What We've Learned Doing This Work
Articles on the problems we run into most often across AI, data, cloud modernization and migration, written by the people doing the work.
Grouped by the Problem They Address
Each one takes a single problem and works through it, in enough detail to be useful rather than just interesting.
Why Most AI Investments Fail to Deliver
Most AI work looks like progress on paper. What separates that from measurable outcomes.
Read Article →Are Your Gen AI Experiments Stuck in POC Limbo?
The signs your pilots have stalled, and why they rarely reach production on their own.
Read Article →Can't Connect AI to Real Business Value?
The executive question with real weight behind it, and how to answer it credibly.
Read Article →An AI Roadmap: 5 Priorities for Getting Started
The priorities that separate AI programs that deliver from the ones that stall.
Read Article →Upskill Internally or Partner for AI?
When adoption lags, the problem is organizational, not technical. How to decide your path.
Read Article →Building POCs That Are Production Ready
How to design proofs of concept that are built to become real production capabilities.
Read Article →Speed vs. Cost in AI Deployments
Compare the main paths from pilot to production by time-to-value, risk, and readiness.
Read Article →From AI Experiments to Real Business Impact
What the programs that deliver do consistently differently from the ones that do not.
Read Article →How POC Fatigue Drains Enterprise AI
The same initiatives on the portfolio slide six months later. How the cycle sets in.
Read Article →AI Adoption Breakdown: Missing the Mark
Tools deployed, models built, nothing changing in the business. That is an adoption problem.
Read Article →Drowning in Data Silos? 7 Red Flags
Three teams, three dashboards, three different numbers. The signs your data is fragmented.
Read Article →The Real Reason Lakehouses Don't Deliver Value
The deployment hit every milestone and the value still never showed. Why that happens.
Read Article →Why Near Real-Time Analytics Fails
Real-time data does not guarantee real-time decisions when speed outpaces structure.
Read Article →Data Lakehouse vs. Traditional Warehousing
The pros, cons, and trade-offs of each, and how to tell which fits your situation.
Read Article →Accelerating Data Maturity: Roadmap and Milestones
How to build toward trust, adoption, and faster time-to-insight across the organization.
Read Article →Lakehouse Adoption: DIY or Partner?
Less a cost question than a time-to-value question, with compounding implications either way.
Read Article →Why Your Analytics Architecture Breaks Down at Scale
Analytics platforms rarely fail on storage. They fail when they never scaled across teams.
Read Article →Is Your Data Structure Slowing Innovation?
When teams reconcile more than they analyze, the structure is the bottleneck.
Read Article →Data Maturity: The Hidden Obstacle to Analytics
Inconsistent dashboards and stalled self-service often trace back to maturity, not tools.
Read Article →Consolidating Data for Near Real-Time Analytics
Without the right consolidation strategy, fast data becomes untrusted data.
Read Article →Is Your Data Center Draining Your Budget?
The on-premises costs that look predictable on paper, and the ones that quietly are not.
Read Article →High Maintenance Costs: A Symptom of the Legacy Trap
Why maintenance keeps consuming more of the budget each year, and what that signals.
Read Article →Cloud Migration: Top Fears and Why They Persist
The cost, risk, and operational worries that stall migration decisions, and why they linger.
Read Article →Why a 7R Analysis Is Critical to Migration Success
The framework for deciding what to do with each application before you move anything.
Read Article →Your Migration Roadmap and TCO
How to weigh cost, risk, and sequence before committing to a major migration investment.
Read Article →Making Cloud Migration Predictable and Safe
The structure and sequencing that let you move with confidence instead of guesswork.
Read Article →Unpredictable Cloud Spend: Is AWS Really More Expensive?
Why spend feels unpredictable, and why the platform is rarely the real cause.
Read Article →Lift-and-Shift Fatigue: Why Apps Aren't Truly Modern
The migration was supposed to be the finish line. Why it often is not.
Read Article →No Time or Money to Migrate?
The real blocker is usually clarity on cost and value, not time or budget.
Read Article →Migrating Out of Legacy: Big Challenges, Hidden Costs
Stable-feeling legacy systems quietly raise cost, risk, and complexity over time.
Read Article →Assessing Legacy Apps Before Migration
Informed prioritization that reduces risk without slowing progress.
Read Article →Lift-and-Shift With Modernization
How combining the two creates a flexible, low-risk path to the cloud.
Read Article →Stuck With Monolithic Applications?
Why architecture, not more resources, is usually the real limit on performance and scale.
Read Article →Speed to Market: Is Your DevOps Holding You Back?
When releases are slow and manual, the constraint is usually the pipeline, not the team.
Read Article →Cloud Costs Rising? Optimize With Modernization
Cloud cost problems often run deeper than usage. Where modernization changes the math.
Read Article →Monolith to Microservices
How to modernize incrementally, without betting the business on a full rewrite.
Read Article →The Journey of Application Modernization
The realistic pathways for taking applications beyond a basic lift-and-shift.
Read Article →Modernizing for Cloud Scalability
Practical roadmaps for getting real scalability and real-time data out of the cloud.
Read Article →Why Modernize Before Adopting AI and Analytics
Outdated application architecture is why many AI and analytics efforts fail to scale.
Read Article →Automating DevOps for Speed and Reliability
How CI/CD and Infrastructure as Code improve deployment speed and consistency.
Read Article →More Articles Coming
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