With a vast experience of over 20 years working in IT-related activities, Anoop Kumar has held several leadership positions in e-commerce, digital transformation, and program management. His rich arsenal of knowledge and skills in artificial intelligence (AI) and machine learning (ML) has enabled him to implement change in various sectors which include high-tech, healthcare, financial services, and automotive. During this interview, Anoop speaks about the evolution of e-commerce, how AI and ML will enhance user experience, and the obstacles that a program manager faces while dealing with many complex interactions in a digital world.
Q1: Anoop, you’ve led large-scale digital transformations across multiple industries. Particularly in e-commerce, where do AI and ML come in your work?
A: In the e-commerce space, AI and ML are seen more like a platform than an expansion capability in the sense that they are less likely to be used as a mere enhancement of customer service but expansion of businesses. As an e-commerce technical program manager, for instance, I have seen how AI can advance in real-time to make the shopping process more relevant while supplementary ML technology is working on the supply chain and inventory control as optimization scenarios. In e-commerce contexts, for instance, AI is utilized in recommendation systems that support market makers by accurately analyzing web users’ actions when they’ve been looking for goods and so, recommending the right products. This close level of personalization makes consumers happy and increases the rates of conversion.
I had the pleasure of using these technologies in my projects to optimize the activities in all directions. Be it the use of AI for price optimization or the use of ML techniques for anticipating the demand for a particular product, there is more than what meets the eye in terms of their possible applications. Of great interest to me is the ability of such technologies to take over and speed up a lot of things that require decision-making from the huge amounts of data that exist within the business to increase competitiveness.
Q2: You mentioned that you have applied both AI and ML in several fields. How is e-commerce different compared to other applications in that respect?
A: E-commerce is unique as it operates in a space where there is a constant change in user behavior. In the case of other sectors, such as healthcare or automotive even though they apply technologies, the efforts may be directed more towards processes or long-term results rather than actual engagement with the client as is the case with e-commerce where the aim is to impact the client in real time. AI and ML in e-commerce have no room for complacency as they must respond quickly to changing customers, markets, and inventories.
In my view, AI and ML applications should not only be very scalable but also be a very dynamic e-commerce environment. For instance, when it is that time of the year or when there is a marketing campaign; the number of customers visiting and purchasing from the website is at its peak. This is where AI comes in handy because it can automate some of the things by changing the content of the website dynamically, anticipating stockouts, and even monitoring transactions for any fraudulent activities in real-time. Machine learning assists in improving the efficiency of the search for products, the routing of deliveries, and the forecasting of customers’ preferences. This makes e-commerce one of the most wonderful fields for AI and ML applications due to its level of competitiveness.
Q3: One of the key challenges in program management is ensuring all the teams can work together efficiently as a unit. How do you cope with cross-functional teams when dealing with new technologies in the field for instance AI, ML, and others?
A: Working with cross-functional teams, especially with the emerging technologies in focus, is not just working with numbers but with several other parameters of the project: business. When inter-disciplinary teams involving AI and ML are concerned, the collaboration between data scientists, engineers, and business people around the project, its goals, and deliverables becomes very important. My response is to provide a universal understanding and structure for working together. For example: I have all the team members know the business value of the technology- how AI or ML will improve the customer experience and how it can improve business productivity.
Furthermore, I also emphasize the building of a culture of trust and responsibility. Frequent meetings, a shared commitment to deadlines, and a collective work environment established through means such as Jira and Confluence assist in making people consistent. One more thing that needs to be taken into account is the necessity of being agile in such projects. Such technologies as AI and ML develop in a very fast-paced manner and there will be instances when the project team will need to change data models or algorithms or even the business. In managing teams with members from different units, everyone needs to be vested in the project’s objectives and not just take each of the tasks as ‘work’.
Q4: Working in several organizations, you held leadership to construct and execute multi-million projects. What would in your opinion be the decisive elements while introducing AI and ML to extensive-scale e-commerce activities?
A: The two most important aspects in the context of large-scale e-commerce initiatives regarding the use of AI and ML are the scalability of the design and the quality of the data. A good AI or ML model is as good as the data that goes into it, and the first step is making sure that your data is clean, complete, and relevant. In e-commerce, this can include the data coming from the customers used: their orders, product catalogs, and inventory to the open sources or social.
