The Evolving Revolution: AI in 2025

The AI landscape of 2024 was nothing short of explosive, with generative models capturing global attention and sparking both excitement and concern. As we look ahead to 2025, the question on everyone’s mind is: how is AI evolving, and what breakthroughs or challenges lie just over the horizon?

To explore these questions, we gathered insights from a diverse panel of experts including Andrew Brust, Chester Conforte, Chris Ray, Dana Hernandez, Howard Holton, Ivan McPhee, Seth Byrnes, Whit Walters, and William McKnight. Their perspectives paint a picture of an industry in transition—moving from experimental enthusiasm to practical, vertical-specific applications.

What’s Still Hot? Where Are AI Use Cases Seeing Success?

The consensus is clear: AI is maturing beyond the experimentation phase. Chester Conforte notes a significant shift toward “true, vertical-specific use cases” being developed across industries. In healthcare, for instance, AI is being fine-tuned for specialized applications like auditory tools that help doctors stay present during patient conversations while AI handles note-taking.

“I believe ‘small is the new big’—that’s the key trend,” Chester explains. “We’re seeing hematology versus pathology versus pulmonology applications. AI in imaging technologies isn’t new, but it’s now coming to the forefront with new models used to accelerate cancer detection.”

However, Chester emphasizes an important caveat: “It has to be backed by a healthcare professional. AI can’t be the sole source of diagnoses. A radiologist needs to validate, verify, and confirm the findings.”

Dana Hernandez echoes this industry-specific focus in her analysis. “In my reports, I see AI leveraged effectively from an industry-specific perspective. For instance, vendors focused on finance and insurance are using AI for tasks like preventing financial crime and automating processes, often with specialized, smaller language models.”

William McKnight points to operational improvements: “We’re seeing cycles reduced in areas like pipeline development and master data management, which are becoming more autonomous. An area gaining traction is data observability—2025 might be its year.”

Andrew Brust highlights the practical successes in code generation and natural language interfaces for querying data. “More interesting are advancements in the data layer and architecture,” he says. “I see a shift from the ‘wow’ factor of demos to practical use, using the right models and data to reduce hallucinations and make data more accessible.”

How Are We Likely to See Large Language Models Evolve?

The evolution of LLMs appears to be taking different paths across the globe. Whit Walters offers a fascinating geopolitical perspective: “Globally, we’ll see AI models shaped by cultural and political values. It’s less about technical developments and more about what we want our AIs to do.”

Walters draws a stark contrast between different approaches: “Consider Elon Musk’s xAI, based on Twitter/X. It’s uncensored—quite different from Google Gemini, which tends to lecture you if you ask the wrong question.”

This divergence, Walters predicts, will lead to “a rise in models without guardrails, which will provide more direct answers.” The implication is clear: we’re moving toward a fragmented AI landscape where models reflect regional values and regulatory environments.

Ivan McPhee brings attention to the critical role of structured prompts: “There’s also a lot of focus on structured prompts. A slight change in phrasing, like using ‘detailed’ versus ‘comprehensive,’ can yield vastly different responses. Users need to learn how to use these tools effectively.”

Whit Walters agrees, emphasizing that “prompt engineering is crucial. Depending on how words are embedded in the model, you can get drastically different answers.” He anticipates the emergence of “domain-trained prompting tools—agentic models that can help optimize prompts for better outcomes.”

How Is AI Building on and Advancing the Use of Data Through Analytics and Business Intelligence?

The relationship between AI and data analytics is becoming increasingly symbiotic. Andrew Brust explains: “Data is the foundation of AI. We’ve seen how generative AI over large amounts of unstructured data can lead to hallucinations, and projects are getting scrapped.”

However, Brust sees a promising convergence: “We’re starting to see a marriage between AI and BI, beyond natural language querying. Semantic models exist in BI to make data more understandable and can extend to structured data. When combined, we can use these models to generate useful chatbot-like experiences, pulling answers from structured and unstructured data sources.”

