Information Technology

<h2 style="text-align: justify;"><span style="font-size: 12pt;">Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?</span></h2><p style="text-align: justify;">I have served as a senior executive in the IT industry with an experience spanning more than two decades. I have held key leadership positions in technology, business development, and operations roles. I am currently serving as a senior client partner with Sutherland, a well-known digital business services firm. I have managed large digital transformation programs such as cloud migration, S/4 HANA implementation, DevSecOps pipeline design and management, and data warehousing. I have worked with clients in the US and Europe. I have extensive expertise in providing solutions to customer RFP/RFI's and engaging in the sales process from commercial modelling through customer negotiations and contracting. I have led large teams with up to 300 members. Revenue forecasting, P/L management, resource optimization, and cost monitoring have all been my forte when delivering large programs or designing solutions.</p><p style="text-align: justify;">I am well attuned to the latest changes in technology and I am a hands-on leader. I have been at the forefront of turning my current organization into an AI-first, Cloud-first company.</p><p style="text-align: justify;">Based in Bangalore, I also work for local civic organizations in my free time and take keen interest in sports.</p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q2. What regulatory concerns are emerging around agentic AI in sensitive domains, especially related to decision transparency, bias, or compliance automation?</span></h2><p style="text-align: justify;">Regulatory concerns around data privacy, copyright violation, decision biases, lack of accountability, lethal weapons, violation of laws, and unethical behaviour are arising around the use of AI.</p><p style="text-align: justify;">AI runs on data as fuel. It is not always possible to eliminate personal identification information (PII) from petabytes of data used to train large language models. Further, each individual's private information is diversified in terms of velocity, volumes, and variety. Data ownership is widespread, from your next-door grocer to large insurance and credit firms. There is no single solution that can convincingly capture information flow across digital systems, and therefore, data privacy becomes a victim.&nbsp;<br>Many lawsuits complaining of copyright violations have already been filed against big tech for using copyrighted text to train AI models. Intellectual property rights are often overlooked in the hunger for big data and cutting-edge language models. In the age of AI, copyright issues are becoming a major issue for regulatory bodies, governments, and justice departments worldwide.</p><p style="text-align: justify;">AI-based decision-making is another area of deep concern. Be it for recruitment, sanctioning loans, diagnosing diseases, or in criminal justice, AI inputs should only be seen as an enabler but should not replace human decision-making, which considers specific conditions rather than just broad-based knowledge. AI algorithms that make high-frequency trades do not tend to obey regulations and may increase systemic risk. The SEC proposed rules require investment firms to ensure AI tools don't put their interests above those of clients. Regulatory agencies like the Federal Reserve and FINRA are cracking down on AI-enabled fraud in the financial world.&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://kradminasset.s3.ap-south-1.amazonaws.com/ExpertViews/Venugopalimg1.png" width="456" height="459"></p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">Overall, it is a question of when, not if, governments will increase regulations on AI. Currently, the rules are either non-existent or minimal in most parts of the world. But as AI becomes cheaper and increasingly commonplace, Governments will feel inclined to exert control in some form or another, for good or bad reasons.</p><h2 style="text-align: justify;">&nbsp;</h2><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q3. Which early-stage GenAI-native SaaS companies are gaining enterprise traction in Retail, Utilities, and Manufacturing?</span></h2><p style="text-align: justify;">GenAI-native SaaS companies are finding traction in different sectors of the economy, and retail, Utilities, and Manufacturing are no exception.</p><p style="text-align: justify;">In the retail sector, GenAI is rapidly taking over fashion retailing. Glance AI, an India-based platform, delivers personalized, AI-generated fashion looks directly to smartphone lock screens, merging entertainment with e-commerce. This approach particularly resonates with Gen Z and millennial consumers and is being adopted by large retailers for customer engagement and conversion.</p><p style="text-align: justify;"><strong>iOPEX</strong>: Their GenAI-powered solutions automate marketing operations, campaign management, and customer engagement. iOPEX has demonstrated significant efficiency gains (up to 40% in marketing operations) and sales uplift for enterprise retail clients, managing large-scale campaigns for thousands of brands.</p><p style="text-align: justify;">Multiple other startups have use cases ranging from inventory and logistics management to personalized recommendations.