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Hear from Kanerika's Data & AI Experts

Is Microsoft Fabric alone enough to transform manufacturing data into measurable business outcomes? In this episode of The Digital Shift, Kanerika explores how manufacturers can unify siloed data, improve operational visibility, and build a data foundation for AI. Host Paridhi speaks with Kshitij Singal, Head of Sales and Business Development at Kanerika, about manufacturing data challenges and a real-world engagement with Southern States Material Handling (SSMH), also associated with the Raymond and Toyota forklift brands. In this episode, you'll learn: - *Manufacturing Data Challenges:* Why disconnected plant, ERP, CRM, supply chain, and IoT systems make it difficult to establish a single source of truth. - *Why Microsoft Fabric:* How a unified data platform can support analytics, faster insights, and AI readiness. - *Business-First Data Modernization:* How Kanerika aligns data engineering initiatives with measurable business KPIs. - *The SSMH Case Study:* How business analysis, data integration, and Microsoft Fabric helped address parts inventory, service operations, dealership visibility, and labor utilization. - *Modern Data Architecture:* How bronze, silver, and gold layers, semantic models, and Power BI dashboards turn enterprise data into actionable insights. - *Self-Service Analytics:* How business users can build dashboards with less dependence on IT teams. - *Measurable Business Outcomes:* The engagement reported more than 90% data visibility, over 85% operational visibility, 8–10% inventory cost optimization, and 3–5% improved labor utilization. The key takeaway: Procuring Microsoft Fabric is only the beginning. Enterprises need a partner who understands their business processes, defines the right KPIs, and designs the data architecture to deliver measurable outcomes. 🔗 Talk to Kanerika about Microsoft Fabric and manufacturing data modernization: https://kanerika.com/contact-us/?utm_source=youtube&utm_medium=description&utm_campaign=p0bIquQi46Q 🔔 Subscribe for more enterprise data, AI, and digital transformation insights: https://www.youtube.com/@kanerikainc #MicrosoftFabric #ManufacturingAnalytics #DataModernization #PowerBI #Manufacturing #CustomerSuccess #EnterpriseAI #Kanerika #TheDigitalShift 12:14Microsoft Fabric for Manufacturing: SSMH Case Study & Business Impact

Microsoft Fabric for Manufacturing: SSMH Case Study & Business Impact

Kanerika Inc. October 23rd

0:00 Meet Kanerika's CISO Lalit 1:05 What AI Security Really Means 3:43 Kanerika's ISO 27001 and SOC 2 Posture 7:08 Agentic AI Risks and Access Control 10:28 Client Data Handling When Agents Go Live 13:32 Agent Failures, Observability and Prompt Manipulation 16:35 What Clients Ask in Security Reviews 19:08 Security Differences by Industry 22:01 Microsoft Purview in Client Deployments 24:58 AI Bias and Zero Trust Validation 28:03 Vetting AI Vendors Before Signing 29:57 Final Advice: Use AI Responsibly AI security means different things to different people. Some mean protecting the data that goes into a model. Others mean protecting the business from the decisions the model takes. In this episode of The Digital Shift by Kanerika, Lalit Rawat, Kanerika's Chief Information Security Officer, an eight-year Microsoft Azure MVP and the author of two books on Azure, draws the line between the two and walks through what Kanerika holds itself to before any AI agent touches a client's systems or data. What Lalit covers: - Why the public conversation about AI and data (image generators, training on user uploads) is not how an enterprise should think about its data strategy - Where AI security starts: Well-Architected Framework for AI, landing zones, and policies, procedures and controls defined before a model is built - Why models should be trained and tested on dummy data first, and why PII and HIPAA-regulated data such as medical records and phone numbers should be restricted from training sets - Prompt injection as the biggest current challenge, with behavioral analysis and guardrails as the controls that hold up over the long term - The guidelines Kanerika follows: NIST and OWASP, turned into internal policies, with data retained on-site whether it belongs to a client or to Kanerika - Kanerika's own posture: ISO 27001 and SOC 2, plus an internal tool that scans every piece of code before it reaches production, including code written with AI, for secure development lifecycle compliance, exposed credentials, encryption and infrastructure choices - Why security has to cover every layer, from infrastructure and network to the AI layer and the application, with checks enforced in the DevOps repo before any code is merged Kanerika builds and governs enterprise AI systems under ISO 27001, ISO 27701 and SOC 2 certifications, and maps its generative AI controls to the OWASP Top 10 for LLM Applications and NIST's Generative AI Profile. Resources: AI governance consulting: https://kanerika.com/services/ai-governance/ Generative AI security, layer by layer: https://kanerika.com/blogs/generative-ai-security/ LLM security guide: https://kanerika.com/blogs/llm-security-guide/ AI security frameworks, types and controls: https://kanerika.com/blogs/ai-security-framework/ Data security in AI with Microsoft Purview: https://kanerika.com/blogs/data-security-in-ai/ Kanerika leadership announcement: https://kanerika.com/newsroom/kanerika-strengthens-leadership-across-ai-and-security/ Related videos: Enterprise AI Adoption: Why Employees Resist and Most AI Projects Fail https://www.youtube.com/watch?v=4l9_RMRNMPQ What Does It Take to Deploy an Enterprise LLM? https://www.youtube.com/watch?v=vViJp2cMPds State of Enterprise AI 2026: Data Foundations, Maturity Stages & ROI https://www.youtube.com/watch?v=2WRFS6NLSjo Putting an AI partner through a security review? Talk to Kanerika: https://kanerika.com/contact-us/?utm_source=youtube&utm_medium=description&utm_campaign=BDQAEx7BFQU Subscribe to The Digital Shift by Kanerika: https://www.youtube.com/@kanerikainc #AISecurity #EnterpriseAI #PromptInjection #ISO27001 #SOC2 #AIGovernance #CISO #DataSecurity #TheDigitalShift #Kanerika 31:45AI Security for Enterprises: Kanerika's CISO on Prompt Injection, ISO 27001 & SOC 2

