An enlightening conversation with Pradeep Gulipalli, Co-founder and Chief Executive of Tiger Analytics, sheds light on Tiger’s approach to autonomous systems, talent acquisition in a niche market, and their strategies for scaling from a startup to an AI powerhouse amidst changing industry dynamics.
The landscape of analytics and AI has undergone significant shifts in recent years, marked by rapid advancements in technology and a growing emphasis on data-driven decision making. This evolution has seen traditional data analysis expand into realms of sophisticated AI, such as machine learning and predictive analytics, fundamentally altering the way businesses strategise and operate. As a result, the demand for specialised skills in AI and data engineering has skyrocketed, creating a competitive talent market where expertise in these areas is at a premium.
Organisations are now grappling with not only the integration of these advanced technologies into their operations but also the challenge of nurturing a workforce adept in these new paradigms. This changing landscape has necessitated a unique blend of strategic foresight and innovative talent management, aspects that Pradeep Gulipalli, Co-founder and Chief Executive of Tiger Analytics, has navigated with his team. In our interview, Gulipalli offers insights into how Tiger Analytics has adapted its business strategies to these technological shifts and how it’s addressing the complexities of talent development in an industry that is constantly evolving and expanding its horizons.
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Could you explain Tiger Analytics’ growth in business and talent development over the years?
In the initial stages of Tiger Analytics, we primarily focused on data analysis to aid human decision-making. However, we soon ventured into developing autonomous systems, a significant leap from our original scope. These systems were designed to make decisions independently, reducing the need for human intervention.
Our first venture into autonomous analytics involved creating a digital advertising system. This system autonomously identified optimal advertising locations, managed ad bids, and selected effective images, allowing our clients to focus solely on content creation.
Another pioneering project was in railway network maintenance. We developed a system integrated with railway sensors that could autonomously detect and address issues on rail tracks, automating problem identification and facilitating prompt maintenance actions.
These developments were early manifestations of what we now recognise as AI – which encompasses capabilities like processing speech and interpreting images and text. Initially, our endeavours in this niche field seemed ahead of their time, but they aligned well with the burgeoning AI industry, positioning us at the forefront of technological advancement.
Our growth trajectory has mirrored these technological advancements. In our first five years, we were a small team of 100, but as the industry evolved and new technologies emerged, we expanded rapidly. From a team of around 500 just a few years ago, we have grown to nearly 4,000, adapting swiftly to industry demands.
A key aspect of our growth has been providing a framework for problem-solving in AI, a concept applicable across various industries. This approach allowed us to onboard individuals who might not have complete expertise initially but could be empowered with our tools and frameworks, contributing significantly to our growth and innovation in AI.
There’s a noticeable excitement among our employees to work on new, cutting-edge technologies. However, the challenge lies in matching these interests with practical client projects where generative AI is applicable and beneficial. Not every project requires such advanced tech, leading to a mismatch between employee desires to work on these technologies and the actual opportunities available.
Tiger Analytics’ rapid growth from 100 to 4,000 employees raises questions about talent acquisition challenges. Was attracting quality talent easier during the initial growth from 100 to 500 employees?
Indeed, during our initial expansion from 100 to 500 employees, the talent pool in our field was not as vast, and our recruitment needs were more modest, focused on a specific 300-400 person expansion over a few years. Given our unique position in a then-niche market, attracting the right talent was relatively straightforward. We could easily engage potential candidates through platforms like Naukri, often finding them readily convinced by our value proposition.
However, the scenario has evolved considerably in scale from 500 to 4,000 employees. There’s now a larger pool of talent, but our requirements have also grown more complex. The current challenge lies not just in attracting talent but in discerning genuine expertise and experience amidst a diverse array of candidates. This means scrutinising for authenticity in skills and experience, as we now seek individuals who align with our company culture in both aptitude and attitude. The focus has shifted from merely attracting talent to strategically identifying and integrating the right individuals into our team.
Has the focus shifted more towards developing talent rather than just recruiting?
That’s an accurate assessment. Previously, our challenge was locating and attracting the right talent. Now, as we’ve grown, we recognise that simply finding talent isn’t sufficient. We need to cultivate it. Organisations seeking top AI talent might look to us as a source, so it’s essential that we not only hire but also actively develop and nurture this talent internally.
During the COVID period, Tiger Analytics reportedly maintained its hiring efforts. In light of the anticipated economic slowdown, how do you reflect on the decision to continue hiring?
During the economic challenges, including the recession and tech layoffs, we chose not to let go of our talent. This decision, made during tough times, has been remembered by our employees, fostering trust in the company’s stability and decision-making. This approach has been beneficial, especially in terms of reducing attrition. Currently, despite talks of a slowdown, our recruitment team is actively hiring. This is partly because the AI industry seems somewhat insulated from recessionary pressures, with a strong demand for AI capabilities. Our clients continue to seek AI-enabled solutions, allowing us to maintain business and hiring momentum regardless of broader market conditions.