Scalability is another factor to consider. Many e-commerce companies are also struggling with fluctuations in demand due to the seasonality of the business. AI and ML solutions have to increase in capacity to deal with the bigger loads of traffic, number of transactions, and quantity of data to process. I’m familiar with projects where such recommendations with the help of AI had to be implemented on the fly, such as changing product lists or prices, proposing through a chatbot, or contacting the customer via a messaging app to offer assistance. Scalability ensures that the system can operate efficiently under peak loads without compromising customer permissions.
At the end of the day, it is essential to outline the ROI as well. AI and ML are powerful approaches, but there are expenditures associated with them: computing, time, and management of data to name a few. Companies are compelled to search and define how these technologies will be beneficial in terms of growth and cost-effectiveness within operations before taking on these projects on a wider scale.
Q5: E-commerce is a very complex field to implement AI and ML technologies into. What are some of the key difficulties you faced when managing these projects and how did you overcome them?
A: Defusing conflicts among different stakeholders is very important while leading AI and ML projects in e-commerce for instance being faced with demands from investors. Typically, there is a gap between the capabilities of the technology and that which business suits expect. Unfortunately, AI and ML cannot be viewed as the cure-all—what is required are time, data, and continuous changes in strategy. In my case, I strive to be very precise and articulate whilst making it clear what expectations are being set from the very beginning to prevent such occurrences. I also make certain that the right data is being gathered and most importantly that there is a clear and relevant strategy for how AI and ML will in turn influence the business.
Another difficulty is the ever-changing nature of the technology in the world where AI and ML are very relevant. In the domain of e-commerce, for instance, AI and from time to time apply ML to the same environment. Given that the pace at which e-commerce operates is very fast, AI and ML models may not last for long. To offset this, I foster a culture of learning and experimenting within my teams. Models are reviewed; A/B tests are conducted, and AI and ML advancements are implemented to ensure that we are not on ‘the wrong side of history.’
Data privacy and security are, however, becoming a concern where there is always the need to use sensitive customer information. Therefore, the incorporation of comprehensive data governance policies and adherence to certain laws such as the General Data Protection Regulation is very necessary. We have had to build systems that both leverage AI and ML for value creation as well as help in protecting customer trust.
Q6: What is your opinion on the changes that AI and ML technologies will facilitate in the future of doing business, especially online?
A: AI and ML will go beyond this e-commerce and transformation in the other three areas. The most interesting one is hyper-personalization. Now businesses can have very specific personalization across the process from product propositions, marketing messages, etc to every customer, thanks to AI. Going forward your shopping experience will feel personalized to a level where there is a one-to-one relationship a customer has with a store without any threats of generic messaging raining on them, thanks to more complex algorithms. As for risks, AI will allow tackling supply chain and logistics as well. The demand can be forecasted more effectively with machine learning models, suggesting how scheduling the inventory or last-mile delivery can be optimized. The Race for e-commerce firms to have faster order cycles is already on, and AI will be at the center of these improvements.
Another area that will develop is customer service but enhanced with AI. E-commerce is about to acquire 24/7 support from shopbots to virtual shopping aids due to AI, which would also lower cost operations. Gradually, AI will pick up the skills of resolving complex queries posed by customers hence becoming a crucial aspect of customer relationship management.
Going forward, there will come a time when AI and ML will not only improve processes already in place but also facilitate the creation of new businesses. For example, through the application of predictive analytics, companies will easily know the demands of customers and avail goods or services even before the consumers are in search of the products.
Q7: How important is leadership in ensuring the accomplishment of an AI and ML strategy, especially in e-commerce-related projects?
A: Leadership is critical when it comes to the success of AI and ML initiatives. This is especially necessary in industries that are more complex, like e-commerce. The AI and ML projects must combine three characteristics: knowledge of technology and engineering, strategic vision, and, of course, insight into what the company’s customer needs. I see myself as a middleman between the technical specialists doing the construction of the projects and the business specialists who are expecting the fruits of that work.
As one of the senior leaders, one of the roles is amongst others the chief innovation officer. And thus, involves building organizations that tolerate risks, and that enable creative thinking. Considering that AI ML is still in the infant stages, adopting a trial-and-error approach is critical in coming up with great ideas. At the same time, however, leaders have to show and tell where else the AI strategy will take the company. In such cases for example within e-commerce. It is often about how to better serve customers, streamline the processes, and increase sales.
As in a sports team, it is important to play an advocatory role towards the issue of collaboration. Many different people participate in a range of roles in AI and ML projects: data scientists and data engineers, product managers, and marketing specialists. A leader should make sure that all these teams work in synergy towards a single end.
