Unlocking the Future of Tech from FinOps to AI: Sreekrishnan Venkateswaran, Kyndryl India

Sreekrishnan Venkateswaran, CTO, Kyndryl India provided profound insights into the ever-changing dynamics of technology.

Punam Singh
New Update
Sreekrishnan Venkateswaran, Chief Technology Officer (CTO), Kyndryl India

Sreekrishnan Venkateswaran, Chief Technology Officer (CTO), Kyndryl India

In an exclusive interview with DataQuest, Sreekrishnan Venkateswaran, Chief Technology Officer (CTO), Kyndryl India provided profound insights into the ever-changing dynamics of technology across diverse sectors. The discussion covered key trends in FinOps adoption in hybrid and multi-cloud environments, the industry's utilization of AI at the edge for efficient operations, the impact of Generative AI on cybersecurity, how digital workplace technologies enhance employee and customer experiences, and strategies for addressing skill gaps while integrating new technologies like AI.


From the mainstream adoption of FinOps to the transformative potential of AI at the edge, the interview shed light on the critical intersections of technology and business strategies. The discussions highlighted the industry's determination to address evolving challenges and embrace innovative solutions, emphasizing the multifaceted approaches employed to ensure seamless integration, efficient operations, and the development of robust skill sets in the dynamic technological landscape.



Sreekrishnan Venkateswaran: In the landscape of hybrid and multi-cloud environments, the mainstream adoption of cloud computing has popularized consumption-based billing of technology, giving rise to the practice of Financial Operations (FinOps). The ease of provisioning and fulfillment on the cloud has induced a softening of cost controls. In response, financial management paradigms are adapting to infuse cost-sensitivity into the realm of software development and operations (DevOps) on the cloud. Earlier, proficient coders only needed to focus on maximizing algorithmic performance, but with the advent of cloud computing, developers must also optimize operating expenses. This is blurring the traditional lines between engineers and finance managers.

In parallel, cloud run rates of businesses are steadily increasing; it is no longer uncommon to witness monthly cloud spends in the millions of dollars. This means that you might be entrusting your corporate fiscal health to your developer or SRE when you are assigning them technical deliverables. Therefore, to harness the power of hybrid and multi-cloud computing for driving innovation, real-time cost awareness must be built into technical solution development.

This shift in spend control levers from traditional departments to development teams and Site Reliability Engineers (SREs) underscores the growing significance of FinOps in effectively managing costs in hybrid and multi-cloud environments.


DQ: How is the industry utilizing AI at the edge for efficient operations?

Sreekrishnan Venkateswaran: Edge Computing, a paradigm that brings processing capability closer to data sources, has emerged as a pivotal approach for industries aiming at efficient operations. Because edge computing transfers AI-based decision-making ability near to where sensors generate data, it has triggered amazing innovation across industries.

Any device with computing and connectivity can function as an edge node. It can range from telco towers and trains to factories and farms. For example, in-car edge software brings your location, smartphone, street maps, and fuel aggregators together to offer a driving experience that was not possible just a few years back. AI at the edge is revolutionizing rural farming, enabling precision irrigation and spot fertilizer delivery. In the mining sector, edge intelligence is optimizing energy usage and reducing water consumption. Edge analytics is enabling airports to improve operational efficiency by predicting counter allocations in real time based on passenger arrival patterns.


AI-powered automation and orchestration are technologies on which edge deployments have critical dependencies. Because the edge implies a massive scale, the concept of "zero-touch" deployments, sometimes referred to as "extreme automation," assumes importance. When there are 10s of 1000s of containerized edge nodes, for example, in the case of the 5G Radio Access Network (RAN), the system depends on many layers of AI and automation enablers.

DQ: How is the rise of Generative AI impacting cybersecurity in the industry? How is the industry addressing the need for robust data governance in AI applications?

Sreekrishnan Venkateswaran: The emergence of Generative AI has unlocked unparalleled creativity but has posed novel challenges to cybersecurity. As industries embrace this transformative technology, establishing robust data governance becomes essential to navigate intricate ethical and security considerations.


The cyber risk profile has evolved, presenting new threat vectors, both direct and indirect. An example is model inversion, where unauthorized access to the internal workings of a model is gained to manipulate its parameters and influence its output. Another example of a cyber-attack is data/model poisoning, which involves devising training data to compromise the model's integrity. Large language models may "hallucinate" on rare occasions, producing answers that are unexpected and not present in underlying knowledge bases. And because the underlying foundational models are probabilistic, they may produce different results during different runs for the same input, making problem re-creation difficult.

Generative AI models are 'black boxes' from an applied perspective, meaning their inner workings are not transparent. In other words, the results of a generative AI application may not be traceable to a cause. Additionally, these models may, albeit infrequently, produce output containing dangerous information or vulgar content. They may also reuse the information provided via 'prompts' – inputs or instructions - when generating answers to queries posed by others. Thus, in the Generative AI landscape, practitioners must grapple with new challenges across various dimensions, including fairness, bias, ethics, culture, safety, and responsibility. Cybersecurity and auditability are rapidly advancing AI governance to navigate the ethical and security considerations associated with this transformative technology.

DQ: How is the industry enhancing employee and customer experiences through digital workplace technologies?


Sreekrishnan Venkateswaran: In the unfolding landscape of the modern workplace, digital technologies play a pivotal role in shaping employee and customer experiences. Particularly, in the aftermath of the pandemic, the paradigm of hybrid working has become the prevailing trend.

Looking ahead, a transformative aspect of the digital workplace evolution involves the creation of an immersive metaverse experience. Employees will be able to virtually place themselves at an office location, 'walk around,' 'meet' people around them, and sit on virtual seats near co-workers whom they want to whiteboard with.

This is being facilitated by the convergence of cutting-edge technologies such as computer vision, digital signage, augmented reality, and location awareness, seamlessly integrated into modern collaboration software. Employees will have a 'digital twin' representation on the floor of either a physical or virtual office space. In conclusion, the integration of modern technologies not only supports the shift towards hybrid working but also enhances employee collaboration and elevates customer interactions in unprecedented ways.


DQ: What are the industry strategies for addressing skill gaps and ensuring the integration of new technologies like AI with the existing landscape?

Sreekrishnan Venkateswaran: In addressing skill gaps and ensuring the seamless integration of new technologies, particularly AI, the industry employs a multifaceted approach that combines knowledge, certifications, and practical experience.

Knowledge is familiarity with factual information; skill, on the other hand, is the ability to apply that knowledge to specific scenarios. Certifications play a crucial role in validating one's knowledge comprehensively and skills to a certain extent. However, to truly elevate one's skills to a saleable level, practical experience on real projects used by real users is indispensable.

Markets are where the rubber hits the road. Building solutions for clients is where it all comes together. Learners start by shadowing experts, learning on the job, and gradually applying their knowledge and skills to contribute meaningfully to the development of solutions for clients. This journey turns them into practitioners, helps them gain valuable experience, and in the process, builds technical eminence.

This industry strategy of combining knowledge, certifications, and practical experience, particularly through real-world projects, not only addresses skill gaps but also ensures the effective integration of new technologies like AI into the existing landscape.