Has the competition for acquiring talent in the market increased, especially given the emerging technologies? Additionally, how does India fare in this scenario, particularly in terms of talent for new and advanced technologies?
Yes, the talent competition has significantly intensified. We’ve seen various trends over the years, like the late 90s tech boom, and recently, a surge in demand for data engineering talent, crucial for building AI systems. The demand for AI skills continues to outpace supply. In the U.S., there’s a notable talent shortage in emerging technologies. In India, while there’s a large talent pool, the challenge lies in the depth of experience. Many have certifications in AI and related fields, but complex tasks require real-life problem-solving experience. India, with its strong foundation in engineering and IT, sees many professionals attempting to transition into AI, partially addressing this challenge. However, seasoned professionals with hands-on experience remain scarce globally, and India is no exception.
Can you elaborate on the types of analyses Tiger Analytics conducts for understanding employee sentiment and predicting long-term employee retention? How do you balance data analytics with human interactions in these processes?
At Tiger Analytics, we use data extensively for various internal purposes, including predicting hiring needs and evaluating employee success probabilities. For instance, we analyse factors like the number of job offers to make and the likelihood of candidates accepting them, as well as their potential success based on historical data. However, when it comes to understanding employee sentiment, we’ve found that direct human interaction often yields the most genuine insights. While we do collect a range of data through methods like team health checks, where teams self-assess and colour-grade their status, and discussions with talent partners, the core of our approach relies on personal conversations. These interactions provide valuable data, which we then analyse to identify trends like potential risks or dissatisfaction within teams. Despite being a data analytics company, we emphasise the irreplaceable value of the human element in understanding our employees, blending data-driven insights with personal engagement to address any issues effectively.
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Considering your emphasis on human interaction with employees, how do you differentiate between addressing concerns through AI, like your new bot, and through direct human engagement?
This is a crucial consideration for us, especially with the introduction of our AI bot for employee interactions. We’ve been deliberate in determining the bot’s role: it’s designed to handle operational queries like leave policies, reimbursements, project changes, and other process-related questions. These are areas where the bot’s efficiency can be maximised.
However, for more nuanced issues, especially those related to employee well-being or deeper workplace concerns – like dissatisfaction with a manager or questions about compensation – we rely on human interaction. These issues often reflect underlying sentiments that a bot might not adequately address.
The bot is currently in its pilot phase, primarily serving new employees who mostly have operational inquiries as they settle in. We’re learning from these interactions to fine-tune the bot for broader deployment, which we anticipate will take another six to nine months. Besides handling operational tasks, the bot also serves as a knowledge assistant, providing information about company projects or directing employees to the right resources. It’s a balance of using the bot for efficiency in certain areas while preserving the human touch for more complex, personal interactions.
As we approach the end of the year, could you share a key talent or employee challenge that Tiger Analytics has faced in 2023?
A major challenge this year has been aligning employee interests with client needs, especially regarding generative AI technologies like ChatGPT. There’s a noticeable excitement among our employees to work on new, cutting-edge technologies. However, the challenge lies in matching these interests with practical client projects where generative AI is applicable and beneficial. Not every project requires such advanced tech, leading to a mismatch between employee desires to work on these technologies and the actual opportunities available. Our task has been to navigate these technological trends, ensuring that while our team stays engaged with innovative work, it also aligns with the real-world needs of our clients.
The current challenge lies not just in attracting talent but in discerning genuine expertise and experience amidst a diverse array of candidates. This means scrutinising for authenticity in skills and experience, as we now seek individuals who align with our company culture in both aptitude and attitude.
With your significant hiring plans, can you elaborate on the types of roles Tiger Analytics is looking to fill? Have any new roles emerged in recent years?
Over the next 12 months, we plan to hire around 2,000 people. In recent years, we’ve seen the emergence of new roles, particularly in the realm of experience consulting, commonly referred to as UX (User Experience). This role is increasingly crucial as we integrate AI into our solutions. It’s not just about AI systems interacting with other software; it’s also about how these systems interact with users. Effective communication between humans and machines is key, and that’s where UX plays a significant role. Our UX team, which has been developing for over a year now, focuses on information visualisation and designing human-machine interactions. As AI becomes more prevalent, roles like these are becoming increasingly important and valuable in our industry.
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Among the 2,000 positions you plan to fill, is the role of experience consulting or UX one of the key areas you’re focusing on for recruitment?
Yes, we are actively seeking candidates for UX roles, though it represents a smaller segment of our overall hiring strategy. The bulk of our recruitment, about 80%, is focused on data engineers, AI engineers, machine learning experts, and data scientists. However, we’re also placing a significant emphasis on emerging areas like UX design and interface development, recognising their growing importance in our field. These roles, though not the majority, are a vital part of our expanding team.
About the expert: Pradeep Gulipalli, a passionate technologist, has been instrumental in establishing the core foundations of Tiger Analytics. His leadership and deep expertise in technology have been pivotal in developing the company’s advanced capabilities, processes, and teams.