In this episode of The Tech Trek, Amir Bormand sits down with Nirmal Ranganathan, CTO, Global Public Cloud at Rackspace, to dissect one of the hottest and most crucial topics in today’s tech landscape—trust in AI applications. They explore how enterprises can drive adoption of AI solutions, what key factors are needed to foster trust, and why guardrails, security, and change management play a pivotal role. Whether you're a developer, tech leader, or AI enthusiast, this episode dives deep into the challenges and opportunities shaping the future of AI adoption.

Key Takeaways

• Trust is the Cornerstone: For AI adoption to succeed, users must trust the output. Trust hinges on data quality, security, responsible use, and model transparency.
• Change Management Matters: Adoption in enterprises isn't about trends—it’s about clear processes, education, and user enablement.
• Guardrails Are Non-Negotiable: Especially when AI is exposed to external users, organizations need strong safety checks—think toxicity filters, bias mitigation, and strict data governance.
• Scaling AI = Scaling Costs: Unlike typical systems, scaling AI comes with heavy computational costs. Patterns like caching and model optimization are essential for sustainability.
• Prompt Engineering & Peer Learning: The secret to effective enterprise AI adoption is empowering users to master prompt engineering and fostering peer collaboration.
• Future of Adoption: 2025 might not yet be the year of mass AI production rollout, but the curve is gradually climbing—especially with evolving architectures and better model accuracy.

Timestamped Highlights

• [00:00:00] Introduction to Nirmal Ranganathan & the importance of trust in AI
• [00:01:34] Why adoption is key—and why most tech projects fail due to lack of it
• [00:02:50] Three pillars of successful AI adoption: Trust, Change Management, Functionality
• [00:05:02] The trust barrier: Hallucinations, relevance, and grounding AI responses in enterprise knowledge
• [00:10:01] Why most AI projects are stuck in POCs—and what's preventing full-scale deployment
• [00:11:43] Technical guardrails: Security, scalability challenges, and compliance considerations
• [00:14:56] Cost & infrastructure challenges when scaling AI solutions to millions of users
• [00:17:52] How tech companies differ from enterprises in deploying AI—data privacy, safety checks, user unpredictability
• [00:20:00] The role of prompt engineering, peer learning, and experiential training in ensuring AI adoption success
• [00:22:16] What the future holds for AI adoption—and why the heavy lifting might get easier

Featured Quote "AI adoption compounds all of our existing challenges—and then multiplies them by five or ten times." — Nirmal Ranganathan

Connect with Nirmal

• LinkedIn: https://www.linkedin.com/in/rnirmal/

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In this episode, we dive into the real-world experimentation of Generative AI (Gen AI) with Naveed Asem. Naveed shares his hands-on experience in identifying, testing, and scaling AI-driven solutions. We discuss how organizations should approach experimentation, set success metrics, manage stakeholders, and navigate governance challenges.

If your company is exploring Gen AI or struggling with moving AI pilots to production, this episode is packed with insights to help you move forward.

Key Takeaways

🔹 The 4P Framework for AI Adoption – Platforms, Potential, People, and Policies form the foundation for AI experimentation and implementation.

🔹 Prioritization Strategy – Wex uses an impact vs. complexity matrix to determine which AI projects to pursue.

🔹 Iterative Product Development – AI projects need constant evaluation due to the rapidly evolving technology landscape.

🔹 Governance and Risk Mitigation – Hallucination-free AI is critical for financial institutions, requiring strict regulatory and security measures.

🔹 Measuring ROI in AI – The challenge isn’t just in cost savings but also in tracking efficiency gains and organizational impact.

🔹 Change Management – Early AI adoption requires executive buy-in and employee education to drive acceptance.

