Reclaiming 70% of your analysts' time with AI agents - WRITER

Alpha, not admin: Reclaiming 70% of your analysts’ time with AI agents

Dilshoda Yergasheva | September 25, 2025

After fifteen years of watching analysts get bogged down in manual data work, I knew there had to be a better way. Here’s how AI agents are redefining financial research, and what that means for your bottom line.

I must have seen it a thousand times in my 15-year career as a portfolio manager. You have a team of brilliant, highly-paid analysts, but they’re buried under a mountain of data, racing against a market that had already left them behind. Every morning started with the same urgent question, “What happened overnight, and where should we focus our attention?” Getting that answer wasn’t a quick, strategic insight. It was a slow, manual slog — pulling performance figures from one system, cross-referencing risk metrics from another, and piecing it all together just to get a baseline view.

By the time we had the full picture, the alpha in the opportunity had often vanished.

It took me years to fully appreciate that the core problem wasn’t our talent. Instead, it was the tools. We were asking 21st-century analysts to work with 20th-century processes, forcing them to spend the vast majority of their time — up to 70% — on the administrative mechanics of financial research, instead of the actual analysis. This basic inefficiency leads to delayed insights, widespread analyst burnout, and a persistent competitive disadvantage in a market where speed and accuracy are everything.

That core frustration is exactly why I came to WRITER. I wanted to build the solution I wish I could have given my own teams. I knew that simply throwing more people at the problem was a losing strategy, as was forcing analysts to become part-time programmers. The real path forward is to augment our best people with AI agents that can finally handle the crushing volume and complexity of financial data.

Why traditional research processes kill alpha generation

When I talk about that “administrative friction,” I’m not referring to a minor inconvenience. I’m talking about the daily reality that defined my team’s work and consistently undermined our ability to generate alpha. To truly understand the scale of this inefficiency, let me walk you through the research cycle I saw play out time and time again.

It always started with the “great data hunt” (1–2 days)

I’d watch my analysts begin every new project with a scavenger hunt. They had to manually pull the latest earnings reports from company websites, swivel their chairs to a Bloomberg terminal for market data, navigate a dozen different broker portals, and then dig through our own shared drives for past notes. Each source was a separate silo with its own sign-in and its own unique data format. It was a tedious, fragmented process before we could analyze a single number.

Then came the days of wrestling with spreadsheets (2–3 days)

The next phase was always the most nerve-wracking for me as a manager. My analysts would spend days wrestling with massive, error-prone Excel spreadsheets — painstakingly cleaning data, normalizing formats, and building out valuation models.

From there, it was a slow march to the presentation (3–5 days)

Once the model was ready, the work shifted from analysis to communication. This meant manually creating every single chart, formatting them to fit our presentation template, and then writing the executive summaries to explain the findings. It was a slow, laborious process of turning numbers into a story.

Finally, we’d have the last-minute scramble (1–2 days)

By the time the report landed on my desk, the final review was rarely a high-level debate over the investment thesis. Instead, it was a frantic scramble to double-check calculations, validate data points, and incorporate feedback under pressure. Our focus shifted from sharpening insights to simply avoiding mistakes.

And after all that work — that week-long marathon — the result was almost always the same: The insight was stale on arrival. This inefficiency led straight to missed opportunities for outperformance and a reactive stance in a market that rewards only proactivity.

How AI in financial services delivers the portfolio analysis tools I always envisioned

Whenever my team was drowning, the institutional reflex was always to hire another analyst. I fought for that headcount myself, believing that more smart people would solve the problem. But I learned over time that this was just a temporary patch. We were adding more people to a fundamentally broken process, and the administrative burden would inevitably scale right alongside the team. The real solution wasn’t more bodies — it was a better, smarter way of working. We needed to augment our team with an AI tool built for the specific challenges of investment research.

As a former PM, here are the core capabilities I knew it had to have:

When you put it all together, you can think of it as buying back that 70% of an analyst’s week. The agent handles the mechanics of research — the data gathering, the cleaning, the calculations, the formatting. This allows our human experts to dedicate their time and intellectual energy to what truly matters — strategy, critical thinking, and insights that drive portfolio performance.

How to automate a PM morning briefing with AI agents

Let’s make this concrete with a scenario that used to be a regular occurrence for me. I’d come in to find my portfolio was down 50 basis points from the day before. My first request would be direct and urgent, “I need a full briefing by morning on which positions drove the underperformance, what our key factor exposures were, and if there was any market-moving news I missed.”

The traditional approach: The data scramble

I’d have to pull a junior analyst off their primary research to begin the scramble.

The AI-powered approach: The pre-coffee workflow

Now, let’s run that exact same scenario, but with the AI agent I always wished I had. I would give the agent the same prompt: “My portfolio was down 50 bps yesterday. I need a full briefing by morning on which positions drove the underperformance, what the factor exposures were, and if there was any news I missed.”

Instead of triggering a human fire drill, the agent initiates an automated workflow that autonomously connects to the necessary systems to pull portfolio and market data.

How AI drives alpha and solves for analyst burnout

When I speak with CIOs and Heads of Research today, the conversation quickly turns to the ROI of a tool like this. Here’s how I frame the business outcomes, based on what I always tried to achieve as a manager.

Direct driver of alpha and competitive differentiation

In our industry, alpha is often a game of inches and seconds.

Solution to analyst burnout and the key to attracting talent

I’ve lost great analysts over the years because the work wasn’t strategic enough. By automating the 70% of the job that is tedious and repetitive, you empower your analysts to be the strategists and critical thinkers they joined the company to be.

Delivers true scalability and cost optimization

This is how you scale your research capabilities without automatically scaling your headcount.

Will AI replace the investment analyst?

My answer is always the same: absolutely not. It’s going to make you better at your job.

The future of our industry isn’t man vs. machine. It’s the augmented analyst vs. the traditional analyst. The firms that thrive in the next decade will be the ones that empower their talent with tools that handle the data grunt work.

From vision to reality: The WRITER research desk action agent

The solution I’ve been describing isn’t just a theoretical concept — it’s the core of what we’ve built at WRITER with our research desk action agent.

As a manager, this gives you strategic options you never had before.

What sets the WRITER approach apart

From theory to real-world results

Here are real examples of how investment firms are using our AI agents to:

An invitation to the future of finance

The financial industry is at an inflection point. The future belongs to firms that successfully and thoughtfully augment their human talent with agentic AI.