The Tech Trek

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The Tech Trek is a podcast for founders, builders, and operators who are in the arena building world class tech companies. Host Amir Bormand sits down with the people responsible for product, engineering, data, and growth and digs into how they ship, who they hire, and what they do when things break. If you want a clear view into how modern startups really get built, from first line of code to traction and scale, this show takes you inside the work.

  1. How Data Teams Scale Project Management Without Slowing Down

    VOR 8 STD.

    How Data Teams Scale Project Management Without Slowing Down

    Cam Crow, Director of Data and Analytics at Vacatia, joins The Tech Trek to unpack what happens when a startup outgrows informal ways of working. This episode looks at how data teams can introduce project management frameworks without killing speed, how to manage stakeholder demand as complexity rises, and why the right operating model matters even more as AI begins to reshape analytics work. Cam shares a practical view from the middle of real growth, from startup scrappiness to acquisitions, migrations, and a much wider stakeholder base. He explains when process becomes necessary, how to build trust during that shift, and where AI is starting to change both delivery workflows and the future of business insights. In this episode • Why early stage teams should add process cautiously, not by default • The moment speed and quality start breaking under too many competing requests • How public communication and domain based stakeholder channels reduce friction • Why planning routines matter as much for stakeholders as they do for the data team • Where AI fits today, from faster delivery to semantic layers that support better answers Highlights 00:00 Cam Crowe joins the show to discuss project management frameworks through the lens of data, startup growth, and stakeholder alignment 01:58 Why Cam resisted formal sprint planning in the startup phase and why that made sense at the time 05:58 The tipping point where too many priorities start hurting both velocity and quality 11:49 How moving conversations out of direct messages and into domain channels changed team operations 15:03 Inside the two week development cycle and the planning week that keeps stakeholders engaged 21:08 How Cam is thinking about AI, semantic layers, and the future of on demand analytics A standout idea from this conversation, process should be added conservatively, only when the business truly needs it. Practical takeaways • Do not formalize too early, but do not wait until the system is already breaking • Make prioritization visible once demand exceeds capacity • Use shared channels instead of one to one communication to reduce bottlenecks • Build stakeholder rituals into the operating model, not just team rituals • Treat AI readiness as an infrastructure challenge, not just a tooling decision Follow The Tech Trek for more conversations with operators, builders, and technology leaders shaping how modern teams work and scale.

    30 Min.
  2. Why Enterprise AI Fails Without Better Data and Business Process Design

    VOR 1 TAG

    Why Enterprise AI Fails Without Better Data and Business Process Design

    Deep Sogani, SVP and Group Data Management Officer at Datasite, joins The Tech Trek to unpack why data governance, lineage, and business process design have become mission critical in the age of AI. This conversation gets past the surface level AI hype and into the operational reality, how companies actually build trustworthy systems, where AI initiatives break down, and why strong data foundations now shape business outcomes in real time. This episode explores the shift from downstream analytics to data that actively drives live decisions, workflows, and automation. Deep explains why many AI projects fail before the model even matters, how business architecture should lead technical design, and why human oversight still matters in high stakes environments. In this episode Why AI has made data governance and data lineage far more operational Why business process clarity matters before data architecture or tooling decisions How real time AI changes the demands on data quality and system design Where agentic AI fits, from workflow automation to more advanced decision support Why human judgment still matters in AI systems shaped by risk, ethics, and security Timestamped highlights 01:47 Why AI raises the stakes for governance, lineage, and trust in data 04:57 Why business architecture has to lead before technical design 09:11 The progression from predictive models to agentic AI workflows 17:55 Why the human in the loop is still essential 21:16 What makes an AI project worth prioritizing 26:06 What has changed, and what has not, in AI related change management Standout line“Business architecture and business thinking should dictate the what and the why, and the data architecture is the how part which needs to follow.” Practical takeawayIf you are evaluating AI inside the enterprise, do not start with the tool. Start with the business problem, the workflow, the decision risk, and the quality of the data behind it. Strong models on the wrong problem still fail. Follow The Tech Trek for more conversations with leaders shaping technology, data, AI, and the future of modern business.

    29 Min.
  3. How Data Leaders Build New Technical Capabilities

    VOR 4 TAGEN

    How Data Leaders Build New Technical Capabilities

    Suresh Martha, Head of Data Driven Innovation and Analytics at EMD Serono, joins The Tech Trek for a practical conversation on what leadership looks like when your team is asked to take on new technical capabilities. This episode is about extending team impact, evaluating new tools, building credibility with stakeholders, and leading through change without pretending to be the deepest expert in every domain. For data leaders, analytics managers, technology executives, and operators, this conversation gets into the real work behind capability building. Suresh breaks down how to assess whether a new technology is worth pursuing, when to start with a pilot, how to upskill internal talent, and how to hire for skills your team does not yet have. In this episode • How to evaluate whether a new tool or technology actually adds business value • Why small pilots help leaders build trust before asking for larger investment • What it takes to lead technical work you have not personally done yourself • How to hire for capabilities your team does not yet have • Why business context and data knowledge still matter as much as technical depth Timestamped highlights 00:04 Extending technical impact as a leader when new capabilities land on your team 03:37 A simple framework for evaluating new tools, investment, and fit 05:28 Hiring for skills your team does not yet have 07:44 Upskilling as a leader so you can guide the work with confidence 12:06 Managing experts whose technical depth goes beyond your own 15:21 Making room for learning and experimentation while still delivering Standout line As long as I understand the intricacies and can explain that, that is what matters, especially for a leader. A practical takeaway Start small. Pick a real business problem. Run a focused pilot. Measure the outcome. Earn the right to scale. Follow The Tech Trek for more conversations with leaders building teams, systems, and technical capability inside modern businesses.

