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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  • Output quality degrades on niche or highly technical topics where training data is sparse or outdated.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

This use case is part of a larger Data & AI catalog built from 50+ enterprise transformation programs. Take the free diagnostic to see how it ranks against your specific context.