This integration, Brust argues, “creates business-useful outputs while reducing hallucinations through contextual enhancements. This is where AI will become more grounded, and data democratization will be more effective.”

Howard Holton provides a sobering reality check: “BI has yet to work perfectly for the last decade. Those producing BI often don’t understand the business, and the business doesn’t fully grasp the data, leading to friction.” He emphasizes that “this can’t be solved by Gen AI alone—it requires a mutual understanding between both groups.”

What Other Challenges Are You Seeing That Might Hinder AI’s Progress?

Several significant challenges loom on the horizon. Andrew Brust identifies a critical misallocation of resources: “The euphoria over AI has diverted mindshare and budgets away from data projects, which is unfortunate. Enterprises need to see them as the same.”

Whit Walters highlights the unsustainable startup ecosystem: “There’s also the AI startup bubble—too many startups, too much funding, burning through cash without generating revenue. It feels like an unsustainable situation, and we’ll see it burst a bit next year.”

Chris Ray points to the premature security market: “I am seeing vendors build solutions to ‘secure’ GenAI/LLMs. Penetration testing as a service (PTaaS) vendors are offering LLM-focused testing, and cloud-native application protection (CNAPP) has vendors offering controls for LLMs deployed in customer cloud accounts. I don’t think buyers have even begun to understand how to effectively use LLMs in the enterprise, yet vendors are pushing new products/services to ‘secure’ them.”

William McKnight raises regulatory concerns: “Another looming factor for 2025 is the EU Data Act, which will require AI systems to be able to shut off with the click of a button. This could have a big impact on AI’s ongoing development.”

The Million-Dollar Question: How Close Are We to Artificial General Intelligence?

The panel’s response to AGI was notably skeptical. Whit Walters is blunt: “AGI remains a pipe dream. We don’t understand consciousness well well enough to recreate it, and simply throwing compute power at the problem won’t make something conscious—it’ll just be a simulation.”

Andrew Brust agrees: “We can progress toward AGI, but we must stop thinking that predicting the next word is intelligence. It’s just statistical prediction—an impressive application, but not truly intelligent.”

Walters elaborates: “Even when AI models ‘reason,’ it’s not true reasoning or creativity. They’re just recombining what they’ve been trained on. It’s about how far you can push combinatorics on a given dataset.”

The experts’ collective wisdom suggests that while AI will continue to advance and transform industries, the dream of artificial general intelligence remains distant. The focus for 2025 appears to be on practical applications, vertical specialization, and addressing the significant challenges of data quality, regulatory compliance, and sustainable business models.

As we move into 2025, the AI revolution continues to evolve—not toward the sci-fi vision of sentient machines, but toward increasingly sophisticated tools that augment human capabilities in specific, valuable ways. The journey promises to be fascinating, challenging, and transformative.

tags

AI evolution 2025, large language models, artificial general intelligence, AI use cases, healthcare AI, finance AI, data observability, prompt engineering, AI and business intelligence, EU Data Act, AI startup bubble, generative AI, machine learning, AI security, data democratization, vertical AI applications, AI hallucinations, semantic models, AI regulation, AI consciousness

viral_sentences

“Small is the new big” in AI specialization
AI can’t be the sole source of diagnoses—radiologists must validate
The AI startup bubble is about to burst
Prompt engineering is the new coding skill everyone needs
We’re moving from “wow” demos to practical AI applications
Different cultures will shape fundamentally different AI models
The EU Data Act could change everything for AI development
Predicting the next word isn’t intelligence—it’s statistics
AI reasoning is just sophisticated recombination, not true reasoning
The marriage of AI and BI is where the real magic happens
Data projects are suffering because everyone’s obsessed with AI
2025 might be the year data observability finally takes off
We don’t understand consciousness well enough to create it artificially
AI models without guardrails are coming—and they’ll be controversial
The friction between data teams and business teams hasn’t gone away

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