AI Security for Enterprises: Kanerika's CISO on Prompt Injection, ISO 27001 & SOC 2

Kanerika Inc. October 15th

Samidha Garud, co-founder and CEO of Kanerika, traces her path from a small-town school auditorium in Aurangabad, where students were told not to touch the first computer they had ever seen, to running a bootstrapped data and AI company that turns 11 this week. In this episode of The Digital Shift by Kanerika, she talks about starting at TCS during the Y2K push, why entrepreneurship happened to her as a side effect rather than a plan, how a healthcare product became a data consulting business, and what FLIP does for enterprises migrating thousands of reports onto AI-ready platforms. What the conversation covers: - The 8th-grade moment: a desktop wheeled into a school auditorium, students pushed back from the machine, and a decision to grow up and use it - Joining TCS in February 1997 and being in the US by October as a fresher who had never seen an aeroplane, and why that early global exposure taught her to think business and technology together - Being part of one of the first CMMI assessments in the world, and why the core of consulting has not changed even as the technology has - Why she never planned to be an entrepreneur: co-founder Bhupendra Chopra was starting alone this time and she stepped in to support, then six months later wished she had done it sooner - 2015: a women's health product, a market full of healthcare startups, no easy funding, and an engineering-center opportunity to modernize a freight audit platform that became Kanerika's first client - 18 months running the product and the consulting side in parallel, the decision to shelve healthcare and stay niche in data, and seven clients in the first three or four years, all through personal networks - How the founders split roles and when Kanerika started talking to strangers and formalized lead generation and sales - Staying bootstrapped, cash positive and debt-free, investing reserves into an innovation lab, and building FLIP on three pillars: agentic AI, migration accelerators and ETL - What changed when she stepped into the CEO role: a formal innovation lab with financial tracking for capitalization, and CMMI Level 3 to prepare the organization for scale - FLIP explained: an AI-powered workflow platform built so non-technical teams can automate workflows, analyze data and manage migrations without depending on IT - The migration landscape for enterprises sitting on 2,000 to 50,000 reports: discovery, conversion, validation and optimization, and how FLIP's automated discovery report replaces months of manual mapping - Her account of the results: 70% of migration effort automated, an AI agent validating that figures match across old and new platforms, and around 85% total effort saved against conventional methods - Why some enterprises never leave the pilot stage and Kanerika's clients have legal, compliance, customer support and sales agents live: AI embedded in business users' workflows with those users in the loop from the start - How Kanerika steers a client to automation, analytics or agentic AI depending on where they are in their data journey - FabCon Europe 2026: showcasing agentic AI workloads already in production on Microsoft Fabric and migration accelerators for European enterprises, after doing the same for North America Kanerika is a sponsor at the European Microsoft Fabric Community Conference, September 28 to October 1, 2026, in Barcelona, Booth #38, showing how enterprises migrate to Microsoft Fabric 5X faster with FLIP accelerators. Karl, Kanerika's AI data insights agent, and the Azure to Fabric Migration Accelerator, powered by FLIP, launched as native Microsoft Fabric workloads at FabCon 2026 in Atlanta. Meet Kanerika at FabCon Europe 2026: https://kanerika.com/events-and-webinars/european-microsoft-fabric-sql-community-conference-2026/ FLIP, Kanerika's intelligent workflow automation platform: https://kanerika.com/product/flip/ Migration accelerators for Fabric, Databricks, Snowflake and more: https://kanerika.com/services/migration/ Karl and the Azure to Fabric accelerator as native Fabric workloads: https://kanerika.com/newsroom/kanerika-debuts-two-native-microsoft-fabric-workloads-at-fabcon-2026/ About Kanerika and its leadership: https://kanerika.com/about-us/ Planning a move to Microsoft Fabric or taking an AI agent from pilot to production? Talk to Kanerika: https://kanerika.com/contact-us/ Related videos: Data Platform Migration with FLIP: Up to 5x Faster, 60% Lower Cost https://www.youtube.com/watch?v=yQVpBem-RyE Kanerika on Microsoft Fabric: AI Insights with Karl + Faster Azure Migration https://www.youtube.com/watch?v=7ggP29U2jA0 AI Data Analyst Agent: How Karl Turns Questions into Governed Answers https://www.youtube.com/watch?v=CmfAgEbbnp8 Subscribe for more on data, AI and digital transformation: https://www.youtube.com/@kanerikainc #SamidhaGarud #Kanerika #FounderStory #WomenInTech #Bootstrapped #FLIP #MicrosoftFabric #FabConEurope #DataMigration #EnterpriseAI #AgenticAI #TheDigitalShift 29:35From Engineer to CEO: Samidha Garud on Bootstrapping Kanerika, FLIP & Enterprise AI

