforce Friction Points: Clinicians readily embrace AI when it relieves systemic burdens like "pajama time" (the hours spent typing clinical documentation into EHRs at night). Ambient listening is the first step toward creating a collaborative AI assistant that transforms how medicine is practiced. Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/

    The Role of AI in Healthcare
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On this podcast, I sit down with business leaders, researchers and executives to explore innovative technology solutions and products, whether they’re transforming industries today or still in development. But we go far beyond the tech itself. From real-world use cases and business implementation journeys to cybersecurity challenges and future trends, we uncover what’s shaping the digital landscape.We also dive into topics that matter to every tech professional: Work/life balance, business communication, education and training. Think of it as your one-stop shop for meaningful technology discussions that inspire and inform.

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  • 제작진
    Amanda Razani
  • 방송 연도
    2025년 - 2026년
  • 에피소드
    54
  • 등급
    전체 연령 사용가
  • 저작권
    © 2026 Full Tech Ahead
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    Full Tech Ahead