Timestamped Highlights

[00:00] – Introduction to the episode and guest, Naveed Asem

[00:01:20] – Overview of Wex and its role in fintech and payments

[00:02:10] – How to identify business needs that align with AI solutions

[00:03:45] – The 4P framework: a structured approach to AI adoption

[00:06:00] – How Wex prioritizes AI experiments using an impact vs. complexity framework

[00:08:41] – Establishing measurable goals for AI projects – revenue, productivity, and customer satisfaction

[00:12:03] – The AI product development lifecycle: discovery, delivery, and optimization

[00:14:50] – Navigating challenges with generative AI: hallucinations, governance, and security

[00:16:46] – The role of governance in AI – from acceptable use policies to ethical considerations

[00:21:11] – The impact of AI on jobs and processes – change management in action

[00:25:28] – How companies are evaluating ROI for AI and tracking efficiency improvements

[00:28:50] – Where to connect with Naveed Asem for further discussion

Quote from the Episode

"AI governance isn’t just about policies—it’s about ethics, security, and ensuring that what we build is trustworthy and aligned with real-world needs." – Naveed Asem

Connect with Naveed Asem

📌 LinkedIn: Naveed Asem

In this episode, Amir Bormand sits down with Jeremy Goldsmith, VP of Engineering at Branch, to explore leading with purpose and how it impacts engineering teams. Jeremy shares his philosophy on leadership, the psychology behind motivation, and how connecting individual contributions to a larger purpose can unlock potential and drive performance.

This conversation is a must-listen for engineering leaders, tech managers, and individual contributors who want to cultivate a stronger sense of purpose in their work and teams.

Key Takeaways

🔹 Clarity in Purpose Fuels Performance – Helping engineers understand the bigger picture leads to higher engagement, motivation, and job satisfaction.

🔹 Connecting the Dots – Leaders must translate business strategy into meaningful work for individuals and teams.

🔹 Balancing Strategic & Tactical Thinking – The best leaders can zoom in and out, ensuring both long-term vision and day-to-day execution are aligned.

🔹 Psychology in Leadership – Jeremy's psychology background plays a big role in how he manages and motivates his teams.

🔹 Hiring for Purpose Alignment – Engineers often self-select into mission-driven companies; leaders should recognize this when hiring and retaining talent.

🔹 Authenticity Matters – You can’t fake purpose—people see through it. Leadership must be genuine in their messaging and actions.

🔹 Vulnerability as a Strength – Great leaders model growth and development, making it easier for their teams to do the same.

Timestamped Highlights

⏱ [00:00:00] Introduction – Jeremy Goldsmith joins the show to discuss leadership and purpose-driven engineering.

⏱ [00:01:00] What Does Branch Do? – Jeremy explains deep linking and how Branch enhances digital experiences.

⏱ [00:03:00] Defining Leading with Purpose – Helping engineers see meaning in their work improves engagement.

⏱ [00:07:00] Connecting Work to Strategy – Why engineers need clear links between daily tasks and company goals.

⏱ [00:10:00] Avoiding Corporate Jargon – Leaders should communicate in plain language to build trust.

⏱ [00:12:00] Unlocking Potential – How purpose ties into motivation and high performance.

⏱ [00:14:00] Mission-Driven Workplaces – Jeremy’s experience at Tendril and how mission impacts hiring and culture.

⏱ [00:17:00] Do Engineers Self-Select into Companies? – How job seekers evaluate company missions.

⏱ [00:19:00] Psychology and Leadership – Jeremy’s background in psychology and how it informs his leadership style.

⏱ [00:24:00] Getting to Know Your Team – Why spending time with individuals leads to better outcomes.

⏱ [00:26:00] Being a Work in Progress – Modeling self-improvement as a leader.

⏱ [00:27:00] Part Two Teaser – A future episode on finding the right workplace fit.

Quote from the Episode

"Leading with purpose isn’t just about inspiring teams—it’s about helping them connect their daily work to something bigger. When people see the impact they make, they perform at a higher level." – Jeremy Goldsmith

Connect with Jeremy

📩 LinkedIn – https://www.linkedin.com/in/jeremygoldsmith/ (Mention the podcast when reaching out!)

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In this episode, I sit down with Nate Sullivan, the CTO at Academia.edu, to discuss technology strategy and special projects. Nate shares insights on identifying and prioritizing emerging technologies, balancing innovation with business needs, and collaborating with product teams to drive impactful technical advancements.

