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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 problemKanerika 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=WeE7BYxDhN0Subscribe 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 needsThe 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 outputFour 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 3Industry 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% fasterFive 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-RXinRuYISubscribe 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 migrationsThe 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 acceptanceAlso 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=04Re5HOyRiYSubscribe 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

An AI data analyst agent should do what a good analyst does when a CEO asks why deliveries from one warehouse are late: plan the investigation, pull the right data, connect the dots and come back with an answer you can act on, with the source tables cited. This episode of The Digital Shift by Kanerika goes inside Karl, Kanerika's data analytics AI agent, with Amit Kumar Jena, Development Manager, AI at Kanerika, who explains what it does, how a question becomes a governed answer, and what a business should check before deploying it.What the episode covers: - The problem Karl solves: a report request that takes three weeks, a data team buried in tickets, and a leadership team that needs to dig into a problem today, not after an analyst spends two days on it - Karl vs Power BI or Tableau: dashboards give the well-designed holistic view; Karl is for the next step, when you see a problem and need to explore thousands of data points in combinations no fixed report anticipated - From question to answer: Karl builds a plan first, asks clarifying questions if the prompt or data is ambiguous, generates the SQL, runs a deep analysis of how data sets correlate, then returns a structured answer as text, charts or a dashboard in the report format the user defines - Messy enterprise data: connectors for SAP, lakehouses, warehouses, SQL, MySQL and Postgres, and why a defined data structure and cleanliness standard are needed before Karl is switched on - Why every answer cites its source tables and lineage: a machine answering for the business needs accountability, so governance teams can verify logic and accuracy, keep hallucination under control and rule out bias - Industry rollout: a generic product tuned by forward deployed engineers in one to two weeks, with business rules added in plain English, such as fiscal year January to December instead of April to March, currency in pounds not dollars, time zones and decimal precision - A manufacturing deployment: warehouse operations, stock problems, leakage detection and time zone management answered from thousands of connected data points - What to evaluate before committing: whether your data is AI-ready, and how Kanerika's forward deployed engineers curate a gold data layer if it is notKarl is deployed in healthcare, banking and retail. If your data team is buried in requests or your business users cannot get answers fast enough, Kanerika can show you what a real deployment looks like on your data.Karl, the data analytics AI agent: https://kanerika.com/product/karl/ Karl datasheet: https://kanerika.com/datasheets/karl-data-insights-ai-agent-for-microsoft-fabric/ Forward deployed engineering at Kanerika: https://kanerika.com/services/forward-deployed-engineering/ Talk to Kanerika about a Karl deployment: https://kanerika.com/contact-us/Related videos: LLM Hallucination in Enterprise Data: The Semantic Layer Fix https://www.youtube.com/watch?v=ZwyVBhJbkGo Forward Deployed Engineers: Why AI Teams Need Embedded Engineering https://www.youtube.com/watch?v=XWq6OwJqffkSubscribe for more on data, AI and digital transformation: https://www.youtube.com/@kanerikainc#AIDataAnalyst #KarlAI #ConversationalAnalytics #AIAgents #NaturalLanguageBI #DataAnalytics #PowerBI #EnterpriseAI #Kanerika #TheDigitalShift 13:34AI Data Analyst Agent: How Karl Turns Questions into Governed Answers

