First off, what’s still hot? Where are AI use cases seeing success?

Chester: I see people leveraging AI beyond experimentation. People have had the opportunity to experiment, and now we’re getting to a point where true, vertical-specific use cases are being developed. I’ve been tracking healthcare closely and seeing more use-case-specific, fine-tuned models, such as the use of AI to help doctors be more present during patient conversations through auditory tools for listening and note-taking. 

I believe ‘small is the new big’—that’s the key trend, such as hematology versus pathology versus pulmonology. AI in imaging technologies isn’t new, but it’s now coming to the forefront with new models used to accelerate cancer detection. 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: 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. These industry-specific AI models are a significant trend I see continuing into next year.

William: 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: Generative AI is working well in code generation—generating SQL queries and creating natural language interfaces for querying data. That’s been effective, though it’s a bit commoditized now. 

More interesting are advancements in the data layer and architecture. For instance, Postgres has a vector database add-in, which is useful for retrieval-augmented generation (RAG) queries. 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. Over the next two or three years, vendors will move from basic query intelligence to creating more sophisticated tools.

How are we likely to see large language models evolve? 

Whit: 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. 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. 

Different providers, geographies, and governments will tend to move either towards free-er speech, or will seek to control AI’s outputs. The difference is noticeable. Next year, we’ll see a rise in models without guardrails, which will provide more direct answers.

Ivan: 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: Indeed, prompt engineering is crucial. Depending on how words are embedded in the model, you can get drastically different answers. If you ask the AI to explain what it wrote and why, it forces it to think more deeply. We’ll see domain-trained prompting tools soon—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 (BI)?

Andrew: 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. We’re seeing a lot of disillusionment in the enterprise space, but progress is coming: 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 approach 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: Agreed. 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. However, this can’t be solved by Gen AI alone, it requires a mutual understanding between both groups. Forcing data-driven approaches without this doesn’t get organizations very far.

What other challenges are you seeing that might hinder AI’s progress? 

Andrew: 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: 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. There’s so much churn, and keeping up has become ridiculous.

Chris: Related, 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. This is ripe for popping, although some “LLM” security products/services will pervade. 

Seth: On the supply chain security side, vendors are starting to offer AI model analysis to identify models used in environments. It feels a bit advanced, but it’s starting to happen. 

William: 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 (AGI)?

Whit: AGI remains a pipe dream. We don’t understand consciousness 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: 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.

Whit: Exactly. 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.

Thanks all!

The post The evolving revolution: AI in 2025 appeared first on Gigaom.

Data warehouses consolidate information from all of an organization’s internal and external data sources and store it in a central repository. Specific design choices allow them to facilitate fast analytics over large volumes of data, including a dimensional model, or star schema, massively parallel processing, columnar storage, vector processing, single instruction multiple data (SIMD) operations, and data compression. Data often must meet strict quality requirements to be loaded into the data warehouse; thus, in addition to helping organizations centralize, organize, and derive meaningful insight from their data, data warehouses also play an indirect role in ensuring data quality.

Data warehouses form the cornerstone of many organizations’ data stacks, and are important to all personas and levels of the business. Data engineers cleanse, structure, organize, and model the data. Database administrators monitor and maintain the data warehouse on the back end. Business users, business analysts, and data analysts access the cleansed and organized data in the warehouse through client applications, such as business intelligence (BI) applications, to extract meaningful insights about the business. This insight is presented in reports, visualizations, and/or dashboards, answering executive and C-suite questions and forming the basis for strategic decisions of the business.

Business Imperative
To remain competitive, regardless of industry, an organization’s management and C-suite need to make smart, timely, and informed decisions about the operations and strategic direction of the business. However, relational databases, optimized to store the record-by-record data generated from individual business transactions, are typically not also designed to perform analysis on large volumes of that data. Organizations can’t afford to let data sit when potentially critical and competitive information can be extracted from it. But they also can’t afford to struggle with the poor performance, unacceptable response times, and the incomplete view that can result if operational systems are forced to perform analytical functions they were never designed to handle.

Data warehouses are platforms purpose-built to solve this problem. Since their development in the late 1980s, they’ve existed to enable the data analysis that forms the foundational support for business decisions. Data warehouses assemble a historical, curated, comprehensive collection of the organization’s data into a database that’s specifically architected to perform analytics over that data. The insight thus derived reveals an organization’s current and past performance, uncovers patterns that can lead to predictions about potential future performance, and overall informs both the day-to-day operations and the long-term roadmap of the business.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of deploying a data warehouse solution, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a data warehouse solution, we provide an overall Sector Adoption Score (Figure 1) of 4.4 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a data warehouse solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for data warehouses are explained in more detail in the Sector Brief section that follows.