Where Analyst Time Goes
Ask a junior consultant or analyst at most management consultancy firms how they spend their first week on a new engagement, and the answer is usually a variation of the same thing: gathering information. Reading industry reports, compiling competitor data, pulling financial figures, scanning trade press, building market sizing models from publicly available sources.
This desk research phase is essential — a good strategy engagement is built on solid market intelligence — but it is also extraordinarily time-consuming. An analyst might spend three to five days producing a competitive landscape document that a partner will review for thirty minutes before the team moves on. The ratio of input time to strategic value is poor, and it is one of the clearest opportunities for AI automation in professional services.
What Research Automation Can Cover
The scope of automatable research work is broader than most people initially assume. Here are the main categories:
Competitor Monitoring
For ongoing client engagements or retained advisory relationships, keeping track of competitor activity is a continuous task. What has a competitor announced in the last month? Have they made acquisitions, launched new products, changed pricing, published thought leadership that signals a strategic shift? Manually, this means someone checking websites, press release feeds, and news aggregators on a regular basis.
An automated system can monitor a defined list of competitor websites, Companies House filings, regulatory announcements, and news sources continuously, extract structured updates, and deliver a weekly briefing to the engagement team — without a single hour of analyst time beyond the initial setup.
Market Sizing and Data Aggregation
Market sizing work often involves pulling data from multiple public sources: ONS statistics, industry association reports, Companies House financial data, sector-specific databases. An AI pipeline can be built to pull from these sources systematically, extract the relevant figures, and populate a model. The analyst's role becomes reviewing and interpreting the assembled data rather than hunting for it.
News and Regulatory Intelligence
For clients in regulated industries — financial services, healthcare, energy — keeping track of regulatory developments is critical. Automated pipelines can monitor the FCA, CMA, HMRC, sector regulators, and relevant parliamentary committee activity, summarise relevant items, and flag those that affect a specific client's business.
Stakeholder and Expert Mapping
Early-stage research often involves mapping who the key players are in a market: which organisations are active, who the senior figures are, what positions they hold publicly. AI agents can systematically gather and structure this information from public sources — LinkedIn, company websites, industry press — in a fraction of the time a researcher would take.
How It Feeds into Deliverables
The goal is not to produce raw data — it is to feed structured, reliable intelligence directly into the deliverables consultants actually produce. A well-built system does not just gather information; it organises it in the format that the engagement team uses.
For example: a competitive landscape tracker that automatically maintains a structured database of competitors — with columns for revenue, headcount, product lines, recent announcements, and strategic positioning — means that when a consultant needs to build a slide, the data is already there, current, and formatted. They are writing the analysis, not building the underlying table from scratch.
Similarly, a market intelligence digest delivered every Monday morning — summarising the previous week's relevant news, regulatory updates, and competitor activity in a structured format — means client teams start each week informed without spending time on information gathering.
A Practical Example
A boutique strategy consultancy working with clients in the UK logistics sector wanted to offer better ongoing advisory value between major engagements. We built a system that monitors 40 competitor and sector-relevant organisations across their websites, Companies House filings, and trade press. Each week, a structured briefing is generated covering: new announcements, financial filings, senior personnel changes, and relevant regulatory developments. The briefing is formatted as a PDF and delivered automatically.
The consultancy now uses these briefings as the basis for monthly client calls, positioning them as a source of ongoing intelligence rather than project-only advisors. What previously required two to three days of analyst time per month to produce informally now runs without ongoing staff input.
What Automation Does Not Replace
Research automation handles the gathering, structuring, and initial summarisation of information. It does not replace the strategic interpretation — the so-what analysis that turns market data into a recommendation. That is where senior consultants add their value, and it is where they should be spending their time.
The aim is to eliminate the information-gathering overhead so that the analytical and advisory work gets a proportionally larger share of the engagement's hours. That benefits the client (better-informed analysis), the firm (higher-value work per hour billed), and the analysts themselves (more interesting work).
Getting Started
The best entry point is usually a specific, recurring research task that already happens on a regular basis — a monthly competitor review, a weekly news digest for a particular client, a sector-specific data-gathering exercise. Building an automated version of something that already exists is faster than designing a system from scratch, and the time saving is immediately measurable.