Most businesses are already using AI — often more than they realize. But without structure or oversight, that early “AI innovation” can quickly turn into AI chaos. Employees experiment with tools like ChatGPT, Gemini, and dozens of industry-specific apps every day. While that can spark fast wins, it also creates real risks: data leakage, duplication, […]
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AI tools are transforming business operations — but without proper governance, they can also expose your organization to serious data, security, and compliance risks. To help you identify blind spots, IronEdge created the AI Governance Checklist: Is Your Business at Risk? This quick self-assessment will help you evaluate whether your business has the right visibility, controls, and safeguards in place. You’ll uncover: Where unapproved AI tools
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Artificial intelligence has evolved from experimental novelty to indispensable business utility. According to the 2025 World Economic Forum’s Future of Jobs Report, 50% of employers globally are planning to reorient their business to target new opportunities resulting from AI technology. Therefore, the conversation for small and midsize businesses (SMBs) is no longer if you’ll use AI — it’s how you’ll do it securely, affordably, and at scale. That’s where a multi-model Large Language Model (LLM) strategy comes in. It’s the next
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Challenge For IT leaders, strategic planning often demands extensive research, cross-department collaboration, and constant information gathering. Building technology roadmaps, assessing compliance requirements, or evaluating vendor solutions can consume hours or even days of effort. Without a dedicated research team, IT directors must independently source information from peers, vendors, and countless online resources — then validate
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Challenge In a fast-paced technical support environment, efficiency is everything. Support technicians are expected to quickly diagnose issues, communicate solutions clearly, and maintain high-quality service for every user request. Before using IronAI, troubleshooting often required switching between multiple AI tools or manually searching for next steps across different platforms. Public AI tools like ChatGPT were
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Challenge For technical engineers and solution architects, proposal development can be one of the most time-intensive responsibilities. Each project requires crafting detailed scopes of work, defining the level of effort (LOE), and writing technical outlines that are comprehensive enough for experienced engineers — yet still clear and approachable for clients. These repetitive tasks slowed down
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Challenge For Account Executives, prospecting and research are often the most time-consuming parts of the sales process — essential but repetitive work that slows down momentum. Before connecting with a new lead, each account required detailed preparation — manually reviewing company websites, LinkedIn profiles, and recent news to identify potential pain points and key decision-makers.
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Challenge For data analysts, time is divided between managing client deliverables, building reports, and troubleshooting technical issues. The constant context switching between projects and support tickets made it difficult to stay efficient. Before IronAI, solving complex data issues in SQL, Power BI, or Azure Data Factory could take hours — especially when debugging formulas or
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Do you know how your employees are using AI tools right now? You might be surprised. In one recent AI assessment, IronEdge discovered 4,193 AI queries by 40% of employees in just 30 days — at a company where leadership believed usage was zero. Our ManagedAI™ solution, IronAI™, help uncover and manage hidden AI usage like this. In
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Artificial Intelligence is transforming the workplace — but most companies aren’t ready for what comes next. Our new infographic, “5 Facts About AI at Work: The Shift from Public Tools to Secure LLM Platforms,” breaks down the data behind this rapid shift — and what it means for small and midsize businesses. You’ll discover: Why AI adoption
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