Stefano Puntoni on the Missing Piece in AI Strategy: Humans | The Data Chief
95% of AI projects fail. Is your "tech-first" mindset to blame? @wharton’s Stefano Puntoni breaks down the Human-AI gap with podcast host Cindi Howson on.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Connecting On-Premises LLMs to Enterprise Databases and APIs | DreamFactory
As organizations increasingly recognize the value of generative artificial intelligence, many are moving away from cloud hosted models in favor of on premises Large Language Models. This shift is primarily driven by the need to protect sensitive corporate data, maintain regulatory compliance, and reduce latency. However, an isolated local model offers limited utility. To truly unlock the potential of an on premises LLM, enterprises must connect it to their internal databases and APIs.
Stop AI Hallucinations at the Source | Simba Intelligence
AI isn't failing because the models are weak. It's failing because the data beneath them is broken. 88% of AI pilots never make it to production. 74% of companies haven't seen value from AI. The uncomfortable truth? These failures aren't about intelligence—they're about access, governance, and context.
In case you missed it | Meet Smartbear BearQ + application integrity
Missed the live event? Here’s a quick look at what we unveiled. AI has fundamentally changed how applications are built, creating a growing gap between development velocity and your ability to validate what’s being built. That’s why SmartBear delivers application integrity for the AI era – ensuring continuous, measurable assurance that your software just works as intended, with governance to operate at AI speed and scale.
A Wharton AI Research Leader's Formula for Responsible AI
Learn why scaling AI is as much a human challenge as it is a technological one. Stefano Puntoni, Co-Director of Wharton Human-AI Research and Professor at The Wharton School, examines the limits of data-driven decision making in the age of AI and why insights so often fail to translate into action. He breaks down the psychology behind AI resistance and outlines the leadership and change management strategies needed to turn AI potential into real organizational impact.
Why we built vision AI into TestComplete: Solving the complex app testing challenge
When we talk to testing teams at enterprise organizations, we hear the same frustrations repeatedly: “Our automation breaks every time the UI changes.” “We can’t test this application because it doesn’t expose accessible properties.” “We spend more time maintaining tests than creating new ones.” These scenarios block test automation adoption for teams that need it most.
Ep 66 | Women Leaders in Technology: AI Agents Are Your New Team- Now What?
From econometrics to anthropology to leading roles at Salesforce, AWS, and Nextdoor, Tatyana shares how her background shaped a fundamentally different approach to leadership. Drawing on her unconventional journey, she explains why agentic AI is forcing leaders to rethink how they manage technology, shifting from systems to a focus on teams, culture, and governance. Together, Tatyana and Paul share their perspectives on.
Data Silos Could Be Your Biggest Cloud Liability
In an always-on industrial economy, fragmented data is a liability. Your analytics reports may look flawless, but if they’re built on data silos scattered across edge, core, and cloud, they’re built on a fault line. Data silos drive-up costs, distort the critical decisions meant to drive competition, and prevent organizations from reaching a state of data singularity — where data becomes unified, portable, and continuously usable for AI.
Perforce 2026 State of Open Source Report Highlights Shift Toward Digital Autonomy in the EU
Fear of vendor lock-in is a top factor for moving to open source, up 68% this year compared to last year's report.
Ai-Powered Test Automation: A Complete Guide for Engineering Leaders
Your developers are shipping more code than ever. GitHub Copilot, Cursor, and tools like them have fundamentally changed developer throughput - some teams are seeing 40-76% more code per person per sprint. That is the headline everyone celebrates. The part that keeps engineering leaders up at night is the other side of that equation: your testing pipeline has not changed at the same pace. Tests that used to gate two releases a week now need to gate ten.