Stratishield AI

Excited to share that I was featured in Gaming & Leisure® magazine for my article on AI analytics! 🎉 Businesses are moving beyond relying on a single AI model. By combining Large Language Models (LLMs) and Large Graphical Models (LGMs), we can move from reactive to predictive analytics, gaining more comprehensive insights. LLMs excel at understanding and generating human-like text. LGMs shine in analyzing complex data structures and relationships. Together, these tools offer a more holistic approach to decision-making. Check out the full article here: https://lnkd.in/e6nRVey9 Gaming & Leisure LinkedIn here: https://lnkd.in/gX_Bmt4P Jeannie Caruso

In Episode 32 of AI Insights: The Executive Brief host Benjamin Bohman with Hani ElGebaly, founder and CEO of AvidBeam Technologies, to explore the cutting-edge world of AI-powered video analytics. Discover how Avid Beam is revolutionizing industries with computer vision—shifting from traditional surveillance to actionable business intelligence and safety solutions. Hani shares his journey from Intel to founding Avid Beam, the challenges of scaling AI, and the exciting possibilities of combining computer vision with generative AI. Whether you’re interested in smart cities, retail insights, or the future of video analytics, this episode offers thought-provoking insights into the transformative potential of AI. 👀 Watch now and join the conversation with Ben Bohman and Hany Elgwali! Full Episode:https://lnkd.in/gz24PYXd 🔥 Key Topics Covered: • How AI is transforming video analytics • Real-world use cases in smart cities, safety, and retail • Scaling AI solutions for real-time video processing • The future of computer vision with generative AI 📢 Hashtags: #AIInsights #BenBohman #VideoAnalytics #ComputerVision #SmartCities #GenerativeAI #BusinessIntelligence #AvidBeam #AIApplications

Episode 32 of AI Insights: The Executive Brief is live! In this episode, I sit down with Jason Hart, Managing Partner at ARG - IT Clarity, to discuss AI inflection points and their transformative impact on technology and business. From his early days at CDW to spearheading innovative AI and cloud solutions for enterprises and SMBs, Jason shares a wealth of insights and real-world applications of AI. 💡 What we cover: • The evolution of cloud computing and its parallels with AI adoption. • How businesses can bridge data silos for real-time insights. • The critical role of AI in enabling smarter decision-making and scaling innovation. Whether you’re leading a tech strategy, exploring AI adoption, or just curious about how AI is reshaping industries, this episode is packed with actionable advice and thought leadership. 👉 Don’t miss it! Watch the full episode here: https://lnkd.in/gkNuVFSq Let’s shape the future of AI together. What’s your biggest takeaway from the current AI revolution? Share your thoughts below! 💬 #AIInsights #TechnologyLeadership #AIInBusiness #InnovationInTech #DigitalTransformation #AIInflectionPoints #FutureOfAI

4 tips to measure the ROI of your AI integration: 𝟭. 𝗚𝗲𝘁 𝗰𝗹𝗲𝗮𝗿 𝗼𝗻 𝘄𝗵𝗮𝘁 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲. You can't find ROI without that. Set specific metrics for your AI integration—like cost savings, efficiency gains, or increased revenue. Make sure you know exactly what you're tracking from day one. Without metrics, ROI is just a guess. 𝟮. 𝗖𝗵𝗲𝗰𝗸 𝗶𝗻 𝗼𝗳𝘁𝗲𝗻 Measuring ROI isn't a one-time thing. Keep an eye on progress and compare results against your set metrics. Regular monitoring helps you understand what's working and what needs adjusting. 𝟯. 𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝘁𝗵𝗲𝗻 𝘃𝘀. 𝗻𝗼𝘄 To really see the impact, compare the before and after. Look at how things worked before AI and how they improved after integration. The numbers should speak for themselves, whether it's reduced workload or faster turnaround times. It gives you a bird-eye view. 𝟰. 𝗚𝗶𝘃𝗲 𝗶𝘁 𝗮 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲 Create a plan on how long you will test. If AI isn't delivering results within that time frame, it's time to reassess and change variables. However, make sure you give it enough time to show real impact. Deadlines keep expectations realistic and help you adjust if needed. Want to build one? DM me.

5-step guide to bulletproof your AI governance framework 𝗦𝘁𝗲𝗽 𝟭: 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗰𝗹𝗲𝗮𝗿 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 Ownership matters. Someone must lead. You need to know who's responsible—whether it's IT, Legal, or the Data Science team. Without clarity, things will fall apart. The best approach is to create a cross-functional team. Assign roles. Make it clear. 𝗦𝘁𝗲𝗽 𝟮: 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗱𝗮𝘁𝗮 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Data unity is everything. Different departments can't have different rules. If they do, the process just becomes much more frustrating. What you need is unity. Collaborate across teams. Standardize the process. Create a unified policy. 𝗦𝘁𝗲𝗽 𝟯: 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗴𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀 Think ethically. Act ethically. AI needs a moral compass. It's important to make sure that our AI applications reflect the values you hold as a company. Avoid bias. Protect privacy. They guide decision-making. They protect your brand. 𝗦𝘁𝗲𝗽 𝟰: 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Monitor often. AI changes pretty fast. That's why regular checks are necessary: • Define metrics • Keep outputs relevant • Review AI performance And do it often. 𝗦𝘁𝗲𝗽 𝟱: 𝗔𝗜 𝗮𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 AI governance needs buy-in. Employees must understand AI's role. They must see its value. It’s not just for leadership. Build trust. Encourage questions. Promote learning. AI governance works when everyone is on board. If you’re looking for help on this, DM me.

