object(WP_Post)#8429 (24) { [“ID”]=> int(40160) [“post_author”]=> string(2) “36” [“post_date”]=> string(19) “2025-05-26 02:54:37” [“post_date_gmt”]=> string(19) “2025-05-26 02:54:37” [“post_content”]=> string(3706) “ Across the communications landscape, teams are being asked to do more with less, while staying aligned, responsive and compliant in the face of complex and often shifting stakeholder demands. In that environment, how we track, report and manage our relationships really matters. In too many organisations, relationship management is still built around tools designed for customer sales. CRM systems, built for structured pipelines and linear user journeys, have long been the default for managing contact databases. They work well for sales and customer service functions. But for communications professionals managing journalists, political offices, internal leaders and external advocates, these tools often fall short. Stakeholder relationships don’t follow a straight line. They change depending on context, shaped by policy shifts, public sentiment, media narratives or crisis response. A stakeholder may be supportive one week and critical the next. They often hold more than one role, and their influence doesn’t fit neatly into a funnel or metric. Managing these relationships requires more than contact management. It requires context. The ability to see not just who you spoke to, but why, and what happened...

Since AI is advancing faster than regulation and innovation is outpacing understanding, two principles must rise above the noise: trust and responsible innovation. That was the central message for industry leaders during another day of SAS Innovate 2025. Show host and recently named Chief Operating Officer Gavin Day said this core tenet begins before the first line of code is written. “These are really volatile and uncertain times, perhaps the most volatile we’ve faced in our careers,” he said. But it’s not new territory for SAS, which is on the brink of 50 years in business. “We’ve seen everything, so we’re here to help you through anything, especially when we know market forces are working for and against you.” Gavin Day speaks about trust and innovation Goodnight and Nadella on the big picture But even with a partner like SAS, he believes organizational success and the ability to forge through challenging times begin at the top. Day said there are no better examples – “titans,” he called them – than SAS CEO Jim Goodnight and Microsoft Chairman and CEO Satya Nadella. The two engaged in a recorded video discussion that helped set the tone for the event, discussing topics that included...

Photo by Jessie McCall / Unsplash Selling custom-made furniture in this age of mass production is not easy. But this mid-sized, Europe-focused furniture retailer was making it work. Their secret? Letting customers choose everything — from fabric choices to sofa leg styles, even down to the color of decorative nails. However, as sales grew, the made-to-order created a major problem. Each order was unique — different fabrics, custom features, and special requirements. The team had to carefully handle each customization, create detailed specifications for suppliers, and ensure every special request was correctly manufactured. When suppliers sent back order confirmations, the real challenge began. With an 8-week order cycle, processing delays meant customers waited without updates. Order backlogs grew, negative reviews increased, and 20-30% of all orders were experiencing some form of error or issue. They needed a way to process these documents accurately without hiring more staff. The real cost of manual supplier order management The supplier documents arrived as complex PDFs — some up to 16 pages long, in multiple languages, and with different technical notations. Their seven-person operations team spent 10-15 hours per week per person processing these documents. That’s 70-105 hours weekly just matching...

Participants and organizers of the TriCon AI Workshop: (L-R) Travis Wagner (Trinnex), Alana Gildner (BV), Yudu (Sonia) Wu (WSP), Madeleine Driscoll (Hazen and Sawyer), Craig Daley (City of Baltimore), John Smith (Haley Ward), Brian Ball (VA Engineering), David Gisborn (DC Water), and Davar Ardalan (TulipAI). Brandon O’Daniel of Xylem, one of the speakers, was not present in the photo Water industry professionals explored the intersection of artificial intelligence (AI) and machine learning (ML) during a pre-conference workshop in Ocean City, Maryland yesterday, discovering that while AI’s roots go back to 1948, today’s Generative AI has the potential to completely upend their industry. Designed to make AI technologies accessible and relevant, the sessions emphasized the critical role of data and the importance of data governance, sparking excitement and curiosity among participants — all leading up to the Chesapeake Tri-Association Conference (TriCon), the water industry’s premier event. Craig Daly, Chief of Water Facilities Division, City of Baltimore DPW on the fundamentals of AI and ML Professionals from the City of Rockville, WSSC, City of Baltimore, DC Water, and regional engineering companies gathered to explore how AI can be effectively applied to their field. Presented by the CWEA and CSAWWA Asset Management Committee,...

