Barada Sahu(Barry) is the founder & CEO of Mason - the AI for Commerce. Prior to Mason, Barada was the founder and CTO of Storeo, where he pioneered media personalization by amplifying audio voices from Southeast Asia. 

Noteworthy for shaping content platforms and storefront architecture at Myntra, Barada is a Forbes Technology Council member and was Founder/CTO of Novogreen Energy Solutions, showcasing commitment to social impact. 

Coming to education, he studied Engineering from the Indian Institute of Technology, Kharagpur. With over 20 years of experience, Barada's journey previously showcased impactful leadership at Native5, Kony Solutions, Capgemini, and Ocimum Biosolutions. 

Let’s learn more about Mason and AI’s role in revolutionizing retail.

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We’re excited to have you here.

Thank you for inviting me over, Yasir. I'm happy to share my learnings with the community, having worked across consumer, enterprise, and SMB spaces. While straddling a large part of my career across product and technology, I find that product remains close to my heart due to its impact on our everyday lives. Feel free to ask anything, and I'd love to share my views. I enjoy the occasional workout, podcasts, and reading on a diverse range of topics. I would say I am a techno-optimist, and in the era of AI, my view is that we are hopefully about to birth a new species in our evolution.

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As we get started, could you share a little bit about what's happening in retail tech? Is that the 4th wave in retail?  

Well, I would share something about how I see Retail evolving over the years. If you observe, we are kind of keeping pace with the overall industrial revolution, leaning into more robotics and automation (the industry 4.0 wave). On the retail side, it has gone through some transitional shifts:

- 1st wave: Marketplaces and Amazon - about 30 years old now. We've seen the rise of online marketplaces as evolving forces in commerce, spreading goods and products worldwide. While Amazon continues to be a juggernaut, vertical and horizontal marketplaces continue to evolve, embracing technology along the way.

  - 2nd wave: Platforms - Shopify being one of the dominant stories globally. From SMB and creator commerce platforms to more enterprise-grade ones like Salesforce, SAP, and Magento.

  - 2.5 wave: Platforms have started to become more customizable, and we've seen a rise of more composable infrastructure guiding the rise of retail across multiple channels and best-in-class products. We are still in both 2/2.5 waves currently.

  - 3rd wave: The upcoming AI-native revolution - consumer-first commerce. Consumers expect retailers and brands to unify the shopping experience across various channels. It's a consumer-first commerce movement driven by AI at its heart. I'm excited to see this 3rd wave come to life, with innovation rising out of Asia and India.

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How did your experiences at Myntra and Storeo shape your approach to product management at Mason? Can you share some key challenges you faced in developing AI for Commerce at Mason, and how you addressed them?  

I'll try to give a perspective on both. Storeo was a passion project born out of my love for podcasts. My key learning was that markets need specific solutions, and timing is crucial. Build and launch fast, but learn fast and know when to let go.

On Myntra - working with a pack of the smartest consumer brains in India. It was an exciting time with talented teams in product, technology, and business. The learning on the product was about execution and quick bets while managing long-term vision and innovation. This feeds into what we are doing with Mason, balancing quick wins and focus on execution vs long-term vision. We've faced challenges in product-market fit (PMF), realizing it's not binary but a continuous journey in vertical software where understanding the problem space is as important as the GTM fit.

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Can you elaborate on how a person can transition into product without having a tech or MBA background? What are the steps you would recommend to proceed?  

While it depends, and I would say a technical background helps - I have always seen some unmistakable characteristics about good PMs:

- Be inquisitive and curious.

- Have empathy.

- Think on your own feet.

If you possess these characteristics, you do not really need a technical background. In today's world, learning about things is becoming increasingly easier - you can ask, lean in on ChatGPT (my favorite learning assistant, by the way), or just be part of communities and read. The knowledge is truly open source today. It's simultaneously very easy to get started but also super hard to be good at Product.

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How long did it take for the company to land their first 10 customers?  

The first 10 customers came real fast - we had launched on the Shopify app store as a channel, and it took us, I think, less than a week. We were fairly new in the category, and it worked as a flywheel.

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Shipping within a week is truly exceptional! Could you elaborate on how you go about building the tech? Was it like a bunch of APIs or highly technical?  

Ah, the build didn't take us a week - we had taken a very narrow use-case initially: how do we automate visual content updates in a webstore based on changes in business conditions like out of stock, new arrivals, and so forth. The build of the product took us about 15 days - but post-launch, it was real soon that we got some initial traction. A lot of it was - the use case was underserved and a very real one. Incidentally, it had come from an observation from a prior enterprise use-case we had worked on for a customer. While that was a custom build, we realized very soon that a very similar use-case could be relevant for a large number of SMB merchants - we productized it, and the rest was the genesis of ModeMagic. ModeMagic (our initial product) was a small experiment as part of our product portfolio, but it gave the initial wings for us to keep going down and doubling down on the adjacent use-cases in retail.

