Knowledge work just got an upgrade—don’t lag behind

The information age fundamentally changed how knowledge workers do their jobs. Now, with the introduction of AI, a new era is upon us that some might call the “automation or super-information age.” But what does that mean for those who live, eat, and breathe analytics?

If they’re smart, it means they’re staying ahead of the curve. AI is clearly here to stay, as evidenced by the massive investment in GenAI startups and funding at organizations across all sectors and geographies catalyzing top-down AI strategies. Adapting takes smarter, harder work, but there are resources to streamline analysis and research with trusted sources built in.

According to S&P Capital IQ Pro, GenAI startup applications (including foundational model providers like OpenAI, Anthropic, and xAI, as well as niche providers like ModelML, Cohere, and Mistral AI) raised over $95b across 143 funding rounds in 2025—over double from the year prior.

Here’s a playbook from S&P Global Market Intelligence to help those in the noble pursuit of strategic business insights better understand:

• how AI redefines the role of knowledge workers
• how knowledge workers can accelerate data extraction
• what the future holds for knowledge workers

This is a resource for the AI-curious, the AI-proficient, and everyone in between.

Redefining roles

Knowledge workers no longer need to spend hours in training to get up to speed in their focus areas. AI use cases and true adoption remain primarily focused on reducing day-to-day manual work. Customer service and decision-support functions remain the top adoption areas to date.

Defining corporate use cases and ensuring the workforce has the proper tools and skills necessary to capitalize on AI tooling remain organizational challenges. While many knowledge workers are increasingly more proficient in skills necessary to capitalize on AI, such as prompting, rapid technological change makes upskilling more necessary than ever.

For example, knowledge workers would historically spend hundreds of hours reading textual-based data sources to glean insights. GenAI enables a more streamlined document analysis experience, so users can now ask questions directly of documents—now possible via Document Intelligence within the S&P Capital IQ Pro platform.

You can’t sprint before you learn to walk, and as you fine-tune key research skills, it opens the door to other AI analytics use cases that we see leading companies developing. The job then shifts from just knowing your area of expertise to also knowing the tools needed to stay up-to-date on market events and extracting niche data insights for quick, strategic decision-making.

Agentic and MCP (Model Context Protocol) were certainly some of the most popular buzzwords of 2025. Development focus shifted to agents, but there’s still plenty of work to do for AI and agents before they can function like humans. MCP adoption is not without its challenges as organizations grapple with authentication, security, and scalability. MCPs are, however, making it easier for knowledge workers to access data within AI tools or applications.

This means the knowledge worker’s solo campaign is now a cooperative journey, with agents as junior partners. While they’re not ready to act independently, with proper oversight, training, and good data, they can learn how to best support you.

Accelerating skill evolution

Even the earliest adopters and firms with the most advanced AI strategies are still evolving, with a continued focus on data quality and accuracy. A deeper understanding of users’ workflows and what AI tooling can support is critical as strategies progress. Data quality, accuracy, and differentiation, both proprietary and third-party, remain the critical foundations for AI solutions.

AI-powered tools can help you fast-track a host of analytical skills. Examples of the most critical workflows include:

• deal ideation
• market mapping
• company-level performance metrics research
• company earnings analysis

Deal ideation

You can combine thousands of quantitative and qualitative criteria to screen transaction types, equities, activist campaigns, insider holdings, and more with S&P Capital IQ Pro. This helps efficiently generate, develop, and pressure-test new deal hypotheses and quickly identify relevant investment targets.

By leveraging the platform’s Document Intelligence features, you can analyze multiple documents simultaneously to gauge dealmaking appetite from earnings call transcripts, broker research reports, public filings, and more. Additionally, you can utilize ChatIQ, the natural language chat, to query and share cited answers or lists based on deep company-level data. Users also have access to 300+ niche industry topic tags generated by an AI-based model, enabling enhanced sub-industry analysis and screening for niche topics like health diagnostics, cloud infrastructure, and online payments.

Market mapping

Identifying the full picture of targets, peers, or competitors in a market is easier with AI-powered, LLM-based research tools, plus advanced classification and tagging features to assess companies. Leveraging fine-tuned classification language models grants access to detailed insights into company operations and niche sub-sector markets.

Move away from blanket approaches that deliver a big picture without granular analysis worked into the mapped output. AI-driven tools also support spatial analysis, helping you visualize relationships, identify trends, and perform proximity and demographic analyses for more informed decision-making. Integrating this into market research or competitive intelligence initiatives allows analysts to filter, search, and analyze data with greater precision at scale and with more efficiency.

Company-level performance metrics research

There are now several ways to conduct deep research or due diligence on companies to quickly find opportunities and make strategic decisions with more context.

The ability to quickly query and summarize or compare multiple text-heavy documents with the help of advanced AI tools integrated into the research workflow on S&P Capital IQ Pro means that you can easily interact with millions of data points, including financials for millions of private and public companies, then uncover or assess hard-to-find intelligence. From leveraging smart summarization to assessing natural language processing-derived sentiment scores, this changes the stories analysts can tell with critical insights for corporate strategy and executive audiences.