    21 Min.
  4. Machine Learning: What Businesses Might Actually Need

    VOR 5 TAGEN

    Machine Learning: What Businesses Might Actually Need

    Sourish Samanta, Director AI and ML at Advance Auto Parts, joins The Tech Trek for a grounded conversation on where machine learning still creates the most business value, where generative AI fits, and why many teams are chasing the wrong solution. This episode is worth your time if you want a clearer view of how serious operators think about AI strategy, product delivery, and practical use cases that can ship now. This conversation cuts through the noise around AI and gets back to first principles. Sourish explains why machine learning remains the foundation behind today’s AI wave, how to choose between deterministic and creative systems, and what it actually takes to build production ready products that solve real business problems. In this episode: Why machine learning is still the core layer behind modern AI When to use machine learning, when to use generative AI, and when simple analytics is enough What a real product mindset looks like for AI and ML teams How pod based teams can ship faster with better cross functional alignment Why AI and ML talent need to spend time continuously reskilling Timestamped highlights: 00:00 Why machine learning remains the foundation of today’s AI stack 01:57 The difference between ML teams, AI teams, and agent focused workflows 05:56 Choosing the right solve, from forecasting and inventory to creative content generation 10:09 The product mindset required to turn AI ideas into working systems 13:51 Why some business problems need analytics, not AI 15:52 Why AI teams need to spend part of their time learning, testing, and staying current Standout line: AI is not the strategy. Solving the right problem is. Practical takeaway: If you are leading an AI initiative, start by classifying the problem. If the outcome needs consistency, prediction, or forecasting, machine learning may be the better path. If the outcome needs creativity or flexible generation, generative AI may be a better fit. And in some cases, the best answer is still a clean dashboard and strong analytics. Follow The Tech Trek for more conversations on AI, data, engineering, and how technology actually gets applied inside real businesses.

    20 Min.
  5. How Robotics Could Transform Construction

    VOR 6 TAGEN

    How Robotics Could Transform Construction

    Shamoon Siddiqui, CEO and Founder of Human Friendly Robotics, joins The Tech Trek to break down what it really takes to bring robotics into construction. This is not a futuristic thought experiment. It is a grounded conversation about where robots can create value now, why construction has lagged so badly on productivity, and how focused automation could reshape one of the world’s biggest industries. At the center of the discussion is Tyler, a tile laying robot built as a practical entry point into construction automation. Shamoon explains why repeatable workflows matter, where human skill still wins, and how robotics can improve speed, safety, and job site economics without needing to look like a science fiction demo. In this episode• Why construction productivity has moved backward while other industries have surged ahead• Why tiling is the right entry point for construction robotics• How Human Friendly Robotics thinks about deployment, rentals, and product iteration• Where robots can reduce hidden job site injuries tied to repetitive strain• Why the long game is much bigger than tile, with plumbing, electrical, and HVAC in sight Timestamped highlights00:35 Why construction is the right market for robotics right now03:56 The bigger shift from humans moving atoms to machines handling more physical work08:29 Why the business model is built around rentals, not one time equipment sales10:24 The wedge strategy today and the larger vision across licensed trades12:12 The overlooked safety problem of repetitive strain in construction20:44 Why useful robots matter more than robots built for flashy demos “Version one is not going to be as good as version five, but if you continue to rent it from us, we can make sure you get version five when it’s ready.” Practical takeawayThe smartest automation wedge is not the flashiest one. Start with repetitive, measurable work, prove productivity gains in the real world, and expand from there. Follow The Tech Trek for more conversations on robotics, AI, startups, and the technologies changing how real work gets done. #ConstructionTech #Robotics #Automation #ai #FutureOfWork

