Santosh Vutukuri is an adjunct faculty member in various analytics stacks, from business intelligence to AI and ML. 

He is well-known in the industry for his outstanding data visualization skills, mentorship in data science, blogging, and podcasting. 

INDIAai interviewed Santhosh to get his perspective on AI.

How did you get started in AI as a mechanical engineer? What inspired you to continue?

My journey in AI has started as a byproduct of my continuous learning mindset. I started my career in 2006 as a quality assurance consultant through campus placement. As part of the role, I am supposed to interact with people at various organizational levels to follow standard practices. For more than a decade, I was in this role at various organizations and created a positive impact for myself. Technically, before I leapt to undergo the Advanced Management Program in Business Analytics (AMPBA) from Indian School of Business (ISB) during 2017-18, I used only Microsoft Office stack, but I learnt the power of processes and convincing capabilities to various people around me. The Quality Assurance role emphasized me to be a continuous learner and drive process improvements. Fortunately, my aspiration to study at ISB led to entry into Analytics and AI. Every day, I am motivated to continue in the AI stream because it has three components: EXPLORE, EVALUATE, and EXPERIENCE, with VALUE at the centre.

What challenges did you face at the beginning of your career?

Of course, there were challenges. While I prepared well in every stage of Analytics academics at ISB and secured the best capstone and deans listed, starting a new career was tough. Why would someone want to accept someone into a technical role in Analytics with ten years of non-technical quality assurance experience? It is a perception problem. Having a career in Analytics and AI doesn't mean you are dumping your experience and starting afresh. Your perception has moved towards a data-driven decision-making process. 

What was the most significant difference between your analyst and data scientist role?

As an analyst, I used to focus much on analyzing the data, processes and people with an objective of repeatability and eliminating people or heroic culture in organizations. In contrast, as a data scientist and analytics consultant, I aim to get the best use of data for enabling business decisions through diagnostic, predictive, and prescriptive analytics complemented with AI. AI questions your decision process with proven historical objective data such that the chances of failure are minimal.

Please describe your daily routine as your organization's Director of Analytics and AI.

While the designations don't matter in this data revolution, I act as a team member, mentor, individual contributor, solution designer, business analyst, problem solver and developer. I handle multiple use cases under the text intelligence vertical of the analytics and AI team. We build text intelligence-enabled platforms for our clients and internal operations team, who act as SMEs (Subject Matter Experts). I work in the financial domain, publishing journals and articles, ESG investing, document extraction, generative AI, deep learning, sales support, POC development, team mentorship, team management, strategic and tactical dashboards, brainstorming for ideations, leadership and knowledge talks.

Is it necessary to have a good programming background to seek a job in AI? What are your opinions?

As I said, it's essential to have an analytics mindset. I am very passionate about building macros in Excel while I was in the Quality Assurance role and being passionate about Excel is like being analytical.

With the advent of Generative AI, programming has become a secondary skill, but is required. More important in an Analytics and AI career is a structured problem-solving mindset with data. All technologies and tools follow later.

What is the one quality that every AI professional must have?

Questioning mindset or Inquisitiveness, Truthful, Exploratory and Experimental Mindset. Dare enough to speak the truth, Value Only mindset and lastly, continuous learning and networking

What are some of the most intriguing research articles and publications you have encountered while researching AI?

The work which we did and are doing at our current organization directly compliments climate improvement through ESG (Environmental, Social and Governance). We extract the ESG data for 100's of companies over a while from 1000's of documents like annual reports, sustainability reports, and 10k files. Scaling the deep learning solution is the most challenging problem we ever faced. An outstanding research towards sustainable living using AI is something inspired by. We are improving our solutions in each use case through generative AI capabilities.

What advice would you offer students and professionals interested in pursuing a career in AI?

Start by reading "Artificial Intelligence News" on Google every day. It will be followed by taking a few data samples from data.gov.in and exploring using Excel. Take your own WhatsApp data, start asking questions and find answers. While this is to kick-start your brain towards analytics, I strongly recommend going through a formal academic program because paying and learning make you disciplined and motivated. Peer group learning is the essence of formal learning. Your motivation should be "How can I put data to use to generate better decisions?" which is the core objective of Business Analytics.