Across the business landscape, digital transformation is the focus du jour. But as companies large and small look toward a post-pandemic existence, Rajashree Ramakrishnan believes they could learn a lot from retail about how to keep those trends in vogue.

Prior to being named chief marketing officer at Tata Consultancy Services earlier this year, Ramakrishnan led the Retail Solutions Group at TCS, India's largest IT services company, for more than 15 years. Like many in the industry, she saw the writing of digital transformation on the wall. But advocating for change and working with companies to actually implement those changes as the world has undergone a digital shift are separate beasts.

A recent TCS survey of nearly 300 companies across the globe found that only 24% have highly automated core business processes, a tenant of transformation Ramakrishnan believes should be central to every business. Now, as small and large businesses alike aim to find new ways to modernize their digital experiences, Ramakrishnan sees the lessons learned from the battles between ecommerce and physical stores as relevant today to all businesses as they have been retail in the past decade.

In an interview with Protocol, Ramakrishnan described the advantages she sees small businesses having in the current environment, the role of data-led projects across the value chain and the innovations she believes will define the next era of hybrid business operations.

This interview has been edited and condensed for clarity and length.

For years we've been hearing about the role of digital operations and innovation in retail, and in the last six months, we've seen acceleration in the adoption of those strategies. What's changed in the way you now advocate digital transformation to businesses?

Retail actually was one of the industries which had a much more advanced digital transformation agenda, and a lot of the other industries were trying to catch up. Needless to say, digital transformation is now really becoming very important across both the customer experience, but also the employee experience and the workplace of the future.

We've been hearing all these conversations about remote work and going to go back to work, and this actually takes me back to maybe about seven or eight years ago when the first online retail started to come into place. In those days, we used to go to every meeting, and the clients would ask us "are stores going to survive?" I remember talking to a very big grocery retailer in the U.S. who told me that you can never smell bread online, so there were these guys who said stores are going to continue to remain very important. And there were guys who said that stores are going to die. There was this constant fight between them, but what really happened was we came to this interconnected or omnichannel customer experience.

Omnichannel has been described very well in the context of retail, but I believe that's going to happen to every industry. Whether it's banking or manufacturing, there is going to be a space for the physicality of that activity, but there's also going to be a very strong interplay of digital.

That merger between the physical and digital experiences is something that would seem to be easier if you had the capital and scale to invest in the infrastructure to make it possible. But for smaller businesses, what's the step that needs to be taken to move toward that level of transformation?

Actually, I really believe this is an advantageous situation for small businesses. What stops us from taking some of these ambition journeys is really the mindset, and that sometimes works against large businesses. The challenge was never about funding. The challenge was never about the technology. The technology was available at a very cheap cost.

In the context of retail, many small businesses were actually threatening large businesses in more ways than one. If anything, technology has become more democratized, and this has advantages for small businesses because they do not have the barriers in the mind when it comes to making those transformative changes. They have organizations, which are a lot more agile [and have] less internal politics. We work with a number of large businesses, and we've been pushing them to think like small businesses.

I'll give you an example: I was talking to a customer in private banking, and private banking is a very high-touch business. You go to a bank, and you get treated like royalty because you have millions of dollars in your bank. They are now telling me this entire experience is shifting more and more to videos. So it's a great opportunity for a small business to quickly catch on and build that infrastructure. You don't have to build these fancy branches anymore.

Let's dig into that open playing field idea: With the increased emphasis now on technology adoption, what comes next after these ideas go from being strategies you've advocated for to requisites for business?

I would distinguish this into different technology layers. What I would call foundational, or what we at TCS called the digital core, is the adoption of cloud and getting security systems in place. What we've learned in the last six months is that the adoption of the foundational layer has become very rapid, but the full power of technology really comes into play when you go to the process layer and the business model layer.

[It's there] where these technologies are going to transform the way you serve your customers or drive efficiency, and people have still not explored that layer. I think that is where the conversation is starting to happen. Cloud has [already] happened or will happen, so I don't think our focus needs to be on that core foundational layer, but on the next set of opportunities. In my view, the next 12 months is going to be about learning from each of the other industries.

I would assume that a lot of that learning comes from new data, and as businesses continue to access new physical or digital channels, they'll be creating more of it. Again thinking about small businesses and bigger businesses alike, where's the evolution of that data collection leading?

This is a topic very close to my heart because I spent nearly 20 years in the retail industry, and in the last couple of years, we launched an industry framework called algo retail. The premise of that concept was that retail businesses were increasingly competing with businesses which were run by machines. Take Amazon as an example: Every retailer will tell you that Amazon is their No. 1 competitor, but Amazon is not a retailer. Amazon is a company which is run by algorithms. I used to tell retailers that this is an uneven battle; you have machines and humans fighting against each other.

Therefore, it's very important that businesses become algorithmic. There are three stages of evolution: One is automation, the repeated tasks which machines can do for you. The second is amplifying intelligence, which is using machines or algorithms to process information. And the third is a stage where the machine can think like a human. What we continue to tell our customers is that every process in the organization needs to get into one of these three stages.

If I take a process like pricing, Amazon does some millions of pricing shifts every day. But you will still find in retail organizations 200 people who are sitting and making those pricing decisions. That's an example of a process that can become completely algorithmic.

But what about a question like what is the style for the next season? That cannot be an algorithmic decision completely, but machines can provide you data insights. There are algorithms today which use computer vision to draw insights from every fashion show, and they can tell you what color, what silhouette, what size is going to work for the next season. That information can be fed into a designer or a fashion analyst at a large retailer to help them design the fashion better. And I can guarantee the companies who've [adopted algorithms] have performed much better than the ones who have not.

Shifting away from design or pricing, data is integral to the supply chain. It's an example where many businesses of all sizes are dealing with similar levels of disruption. For those companies looking to innovate coming out of this, what's stopping the next set of algorithms they rely on from becoming garbage-in, garbage-out?

No historic data could have helped you to design or manage supply chains in these last six months. It requires more similarity techniques and more artificial intelligence techniques which can simulate the current situation. There are three perspectives to this if you look at traditional systems. We always had systems that use data. I think the challenge with some of those systems was the use of a very small sliver of data. You use maybe four barometers, five barometers. When businesses go more digital, you gather more signals about what's happening in the world, what's happening with your customers and what's happening with your suppliers, your vendors. The expanse of the signals that you capture has exponentially gone up.

Second is the techniques of the cloud technologies, the AI models, which are now available to process those signals at scale. If you talked to researchers five years back, they would run a model, go home, come back in the morning and see the results, as opposed to being able to run that model in real time. You still use things like regression and time series which are not new mathematical models, but the underlying technology and tools that you now have to process these signals have become extremely strong, and that is what businesses are starting to leverage more.

The third is that business teams understand technology much better. If I take retail, for example, the way we talk about merchandising and retail was a lot like art. If you're a fashion retailer, you're supposed to understand fashion and you're supposed to understand colors and designs. But I think the science element of businesses everywhere has become pretty strong. Take a company like Stitch Fix. Stitch Fix has 50 Ph.D.s. Businesses now have people who actually understand the adoption of these technologies.

Some of the new developments, like reinforcement learning, are also very important because we traditionally had huge dependence on historic information. I think some of the new techniques which have come up are very attractive for businesses. And computer vision has been really game-changing for a lot of businesses. For medical science, retail, manufacturing and a lot of businesses, computer vision is available at scale, which was never the case even three or four years back. I think businesses need to now make this an important priority for them. A lot of things are falling together, and the pandemic, if anything, has shown us that this is the way to go.

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