    25 Min.
  6. Why Most Companies Still Struggle to Operationalize AI

    10. MÄRZ

    Why Most Companies Still Struggle to Operationalize AI

    Mary Elizabeth Porray, Global Vice Chair Client Technology and COO, Growth and Innovation at EY, joins The Tech Trek for a grounded conversation about what it actually takes to operationalize emerging technologies inside a global enterprise. This episode goes past the AI hype cycle and into the real work of adoption, change management, process redesign, workforce trust, and leadership in ambiguity. A lot of companies are asking what AI can do. Fewer are asking what needs to change for AI to actually work. Mary Elizabeth shares how EY is thinking about experimentation, employee experience, guardrails, internal adoption, and the cultural shifts required to move from curiosity to real impact. In this episode Why culture, not technology, is often the biggest blocker to emerging tech adoption Why AI is not a magic wand, but can help teams solve problems in a different way How leaders can identify the right starting points by listening for real pain points Why productivity gains have to create psychological space, not just more work How affinity groups, storytelling, and visible leadership help drive adoption Timestamped highlights 01:58 Why cultural norms often slow down emerging technology adoption 03:25 AI hype, false expectations, and what the technology can realistically change 05:55 The mental load of AI at work, and why EY created Thrive Time 11:20 Why AI pilots need to go deeper than surface level experimentation 15:19 How AI is creating a shared language between business and technology teams 29:29 How storytelling, affinity groups, and positive momentum help people lean in One line that sticks: AI is not something you dabble in. A practical takeaway The best place to start is not with the flashiest use case. It is with a real pain point. If a process should take one week and actually takes eight, that is a signal worth following. Follow The Tech Trek for more conversations with leaders building through change, scaling technology, and shaping how modern work actually gets done.

    35 Min.
  7. From Engineer to CEO, Building an AI Mortgage Company

    9. MÄRZ

    From Engineer to CEO, Building an AI Mortgage Company

    Michael White, Co founder and CEO of Multiply, joins the show to talk about the path from engineering leadership to the CEO seat, and what it really takes to build in a high trust, high complexity market. If you are thinking about founder readiness, leadership growth, or where AI creates real value in fintech, this episode gets into the parts that matter. Michael shares how early entrepreneurial instincts showed up long before Multiply, what changed as he moved from builder to company leader, and why some of the most important skills in leadership have less to do with code and more to do with communication, conviction, and influence. He also breaks down how Multiply is using AI to improve the mortgage experience without removing the human element people still need in a major financial decision. In this episode: • The mindset shift from engineer to CEO • Why leadership becomes a form of sales • How founder timing can be an advantage, not a delay • Where AI fits in the mortgage process, and where it does not • Why startups can move faster than legacy players in AI adoption Timestamped highlights 00:43 What Multiply is building, and why an AI native mortgage company sees a better path to homeownership 01:47 The childhood business story that hinted at an entrepreneurial future 06:20 What changed in the move from engineering leadership to founder and CEO 08:45 Why so much of leadership comes down to influence, alignment, and selling the vision 17:19 Why mortgages are such a strong use case for AI, and why the back office is the real opportunity 22:39 The startup advantage in AI, speed, focus, and freedom from legacy systems Follow the show for more conversations with founders, operators, and technology leaders building what comes next.

    25 Min.
  8. What VCs Really Want From AI Startups in 2026

    6. MÄRZ

    What VCs Really Want From AI Startups in 2026

    Susan Liu, Partner at Uncork Capital, joins Amir to break down what actually matters when backing early stage AI companies. From founder market fit to product wedge to the reality of churn, this conversation gets past the hype and into how strong companies separate themselves in a crowded market. If you are building, funding, or evaluating AI startups, this episode gives you a sharper lens on where the market is heading, what Series A investors now expect, and why real ROI is becoming the line between momentum and fallout. What stood out • The best early stage founders usually have earned insight, meaning they have lived the problem before building the solution • In crowded AI markets, the goal is not to be interesting, it is to become one of the few companies that actually wins • AI buyers still care about the same core question, does this drive revenue or cut cost in a measurable way • The Series A bar has moved up fast, and strong growth alone is not enough if retention is weak • Some of today’s biggest AI winners may still face painful churn if they are not truly essential to the customer Timestamped Highlights 00:37 Susan breaks down how Uncork Capital invests at seed and what it takes to get real conviction early 02:00 The three-part framework she uses to evaluate companies, team, market, and product wedge with traction 09:42 Why crowded AI markets are not necessarily a red flag, and how winners still pull away from the pack 17:04 The ROI test every AI startup has to pass if it wants to survive renewals 19:05 Susan’s honest take on 2026, cautious optimism, bigger impact, and a likely wave of churn 24:33 What founders need now to raise a strong Series A in a market where the bar is higher than ever One line that stuck “If you cannot prove one of these two, it is going to be a tough sell. Companies are not going to renew.” Practical takeaways for operators and founders • If your product cannot clearly tie to revenue growth or cost savings, buyers will eventually cut it • Founder credibility matters more when the market gets noisy, especially in AI • A compelling wedge wins attention, but retention is what keeps the story alive • Happy customers who will speak for you can be one of the strongest assets in a fundraise Stay connected If this episode gave you a better lens on AI startups, venture, and what actually drives durable value, follow the show, share it with a founder or operator in your network, and keep up with Amir on LinkedIn for more conversations like this.

    29 Min.
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The Tech Trek is a podcast for founders, builders, and operators who are in the arena building world class tech companies. Host Amir Bormand sits down with the people responsible for product, engineering, data, and growth and digs into how they ship, who they hire, and what they do when things break. If you want a clear view into how modern startups really get built, from first line of code to traction and scale, this show takes you inside the work.

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    Elevano
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    2020 - 2026
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    The Tech Trek