Sell-side broker dealers and investment research businesses are entering a decisive phase in the evolution of their operating model. The pressure is no longer simply to cut costs. It is to expand coverage, improve timeliness, deepen differentiation, and connect research more directly to client engagement and monetization.
That is a meaningful shift.
For years, research businesses have been caught between two structural realities. On one hand, cost pressures, MiFID II, and the ongoing effects of unbundling reducing the revenue pool and forcing firms to do more with leaner analyst teams. On the other, buy-side clients increasingly view undifferentiated research as commoditized; and consequently, not worth compensating. In that environment, the central question for research leadership has become far more strategic: how can each analyst produce more, cover more, and still create genuinely differentiated insight? For the most part of the last 2 decades, the sell-side research industry has failed to address the above headwinds in any meaningful manner.
This is precisely where AI needs to be deployed not as a novelty, and certainly not as a replacement for analyst judgment, but as an operating lever to increase output quality and coverage capacity simultaneously. ANALEC’s own research workflow vision reflects this shift, with ANALEC Resonate focused on intelligent authoring, workflow discipline, compliance control, digital delivery, and deeper engagement across the research lifecycle.
The most important use case for AI in sell-side research today is not generic productivity. It is coverage expansion.
Research heads are increasingly asking whether one analyst can cover 1.5 to 2 times more companies without compromising quality, timeliness, or compliance. That is the benchmark that matters. AI is helping firms move toward that goal by shifting analysts away from repetitive drafting and maintenance work, and toward higher-value interpretation and differentiated views.
One of the clearest applications is automated first-draft generation. Earnings notes, company updates, sector summaries, and quick takes on filings or news flow can now be assembled far faster from structured and unstructured inputs. In practical terms, this moves the analyst’s role from manual drafting toward review, refinement, and insight generation. ANALEC’s Resonate platform already reflects this model through smart templates, AI-assisted content injection, centralised data retrieval, and the ability to generate a multi-page note within seconds while preserving consistency and workflow control.
Financial model support is another major lever. Analysts are increasingly looking for AI to help with data extraction from filings, model population, variance analysis versus consensus, and scenario generation across bull, base, and bear cases. This does not eliminate analyst responsibility. It simply compresses the time spent on mechanical work and creates more room for judgment. The strategic benefit is obvious: less time maintaining coverage, more time making the value-added assessments and engaging clients. At ANALEC, given our sell-side research expertise and background, we can create detailed financial forecast models with significant level of granularity, DCF and Sensitivities analyses, as well as valuation summaries; all produced through our prompts within 15-20 minutes, which in the traditional day would consume, at a minimum, 4-5 valuable days of an analyst.
Monitoring and alerting are equally important. AI agents can continuously track companies, sectors, macro indicators, and news flow, surfacing only what is material. That matters because much of an analyst’s day is often consumed by low-value monitoring overhead. When AI filters noise and increases relevance, the analyst’s bandwidth is reallocated to work that differentiates the franchise.
Most LLMs (or, for that matter, SLMs) have an inherent bias to “please” the user. With ANALEC’s expertise in the middle, we ensure the output reduces the scope of “pleasing” biases and delivers work that stands up to professional scrutiny.
In research, speed has always mattered. Today, speed-to-market is of paramount importance and is directly tied to commercial success.
A broker-dealer who gets a well-structured, high-quality note into clients’ hands minutes after an earnings call is not just being efficient; it is driving top-of-mind recall. It is improving the monetization potential of its research franchise. AI is accelerating this cycle materially through instant transcript summarization, dynamic note generation during live events, and pre-populated templates that reduce post-event turnaround time.
This is where workflow technology matters as much as the financial forecast model itself. AI on its own is not enough. A fragmented workflow process creates bottlenecks and the resulting loss of speed-to-market. The real advantage comes when AI is embedded inside the authoring, approval, compliance, and distribution process, in a seamless manner, delivering maximum client impact, while ensuring appropriate guardrails and compliance control across the digital process.
ANALEC Resonate’s research publication and workflow solution and its design philosophy is particularly relevant here: intelligent authoring inside familiar environments such as MS Office (includes Microsoft 365), structured approvals, complete audit trails, smart disclaimer/disclosure management, and automated distribution to clients and aggregators all contribute to turning AI-assisted drafting into publishable, compliant research at speed.
The forces of creative destruction are at work in full force. There is no point fighting them. What is important to understand is how the future will unfold and how research organisations will have to evolve. There are many concerns about how to nurture junior talent in your team if all that work is being automated by AI tools. How do you create future research leaders from internal teams? These are all legitimate questions, and the only way to get to a happy place is to evolve the organisation's culture and mindsets.
Training new (junior) analysts in the fundamentals of corporate valuation and techniques, and in understanding specific industry drivers, will see rapid progress with AI tools. “Junior” resources will deliver significantly greater value over shorter time horizons if they embrace AI-enabled capabilities. We have seen the pressures on sell-side teams over the last decade and more, and the notion of “do more with less” has been a real challenge. Generative AI and its components enable all of the above.
In my next publication, I will elaborate on what defines good research (in the future) and how to protect (or evolve) the commercial model for sell-side research, while ensuring appropriate risk management and compliance guardrails.
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