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How can product people from non-tech backgrounds leverage AI use cases or capabilities in a product?  

I would iterate on the same things - think from first principles. Take the example of any traditional use-case - say some workflow you are enabling or some new consumer experience which can be powered. How can AI (or any intelligence) really help with any workflow or use-case or consumer experience? Then you look at really seeing whether you can add in a degree of intelligence so that you make the workflows automated or make it faster. Can the AI go wrong - so how do I check for them, how do I need to train it - so what kind of data will I need to find (much like a younger junior person who joins your team and has no idea of what you do or how you do). Again, a lot of how to do things depend on context but importantly on your ability to really see past the tools available and really understand how intelligence (forget about the artificial part of it) can really help it... Then you say can you bring in AI with today's capabilities to really make that richer experience or automate the workflow. No easy answer here unfortunately - my thinking has been to look past the haze - the low hanging fruits are where it's always the easy things. I can build a wrapper around GPT - and there's probably a lot of them today. You should start from the consumer and the problem you are solving and work to the solution (where AI can really help).

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PMF is a challenge for many companies. Would you be able to share your journey for PMF?  

I have a very contrarian view on PMF - I honestly think it depends on the domain and the type of product/industry you are in. In a vertical industry, oftentimes you start with a prior understanding of problems and inefficiencies - so you need more than just a product to be able to make a solution that is adopted. While building products is essential - adoption of product and product love is really the fuel that gets all of us as builders going. So PMF, in a vertical industry - depends a lot on your understanding of the problem, your adoption of the solution, and whether you found design partners (early adopters) to build with. Unfortunately, it often isn't a binary indicator in such cases. In horizontal software - say the example of Loom, it's quite different - you are looking for a very specific pain point which is universal. In such a scenario, a deep understanding of consumer needs and workflows is super essential. While you can iterate often times, this requires this imaginative creation + relentless editor mindset - to know what's gonna really fit to make the workflow so smooth - that it becomes the de facto norm. You often have inflection points in such situations - there is a pre-PMF and post-PMF moment. But remember PMF can also be super fleeting, take the example of all the dozen video conferencing solutions that emerged during covid, take the case of Prisma - image editing app looked like the next Canva, or that of Clubhouse - went viral for a brief moment in our lives. True PMF - really arises by keeping your head close to who your product is for - the consumers. For us, it has been a journey across multiple types of iterations - we went viral, we gained a lot of growth, we had to face massive churn (post-covid effect), we had to go from growth to monetization and the right model of monetization and this journey has been very humbling because it has shown me several types of PMF - and how transitory it can be as well. I would say the good thing is we have been able to stick through both ups and downs to realize that now and understand that PMF is an ongoing journey where if you are not reinventing yourself - your PMF is eventually going to be eaten by the next new builder. Long drawn but hope that was insightful.

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Could you share how AI has impacted the retail engine at Mason? Secondly, whether, as an impact of AI, are there some things you had to rebuild at Mason?  

As we have worked in retail for a while - we have also started to see both the consumer side as well as the brand and the operator side of retail. 

Honestly, there are lots of inefficiencies in retail today - where AI can play a big role - and we see this shift coming rapidly as consumers move and switch channels, attention and loyalty. The analogy I like to give is - Games are always built with a Game Engine but unfortunately products and storefronts still are in the era of builders - we manually build and keep operating them to no end. 

As retail evolves to become more experiential - more consumer-first you will need an operating engine - and that's what we are doing at Mason today - bringing the AI based operating engine for retail. Our hope - in the future for brands - they will have a shopping engine much like games have a game engine. We had to keep building lots of parts but also trimming down scope - the key challenge with innovation in a new space where it seems like a red ocean is taking a different lens on the whole thing. We could have built a ton of tools - but the focus for us has been how do we synthesize these tools to finally enable retailers and brands - and so prioritization of what tools, how they connect to make the consumer shopping experience easier is really what drives product for us. 

With AI - it's been more upgrades rather than rebuilds - we have been working with AI for a while - I used to be part of the AI team at Myntra and worked on a bunch of initiatives so we have always kept close to the evolution cycle. Like I say - if you look at AI as evolution - you will not have a revolution

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With the market saturated with thousands of brands in each category, how can brands leverage AI tools, like those provided by Mason, to not only differentiate themselves but also stand out and capture the attention of their target audience?  

Again, look at what the shopper and consumer want and work backward from how you can use AI to enable that - whether on the experience side or on the operations side of the business. Consumers need convenience and want to be understood - AI enables that today. That said I am looking for all you folks to put on your thinking hats - what can AI do? Think a bit creatively, think beyond the box, and make the imagination fly - you will be sure to teach me a thing or two. 

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This is a very fantanstic and engaging session by Barada. For those who’d like to know about what’s happening in the Retail and AI, you can reach out to Barada over Linkedin(link here)

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