AI assistant features like ChatIQ can pull key information from a range of documents—including public filings, earnings transcripts, news, and research reports from more than 1,800 brokerage, independent, and market research providers—saving you time. The inclusion of citations and sourcing data makes the data points usable for high-visibility reporting or use in financial models and pitch books. Plus, you can view GenAI-driven summaries of news and significant events for a company’s stock price with Chart Explainer.

Company earnings analysis

Now you can streamline how they track the impact of market shifts on stock prices, distill sentiment from broker analysts to match earnings-call messaging, and address strategic changes among competitors or a specific portfolio. Additionally, you have the option to tap into differentiated data sources. For example, Visible Alpha Estimates can standardize, normalize, and extract granular line-item or product-line-specific estimates or market forecasts without the need for manual, time-consuming modeling.

Analysts from investor relations and finance departments alike can also efficiently extract insights for earnings reports and investor communications from transcripts, financials, and broker research en masse. So that guidance is not misinterpreted, transparent data sources and citations become especially important to validate reporting and strategy when using natural language processing (NLP) tools to identify sentiment and investor insights. Knowledge is power, and time is money. So the more data you can extract and interpret during critical times such as earnings season, the easier it is to stay on your A game.

The future of knowledge workers

The future of knowledge work will include AI and organizations remain focused on finding effective ways to implement it across departments for positive impact. By letting solutions like S&P Capital IQ Pro do the heavy lifting when it comes to finding data, analysts can spend less time hunting for niche data and more time supporting important decision-making.

In this high-tech environment, research with S&P Capital IQ Pro could look like this:

• Document Intelligence eliminates time-intensive manual research by searching across lengthy documents to find the resources relevant to your search.
• ChatIQ can then expedite analysis by allowing you to ask plain-language questions about multiple documents and get answers with source citations.
• Similarly, you can quickly contextualize price movements and link directly to sources with Chart Explainer. It provides a summary of historical events and news coverage within the selected time frame, again with direct sources.

Now, what would’ve taken hours (if not days) of sifting through sources, interpreting charts, and trying to connect the dots between data points can take just a few minutes. With quick access to all of the data available to them, analysts can go deeper into their findings.

This creates a two-pronged advantage for analysts who best implement the tools available to them. First, it speeds up their research process without losing credibility. In fact, by uncovering niche insights that would otherwise require extensive research processes to find, it helps build expertise and deepen insights. Second, it allows them to best package that information for others who may not have the same background in data analytics.

With AI-ready data as the foundation and AI solutions as the engine, organizations unlock opportunities and move with confidence in every decision. S&P Global sees a future where there’s less guesswork, more trusted data, and more opportunity to act quickly with precision.

A trusted data partner

When it comes to advancing along your AI journey, it’s beneficial to work with a company that provides trusted, AI-ready data and already collaborates with AI’s heavy hitters. S&P Global has strategic partnerships with 40+ AI-technology firms, including Anthropic, OpenAI, Google, Microsoft, Databricks, Snowflake, Salesforce, and workflow-specific providers to drive technology infrastructure, distribution, and agentic capabilities.

These partnerships help:

• Link companies up with hyperscale data infrastructure, such as Google, which provides building blocks for S&P Global GenAI solutions.
• Connect S&P Global data to AI-based tools, such as Claude, ChatGPT, and more, via an MCP server powered by the Kensho LLM-ready API.
• Provide AI-ready datasets for seamless integration into cloud data environments, allowing users to directly access and query S&P Global and select third-party data.
• Create agentic solutions for various datasets to decrease time-to-market and solve niche workflow challenges.

S&P Global Market Intelligence is flexing to meet the reality of the modern data era to help you take the next step forward in your team’s AI and analytics strategy.

As clients develop their own custom, GenAI–powered tools, S&P Global Market Intelligence can supply AI-ready data to deepen the insights clients draw from their internal data. Each layer is built for scale, interoperability, and transparency—with comprehensive metadata that provides the context AI systems need to accurately interpret data.

This includes not only structured and standardized datasets but also Python-wrapped data designed for seamless integration into data science environments. Flexible distribution channels then make data available to meet analysts where they are and where they’re going.

Data governance, AI strategy goals, and enabling execution with flexible data delivery are imperative for these builds. Once you have those clearly defined, companies are advancing their analytics with AI to:

• Build custom dashboards or applications for regularized insights and monitoring.
• Deepen CRM insights and client knowledge with fundamental or standardized S&P Global data.
• Design human-in-the-loop models for agents to streamline knowledge workflows.
• Scale up ERP (enterprise resource planning) processes enhanced by S&P Global data.

There is a bright future ahead for knowledge workers. It is not one you can simply coast into, however. With the right data partner, you can adapt to new industry standards, level up the skills you need to stay competitive, and forge your own path forward with fewer distractions.