Organizations are eager to move into the era of agentic AI, but moving AI projects from development to production remains a challenge. Deploying agentic AI apps often requires complex configurations and integrations, delaying time to value.  Barriers to deploying agentic AI:  Knowing where to start: Without a structured framework, connecting tools and configuring systems is time-consuming. Scaling effectively: Performance, reliability, and cost management become resource drains without a scalable infrastructure. Ensuring security and compliance: Many solutions rely on uncontrolled data and models instead of permissioned, tested ones Governance and observability: AI infrastructure and deployments need clear documentation and traceability. Monitoring and maintenance: Ensuring performance, updates, and system compatibility is complex and difficult without robust monitoring. Now, DataRobot comes with NVIDIA AI Enterprise embedded — offering the fastest way to develop and deliver agentic AI.  With a fully validated AI stack, organizations can reduce the risks of open-source tools and DIY AI while deploying where it makes sense, without added complexity. This enables AI solutions to be custom-tailored for business problems and optimized in ways that would otherwise be impossible. In this blog post, we’ll explore how AI practitioners can rapidly develop agentic AI applications using DataRobot and NVIDIA AI Enterprise,...

In an extremely aggressive promotion, Google is offering U.S. college students a free, one-year ride on Google One AI Premium — a fierce competitor to ChatGPT. The deal translates into $20/month savings for a year — and gives those students access to some of the most advanced AI on the planet, including the Gemini Advanced chatbot, Deep Research, text editor Canvas and auto-video generation. Observes Josh Woodward, vice president, Google Labs & Google Gemini: “To top all of this off, you’ll get 2 TB of storage, providing plenty of space for school projects, research, high-resolution media and your personal photos or videos.” Currently, students are the number one users of Google’s chief competitor, ChatGPT, according to ChatGPT-maker OpenAI. In other AI news and analysis: *New ChatGPT AI Engine Smarter than 98% of Humans: Stick a fork in it: Apparently, the battle of wits between humans AI is so yesterday — and we flesh-bags have lost. New test results from Mensa — the global group of the rumoredly smartest people in the world — show that one of ChatGPT’s newest AI engines, o3, has an IQ of 136. Observes writer Liam Wright: “The score, calculated from a seven-run rolling average, places...

What is this Every Thursday, I will share a dev diary about what we’ve been working on over the past few weeks. I’ll focus on the interesting challenges and solutions that I encountered. I won’t be able to cover everything, but I’ll share what caught my interest. Why am I doing it I want to bring our community along on this journey, and I simply love writing about things I’m passionate about! This is my unfiltered dev journal, so please keep in mind that what I write here are my thoughts and will be outdated by the time you read this, as so many things change quickly. Any plans I mention aren’t set in stone and everything is subject to change. Also, if you don’t like spoilers, then don’t read this. Space Engineers 2 On Monday we released our first update (VS 1.1) – Steam Workshop. Now players can share their blueprints and worlds. I can see a lot of activity there. We are now focusing on VS 1.2 and polishing the last few things, and then the testing period begins. Our graphics team is hard at work on planetary features planned for Vertical Slice 2. They’re currently focused on...

Image credit: University of Waterloo. University of Waterloo researchers have developed new artificial intelligence (AI) technology that can accurately analyze pitcher performance and mechanics using low-resolution video of baseball games. The system, developed for the Baltimore Orioles by the Waterloo team, plugs holes in much more elaborate and expensive technology already installed in most stadiums that host Major League Baseball (MLB), whose teams have increasingly tapped into data analytics in recent years. Waterloo researchers convert video of a pitcher’s performance into a two-dimensional model that PitcherNet’s AI algorithm can later analyze. (Credit: University of Waterloo) Those systems, produced by a company called Hawk-Eye Innovations, use multiple special cameras in each park to catch players in action, but the data they yield is typically available to the home team that owns the stadium those games are played in. To add away games to their analytics operation, as well as use smartphone video taken by scouts in minor league and college games, the Orioles asked video and AI experts at Waterloo for help about three years ago. The result is a comparatively simple system called PitcherNet, which overcomes challenges such as motion blurring to track the movements of pitchers on the mound,...

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Call it what you want – an arms race, a land grab, a gold rush – but AI is now the centerpiece of most business strategies. Executives aren’t just curious about AI anymore; they are positioning their organizations for the technology’s future. The problem? Most organizations aren’t ready. Even as interest in generative AI (GenAI) and autonomous systems explodes, only a few organizations are building the foundations to scale AI responsibly. The rest are rushing in without a plan, missing the infrastructure, governance and cross-functional collaboration to make AI work in the real world. The answer lies in building on the existing infrastructure, applying AI thoughtfully across people, processes and technology and strategically using tools like SAS® Viya® to solidify that foundation. Let’s explore what distinguishes AI leaders from laggards, why agentic AI marks the next significant shift and how SAS technology is helping organizations take a more innovative, structured approach. The AI gold rush We are witnessing an AI gold rush, with organizations eager to tap into AI’s potential. While many are excited about what AI can offer, few have the right approach to make the most of it. The rush is on, but only a few are laying...