AI Data Analyst Agent: How Karl Turns Questions into Governed Answers

Kanerika Inc. August 24th

Snowflake Summit 2026 rebranded two of Snowflake's biggest AI products and the CEO's argument was blunt: the models are a commodity, your data is not. In this episode of The Digital Shift, host Kanishka Goel talks with Shaurya Singh Chauhan, Lead Software Engineer at Kanerika, who has worked with Snowflake across multiple enterprise implementations, about what actually changed, how ready the new features are for clients, and how Snowflake fits next to Databricks and Microsoft Fabric.What the episode covers:The commodity argument. Anyone can rent Claude, GPT or Gemini. Two retailers on the same model get different insights because their raw data differs. That is why Snowflake keeps data and AI side by side instead of separating them.The rebrands. Snowflake Intelligence is now CoWork, and Cortex Code is now CoCo. Calling it a coworker signals a product that sits next to a business user, and CoCo is an agent that completes tasks rather than an autocomplete feature. The audience shift is the point: a finance or marketing user who has never written SQL can get answers without filing a ticket and waiting two to three weeks on the analyst team.Snowflake vs Databricks vs Fabric. Snowflake wins for SQL analytics, high concurrency and near-zero tuning, with Adaptive Compute auto-sizing warehouses. Databricks is home turf for machine learning and heavy unstructured data. Fabric fits Microsoft-centric shops.The external tool question. Pipelines still lean on dbt and Fivetran, which completed their merger in June, while Snowflake adds native Openflow and data streams to reduce that dependency. The stack only hurts when lineage and central governance are missing.How far along the features are. CoWork works in plain English across Slack, Gmail and Salesforce. CoCo reads your lineage and schemas. Cortex AI uses Claude as the default model, and Cortex Sense lifts answer accuracy from roughly 25% to around 80% by grounding responses in business context. Snowpark runs it all, and Apache Iceberg tables are generally available.Governance for autonomous agents. Agent Identity, Horizon AI guardrails and multi-party approvals mean every agent has an audit trail and no single admin can approve a change alone.Running Snowflake alongside Databricks and Fabric. Apache Iceberg keeps one governed copy of the data that all three platforms can use, plus zero-copy links to SAP, Workday and Salesforce. Kanerika sits across OneLake and Unity Catalog as a Microsoft and Databricks partner, and brings FLIP accelerators with an assessment-first approach.Kanerika is a Snowflake Select Tier partner, a Microsoft Solutions Partner for Data and AI and a Databricks Consulting Partner.Snowflake consulting at Kanerika: https://kanerika.com/technology/snowflake/ Snowflake CoWork explained: https://kanerika.com/blogs/snowflake-cowork/ Snowflake CoCo explained: https://kanerika.com/blogs/snowflake-coco/ Snowflake Horizon Catalog and AI governance: https://kanerika.com/blogs/snowflake-horizon-catalog/ Snowflake Iceberg tables: https://kanerika.com/blogs/snowflake-iceberg-tables/ Snowflake Openflow: https://kanerika.com/blogs/snowflake-openflow/ Microsoft Fabric vs Databricks vs Snowflake: https://kanerika.com/blogs/databricks-vs-snowflake-vs-fabric/ Kanerika and Snowflake partnership: https://kanerika.com/partners/snowflake/ Evaluating Snowflake or planning a migration? Talk to us: https://kanerika.com/contact-us/Related episodes: Databricks Lakebase: Architecture, Pricing & Zero-Downtime Migration https://www.youtube.com/watch?v=966x_YkNOyI LLM Hallucination in Enterprise Data: The Semantic Layer Fix https://www.youtube.com/watch?v=ZwyVBhJbkGo Business Ontology on Microsoft Fabric: Global Pharma Case Study https://www.youtube.com/watch?v=YmIT2cO30X0Subscribe for more on AI, data and enterprise technology: https://www.youtube.com/@kanerikainc#Snowflake #SnowflakeSummit2026 #SnowflakeCoWork #SnowflakeCoCo #CortexAI #ApacheIceberg #Databricks #MicrosoftFabric #DataCloud #TheDigitalShift #Kanerika 8:36Snowflake Summit 2026 Explained: CoWork, CoCo, Cortex Sense & Horizon

