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Discover how ServiceNow’s new Now Assist release brings fully autonomous, guard-railed AI agents to HR, Sales, IT Ops, and more. Learn the architecture, use cases, and transparent token-based pricing you need to launch your first agent in weeks.

At Lowe’s, AI has moved beyond experimentation to power daily interactions with shoppers, employees, and the entire supply-chain operation. Here’s a snapshot of where AI is deployed, how the company built the capability, and the tech stack that keeps it humming. Scope of Deployment

What is Agentic AI really—and why are so many products misusing the term? In this blog, we cut through the marketing hype to expose the gap between what’s being promised and what’s actually being built. You'll learn what defines true Agentic AI, why most so-called “agents” are just automation with GenAI wrappers, and how to refocus your AI strategy on outcomes, not buzzwords. Whether you're a decision-maker, builder, or strategist, this is the clarity check you need before committing to the next big AI investment.

In this blog, we break down why observability is essential for AI agents and RAG systems—covering how logs, metrics, and traces enable transparency, trust, and automation. Explore key observability tools like LangSmith, Traceloop, Arize AI, and OpenTelemetry, and learn how observability powers AIOps, performance monitoring, and real-time decision-making across complex, multi-agent environments.

In our first DataOps post , we explored how AI’s success hinges not just on powerful models, but on the quality, accessibility, and governance of the data that fuels them. And it all starts at the source— Pillar 1: Data Sources . Now, in Pillar 2, we shift focus to the movement of data: how raw inputs from disparate systems are seamlessly ingested, integrated, transformed and made AI-ready. By mastering ingestion and integration, you set the stage for continuous, near–real-time intelligence—no more stale data, no more guesswork, and no more missing records. In this blog we will go over: What data ingestion and integration mean in a DataOps context When ingestion occurs (batch, streaming, micro-batch, API, etc.) How integration differs from ingestion—and how transformation (ETL vs. ELT vs. Reverse ETL) fits in The tools you’ll use for ingestion and integration at scale How to handle structured, unstructured, and vector data A readiness checklist to gauge your ingestion maturity An enterprise case study demonstrating ingestion at scale Why Pillar 2 Matters Ingestion Delivers Fresh, Unified Data: If the data doesn’t flow into your ecosystem frequently enough (or in the right shape), everything else breaks. Poor Ingestion Creates Blind Spots: Stale data leads to flawed analysis, subpar AI models, and questionable business decisions. Integration Makes Data Actionable: Merging data across systems, matching schemas, and aligning business logic paves the way for advanced analytics and AI. Acceleration from Pillar 1 : Once you know where your data resides (Pillar 1), you must continuously move it into your analytics environment so it’s always up to date What “Data Ingestion” and “Integration” Mean in a DataOps Context Data Ingestion Ingestion is how you bring raw data into your ecosystem from databases, APIs, cloud storage, event streams, or IoT devices. It focuses on: Automation: Minimizing or removing manual intervention Scalability: Handling growing volume and velocity of data Flexibility: Supporting batch, streaming, micro-batch, and file-based methods Data Integration Integration is the broader stitching together of data for consistency and usability: Aligns schemas Resolves conflicts and consolidates duplicates Standardizes formats Ensures data is synchronized across systems Integration typically includes transformation tasks (cleaning, enriching, merging) so data can be confidently shared with BI tools, AI pipelines, or downstream services. Is Transformation the Same as Integration? Not exactly. Transformation is a subset of integration. Integration is about combining data across systems and ensuring it lines up. Transformation is about cleaning, reshaping, and enriching that data. Often, you’ll see them happen together as part of an integrated pipeline Ingestion Models & Tools Below are the most common ingestion models. Remember: ingestion is about how data gets into your environment; it precedes deeper transformations (like ETL or ELT). Batch Ingestion Definition: Scheduled jobs that move data in bulk (e.g., nightly exports) When to Use: ERP data refreshes, daily or weekly updates, curated BI layers Tools: Talend Informatica Azure Data Factory AWS Glue dbt (for post-load transformation) Google BigQuery Data Transfer Service Snowflake: COPY INTO (bulk loading from cloud storage into Snowflake) Matillion (cloud-native ETL specifically for Snowflake) Hevo Data (batch ingestion into Snowflake) Estuary Flow (supports batch loading into Snowflake) Real-Time Streaming Definition: Continuous, event-driven ingestion with millisecond latency When to Use: Fraud detection, real-time dashboards, personalization, log monitoring Tools: Apache: Apache Kafka Apache Flink Apache Pulsar Redpanda AWS Kinesis Azure Event Hubs Google Cloud Pub/Sub StreamSets Databricks Structured Streaming Snowflake: Snowpipe Streaming (native streaming ingestion into Snowflake) Kafka Connector (Kafka integration for Snowflake) Striim (real-time data integration platform for Snowflake) Estuary Flow (real-time CDC and streaming integration with Snowflake) Micro-Batch Ingestion Definition: Frequent, small batches that balance freshness and cost When to Use: Near-real-time analytics, operational dashboards Tools: Snowflake Snowpipe Debezium (Change Data Capture, or CDC) Apache NiFi Snowflake: Streams & Tasks (native micro-batch processing) Estuary Flow (low-latency micro-batch integration) API & SaaS Integrations Definition: Ingesting data via REST, GraphQL, or Webhooks When to Use: Pulling from SaaS apps like Salesforce, Stripe, Marketo Tools: Fivetran Airbyte Workato Tray.io Zapier MuleSoft Anypoint Hevo Data Stitch Segment Airbyte (open-source connectors to Snowflake) Hevo Data (real-time SaaS replication into Snowflake) Estuary Flow (real-time SaaS integration with Snowflake) File Drop & Object Store Ingestion Definition: Ingestion triggered by file uploads to an object store (S3, Azure Blob, Google Cloud Storage) When to Use: Legacy system exports, vendor file drops Tools: Snowflake External Stages Databricks Autoloader AWS Lambda Google Cloud Functions Azure Data Factory Snowflake: Snowpipe (automatic ingestion from object stores into Snowflake) Change Data Capture (CDC) Definition: Real-time capture of insert/update/delete events in operational databases When to Use: Syncing data warehouses with OLTP systems to keep them up to date Tools: Debezium Qlik Replicate AWS DMS Oracle GoldenGate Arcion Estuary Flow (CDC integration with Snowflake support) Orchestration & Workflow Scheduling Definition: Automating ingestion end-to-end, managing dependencies and error handling When to Use: Coordinating multi-step ingestion pipelines, monitoring data freshness, setting SLAs Tools: Apache Airflow Prefect Dagster Luigi Azure Data Factory Pipelines AWS Step Functions Estuary Flow (pipeline orchestration supporting Snowflake ingestion workflows)

