Analiza uloge

Može li AI zamijeniti vašeg Research Assistant?

Ljudski trošak
£26,000–£38,000/year
AI trošak
£60–£180/month
Godišnja ušteda
£25,000–£35,000

🤖 Što AI rješava

  • 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

👤 Što ostaje ljudsko

  • 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 alati koji obavljaju ovu ulogu

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)
Pravi primjer

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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Pennyjev pogled

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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Pogledajte koje uloge AI može zamijeniti u VAŠEM poslovanju

research assistant je samo jedna uloga. Penny analizira cijelu strukturu vašeg tima i identificira svaku ulogu gdje vam AI štedi novac — s točnim brojkama.

Od £29/mjesečno. 3-dnevno besplatno probno razdoblje.

Ona je također dokaz da funkcionira - Penny vodi cijeli ovaj posao bez osoblja.

2,4 milijuna funti +utvrđene uštede
847mapirane uloge
Započnite besplatno probno razdoblje

Često postavljana pitanja

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.

Research Assistant po industriji

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