From Engineer to CEO: Samidha Garud on Bootstrapping Kanerika, FLIP & Enterprise AI

Kanerika Inc. September 28th

Dynamics 365 and Power Platform go live, everyone signs off, and the critical work still runs on Excel, email and manual approvals. Microsoft MVP Venkata Subbarao Polisetty joins The Digital Shift by Kanerika to explain why that gap exists, how Dataverse ties both platforms together, and why a forward deployed engineering model changes what happens after deployment. About the guest: Venkata Subbarao Polisetty is a Microsoft MVP for Business Applications, a Microsoft Certified Trainer, lead of the D365 Champs community and founder of the Telugu D365 CRM community, with 16+ years across Microsoft business applications from the on-premise CRM era to Dynamics 365 online. What the conversation covers: - The biggest shift in Dynamics 365: from on-premise CRM versions to an online platform with Sales, Customer Service, Marketing and Field Service modules on one data layer - Dynamics 365 vs Power Platform: why both sit on the same Dataverse architecture (data model, security rules, rules engine, APIs) and where each one fits - Forward deployed engineering on Dynamics projects: architects and leads working directly with business analysts and product owners, starting from the customer's pain points rather than the technology - Why out-of-box workshops come first: 70 to 80% of customers find the built-in sales lifecycle (leads, accounts, opportunities, orders, invoices) covers the need without a custom build - Agile show-and-tell delivery: sprint-level demos that shorten the feedback loop and surface problems before go-live - The Excel problem: what it means when users go back to spreadsheets, and how Power Automate, Power Apps and Copilot Studio integrations remove manual tracking and email hand-offs - ROI beyond go-live: pay-per-user licensing against the cost of maintaining on-premise systems, gateways, security and DevOps - Integration ownership across ERP, legacy and document systems: SharePoint for document management, agreed API contracts, and Azure Functions listening for new records - Copilot and AI agents in Dynamics 365: built-in sales agents that score lead conversion likelihood and read prospect sentiment from the conversation, with no separate agent strategy needed - The first question for a CIO who is not seeing value from Dynamics or Power Platform: set the technology aside and define the actual business problem Kanerika brings applications, data, automation, cloud and AI skills to the same business problem for enterprises already invested in Microsoft, across Dynamics 365 CRM, Power Platform, Copilot Studio, Power Automate, Purview, Microsoft Fabric and FLIP migration accelerators. Forward deployed engineering at Kanerika: https://kanerika.com/services/forward-deployed-engineering/ Kanerika and Microsoft: https://kanerika.com/partners/microsoft/ Planning a Dynamics 365 or Power Platform program, or running one that is not delivering? Talk to Kanerika: https://kanerika.com/contact-us/ Related videos: Forward Deployed Engineers: Why AI Teams Need Embedded Engineering https://www.youtube.com/watch?v=XWq6OwJqffk AI Pilot to Production: Why Forward Deployed Engineers Matter https://www.youtube.com/watch?v=6y-RXinRuYI Forward Deployed Engineer vs Data Engineer: Enterprise AI Deployment https://www.youtube.com/watch?v=WeE7BYxDhN0 Subscribe for more on data, AI and digital transformation: https://www.youtube.com/@kanerikainc #Dynamics365 #PowerPlatform #Dataverse #ForwardDeployedEngineering #MicrosoftMVP #CopilotStudio #PowerAutomate #CRM #EnterpriseAI #Kanerika #TheDigitalShift 20:04Dynamics 365 & Power Platform: Why Adoption Stalls After Go-Live

