Insights

As generative AI tools like Microsoft Copilot, ChatGPT, and Google Gemini become increasingly integrated into our daily workflows, they’re transforming how employees write, code, research, and collaborate. Their efficiency and accessibility make them indispensable in many industries, but their rapid adoption also brings a new layer of risk. Every time a user interacts with one of these platforms by asking a question, pasting in a document, or sharing snippets of proprietary code, there’s potential for sensitive data to slip into environments outside the organization’s control.

As artificial intelligence continues to transform the business landscape, today’s CEOs face critical decisions surrounding AI's role in the workplace. To navigate this rapidly evolving environment, it’s essential for leaders to have a clear grasp of the most important AI terminology and foundational concepts. Understanding what AI models are, what they can actually deliver for your business, the considerations for their secure implementation, and how to strategically leverage these technologies are now critical skills for executive leadership.

Join PulseOne on September 17th for a live event at the Microsoft Tech Center in Irvine, "The AI Advantage: Unleashing Copilot Agents Across Microsoft 365 and Dynamics 365". The event will feature speakers from Microsoft, Queue Associates, AvePoint, TouchBrick, and PulseOne to deliver insights and real-world strategies for integrating Copilot capabilities into your Microsoft 365 and Dynamics 365 workflows.

In this episode, Chad talks with Jason Melo, CTO and Co-Founder of TouchBrick, to explore its solution that strengthens organizational data security when working with generative AI chatbots. Jason holds over 15 years of expertise in developing secure, scalable systems across web, mobile, AI, blockchain, and cloud platforms.

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As small and mid-sized businesses (SMBs) increasingly leverage data for AI initiatives, they face a critical challenge: how to utilize sensitive information while maintaining privacy, security, and compliance. Traditional approaches like redaction—simply removing or masking sensitive elements—often strip data of its utility for AI training and analysis.

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As AI adoption accelerates among small and mid-sized businesses, the focus often centers on capabilities and benefits. However, equally important—yet frequently overlooked—are the significant risks associated with AI training data. From compliance violations to bias perpetuation, the data you use to train AI systems can introduce substantial business, legal, and reputational risks.

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In today's rapidly evolving technological landscape, AI has moved from buzzword to business necessity. But for small and mid-sized businesses, the path to AI implementation isn't always clear. While enterprise organizations deploy dedicated data science teams and custom solutions, SMBs need more accessible approaches. This is where AI augmentation shines – offering targeted AI capabilities that enhance existing systems and workflows rather than replacing them wholesale.

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The difference between AI success and failure often comes down to one crucial element: data quality. Even the most sophisticated AI systems will underperform or fail entirely when fed poor-quality data. For small and mid-sized businesses (SMBs) embarking on AI initiatives, effective data preparation isn't just a technical consideration—it's a strategic imperative.

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Climate change is intensifying natural disasters, and IT outages are on the rise. Is your business prepared for floods, wildfires, or storms that disrupt entire regions? In this post, we explore forward-thinking strategies—from predictive analytics to Internet of Things (IoT) sensors—to help SMBs stay ahead of emerging climate-driven risks.