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Inside ITOpsAI

ITOsAI Blog: Applied AI, & Deep Dives

Original blog content covering AI strategy, IT infrastructure, ITSM, enterprise AI adoption, automation, innovation, and more—built to help you move from insight to execution.

AI in Action: Enterprise Case Studies 

From fraud detection to infrastructure orchestration—see how leading enterprises are executing AI to solve real problems, cut costs, and scale smarter.


Curated AI Research & Execution Frameworks

Discover curated third-party research on how AI is being implemented, governed, and scaled—plus the risks, breakthroughs, and strategies shaping what’s next.


Featured Blog


DataOps: The Backbone of Enterprise AI Execution

You can’t operationalize AI without operationalizing your data. Explore the 5 foundational pillars—from ingestion to orchestration—that enable scalable AI and enterprise transformation through true data readiness.

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AI in Action: Enterprise Case Studies

Kainos: GenAI + ServiceNow Transform Customer Support

Industry: Services

Technology: ServiceNow + GenAI

Kainos integrated Now Assist to automate support workflows, generate knowledge articles, and summarize service cases—achieving 99% satisfaction and a 71% reduction in resolution time.

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Industry: Higher Education & Research

Technology: GPU Virtualization

EPFL deployed Run:ai to create a self-service GPU allocation system, cutting idle GPUs by 50% and enabling hundreds of researchers to run scalable AI workloads

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Industry: Financial Services

Technology: Snowflake Cortex AI

TS Imagine unified data across 500+ clients and automated email monitoring—saving 4,000+ hours per year and cutting GenAI integration costs by 30% using Snowflake Cortex AI.

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Want to See More AI in Action?

Explore our curated collection of enterprise AI case studies—showcasing how organizations are automating processes, scaling infrastructure, and delivering measurable results.

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AI Reports, Benchmarks & Strategic Frameworks

Source: OpenAI

Category: AI Infrastructure & Economy

OpenAI’s report outlines the economic and geopolitical importance of AI infrastructure — data centers, power grids, and semiconductor manufacturing — as the foundation for global AI leadership. It projects $100B per 5GW data center and calls for public-private investment

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Source: Stanford

Category: AI Trends & Policy

Stanford's AI Index is a data-rich annual report tracking AI’s global progress, model scaling, policy trends, and the economic impact of generative AI. It reveals U.S. leadership in model development and highlights rising concerns around ethics and regulation

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Source: Google Cloud

Category: Generative AI in Enterprise

This report features 321 enterprise use cases across sectors like healthcare, finance, retail, and manufacturing. It highlights how generative AI is moving beyond pilots and into core business operations—powering virtual agents, intelligent automation, and real-time decision-making.

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Looking for Trusted AI Research & Frameworks?

A curated collection of research reports and execution frameworks—covering AI infrastructure, automation, governance, market trends, and the workforce shifts shaping enterprise adoption.

Browse the Full Research Hub >

A practical guide to AIOps implementation—complete with use cases, pitfalls to avoid, a vendor comparison matrix, and a customizable rollout template.

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Discover how orchestration is the backbone of scalable AI—solving compute bottlenecks, automating ML pipelines, and turning chaos into streamlined enterprise execution.

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Explore the 10 critical areas where IT leaders must lead AI adoption—from infrastructure and data pipelines to performance monitoring, security, and workforce transformation.

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Explore Strategic Insights on AI Execution

Actionable insights, proven approaches, and execution-ready content for leaders building AI-driven IT and enterprise operations.

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ITOpsAI Hub

A living library of AI insights, frameworks, and case studies curated to spotlight what’s working, what’s evolving, and how to lead through it.

What you’ll find in AI Blogs & Insights:

  • Practical guides on AIOps, orchestration, and AI implementation
  • Use case breakdowns, frameworks, and tool comparisons
  • Deep dives on how AI impacts IT strategy and operations

Many AI tools symbols in a vertical row. colors purple and blue.

What You'll Find in Resources:

  • Curated reports, research, and strategic frameworks from top AI sources
  • Execution guides on governance, infrastructure, and data strategy
  • Trusted insights to help you scale AI with clarity and confidence

AI Brain on a circuit board. Colors purple, blue

What You'll Find in Case Studies:

  • Vetted examples of how companies are using AI to automate and scale
  • Measurable outcomes from infrastructure, IT, and business transformation
  • Strategic insights on execution, orchestration, and enterprise adoption

Explore Recent Blogs

By Sherry Bushman April 23, 2025
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
AI Circuit chip in royal Blue
By Sherry Bushman April 21, 2025
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.
AI Tools and Components linked as cogs
By Sherry Bushman April 17, 2025
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.

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