Key Takeaways

The Role of a CTO in Special Projects

Nate focuses on exploring technologies a few quarters ahead while aligning them with business strategy.

Balancing Exploration with Business Needs

Special projects should solve real business problems rather than being just theoretical experiments.

AI & Its Application in Research

Academia.edu has developed AI-powered tools like Academia Answers to extract insights from millions of research papers.

Assessing Emerging Technologies

Instead of chasing distant tech trends, Nate prioritizes technologies with immediate use cases.

Product & Engineering Alignment

Special projects often treat the product team as the customer, ensuring technical capabilities translate into valuable user experiences.

Measuring Success & Knowing When to Pivot

Projects have clear time-bound evaluations—if results don’t align with expectations, it's time to reassess or move on.

Timestamped Highlights

[00:00] Introduction to the episode & guest, Nate Sullivan

[00:01] What Academia.edu does & Nate’s role as CTO

[00:02] Exploring technologies for future implementation

[00:04] AI-powered innovation at Academia.edu (Academia Answers)

[00:07] How far ahead should companies look at emerging tech?

[00:08] Working with the product team—when and how to involve them

[00:12] Deciding when to stop a special project that isn’t progressing

[00:14] Evaluating success: AB testing & impact on users

[00:17] How the special projects team operates within engineering

[00:19] Communicating progress to leadership & aligning with company goals

[00:21] Final thoughts & how to connect with Nate

Notable Quote

"I think the crucial thing is building something that a customer actually wants, which is very hard as we all know. It requires really understanding their needs, but also knowing about tools that they might not naturally think of." — Nate Sullivan

Connect with Nate

Find Nate on LinkedIn: https://www.linkedin.com/in/nate00/

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Join Amir Bormand in a captivating discussion with Anand Madhavan, VP of Engineering at Everlaw, as they explore innovative development models, the art of integrating into new company cultures, and why Everlaw prioritizes quality code over strict deadlines. Anand reveals how Everlaw's unconventional engineering practices cultivate a sustainable, high-quality development environment focused on long-term success.

Key Takeaways:

Embracing a Unique Development Model:

Everlaw's "no deadlines" philosophy promotes quality and sustainable software.

Prioritizing thoroughness over speed helps maintain high developer morale and lower burnout.

Cultural Alignment for Engineering Leaders:

Importance of assessing and understanding company culture in the initial 90 days.

Utilizing frameworks (e.g., "The First 90 Days" book) to align leadership approaches effectively.

Addressing Technical Debt Strategically:

Technical debt is proactively managed through documentation, careful planning, and supporting engineers in tackling issues as they arise.

Engineering managers at Everlaw play a critical role in protecting the team from unrealistic pressures, ensuring long-term code health.

Balancing Customer Needs and Engineering Excellence:

Everlaw commits to "shipping functionality with healthy urgency," ensuring customer satisfaction without compromising technical integrity.

Clear communication and disciplined sales practices prevent the premature selling of undeveloped features.

Timestamped Highlights:

[00:01:00] Introduction to Everlaw and its role as a "truth-finding machine" in litigation.

[00:02:30] Anand’s perspective on assimilating into new engineering cultures using insights from "The First 90 Days."

[00:05:00] Core concepts of Everlaw’s unique engineering model.

[00:09:00] Discussing the "no deadlines" model and its positive impact on developer happiness and productivity.

[00:15:00] Strategic approach to managing and minimizing technical debt.

[00:18:00] Importance of managerial support and autonomy for empowering engineering teams.

Engaging Quote:

"Developing software is like a long hike—take one step at a time, enjoy the view, leave no garbage, and trust the team to make the right decisions."

Connect with Anand Madhavan:

https://www.linkedin.com/in/madhavananand

Enjoyed the episode? Don't forget to like, subscribe, and share your thoughts in the comments!

In this episode, Amir Bormand welcomes Scott Persinger, CEO of Supercog AI, to discuss the rise of agentic AI and its applications in modern businesses. They explore the evolving role of AI agents in knowledge management, team collaboration, and enterprise automation, particularly in environments like Slack. The conversation delves into how AI is reshaping work, privacy and ethical considerations, and the future of human-AI collaboration.

