AI & Machine Learning

Generative AI is helping retailers reduce friction, personalize experiences, and improve margin across digital and physical channels. Retailers are under pressure to deliver more with less—faster personalization, leaner operations, and smarter inventory decisions. Generative AI is emerging as a practical tool to meet these demands. It’s not about novelty. It’s about automating high-volume tasks, improving … Read more

Generative AI is improving clinical workflows, documentation, and patient access across healthcare systems. Healthcare organizations are under pressure to deliver better outcomes with fewer resources. Rising patient volumes, documentation burdens, and staffing constraints are straining systems that were never designed for scale. Generative AI is now increasingly being used as a practical tool to reduce … Read more

To unlock agentic AI later, enterprises must deploy GenAI today with precision, governance, and reuse in mind. GenAI is no longer experimental. It’s embedded in workflows, powering content generation, summarization, and automation across enterprise environments. But most deployments are short-sighted—built for isolated wins, not long-term scalability. That’s a problem. Agentic AI—systems that act autonomously across … Read more

Generative AI is reshaping financial services by automating decisions, reducing risk, and improving client engagement. Generative AI is no longer experimental in financial services. It’s being deployed across core functions—risk, compliance, client service, and operations. The shift isn’t just about automation. It’s about improving precision, speed, and scale in environments where small errors carry outsized … Read more

GenAI is powerful—but not universal. Here’s when it fails to deliver ROI across enterprise IT use cases. Enterprise IT leaders are under pressure to deliver more with less. GenAI promises speed, scale, and automation—but not every use case benefits. Misapplication leads to wasted investment, poor decisions, and exposure to risk. Knowing when not to use … Read more

Enterprise agentic AI adoption requires timing, clarity, and readiness—not reaction to vendor hype. Agentic AI is gaining traction across enterprise software, promising autonomous agents that can reason, act, and coordinate complex workflows. Vendors are moving fast. Conferences are filled with demos. Analyst forecasts are bullish. But the real question isn’t whether agentic AI is coming—it’s … Read more

Generative AI ROI depends on use case clarity, cost tracking, and measurable business alignment. Generative AI is no longer experimental. It’s being deployed across enterprise workflows—from document summarization to internal search to customer support. In fact, 74% of organizations are currently seeing ROI from their gen AI investments, according to a Google Cloud report. But … Read more

Deciding whether to build or buy AI depends on control, cost, data sensitivity, and long-term differentiation. AI is now embedded in enterprise workflows—from document processing to customer support to internal search. But as adoption deepens, so does the pressure to decide: should you build your own AI or buy from a vendor? This isn’t a … Read more

Experimenting with custom AI models can unlock differentiation, but seven hidden challenges often derail ROI before deployment. Enterprise interest in building proprietary AI models is surging. The appeal is clear: tailored capabilities, tighter data control, and the potential to differentiate in ways off-the-shelf tools can’t. But experimentation is not the same as production—and most initiatives … Read more

Build internal AI capabilities with a step-by-step approach that balances control, cost, and business relevance. Enterprise AI adoption is accelerating, but most deployments still rely on generic tools that don’t reflect your data, workflows, or priorities. That limits ROI. Building your own AI—whether through fine-tuning, retrieval-augmented generation (RAG), or lightweight model development—offers a path to … Read more