Dynamics 365 & Power Platform: Why Adoption Stalls After Go-Live

Kanerika Inc. September 18th

State of Enterprise AI 2026: 71% of organizations now use AI in at least one function, yet 95% of generative AI deployments show no measurable return. Kanerika's State of Enterprise AI and Data Modernization Report 2026 explains why, using 161 documented enterprise engagements and 414 measured outcomes from delivered client work rather than surveys. Amit Kumar Jena, Development Manager, AI at Kanerika, walks host Kanishka Goel through the four findings, the five-stage maturity model and what a stage 2 or 3 company should do next quarter. The numbers behind the report: - 161 enterprise engagements delivered between 2015 and 2026, 414 measured outcomes, every figure a verified delivered result - 71% of organizations use AI in at least one function, up from 65% a year earlier - 95% of GenAI deployments see zero measurable return; 30% of GenAI projects are abandoned after proof of concept - Gartner expects 60% of AI projects to be abandoned by 2026 without AI-ready data; poor data quality costs an average of $12.9 million a year; 63% of organizations lack the data practices AI needs The five-stage maturity model: 1. Ad hoc: siloed data, no governance, AI as scattered experiments 2. Foundational: consolidated data, basic quality, access systems, first use case built 3. Integrated: a modern platform such as Microsoft Fabric, Databricks or Snowflake with reusable pipelines. Most enterprises sit at stage 2 or 3 today 4. Governed: embedded governance, lineage and role-based access, AI running in production 5. AI-native: agents working on governed enterprise data, measured on outcomes rather than output Four findings from the report: - The foundation gap is the blocker, not the model: fragmented sources, weak governance and brittle pipelines are where stalled AI initiatives fail - A fixed foundation resets the cost and speed baseline; results cluster at the high end, not the middle - Adoption is the hardest mile: value leaks at the point of use, sustained usage around the 75% mark is where internal rollouts stall, and low-code or no-code delivery plus change management close the gap. Client satisfaction on Kanerika deliveries runs above 95% - Governance is the price of entry: lineage, role-based access and policy enforced in the platform (Microsoft Purview in practice) let BFSI and healthcare teams ship AI that compliance signs off on the first time, with 99% defect detection accuracy across regulated deployments and certifications including ISO 27001, ISO 27701, SOC 2 and CMMI Level 3 Industry results cited: - Investment banking: 100% role-based compliance, 43% faster retrieval, 35% fewer review hours, 99%+ defect detection - Manufacturing and mobility: 85%+ predictive accuracy and 78% faster outcomes after unifying ERP and operational data - A US fuel distributor: 90% fewer manual interventions in accounts payable, 400+ hours saved a month, invoices processed 30% faster Five predictions for 2026: the production gap becomes a board KPI, governed agents move from pilot to default in core workflows, migration timelines compress from quarters to weeks, data quality gets funded before AI budgets are released, and winners measure outcome, cost, value and trust instead of counting demos. The report's closing line: the winners of 2026 will not own the most models, they will own the most trusted data. Readiness checklist for stage 2 and 3 organizations: a single source of truth, documented lineage, automated data quality monitoring, and at least one AI use case running in production. Download the report: https://kanerika.com/whitepapers/the-state-of-enterprise-ai-and-data-modernization-2026/ AI maturity assessment: https://kanerika.com/ai-maturity-assessment/ Data modernization services: https://kanerika.com/services/data-modernization/ Talk to Kanerika about your data foundation: https://kanerika.com/contact-us/ Related videos: AI Governance Framework: Auditing Data, Models & Deployment Risk https://www.youtube.com/watch?v=7rQZaGnjHko AI Pilot to Production: Why Forward Deployed Engineers Matter https://www.youtube.com/watch?v=6y-RXinRuYI Subscribe for more on data, AI and digital transformation: https://www.youtube.com/@kanerikainc #EnterpriseAI #AIROI #DataModernization #AIMaturityModel #DataGovernance #GenerativeAI #MicrosoftFabric #Databricks #Snowflake #Kanerika #TheDigitalShift 13:19State of Enterprise AI 2026: Data Foundations, Maturity Stages & ROI