Q8: About cloud transformation projects, you’ve mentioned some. Where do cloud platforms sit concerning AI and ML technologies in e-commerce, in your opinion?
A: Cloud platforms are critical to the effective deployment of AI and ML to e-commerce. They afford the necessary facilities to handle a great deal of data, process huge computer programs, and increase the reach of AI assets when needed. Up until recently in my work as an architect, clients would hire me to conduct cloud transformation projects. The task would be to transfer legacy systems into an elastic cloud environment like Azure. It would help in cutting down the overheads and at the same time creating an environment for deploying AI and ML.
Cloud systems in e-commerce help manage load at peak hours as well as enable continuity in processes like breeds, AI production of products and recommendations plus stock smooth operation. In addition, cloud structures have inbuilt AI facilities like Azure Sky AI and ML tools that help design and deploy machine learning models more rapidly. This implies that e-business firms will be in a position to change their algorithms within a very short time for better customer satisfaction.
This facilitates teamwork in an organization increased by the use of a cloud structural framework in structures since information can be uploaded at the center or the cloud. In AI systems this is critical since high-quality active data is necessary. On the other hand, in e-commerce projects analog imaging and machine learning are combined using hitherto cloud dependence to give rise to new business opportunities in relation to AI.
Q9: What pieces of advice would you give to new e-commerce program managers, for example, those who want to use AI and ML?
A: For anyone who wishes to embark on a career in e-commerce program management with a specialization in AI and ML, my strongest piece of advice would be to adopt a habit of self-education. In a short period, the two areas will change many times for better or worse, and one will have to be aware of the changes. Every day there is something new appearing regarding different algorithms, frameworks, and applications for the IM program manager, it is important to know the technology and where it is being used from a business perspective.
Another key area is understanding the customer. AI and ML must strictly be treated as technologies for achieving tangible business objectives, not as technologies for the sake of innovation. The e-commerce business is customer-centered, so think about how these technologies would enhance customer satisfaction, encourage them to use the services, or increase revenue from sales. Whether it’s through personalization, the use of dynamic pricing, or predictive analytics, always remember who the recipient will be.
Finally, soft skills are equally as important as technical skills. All leaders need to be able to explain technical ideas to laypeople, work with teams of various skills, and even manage in a compassionate manner. There is a danger that such program managers will tend to improve their technical skills alone and ignore the importance of these managerial skills which are critical to ensuring the delivery of quality products.
Q10:Is there anything you would like to add regarding future trends of the e-commerce industry incorporating AI, ML, and digital transformation?
A: The new era of e-commerce promotes delocalization, offshore outsourcing, and the integration of the consumer, provider, and third-tier. E-commerce activity platforms or software will also know in advance what a consumer will be looking for and provide such shopping but in an even more advanced format than now. The next leap will be to take automation to the next level. Intelligent solutions will include real-time situational awareness that allows supply chain management and operations utilizing IoT systems that react and adjust to changing conditions.
Merging online and offline interactions is one of the areas that will see radical change. E-commerce companies will improve on total integration of different channels with the help of AI. For instance, virtual advisors will rise to the level of providing shopping preferences to customers or helping them look for what they want both in web stores and land-based shops. Furthermore, customers will be subject to better shopping experiences via augmented reality, when they will be able to virtually ‘wear’ certain items before buying them.
In terms of operations, AI and ML will improve on various backend processes – like inventory control, logistics, and fraud detection towards making e-commerce better in terms of cost and consumption. The quest for data application will be even more bearing with a population of companies that can manage this data in a satisfactory manner and be able to enjoy good business insights and even a competitive edge.
All in all, in my opinion, e-commerce would create an Auspicious movement of customers assisted by AI and ML in the future as well, further resuscitating and transforming digitally the needs of the customers along the way.
About Anoop Kumar
Anoop Kumar’s professional journey shows that he guided himself to apply technology such as AI and ML in the fast-growing e-commerce sector. His perspective on program management, digital transformation, and data innovation reflects his adaptability and out-of-the-box thinking. Anoop interfaces himself to accomplish goals of creating cross-functional teams and executing either medium or large-size constructions in such a manner that, reshaping the thinking and practices of many individuals is a clinched matter. As advanced and practical uses of AI and ML technology open new avenues of business, individuals with such vision as Anoop will create the future of e-business which has yet again new standards to achieve and new transformations to accomplish.
