Talented software engineer and AI strategist on responsible AI adoption in banking, enterprise innovation, and the future of machine learning across industries.
Bank of America announced a $4 billion investment in artificial intelligence and advanced technologies, nearly one-third of its total technology budget. The move signals the growing centrality of AI in modern finance, with applications spanning customer service, fraud prevention, and operational automation.
This large-scale initiative places the bank among global leaders in enterprise AI adoption, raising urgent questions about how such technologies should be built, governed, and scaled responsibly in highly regulated industries.
To illuminate these questions, we talked to Saurav Sharma, a software engineer with over 15 years of experience, focusing particularly on back-end architecture. Saurav has delivered high-impact software solutions for global corporations and United States government agencies. At Caterpillar, he resolved recurring manufacturing disruptions by developing a custom application enabling plant supervisors to correct database issues immediately. He later consolidated twelve outdated systems into a single global platform, reducing operational costs and simplifying technical support across facilities in the United States, Canada, and Asia. At the transportation company J.B. Hunt, he automated the client bidding process for Walmart, generating $200,000 in additional revenue within the first month of deployment.
Saurav Sharma’s technical leadership has extended to nationally significant projects. For the healthcare reform portal known as ObamaCare, he developed core modules for insurance eligibility and hospital registration under the United States Department of Health. At the Federal Aviation Administration, he developed backend application programming interfaces (APIs) to support international flight coordination with the Swiss government. At Inovalon, a healthcare data analytics company, he implemented secure, role-specific user interfaces and single sign-on architecture, increasing platform adoption and contributing more than $500,000 in revenue. His achievements earned him the 2025 Cases and Faces Award in Engineering and an invitation to serve as a judge for the Globee Awards for Technology, underscoring his national and international recognition as a leading expert in software development.
Saurav, as an Onshore Lead responsible for mission-critical banking software and coordinating global development teams at Bank of America, how do you currently view the role of artificial intelligence in banking?
Artificial intelligence is becoming a core operational layer. We’re using AI to improve everything from customer engagement to internal efficiency and fraud detection. At Bank of America, for example, AI supports our digital assistant, helps detect transaction anomalies, and automates repetitive backend workflows that previously consumed a significant amount of time.
But more importantly, in a sector like finance, we’re making decisions that can affect a person’s credit, savings, or access to financial services. That means every model we deploy must be explainable, bias-checked, and compliant with evolving regulations. We apply rigorous testing and validation processes before any AI component goes live.
So, AI in banking is both transformative and sensitive. It holds enormous potential, but it must be implemented with caution, transparency, and accountability. We strive to maintain that balance every day.
At Bank of America, you helped establish an internal AI governance committee and validation processes that include fairness testing, bias detection, and documentation of model behavior. How has this changed how AI is developed and deployed at the bank?
Implementing those structures has significantly impacted our approach to AI development. First, it created a shared accountability model—AI decisions are made in collaboration with compliance, legal, and business teams. This has helped us build internal trust and ensure that everyone understands the risks and goals behind each model.
Second, the validation process provides a consistent framework for evaluating AI before deployment. We test for fairness, flag potential bias, and document how models behave under different conditions. This makes it easier to detect issues early and explain our models when needed, for example, during audits or customer reviews. Although it has made our development process slower in some cases, it is safer and more transparent, which is essential in banking.
In addition to your role at Bank of America, you also serve as Partner and Vice President at Baanyan Software Services. What kinds of challenges or opportunities are you seeing as more clients begin to adopt AI solutions across industries?
At Baanyan, we collaborate with organizations across various industries, including healthcare, retail, manufacturing, and education. In nearly every case, AI now emerges early in the conversation, whether it’s automating workflows, enhancing customer experience, or informing data-driven decisions.
The opportunities are significant. We’ve helped clients optimize inventory using demand forecasting models, build chatbots for customer service, and utilize machine learning to identify and mitigate operational risks before they escalate. However, adoption isn’t always straightforward.
The challenges vary. For some companies, the problem is a skills gap; they don’t have in-house teams ready to support or scale AI initiatives. For others, it’s about data readiness—you can’t do much with AI if your data is scattered or unstructured. And then there’s the aspect of change management. Adopting AI often means rethinking how teams work, and that can be just as important as the technology itself.
My role at Baanyan is to deliver solutions and to help clients realistically understand what AI can do for them and how to build toward that goal sustainably. That’s where strategy and empathy go hand in hand.
Mr. Sharma, at Baanyan, you have led AI upskilling initiatives and delivered practical AI solutions, positioning the firm as a hands-on leader in enterprise AI adoption. How are you preparing your teams for AI and ML in practice?
At Baanyan, I’ve focused on ensuring our teams have the skills and tools to work confidently with AI and machine learning. We started by launching structured training programs. Engineers learn to work with real-world data, build models, and understand how these models behave in different business contexts. The goal is to move from theory to application.
One of the most impactful projects was with a retail client struggling with inconsistent store inventory levels. We developed a machine learning model that analyzed historical sales, seasonality, and location data to predict demand more accurately. As a result, they significantly reduced overstocking and out-of-stock situations.
These hands-on efforts give our engineers practical experience and build their confidence. I mentor teams, review ideas, and connect them with client challenges. This is how we stay relevant and help clients see clear value in AI.
Looking ahead, what role do you see AI and machine learning playing in the future of banking, and how do you plan to integrate these technologies into your work at Bank of America and Baanyan?
Over the next decade, AI and machine learning will be central to banking innovation. We will see more automation in software development, IT support, and customer service, particularly using generative AI tools that can write code, troubleshoot issues, or intelligently respond to internal queries. At Bank of America, I’m exploring integrating these tools into our development pipeline to boost productivity and reduce operational friction.
At Baanyan, I’m working on creating an AI Center of Excellence, a dedicated unit focused on building custom AI solutions for our clients. We have extensive experience in finance and healthcare, and I aim to expand our mentorship for young engineers entering the AI space. I care about helping them develop a strong sense of responsibility.
In the long term, I aim to leverage my background in engineering and consulting to create something new, a venture that utilizes AI to address real-world problems in sectors such as fintech or human resources. There’s so much potential ahead, and I want to be at the edge of it responsibly and with purpose.
