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AI USE CASE

AI-Powered Research and Analysis Synthesis

Accelerate consultant research and draft generation by synthesizing multiple sources with GenAI.

Typical budget
€8K–€40K
Time to value
4 weeks
Effort
4–12 weeks
Monthly ongoing
€500–€3K
Minimum data maturity
intermediate
Technical prerequisite
dev capacity
Industries
Professional Services, SaaS, Finance, Cross-industry
AI type
llm

What it is

GenAI tools aggregate and synthesize information from disparate sources, reports, databases, news, filings, into structured draft analyses in minutes rather than hours. Consulting teams typically reduce research time by 40–60%, freeing senior staff for higher-value interpretation and client interaction. Draft quality improves consistency across engagements, and junior analysts can operate at a higher effective level. Firms report faster proposal turnaround and reduced per-engagement delivery costs of 20–35%.

Data you need

Access to internal documents, past reports, and relevant external sources (web, databases, filings) that can be ingested and queried by the AI system.

Required systems

  • data warehouse
  • project management
  • none

Why it works

  • Establish a clear human review step, every AI draft must be validated by a senior consultant before client use.
  • Deploy a private or enterprise-grade LLM instance to protect client confidentiality and meet GDPR requirements.
  • Run structured prompt engineering workshops so all analysts can extract reliable, citation-backed outputs.
  • Start with one repeatable research task (e.g. market scans) to build confidence before expanding scope.

How this goes wrong

  • Consultants over-rely on AI-generated drafts without critical review, leading to factual errors in client deliverables.
  • Proprietary or confidential client data is inadvertently sent to external LLM APIs, creating compliance and IP risks.
  • Tool adoption is low because senior consultants distrust outputs and junior staff are not trained to prompt effectively.
  • Output quality degrades on niche or highly technical topics where training data is sparse or outdated.

When NOT to do this

Avoid deploying a generic public LLM without data governance controls when the firm handles sensitive client mandates or operates under NDA, the confidentiality risk outweighs the productivity gain.

Vendors to consider

Sources

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