LoadSpring

Find the person on your program who fixes the reporting before anyone sees it.

Every team has one.

They know what breaks and how to hide it before anyone notices. And even worse, most of that knowledge lives in their head.

Here’s a suggestion.

Ask them to track every manual fix they make during a reporting cycle. Every workaround. Every reconciliation. Every time they have to stop and correct something by hand.

What you get back is usually more useful than another reporting review or system audit.

It shows you exactly where the operational friction lives.

That becomes your roadmap for getting reporting off tribal knowledge.

“Why does this dashboard show 47 active projects when Finance is reporting 31?”

Just the thing you don’t want to hear while presenting to leadership.

A customer recently shared this experience with us, and unfortunately, she didn’t have an answer.

But...what she did next was pretty simple, and pretty effective. Back at her desk, she emailed three project controls leads and asked each of them to define what an “active project” meant.

The responses came back completely different.

One team started the clock at budget approval. Another at mobilization. A third kept projects active until final accounts were settled.

The systems were working exactly as configured but the definitions underneath them had drifted apart over years.

Over the next few weeks, she worked through the logic with each group and built a standard definition across the program.

Shared definitions are operational infrastructure. Without them, reporting drift becomes inevitable, even in highly structured environments.

Try it at your org. Pick one metric. Ask three people to define it. See what comes back.

If your team spends its days inside Primavera P6, we highly recommend taking a close look at the P6 R25.12 update. This release targets common day-to-day challenges in P6—slow performance, limited visibility during imports, and a web experience that can be hard to work with.

Here’s what users are saying:
• Faster performance on large schedules - Projects with thousands of activities run more smoothly, with less lag during updates and calculations.

• More control over imports - You can review and validate changes before bringing them into your schedule, reducing the risk of unexpected issues.

• A better web experience - The web interface now behaves more like a spreadsheet, making it easier to navigate, filter, and manage data.

• More flexible resource planning - You get more visibility into how resources are assigned, helping you plan with greater accuracy.

If performance and usability have been P6 pain points, R25.12 is worth a look.

See why leading project-driven organizations rely on LoadSpring to run P6 with better performance, simpler upgrades, and smoother user experience.

👉 https://hubs.ly/Q04g6qls0

77% of employees are sharing sensitive company data with AI tools—often without realizing the risk.

If your team is using AI in project controls, here are 5 things worth reviewing:

1. Keep Sensitive Data Inside Your Environment
→ Don’t paste project data into public AI tools
→ Only use enterprise AI or tools that connect directly to your systems

2. Confirm How the AI Gets Its Answers
→ Check with your vendor or implementation team if the AI is generating responses or retrieving them from your system
→ For project data, it should pull exact values from your database

3. Test Role-Based Access Controls
→ Log in as a restricted user
→ Ask for data that role shouldn’t see
→ If it returns it, fix access before rollout

4. Check For Consistency in Responses
→ Ask the same factual question multiple times
→ If answers change, the AI isn’t retrieving data reliably

5. Trace Answers Back to the Source
→ Ask: “Where did that number come from?”
→ Look for a specific query, report, or record behind it

Get these right and AI becomes genuinely useful—more efficiency, better forecasts, earlier risk identification, and project data your team can explore just by asking questions.

Curious what this looks like in practice?

👉https://hubs.ly/Q04fsB9b0

80% of AI projects fail to live up to the hype. We've all seen this or other similar stats.

According to Project Management Institute, data quality (the lifeblood of AI) is a major culprit. Without clean, unified project data feeding your model, AI can’t improve efficiency, and definitely won't surface real risks, scenarios, or insights.

Build AI on quality data and you have the winning formula.

👉 See it in action https://hubs.ly/Q04f3Prp0



Source: Rand: The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed

April 2026: Insights from Industry Conversations

This month we highlight data as the new control layer for modern projects.

Project performance is now driven by the quality, accessibility, and security of the data behind every decision.

Two of our leaders weigh in on what we're hearing across the industry:

1. "How to Improve Project Controls in Construction: 5 Changes That Make a Difference" In this blog, . Asif Sharif examines why better tools haven't improved outcomes — and how fixing the underlying data changes visibility, timing, and decision-making across construction projects.

2. In his blog "How to Keep Data Safe When Using AI in Project Controls" Maciel shares what we've learned from customers navigating AI adoption, with a practical framework for leveraging AI without compromising control over sensitive project data.

One theme runs through both: organizations are rethinking how data is structured, trusted, and protected as it moves through their project environments.

👉 Read both articles here: https://hubs.ly/Q04dTJZ20

50% of project risk is poor communication.

Most project risk is informational. When teams aren’t in sync, things get lost, items are missed, and risk multiplies. Get everyone on the same page with LoadSpring Unified Project Platform.

👉 Reduce Risk through Better Team Collaboration https://hubs.ly/Q04d5XRz0



Source: PwC Insights and Trends: Current Programme and Project Management Practices