Snowflake Summit 2026 Explained: CoWork, CoCo, Cortex Sense & Horizon

Kanerika Inc. August 7th

Enterprise AI adoption rarely fails on the model. It fails when the people who are supposed to use the tool do not trust it, do not understand it, or assume it exists to replace them. In this episode of The Digital Shift by Kanerika, a product manager at Kanerika who has led AI implementations inside real workflows explains what the first conversation with a client should cover, why every SaaS platform's "AI features" still leave a gap, what employee resistance looks like in practice, and how to tell whether an AI rollout is working.What the conversation covers: - The first conversation: moving from "which model" to which bottleneck, which manual process and where time and money are lost - Discovery with the operational teams, since leadership frames AI as productivity while the people doing the work describe process problems - Use case mapping: some findings need generative AI, some need traditional AI or simple automation, and some are process fixes with no technology at all - Feasibility before the pilot: siloed and fragmented data, and why an "AI first" ambition often meets an environment that is not ready - Why built-in SaaS AI features are horizontal by design and what custom enterprise AI adds: grounding in the organization's own knowledge, policies and terminology, workflow integration, and governance with role-based access - Building the solution: plug AI into the exact point where the process breaks, introduce it in small chunks, keep a human in the loop (a support ticket resolution the agent reviews and approves), and orchestrate security, compliance and product stakeholders early with named champions - Direct pushback (refusing to share information, skipping workshops, openly opposing workflow changes) and passive pushback (never using the tool, no feedback, sticking to old methods) - Why resistance comes from uncertainty rather than hostility, and why top-down rollouts handed to employees who had no part in building them stall - How resistance exposes process gaps, since many critical processes run on instinct with no defined workflow - Why AI is not a one-time training problem: clarity on the purpose, cultural signaling from leaders who use the tool themselves, and redesigning the workflow around AI instead of digitizing every inefficiency - Measuring whether it works across four layers: adoption (clicks, repeat users), operational impact (ticket turnaround, manual effort, accuracy, SLAs), business impact (cost savings, revenue, customer satisfaction, margins) and model performance (feedback, data drift, hallucinations) - What a working adoption looks like: AI as part of the daily workflow rather than a separate initiative, and employees asking "can AI do this too" instead of "AI generated this" - Advice for a CEO whose AI projects are slowing down: treat adoption as organizational transformation, involve people from the ground up, align leadership across departments so nobody passively owns the project, and give the organization time to catch up with the technologyKanerika's State of Enterprise AI and Data Modernization 2026 report found that user adoption drains more AI value than technology failure, and most programs budget nothing for it. Kanerika's AI strategy work runs from readiness assessment and use case prioritization to a phased roadmap, governance framework and pilot, with 150+ AI strategy engagements and 85% of roadmaps reaching production.AI strategy consulting at Kanerika: https://kanerika.com/services/ai-strategy/ AI change management: why most initiatives fail: https://kanerika.com/blogs/ai-change-management/ 7 AI adoption challenges and how to overcome them: https://kanerika.com/blogs/ai-adoption-challenges/ Enterprise AI adoption in 2026, key data and findings: https://kanerika.com/blogs/enterprise-ai-adoption-report/ Take the free AI maturity assessment: https://kanerika.com/ai-maturity-assessment/ Struggling with AI adoption, or working out why the last AI project failed? Talk to Kanerika: https://kanerika.com/contact-us/Related videos: State of Enterprise AI & Data Modernization 2026: From Pilots to ROI https://www.youtube.com/watch?v=48qXJlLVIEo Enterprise AI ROI in 2026: Why $2.59 Trillion in Spending Isn't Paying Off https://www.youtube.com/watch?v=7lcr2aGkvyY AI Pilot to Production: Why Forward Deployed Engineers Matter https://www.youtube.com/watch?v=6y-RXinRuYISubscribe for more on data, AI and digital transformation: https://www.youtube.com/@kanerikainc#EnterpriseAI #AIAdoption #AIChangeManagement #AIImplementation #AIStrategy #ChangeManagement #HumanInTheLoop #DigitalTransformation #Kanerika #TheDigitalShift 23:57Enterprise AI Adoption: Why Employees Resist and Most AI Projects Fail

Enterprise AI Adoption: Why Employees Resist and Most AI Projects Fail

Kanerika Inc. July 29th

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