State of Enterprise AI 2026: Data Foundations, Maturity Stages & ROI

Kanerika Inc. September 15th

AI-ready data migration is a different project from getting off a legacy platform. Plenty of enterprises finish a migration, see the dashboards working, close the project, and then find the same data is a problem the moment the AI conversation starts. In this episode of The Digital Shift by Kanerika, Kshitij Singhal, Vice President, Business Development at Kanerika, walks through the 90-day FLIP blueprint that treats migration and AI foundation as one integrated program instead of two. What the episode covers: - How the migration conversation has changed: from "get us off this reporting or ETL platform" to an automotive client asking for the legacy exit and AI readiness in the same engagement - Why a manual migration can be technically successful and still leave the business unprepared for AI: slow, error-prone, expensive, and nobody checks whether lineage, governance and documented business logic survived the move - What FLIP is: Kanerika's AI-powered intelligent workflow automation platform, launched in 2023, with around 20 migration pathways from legacy or expensive technologies to modern cloud-native platforms, covering reports, ETL pipelines and bot migrations The 90-day blueprint, stage by stage: - Days 1 to 30, investigate and plan: an automated FLIP assessment of complexity, risk, dependencies and source inventory, then stakeholder alignment on target architecture, AI scope and a sprint plan - Days 31 to 75, automate, migrate and validate: FLIP converts automatically, engineers handle the leftover tweaks, continuous validation and exception handling under an agile cadence, with client involvement kept to final validation before go-live - Days 76 to 90, toggle and AI foundation kickoff: cutover to the new platform to realize the promised savings, performance monitoring and optimization, and AI-ready data pipelines built in parallel against the use cases identified in phase one - The readiness check: complete transparency and traceability of what was converted, plus AI use cases run as a proof of value for user acceptance Also discussed: the public KBR case study, where FLIP replaced manual ETL, enabled self-service pipelines so business users stopped depending on IT, and left a data foundation on which new AI use cases could be built with little or no model retraining. And why demand is coming from every sector, from regulatory reporting pipelines in banking and HIPAA in healthcare to FDA requirements in pharma and real-time plant data in manufacturing. The closing argument: migration and AI programs should converge into one modernization conversation, because it delivers better synergy, faster time to insight and no refactoring of the data foundation. FLIP is built on that philosophy, whether you have already started your journey or have not started yet. FLIP migration accelerator: https://kanerika.com/product/flip/ Migration services: https://kanerika.com/services/migration/ The State of Enterprise Data Platform Migrations 2026: https://kanerika.com/whitepapers/the-state-of-enterprise-data-platform-migrations-2026/ KBR case study: https://kanerika.com/case-studies/transforming-travel-and-expense-management-for-kbr/ Talk to Kanerika about an AI-ready migration: https://kanerika.com/contact-us/ Related videos: Enterprise Data Migration: 80% Automated with FLIP https://www.youtube.com/watch?v=yQVpBem-RyE Microsoft Fabric Migration: Move Off Informatica, Alteryx & SSIS https://www.youtube.com/watch?v=q542Tw-VDNs Azure Migration Strategy: Hybrid Cloud, Cost Optimization & AI https://www.youtube.com/watch?v=04Re5HOyRiY Subscribe for more on data, AI and digital transformation: https://www.youtube.com/@kanerikainc #DataMigration #AIReady #FLIP #DataModernization #LegacyModernization #ETLMigration #DataPlatformMigration #EnterpriseAI #Kanerika #TheDigitalShift 11:59AI-Ready Data Migration: The 90-Day FLIP Blueprint

AI-Ready Data Migration: The 90-Day FLIP Blueprint

Kanerika Inc. September 3rd

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