As AI moves from proof-of-concept to operational scale, we’re continuing to track how leading organizations are deploying real solutions across IT, customer experience, and security. Every case study here has been manually curated, fact-checked, and vetted to showcase real-world AI execution inside enterprise environments. Each case study highlights: A specific business problem (not just a use case) The AI tools and platforms actually used Measurable results like reduced resolution time, improved customer experience, and scaled productivity Cross-functional innovation from IT operations to customer service to development workflows This month’s additions span sectors from retail to cloud services and showcase how companies are cutting resolution time, scaling insights, and unlocking automation across the stack. Quick Take: Case Study Highlights Vulcan Cyber used Snowflake AI Data Cloud to orchestrate 100+ threat feeds, summarize CVEs with GenAI, and accelerate vulnerability remediation. HP integrated Snowflake + ThoughtSpot to modernize analytics, enable AI-powered self-service, and cut partner turnaround times to <24 hours. Kroger unified observability with Dynatrace AIOps, replacing 16 tools and cutting support tickets by 99%. Camping World deployed IBM watsonx Assistant to automate 8,000+ chats, lower wait times to 33 seconds, and boost engagement by 40%. CXReview used IBM watsonx.ai to automate call summaries, saving agents 23 hours/day and scaling compliance reviews. Photobox leveraged Dynatrace AIOps to cut MTTR by 80% and reduce peak-period incidents by 60%. LAB3 rolled out ServiceNow Now Assist to cut MTTR by 47%, reduce workflow bottlenecks by 46%, and boost self-service by 20%. Fiserv used UiPath GenAI Activities and Autopilot to automate MCC validation with AI prompts—achieving 98% straight-through processing and saving 12,000+ hours annually. Expion Health deployed UiPath’s AI-powered Document Understanding and Computer Vision to automate healthcare claims—boosting daily processing by 600% and cutting manual effort at scale. HUB International scaled enterprise-wide automation using the UiPath AI platform, automating 60+ workflows across finance, underwriting, and compliance to support aggressive M&A growth. American Fidelity combined UiPath RPA and DataRobot AutoML to automate customer email classification and routing—achieving 100% accuracy, freeing thousands of hours, and scaling personalization. Domino’s Pizza orchestrated over 3,000 data pipelines using BMC Control-M—enabling real-time insights and scalable enterprise reporting across 20,000+ stores. Electrolux automated global self-service content using BMC Helix Knowledge Management—cutting publishing time from 40 days to 90 minutes and increasing usage by 10,488%. InMorphis launched three GenAI solutions in four weeks using ServiceNow AI Agents—boosting code accuracy to 73%, hitting 100% SLA compliance, and driving a 2.5x increase in sales productivity. 📊 Full Case Study Table

This guide walks through Amazon’s GenAI Readiness Workbook—a cloud-agnostic, execution-focused framework to assess your AI maturity across infrastructure, governance, and strategy. Includes step-by-step instructions, ownership models, prioritization methods, and execution planning tips.

Discover how industry giants like Netflix, Uber, Airbnb, and Spotify leveraged MLOps (Machine Learning Operations) long before GPT and generative AI took the spotlight. This in-depth guide unpacks DevOps-inspired data pipelines, streamlined ML model deployment, and real-time monitoring techniques—all proven strategies to build scalable, reliable, and profitable AI solutions. Learn about the roles driving MLOps success (MLOps Engineer, Data Scientist, ML Engineer, Data Engineer) .Whether you’re aiming to enhance your machine learning workflows or make a major career move, this blog reveals the blueprint to harness MLOps for maximum impact in today’s AI-driven world.

Pillar 1: Data Sources – The Foundation of AI-Ready Data