🚀 Episode 31 of AI Insights: The Executive Brief is here! 🎙️ In this episode, Ben sits down with Kathleen Hurley, MBA, founder of Sage Inc., a company redefining how small and mid-sized businesses approach technology. Kate shares her incredible journey from a bookworm with aspirations of teaching to becoming a thought leader in technology and AI for SMBs. Discover how Kate is using automation and AI to simplify cybersecurity, regulatory compliance, and business operations—all while keeping humanity at the core of innovation. Her insights into making complex tech accessible and her passion for empowering SMBs are truly inspiring! 🎧 Tune in to hear about: ✅ The human side of technology and AI ✅ Empowering SMBs with tailored tech solutions ✅ How AI and automation create real business impact 👉 Watch now: https://lnkd.in/g25SshR6 Follow Sage Inc: https://lnkd.in/grAZGKFB #AIInsights #Podcast #Episode31 #SmallBusiness #AIInBusiness #Automation #Cybersecurity

Enough with the ChatGPT tricks. We’ve all seen the "100 ChatGPT prompts to transform your business" posts. Prompts might sound cool, but they rarely deliver real results. The problem? Most of them are just fluff– engagement bait. What businesses need are specialized tools backed by real case studies and proven outcomes. Not gimmicks that promise the world but fail to deliver. As the founder of deep learning AI, Andrew Ng, said, "The key to AI success isn’t hype; it’s solving real problems with measurable impact." Stop chasing flashy prompts—start using tools that actually work. PS. Every day, I post 1 tip on the strategic implementation of AI in your business. If you enjoyed this one, leave a follow.

James Betker, research engineer at OpenAI, wrote in his blog: “It’s not the model weights that you are referring to. It’s the dataset.” He claims that almost all LLMs will eventually produce similar responses if you feed them the same data. That’s why you should always find out the source your AI vendor uses for their tool. Is it a general tool making big claims, or has it been trained on niche data to solve specific problems? Because the data you feed is the only differentiator. The output quality depends entirely on what goes in—high-quality, relevant data creates niche tools that solve niche problems. If the data is poor, the AI’s performance will be poor too. My advice after integrating 100+ AIs into businesses: 𝗗𝗼𝗻'𝘁 𝗺𝗲𝘀𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗶𝗳 𝘁𝗵𝗲𝘆 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀. If your AI vendor uses incomplete, outdated, or irrelevant data, the AI’s output will be just as useless. The quality simply won’t be there. In other words, garbage in, garbage out. PS. For more tips on practical AI applications for your business, follow Benjamin Bohman

3 mistakes people make while pitching AI to top management 𝟭. 𝗧𝗮𝗹𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗽𝗿𝗼𝗼𝗳. Management needs more than words—they need evidence. Many pitches fail because they rely on buzzwords and hype without real results. Executives want to see case studies, testimonials, and solid numbers that show AI’s effectiveness. Without proof, your pitch won’t stick. 𝟮. 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝘁𝗼 𝘁𝗿𝗮𝗰𝗸 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝗰𝗹𝗲𝗮𝗿 𝗺𝗲𝘁𝗿𝗶𝗰𝘀. Top management expects measurable outcomes. If your pitch doesn’t include agreed-upon metrics for tracking progress, it becomes hard to justify the investment. Metrics like cost savings, efficiency gains, or revenue growth make it easier to show AI’s impact over time. If you can’t measure it, they won’t buy into it. 𝟯. 𝗡𝗼𝘁 𝘀𝗽𝗲𝗰𝗶𝗳𝘆𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗶𝗺𝗽𝗿𝗼𝘃𝗲. Vague promises won't convince executives. You need to identify three concrete areas where AI will bring improvement. Like reducing manual work, speeding up processes, or enhancing customer satisfaction. Specific outcomes make the value of AI tangible. Want to build one? DM me.

5-step guide to train your employees to use AI: 𝗦𝘁𝗲𝗽 𝟭: 𝗣𝗿𝗼𝘃𝗶𝗱𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 Set up hands-on sessions to teach the basics of AI. Employees need to know how AI works and how to use it in their tasks. Clear training helps them feel comfortable with the tools. 𝗦𝘁𝗲𝗽 𝟮: 𝗔𝘀𝘀𝗶𝗴𝗻 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝘁𝗮𝘀𝗸𝘀 Give employees simple tasks to show how AI can help them. For example, have them use AI to create reports or answer common customer questions. These tasks make it easy to see its value in daily work. 𝗦𝘁𝗲𝗽 𝟯: 𝗛𝗶𝗿𝗲 𝗮𝗻 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝘁 Bring in an expert to guide the setup. They can solve problems, adjust tools to fit your business, and make sure AI works smoothly. 𝗦𝘁𝗲𝗽 𝟰: 𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹 𝘄𝗶𝘁𝗵 𝗽𝗶𝗹𝗼𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Begin with a small task or one department. This helps employees get used to AI without too much pressure. They also let you see what works and what doesn’t before using AI across the business. 𝗦𝘁𝗲𝗽 𝟱: 𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 AI tools keep changing, so employees need to keep learning. Provide chances to refresh their skills. Ask for feedback to improve both training and how AI is used. Regular learning keeps things on track. If you’re looking for help on this, DM me.