How GenAI is Changing Enterprise Workflows and Improving Efficiency

In an exclusive interview with Express Computer, Debashis Singh, Chief Information Officer at Persistent Systems, discusses how Generative AI (GenAI) is reshaping enterprise workflows and boosting operational efficiency. He highlights the challenges enterprises face, such as data quality and privacy concerns, and shares insights into how hybrid cloud environments and ethical AI practices can drive scalable and secure adoption. Singh also sheds light on Persistent’s key initiatives to integrate AI across business operations, ensuring innovation while maintaining compliance and trust.

How do you see generative AI transforming enterprise workflows and operational efficiency?

Generative AI is reshaping enterprise workflows by significantly enhancing automation, reducing repetitive tasks, and accelerating decision-making processes. Historically, AI used for business was rule-based, but with generative AI, we can now handle unstructured data such as text, images, and video at scale. GenAI allows us to analyse this data fluidly and generate actionable insights in real time.

For example, in the past, the market research conducted to develop a new product could take months. Today, with GenAI, this process can be fast-tracked. By crawling the internet and aggregating unstructured data, AI can provide market insights, competitor analysis, and customer sentiment in a fraction of the time. This acceleration reduces time-to-market for new products and improves the accuracy of the insights derived, giving businesses a competitive edge. Enterprises that harness this capability effectively stay ahead in their respective industries.

What challenges do enterprises face when trying to integrate generative AI into their operations, and how can they overcome them?

Enterprises face several challenges when integrating generative AI, the most critical being data quality. AI models, including generative AI, are highly dependent on the quality of the data they are trained on. If the data is incomplete, biased, or incorrect, the output will be equally flawed. Ensuring that the right, clean data is connected to AI systems is the first step to successful implementation. Enterprises need to establish robust data governance frameworks to ensure data is structured, clean, and compliant with regulations.

Another significant challenge is privacy and security concerns, particularly around the exposure of sensitive business data to third-party AI platforms. Since many AI providers operate on a public cloud, businesses worry about the misuse or leakage of proprietary information. To address this, enterprises must ensure that they have strong data encryption, privacy policies, and access control mechanisms. Additionally, using AI in a way that ensures compliance with legal standards and ethical considerations is crucial.

Lastly, the high cost of computational resources for AI models can be a barrier. While generative AI offers immense potential, it requires significant computational power, which can become expensive. Enterprises should evaluate the cost-effectiveness of AI projects based on their specific use case and ensure they are not over-investing in unnecessary resources. Over time, as technology matures, these costs are likely to become more manageable.

What role do hybrid cloud environments play in facilitating scalable AI integration?

Hybrid cloud environments play a pivotal role in facilitating the scalable integration of AI technologies. With AI applications requiring different levels of computation and storage capacity, hybrid clouds offer the flexibility to choose the optimal infrastructure. Enterprises can utilise on-premises resources for sensitive workloads while leveraging the cloud for more scalable, heavy computation AI tasks. This flexibility ensures that businesses can scale their AI operations without compromising on security or performance.

For example, some AI models may require specialised computational resources, such as those for scientific research or large-scale data analysis. In such cases, a hybrid cloud environment allows enterprises to deploy these models on the most suitable cloud infrastructure. It enables businesses to align their AI strategy with the specific needs of their use cases, ensuring they are using the right model and the right infrastructure to achieve the desired outcomes. This approach helps businesses scale their AI integration efficiently while managing costs and optimising performance.

What are some best practices for addressing data security and privacy concerns in generative AI systems?

Data security and privacy concerns are significant when integrating generative AI, particularly when it involves sensitive business information. To mitigate these risks, enterprises should adopt a two-pronged approach: strong data governance and ethical AI practices.

Data governance: Ensuring data is clean, accurate, and compliant with relevant privacy regulations is foundational. Enterprises must establish clear data policies and governance frameworks that define how data is collected, stored, and shared. These frameworks should also incorporate encryption, access control, and anonymization techniques to prevent unauthorised access and safeguard sensitive information.

