Financial analysis has always been data-intensive. Now the data processing, the research aggregation, and the routine model-building are being handled by AI tools at a speed and scale that changes the economics of the role. Buy-side desks are running Bloomberg's AI-powered document insights across 200 million company filings. AlphaSense is summarising earnings transcripts in real time. The analyst who spent three days building a comparable company model is being replaced by one who reviews the AI's output in three hours. It's not whether this is happening, it's which half of the job you're in.
What's already being automated
Bloomberg Terminal AI integrates generative AI directly into the terminal workflow, enabling natural-language queries across live market data, filings, and news, with analysts citing tasks that previously took 30 minutes now completed in seconds.
AlphaSense uses NLP to scan millions of filings, earnings call transcripts, and research reports, summarising long documents and surfacing sentiment shifts in management language, saving equity research teams significant hours during earnings season.
Kensho, under S&P Global, connects text data with structured financial information for event-driven and macroeconomic analysis, enabling investment banks and asset managers to model market reactions to economic events at scale.
What the research actually says
Gartner forecasts that by 2029, AI-driven decision tools will replace 60% of finance's custom analysis work, specifically the ad-hoc modelling and data aggregation that currently consumes analyst time (cited in AI in Accounting and Finance Statistics 2026). A Stanford and MIT study found that finance professionals using generative AI reallocated 8.5% of their working time from data entry toward client communication. And 95% of finance leaders are investing in AI, with 43% calling it critical to their operations by 2026 (Solvexia).
The financial analysts who are most exposed right now aren't the ones whose models are wrong. They're the ones whose entire value was in building the model in the first place.
Two people. Same title. Completely different week.
Financial Analyst A builds financial models from scratch, manually pulls data from multiple sources into Excel, runs comparable company analyses by searching through filings, and drafts research summaries that are largely descriptions of what the numbers show. The AI tools now available can do most of this faster. Their value is in the execution of analysis, and execution is exactly what's being automated.
Financial Analyst B uses AlphaSense to pull the research, lets the model run, and then spends their time on the part that requires judgment: what does this mean for the investment thesis, how does management credibility change the read on these numbers, and how do they communicate a nuanced view to a portfolio manager who's already made up their mind. The AI is their research assistant. The thinking is still theirs.
Get fluent with the AI tools that are already standard in your field, specifically AlphaSense, BloombergGPT, and whatever financial modelling AI your firm is evaluating. Then invest deliberately in the communication and strategic interpretation skills that make the analysis useful to actual decision-makers. Being able to build the model matters less every month. Being able to explain what it means still matters a lot.