Key Takeaways

🔹 Agentic AI vs. Traditional AI – Agentic AI enables systems to plan, reason, and take iterative actions based on real-time feedback, offering more dynamic solutions than static AI models.

🔹 The Evolution of AI Agents – While agent-based AI has been discussed for decades, recent advancements in large language models (LLMs) have made them more effective at executing complex tasks autonomously.

🔹 Enterprise AI Adoption – Companies are sitting on vast knowledge bases, but AI’s ability to search, retrieve, and process unstructured data is still evolving. AI agents in Slack and other platforms can help bridge this gap.

🔹 AI in Slack and Workplace Collaboration – By integrating AI agents into platforms like Slack, businesses can automate and streamline workflows without complex engineering resources.

🔹 The Debate on Vertical vs. General AI – While some believe AI should specialize in vertical applications (like finance, healthcare, or insurance), others argue that general AI will soon be capable of handling multiple domains with high proficiency

🔹 Computer Vision & AI Agents – The next step in AI evolution includes visual data processing, allowing AI agents to analyze screenshots, documents, and interfaces, making interactions even more seamless.

🔹 Privacy & Ethical Concerns – As AI agents gain access to corporate communications and historical data, privacy and governance will play a crucial role in adoption.

🔹 The Role of Humans in AI-Driven Workflows – AI adoption will likely involve human oversight ("human in the loop") in the short term, but as AI improves, trust in fully autonomous systems may increase.

Timestamped Highlights

[00:00] – Introduction to Scott Persinger and Supercog AI’s mission.

[02:00] – The origins and resurgence of agentic AI.

[05:00] – Vertical AI vs. General AI: Which will dominate?

[08:00] – Why knowledge search and retrieval are AI’s low-hanging fruit.

[11:00] – How AI agents in Slack enhance team collaboration and efficiency.

[14:00] – The rise of AI-powered assistants instead of AI replacing human workers.

[16:00] – Computer vision as the next leap for agentic AI.

[18:00] – Privacy and ethical concerns with AI searching historical corporate data

[21:00] – The human-in-the-loop debate and how much control people will retain over AI decisions.

[24:00] – The shift from multi-source research (Google) to single-answer AI interactions.

[27:00] – Final thoughts on AI’s future and how people can connect with Scott Persinger.

Quote of the Episode

"We’re heading towards a world where AI models will know more than any human could, and that will fundamentally change how we trust information and make decisions." – Scott Persinger

Connect with Scott Persinger

📧 Email: [email protected]

🔗 LinkedIn: https://www.linkedin.com/in/scottpersinger

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👍 Like & Subscribe – Don’t miss an episode!

📢 Share – If you found this insightful, send it to a friend or colleague.

💬 Comment – Let us know your thoughts on agentic AI!

🎙️ Tune in next time for more conversations on the future of technology!

In this episode, Amir Bormand chats with Jake Moshenko about his journey from engineer to founder, the challenges of identifying the right startup ideas, and navigating both venture capital and bootstrapping. They also discuss decision-making, failure, and the growing role of AI in authorization systems.

Key Takeaways

Turning an Idea into a Startup: Jake emphasizes the importance of picking the right problems to solve—ones that are painful, widely felt, and worth paying for.

The "Muscle" of Evaluating Ideas: Recognizing which ideas are viable requires experience and learning from past failures.

Bootstrapping vs. VC: Both approaches have trade-offs—bootstrapping requires patience and personal risk, while VC funding adds pressure but accelerates growth.

Decision-Making as a Founder: Founders must make decisions without perfect information and delegate when possible.

AI and Authorization: AI companies face similar security challenges as traditional applications, and AuthZed is helping businesses ensure secure, permission-based access.

Timestamped Highlights

[00:01:00] – Introduction: Jake’s journey from engineer to startup founder.

[00:02:30] – The origins of AuthZed: Identifying a need based on past experience.

[00:05:00] – How Jake evaluates startup ideas using his three criteria.

