With more than 27 years of experience in the field of AI, Dr. Hemant Misra has been at the forefront of developing, deploying, and scaling data-driven solutions. Currently heading the Data Science team at Simpl, he focuses on translating AI models into real-world production systems ranging from fraud detection to complex chatbot solutions.
In an exclusive conversation with CIO&Leader, Dr. Hemant Misra discusses the biggest challenges enterprises face in scaling AI initiatives, how organizations can learn from digital-native companies, and the importance of data governance. Below is an edited version of the interview:
Data Science team Head,
Simpl
CIO&Leader: What are the biggest challenges enterprises are facing in terms of scaling AI, and what’s needed for the industry to overcome them?
Dr. Hemant Misra: There are two major challenges. First, AI systems don’t follow the same linear development process as typical software systems. Traditional software often has fixed specifications before deployment, but AI initiatives need iterative learning and refinement. This requires a mindset shift: start with smaller projects, learn quickly, and improve iteratively, rather than expecting a fully production-ready system from day one.
Second, many organizations struggle with data, either it’s locked in silos or not well understood due to lost institutional knowledge. Even if data is available, its value can be hard to leverage if people don’t know where it resides or how to interpret it. For AI projects to scale, you need an end-to-end strategy: identify high-impact use cases, determine whether the right kind of data exists or can be collected, and ensure that the entire pipeline, from data acquisition to deployment, has been thought through. Once a proof of concept is built, there must be a clear path to production, taking into account real-world constraints such as latency and ROI.
CIO&Leader: From a digital-native company standpoint, which key learnings can large enterprises adopt?
Dr. Hemant Misra: Digital-native firms are typically very conscious about ROI and cost-effectiveness. They also recognize that simpler machine learning solutions can often be as effective as cutting-edge methods, especially in the initial stages. If a classic model solves the business problem well and is cheaper to deploy, there’s no need to complicate things.
It’s crucial to adopt an iterative approach, start with a simpler model, see if it solves the problem effectively, and only add complexity when there’s a clear need. Many younger data scientists may be trained in the latest deep learning frameworks, but classic ML techniques can still be extremely powerful. The overarching lesson is don’t chase complexity for its own sake; always align the solution with practical business ROI.
CIO&Leader: What are the infrastructure challenges CIOs face, and how does that shape the future outlook?
Dr. Hemant Misra: One common challenge is inefficiency arising from outdated or redundant AI models running simultaneously. For instance, when an updated model goes into production, the old one might still be consuming resources, inflating infrastructure costs. These inefficiencies can severely impact the bottom line.
Organizations need to be mindful of cost from the very beginning. This means constantly evaluating whether a complex model truly delivers better ROI compared to a simpler alternative. If the incremental improvement is marginal but the infrastructure demand is significantly higher, then the net benefit might be negative. Constant monitoring and a frugal mindset should guide both deployment and development.
CIO&Leader: What are your thoughts on data governance, and how do you think AI implementation is affecting our perspective on data?
Dr. Hemant Misra: Data governance is essential because end-users rarely know the origins or destinations of their personal data. Governments must establish and enforce regulations to protect citizens’ identities and personally identifiable information (PII). Companies, on the other hand, should be transparent about why they collect data and how it will be used—offering clear benefits or ROI to the end-user for sharing that information.
Regulations such as GDPR are a good example, allowing users to choose whether to accept certain cookies and enabling a greater degree of personalization if they opt in. This clarity and simplicity should be the standard. Organizations also need internal policies outlining how data is collected, stored, and shared. Collecting data “just in case” or planning to sell it to third parties without proper consent is unethical and risks violating both user trust and legal standards. Ultimately, governments, businesses, and individuals all share responsibility for creating an equitable data ecosystem—one where data is protected and leveraged responsibly.