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Since Generative Artificial Intelligence (GenAI) captured mainstream attention a few years ago, businesses have been looking for ways to implement AI into their operations. There are some obvious reasons for this shift: saved time, increased productivity, and decreased need for manual effort. But there’s also another factor at play—the realization that not embracing AI now means getting left behind by the competition.

“AI is the new electricity,” as Andrew Ng, Co-founder of Google Brain and Coursera, famously put it, AI is now the driving force behind unprecedented innovation, reshaping everything from healthcare to finance and beyond. All of these sectors revolve around data.

Processes are at the heart of every government organization. They define how work gets done. Whether it’s determining benefits, inspecting food safety, or supporting defense operations, every mission relies on structured workflows to function effectively. Government agencies face unique challenges that impact their processes: Government organizations are increasingly turning to low-code platforms to overcome their operational challenges and increase efficiency.

For today’s IT Director, the pressure has never been greater. Tasked with delivering business-critical technology in a landscape of shrinking budgets and expanding expectations, IT leaders find themselves at the intersection of business demands and technical realities. On one side, C-suite executives and department heads expect solutions that improve efficiency, drive innovation, and deliver competitive advantage.

Pharmaceutical labeling is an ideal use case for AI because it’s a complex process that requires high levels of accuracy. Inaccurate labeling can result in: With recent breakthroughs in AI technology, pharmaceutical companies have rushed to explore its potential. But many have not seen the impact they expected. The problem isn’t the AI. It’s how pharma companies are using AI.

People are tired of talking about artificial intelligence (AI). They want action. Since the launch of ChatGPT, the financial services industry has been looking for ways to drive value with AI, but it's been a struggle to get real value out of AI experiments and pilot projects. The banking industry prefers to avoid potential risks, so how can financial sector leaders move from AI experimentation to AI value while being mindful of risk tolerance?

Artificial intelligence is now ever-present in many businesses. But where’s the ROI? Many deployments stall in pilot mode, failing to drive transformation. Over the past two years, businesses have rushed to deploy generative AI to try to boost operational efficiency, improve customer experiences, and achieve critical organizational objectives. But without a structured enterprise AI strategy, these efforts have failed to drive tangible business outcomes. The problem?

The era of AI experimentation is over. Organizations want to see ROI. And they will—as long as they understand that the competitive edge isn’t in AI itself. With AI evolving rapidly, businesses need a clear strategy that cuts through the noise and generates ROI. This key strategy is to embed AI into core business processes. This post will cover five enterprise AI trends for the new era of AI and why process is the key to ROI. The most talked-about trend today is agentic AI.