<br>Companies like Gen8, SmartConnect, and Teamsolve are building AI tools that can be deployed in manufacturing, utilities, oil and gas, and similar sectors. Gen8 uses industrial knowledge to build AI prototypes used for operational efficiency management, training, asset risk management, and so on. SmartConnect allows the analysis of edge data and predictive inferences. Any asset-heavy industry can use SmartConnect's platforms for asset management, operations management, and predictive maintenance.</p><p style="text-align: justify;">Digital asset twins are an interesting area where AI is making headway in many industries. They allow scenario simulation, operations optimization, risk planning, training, and maintenance, among many other factors that are critical to manufacturing, transportation, shipping, utilities, mining, and other industries.</p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q4. Which governance platforms are building strong ecosystems via open APIs, connectors with ERP/CRM systems, or integrations with popular ML frameworks?</span></h2><p style="text-align: justify;">All the industry suspects, such as Mulesoft, Boomi, Oracle Integration Cloud, Azure Integration Services, Snaplogic, and SAP Integration Suite, have built API-based integration suites to connect any number of enterprise applications, including AI/ML applications.</p><p style="text-align: justify;">MuleSoft's Anypoint API Governance provides end-to-end governance from API design to deployment, supporting integration with various systems. Its platform allows for defining and enforcing standards, real-time compliance checks, and integration with CI/CD pipelines, making it a strong choice for organizations seeking to centralize and standardize API governance across their ecosystem.</p><p style="text-align: justify;"><strong>Cleo Integration Cloud (CIC)</strong></p><p style="text-align: justify;">CIC is a cloud-based integration platform with pre-built connectors for major ERP systems (SAP, Oracle NetSuite, Microsoft Dynamics 365, Acumatica) and CRM solutions. It supports open APIs, customizable dashboards, and business process automation, enabling seamless data sharing across supply chain, finance, and sales applications.</p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q5. How are vendors improving time-to-value for complex transformations&mdash;through GenAI copilots, modular implementation, or playbook-led delivery?</span></h2><p style="text-align: justify;">Using GenAI copilots alongside playbook-led frameworks and modular implementation strategies, IT service providers are accelerating time-to-value (TTV) for intricate digital transformations. These frameworks help to navigate the challenges of slow-paced enterprise IT projects, including lengthy value realization cycles, resource scarcity, and ever-changing customer requirements.</p><p style="text-align: justify;"><strong>GenAI Copilots: Automating Complexity</strong></p><p style="text-align: justify;">GenAI tools automate the manual coding, documentation, and troubleshooting workload by 20-40%, enhancing real-time decision-making.</p><p style="text-align: justify;">With Microsoft Copilot for IT Operations, executives can now routinely audit their infrastructure, with report generation taking 60% less time, and drastically improve asset utilization through streamlined management processes (25% cost savings).<br>In Telecom, Billing Query Copilots use context-aware scripts to assist agents in resolving customer issues. This enables them to analyse usage and resolve queries in half the average time.</p><p style="text-align: justify;">Engineering work is greatly expedited with self-completion functionalities offered in API integration. Thanks to the assistance from code-generation tools such as GitHub Copilot, development time is saved by 55%. Architects are finally free to architect instead of structure!</p><p style="text-align: justify;">Many IT services' clients now enjoy 15-40% cuts to operational costs thanks to GenAI's predictive maintenance and automated service desk functionalities.</p><p style="text-align: justify;"><strong>Modular Implementation: Incremental Value Realization</strong></p><p style="text-align: justify;">Breaking transformations into discrete, outcome-focused modules allows organizations to create "transformation flywheels." The table below illustrates 3 key advantages and their impact on organizational goals.</p><p style="text-align: justify;"><br><img style="display: block; margin-left: auto; margin-right: auto;" src="data:image/png;base64,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" width="548" height="280"></p><p style="text-align: justify;"><br>This approach creates a "transformation flywheel," where early wins fund subsequent phases while building organizational confidence.</p><p style="text-align: justify;"><strong>Playbook-Led Delivery: Standardizing Success</strong></p><p style="text-align: justify;">Structured frameworks ensure consistency across transformations:<br>Customer onboarding playbooks reduce setup time by 35% through predefined workflows for SaaS implementations (e.g., CRM configuration checklists).</p><p style="text-align: justify;">Agile product teams' playbooks combine design thinking and DevOps practices, accelerating feature delivery cycles by 4x compared to waterfall methods.