Artificial intelligence (AI) has shifted from a bold experiment to a strategic necessity. Recent studies show that 88% of C-level decision-makers want to accelerate AI adoption in 2025. Yet, just a fraction of AI initiatives delivers the expected results.

Did you hear about the self-driving Uber car that hit and killed a woman in Arizona? On another occasion, a facial recognition solution profiled an innocent man of color as a criminal in New Jersey, and Amazon’s AI-powered recruitment tool displayed bias against female candidates.

Artificial intelligence in radiology - use cases and trends

TL;DR Many companies rush into generative AI without realizing just how steep—and unpredictable—the associated expenses can be. Meanwhile, the cost of implementing generative AI in business can range from $0.0005 per 1,000 text units (tokens or characters) if you use a popular commercially available Gen AI tool to over $190,000 for a fully custom solution based on a fine-tuned open-source model.

You can view artificial intelligence as an umbrella term encompassing different types of algorithms that can autonomously perform complex tasks that normally require human intelligence. For instance, AI models can analyze historical data to make predictions for the future. They can detect anomalies in a machine’s functioning before it breaks down. AI can even drive vehicles. And depending on which AI subtype you are looking at, it can compose music and generate artwork.

Retrieval-augmented generation (RAG) is an AI architecture that enhances LLM accuracy by dynamically retrieving relevant, up-to-date information from external knowledge sources RAG significantly reduces hallucinations and improves response accuracy in critical domains like healthcare (96% diagnostic accuracy) and legal (38-115% productivity gains)

2024 proved to be a watershed moment for generative artificial intelligence (Gen AI). A few months after OpenAI launched ChatGPT, McKinsey researchers revealed that one-third of companies were already using generative AI in at least one business function.

Have you ever tried to learn how a large language model works under the hood? Behind every smart AI tool lies an intensive but really fascinating training regimen. Training large language models (LLMs) is not a one-and-done activity—it’s a multi-stage process that turns raw data into powerful instruction datasets. From processing billions of words to mastering the art of not saying the wrong thing—the path from a blank-slate model to a specialized AI assistant is truly extrao

Everyone wants AI, but few know where to start. Enterprises face analysis paralysis with implementing AI effectively when massive, expensive models feel like overkill for routine tasks. Why deploy a $10 million solution just to answer FAQs or process documents? The truth is, most businesses don’t need boundless AI creativity; they need focused, reliable, and cost-efficient automation.

Some experts believe that AI agents are just a passing fad, while others dream of AI-run banks and artificial CEOs delegating work to AI agents and humans alike. Our artificial intelligence consulting company believes that this technology has great potential, but there are limitations that firms considering implementing AI agents need to be aware of.

Unlocking the potential of generative AI for marketing success

Unlocking the potential of generative AI for marketing success

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