Ethical AI and compliance: Businesses must ensure the AI models they deploy are used responsibly. This includes prioritising compliance with data privacy, intellectual property, and copyright laws, especially when AI models are trained on external datasets. Companies should also put in place measures to reinforce that AI outputs are ethical and that AI is used in ways that align with their values and regulatory obligations.

Additionally, AI providers must be transparent about how they handle data and ensure that sensitive information is not inadvertently exposed to unauthorised parties. Enterprises should be cautious when using third-party AI models to ensure their data is not misused or shared with competitors.

In your opinion, how has the role of data-driven decision-making evolved in shaping IT strategies?

The role of data-driven decision-making has evolved significantly over the past few years, especially with the rise of AI and machine learning. Data is no longer just a supporting element in business decisions—it has become central to shaping IT strategies. Enterprises now rely on data to guide decisions about product development, customer engagement, and business operations.

In the past, decisions were often based on intuition or historical experience, but today, AI-powered analytics allow businesses to make decisions based on real-time, actionable data. This shift has made IT strategies more agile, allowing companies to adjust quickly to market changes and customer preferences. With AI and advanced analytics, businesses are not only making data-driven decisions but are also predicting future trends, optimising operations, and identifying opportunities for innovation. This transformation is making IT departments more strategic partners in the business, helping align technology with organisational goals.

How has cloud adoption accelerated digital transformation efforts in recent years?

Cloud adoption has been one of the key drivers of digital transformation, particularly in the wake of the pandemic. Cloud has enabled enterprises to remove the constraints of physical infrastructure and adopt more agile, flexible, and scalable solutions. Cloud platforms allow businesses to operate with greater mobility, collaborating in real-time across teams and geographies.

Cloud adoption has also facilitated the democratisation of data. Data that was once siloed in individual departments or regions is now accessible to the entire organisation, enabling a more cohesive approach to data-driven decision-making. This is especially crucial as businesses look to integrate AI and other advanced technologies into their operations. Cloud environments provide the infrastructure to support AI workloads, making it easier for enterprises to deploy, scale, and manage AI models without the burden of maintaining physical infrastructure.

Furthermore, cloud-based tools enable businesses to implement a platform-driven approach to AI integration, allowing them to leverage the full potential of AI in a seamless, scalable manner.

What are the biggest challenges organizations face when migrating critical workloads to the cloud?

Migrating critical workloads to the cloud presents several challenges, particularly around security, data governance, and legacy system integration.

Data security and compliance: As organisations move sensitive data to the cloud, they need to ensure that the security and privacy of this data are not compromised. Cloud providers offer robust security features, but businesses must configure these features adequately to comply with industry regulations. The challenge lies in ensuring the right security controls are in place for all data, especially in multi-cloud or hybrid environments.

Integration of legacy systems: Many organisations rely on legacy systems that were not designed to integrate with modern cloud solutions. Migrating these systems to the cloud often requires significant reengineering, which can be time-consuming and costly.

Cost management: Cloud adoption requires careful planning and management of cloud resources. Without proper governance, organisations may face unforeseen costs as their cloud usage scales. It is essential for businesses to continuously monitor their cloud environments to avoid over-provisioning and control spending.

What are Persistent Systems’ key initiatives for leveraging AI in the coming years?

At Persistent, we are deeply focused on integrating AI across all areas of our business. Our initiatives include expanding and enhancing our AI-powered platforms like SASVA 2.0, which is designed to accelerate AI-driven software development and business transformation. We are also developing new AI capabilities to address key challenges such as data privacy, business automation, and intelligent decision-making.

We see AI not just as a tool to optimise existing processes but as a means to reimagine the way we create, deliver, and manage solutions for our clients. This includes embedding AI into everything, from product design to customer support, ensuring that AI is a constant and seamless part of the business lifecycle.

Additionally, we are working to ensure that AI is used ethically, focusing on transparency, compliance, and responsible AI practices. This includes implementing strong governance frameworks to safeguard data privacy and ensuring our AI models align with ethical standards.