</p><p style="text-align: justify;">GenAI adoption playbooks guide clients through ethical AI deployment, reducing compliance risks by 50% via built-in toxicity filters and audit trails.</p><p style="text-align: justify;">Providers like McKinsey emphasize metrics-driven playbooks that tie at least a part of the compensation received by IT providers to outcomes like customer conversion rate improvements (20&ndash;30% gains reported) rather than relying only on project completion.</p><p style="text-align: justify;">By combining these strategies, IT service providers are compressing traditional 12&ndash;18 month transformation cycles into 6&ndash;9 month value-realization windows while maintaining 80&ndash;90% stakeholder satisfaction rates. Integrating GenAI into modular, playbook-driven workflows represents a paradigm shift from "big bang" implementations to continuous, measurable improvement loops.</p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q6. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?</span></h2><p style="text-align: justify;">As an investor in a Gen AI-based IT services company or a Gen AI SaaS company, it is important to know the company's USP in the market and how it would defend its market share, essentially the moat. Second, it is important to know how the company proposes to monetize its business model in a scenario of diminishing returns from additional AI-based inference expenditure. The following questions should serve well to obtain initial insights.</p><ul style="text-align: justify;"><li>What proprietary data or infrastructure ensures your solution isn't replaceable by off-the-shelf LLMs?(Moat)<br>Investors are keen on capturing and preserving competitive advantages: Datasets, pipelines, or patents proprietary to specific industries. Unique training datasets, domain-specific fine-tuning pipelines, or patented architectures (e.g., healthcare NLP models trained on anonymized patient records) reduce reliance on generic APIs.</li></ul><p style="text-align: justify;"><strong>Example</strong>: A business that displays creativity and frugal expenditures by using synthetic data creation to circumvent costly third-party data purchasing manifests innovation in cost control.</p><ul style="text-align: justify;"><li>How do you balance inference costs with pricing models as usage scales?</li></ul><p style="text-align: justify;">Gen AI precipitates variable compute expenditures, potentially eradicating profit margins. Providers need to demonstrate alignment of usage and client pricing (e.g., API calls used vs usage tiers for pricing), and optimizations like model quantization or caching led to cost benefits.</p><p style="text-align: justify;">Obligatory metrics that any organization must show: Year-over-year reductions in cost-per-inference, GPU utilization targets, and tiered client pricing.</p><ul style="text-align: justify;"><li>What measurable outcomes differentiate your playbooks from consulting-led competitors?</li></ul><p style="text-align: justify;">Avoid vague "efficiency gains." The company has to show ROI and/or granular KPIs.</p><p style="text-align: justify;"><strong>Examples:</strong></p><p style="text-align: justify;">Productivity: Code-generation tools claiming 40% faster deployment must reduce sprint cycles.</p><p style="text-align: justify;">Revenue impact: Churn reduction % attributed to AI-powered customer success workflows.</p><p style="text-align: justify;"><strong>Adoption</strong>: Tool utilization and conversion rates from sales copilots deployed by the company to assist revenue generation (sales).</p><ul style="text-align: justify;"><li>How are you reducing the risk of hallucinations in mission-critical processes?</li></ul><p style="text-align: justify;">For example, can the company show compliance evidence: ISO 42001 certifications for AI management systems or bespoke SLAs for error thresholds.</p><p style="text-align: justify;">Any company must showcase technical safeguards, such as Retrieval-augmented generation (RAG) accuracy rates, human-in-the-loop validation steps, and audit trails for regulated industries (e.g., finance).</p><ul style="text-align: justify;"><li>What's your strategy for retaining top ML talent amid hyperscaler competition?</li></ul><p style="text-align: justify;">Talent attrition threats: Emphasize equity-linked incentives, opportunities for up-skilling (e.g., collaborations with DeepLearning.ai), or proprietary tooling that attracts research-oriented employees.</p><p style="text-align: justify;"><strong>Example</strong>: A firm providing engineers with 20% "innovation time" to write papers keeps talent in-house when credibility is established.</p><ul style="text-align: justify;"><li>How does your roadmap provide for multimodal AI's infrastructure needs?</li></ul><p style="text-align: justify;">Forward-looking IT service providers are preparing for video and 3D model workloads that require 5&ndash;10x more compute than text.&nbsp;</p><p style="text-align: justify;">The company could answer thus:</p><p style="text-align: justify;">Considering investments in Edge deployments for low-latency manufacturing/retail applications. Collaborations with semiconductor companies optimizing GPU cycles for diffusion models or video/3D model workloads.</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p>