[00:09:30] – Learning from startup failures and developing a critical decision-making "muscle."

[00:14:00] – Decision-making as a founder: Confidence, risk tolerance, and analysis paralysis.

[00:18:00] – Bootstrapping vs. venture capital: The key differences and challenges.

[00:21:00] – AI and security: How AuthZed helps AI companies protect data.

[00:24:00] – Where to connect with Jake and final thoughts.

Quote from the Episode

"You need to fail a lot. You can over-index on success stories, but real learning comes from understanding why things didn’t work." – Jake Moshenko

Connect with Jake

Website: https://authzed.com/

Discord: Join via the link on AuthZed's homepage

In this episode, Srini Rajagopal joins us to discuss how Generative AI (Gen AI) is transforming the engineering landscape. We explore the challenges of integrating AI into legacy products vs. building AI-first solutions from the ground up, the impact on developer productivity, and how teams prioritize AI-driven innovation while bringing stakeholders along for the ride.

🔹 How should engineering teams think about AI adoption?

🔹 Where do AI-driven efficiencies actually go?

🔹 What does success look like in AI integration?

Srini shares actionable insights from his experience leading engineering at Navan Expense, a major travel and expense platform, as they leverage AI to unlock hyper-personalization, automation, and developer velocity.

🎯 Key Takeaways

✔ AI Adoption Strategy: Organizations must retrofit AI based on user needs rather than forcing AI into existing product frameworks.

✔ Legacy vs. Ground-Up AI Integration: Legacy products pose challenges with user experience and expectations, while AI-first solutions provide faster innovation cycles.

✔ AI’s Impact on Developers: Engineers are evolving into problem solvers and editors rather than just coders, shifting left into the business side of decision-making.

✔ AI-Driven Efficiency: AI reduces manual coding time, enabling engineers to iterate faster, focus on strategic problems, and deliver business impact.

✔ Guardrails for AI Implementation: AI-driven solutions require a probabilistic mindset—instead of strict rules, companies must define what "wrong" looks like and use AI to monitor itself.

✔ The Future of AI in Engineering: Expect a shift toward natural language-driven development and more automation in business logic and rules-based programming.

✔ Measuring Success: AI adoption should be tracked through customer value, impact on developers' velocity, and measurable efficiency gains—not just cost savings.

⏱️ Timestamped Highlights

[00:02:00] – The Two Key Factors in AI Integration: Solving existing inefficiencies vs. unlocking new possibilities

[00:04:00] – Personalization at Scale: How Gen AI customizes data views dynamically for Navan users

[00:06:30] – Prioritizing AI Features: Balancing business value, feasibility, and innovation risks

[00:08:30] – Managing Stakeholders: Keeping internal teams engaged even when AI adoption takes time

[00:09:45] – AI’s Impact on Developers: Shifting from code generation to business problem-solving

[00:12:00] – The Future of Engineering: AI will push engineers toward higher-level decision-making and automation

[00:15:00] – The Complexity of Bringing AI into Legacy Products: Navigating accuracy, consistency, and user expectations

[00:17:30] – Lessons Learned: How AI speeds up internationalization and the importance of self-regulating AI guardrails

🔥 Quote of the Episode

"Developers are evolving from just writing code to solving real business problems—AI is pushing engineering toward strategic thinking and automation." – Srini Rajagopal

📢 Connect with Srini Rajagopal

🔗 LinkedIn: https://www.linkedin.com/in/srajagop/

🐦 Twitter: @SriniRajagopal

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👉 Share this with a fellow engineering leader or AI enthusiast

👉 Subscribe & leave a review to help more people discover the show

👉 Got thoughts or feedback? Drop a comment!

In this episode, Amir Bormand is joined by Jay Como to discuss how organizations can simplify data governance. Jay shares insights on shifting the perception of governance from a bureaucratic burden to a business enabler, how emerging technologies like Gen AI can assist in governance challenges, and strategies for making governance policies more effective and accessible.

