The Tech Trek

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.

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.

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 highlights
00:35 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 takeaway
The 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

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.

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.