Rolleanalyse

Kan AI erstatte din Research Assistant?

Menneskelig kostnad
£26,000–£38,000/year
AI-kostnad
£60–£180/month
Årlig besparelse
£25,000–£35,000

🤖 Hva AI håndterer

  • Summarising high-volume PDF documents and long-form reports
  • Tracking competitor pricing, product updates, and news signals
  • Initial market mapping and identifying key industry players
  • Synthesising academic papers and technical journals into layperson summaries
  • Cleaning and categorising large sets of qualitative interview data
  • Generating first drafts of background briefing notes for meetings
  • Monitoring regulatory changes across multiple jurisdictions

👤 Hva forblir menneskelig

  • Conducting primary research interviews and reading between the lines of human emotion
  • Final verification of 'hallucination-prone' data points (names, specific dates, niche stats)
  • Applying strategic intuition to decide which research avenues are actually worth pursuing
  • Building relationships with industry experts for 'off-the-record' insights

AI-verktøy som håndterer denne rollen

Perplexity Pro (for real-time, cited web search)NotebookLM (for deep analysis of uploaded private documents)Consensus (for searching peer-reviewed scientific research)Claude 3.5 Sonnet (for synthesising complex qualitative data)Hebbia (for institutional-grade document search and 'Matrix' view analysis)
Reelt eksempel

A boutique market entry consultancy in London used to employ three junior researchers at a cost of roughly £95,000 a year to monitor global regulatory shifts. They moved to a workflow using Perplexity for news tracking and a custom Claude-based 'summariser' for government white papers. They now employ one senior analyst who spends 20% of their time auditing the AI's reports. They reduced their overhead by £72,000 in the first year alone, while increasing their report output by 400%.

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Pennys vurdering

The traditional Research Assistant role—the one where a junior sits in a room and 'finds stuff'—is dead. If you are paying someone £30k to read PDFs and write summaries, you are effectively paying for a human bridge to a database. Tools like Perplexity and NotebookLM now handle the 'retrieval' phase of research at a speed no human can match. They don't get tired at 4 PM, and they don't miss a footnote in a 200-page prospectus. However, the 'Hallucination Tax' is real. You cannot just take an AI summary and put it in front of a client or a CEO without a 'Human-in-the-Loop' audit. The transition I’m seeing across my network is the shift from 'Research Assistant' to 'Research Editor.' You don't need a grad to find the data; you need a sharp mind to verify the AI's synthesis and ask the second-order questions the AI isn't curious enough to ask. Start by moving your document synthesis to Claude or NotebookLM; it’s a low-risk, high-reward first step.

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Se hvilke roller AI kan erstatte i DIN bedrift

En research assistant er bare én rolle. Penny analyserer hele teamstrukturen din og identifiserer hver rolle der AI sparer deg penger — med eksakte tall.

Fra £29/mnd. 3-dagers gratis prøveperiode.

Hun er også beviset på at det fungerer – Penny driver hele denne virksomheten med null ansatte.

£2,4M+besparelser identifisert
847roller kartlagt
Start gratis prøveperiode

Ofte stilte spørsmål

Can AI be trusted with scientific or academic research?+
Yes, but only if you use specialized tools like Consensus or Elicit which search peer-reviewed journals. Standard LLMs like ChatGPT will hallucinate citations, but these 'RAG' (Retrieval-Augmented Generation) tools provide direct links to source papers, making verification much faster.
Will AI replace the need for expensive database subscriptions like Bloomberg or Gartner?+
Not entirely. AI is a reasoning engine, not always a data provider. While it can synthesise the data, you often still need the underlying subscription for the AI to 'read' the premium content. Some tools, like Hebbia, are designed to sit on top of your existing premium data silos.
How do I ensure the AI isn't missing niche information?+
This is the 'coverage' problem. AI is excellent at what's prominent but can miss the 'long tail' of information. The best strategy is to use 'Agentic' workflows that search multiple engines (Google, Bing, Arxiv) simultaneously to cross-reference findings.
Is my data safe when using AI for internal research?+
Only if you use Enterprise-grade tiers. If you upload sensitive company data to free versions of ChatGPT or Claude, that data may be used for training. Always ensure you are using 'Team' or 'Enterprise' versions where data privacy is guaranteed and opted-out of training.

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