🔑 Key Takeaways

Data Governance Has a Stigma – But It Can Be Overcome

Traditional governance has been slow and bureaucratic. The key to transformation is making governance more business-focused and outcome-driven.

Governance Needs Storytelling and Sales Skills

Successful data governance leaders are not just compliance experts—they understand how to "sell" governance by tying it to business outcomes.

Balancing Governance and Business Agility

Organizations must strike a balance between strong controls and flexibility to support commercial goals.

Gen AI and Data Governance

Generative AI is evolving as a tool to enhance governance processes, from data quality rule identification to metadata collection.

Start Small, Build Trust

If you're looking to simplify governance, start with listening, identify quick wins, and build relationships with stakeholders.

Breaking into Data Governance

You don’t need a governance background to succeed. Strong business knowledge, digital fluency, and a strategic mindset can help professionals transition into governance roles.

🕒 Timestamped Highlights

[00:00] Introduction to Jay Como and the topic of data governance

[01:30] The perception vs. reality of governance—why it feels slow and heavy

[03:45] How to make governance an exciting career choice

[06:30] Balancing strong governance with commercial agility

[10:00] Why governance should be “sold” as a business enabler, not a compliance cost

[12:00] The correlation between data quality and governance complexity

[14:30] How Gen AI can help automate governance processes

[18:00] Steps to simplify governance processes within an organization

[21:00] The cultural aspect of governance—getting leadership buy-in

[25:00] How professionals from outside governance can transition into the field

[28:00] How to connect with Jay Como

📢 Quote of the Episode

"Governance should not feel like a roadblock—it should be the foundation that enables better business decisions, faster execution, and stronger data quality." – Jay Como

🔗 Connect with Jay Como

📍 LinkedIn: https://www.linkedin.com/in/jaycomoiii/

📢 Enjoyed the episode? Like, subscribe, and share with someone who would benefit from this conversation!

In this episode, Amir Bormand sits down with Ganesh Datta, Co-founder & CTO of Cortex, to dive deep into engineering excellence—what it means, how to measure it, and how to build it into the culture of a technology organization. They explore product thinking, shared standards, accountability, and continuous improvement, as well as the challenges of maintaining excellence across different types of companies.

Whether you're an engineering leader or a developer striving for high standards, this episode provides valuable insights into how to define, implement, and sustain engineering excellence in your organization.

Key Takeaways

Engineering Excellence is Continuous: There’s no final state of “excellence”—it’s about ongoing improvement and iteration.

The Four Pillars of Engineering Excellence:

Velocity – How fast can the team deliver?

Efficiency – Are resources being used optimally?

Security – Is the system safe and resilient?

Reliability – Can users trust the system to work as expected?

Business Alignment Matters: Excellence should align with business goals, whether that’s innovation, efficiency, or reliability.

Engineering Culture is Key: Excellence isn’t just about processes and metrics—it’s about visibility, accountability, and fostering a mindset of improvement.

Standardization vs. Flexibility: While setting clear standards is crucial, organizations must adapt their definitions of excellence based on their unique challenges and priorities.

Timestamped Highlights

[00:00:00] – Introduction: Who is Ganesh Datta, and what is Cortex?

[00:02:00] – Defining engineering excellence and why it differs by company.

[00:05:00] – Engineering excellence as a cultural foundation, not just an end goal.

[00:07:30] – Measuring excellence: The role of metrics and how to avoid focusing on lagging indicators.

[00:10:30] – Overcoming resistance to engineering standards and ensuring adoption across teams.

[00:12:30] – How business drivers shape engineering standards.

[00:15:30] – Why excellence is different for every company: Comparing OpenAI vs. a large financial institution.

[00:18:00] – How CTOs can translate business goals into engineering priorities.

[00:21:00] – Ensuring consistency: How to sustain high standards year after year.

[00:23:00] – Where to connect with Ganesh Datta for follow-up questions.

Quote of the Episode

“Engineering excellence is not an end state—it’s a culture of continuous improvement. You’re never truly excellent, just more excellent than before.” – Ganesh Datta

Connect with Ganesh Datta

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

Email: [email protected]

Cortex Website: cortex.io