A management consulting boutique automated proposal writing with GPT-4 and reclaimed $280K of senior partner time annually.
A 25-person management consulting firm saw senior consultants spending 30-40% of their billable time on proposal writing, engagement setup documents, and template-heavy deliverable sections, rather than strategy and client problem-solving.
Each new proposal required 15-20 hours of senior consultant time, much of it copying past engagement structures, reformatting standard sections, and drafting boilerplate statements that already existed in archived proposals. Consultants had no search mechanism across historical work, and document versioning was manual. No centralized quality review.
Partners wanted to scale client capacity without hiring more senior staff. The bottleneck was not client thinking, it was document production.
Three years of completed proposals and deliverables lived in SharePoint, representing firm methodology and writing voice. That historical work was the ideal training data for a fine-tuned AI assistant.
With the right tool, consultants could describe the engagement, and a GPT-4 model would draft sections. Humans would review and polish. The firm could reclaim dozens of partner hours per month, reinvest them in strategy.
We used the Aizen framework, purpose-built for rapid, human-centered AI adoption: Explore, Experiment, Embed, Expand.
A base GPT-4 model wrote in generic consultant language. Fine-tuned on this firm's historical proposals, it matched tone, methodology, and client communication style. That credibility and speed difference was the adoption lever.
We framed the tool as a draft accelerator, not an automated writer. Every proposal still required consultant review and sign-off. That human checkpoint built confidence. No shortcuts, faster workflow.
We built a simple dashboard showing time saved per proposal, sections generated, and consultant productivity trends. Partners saw the value weekly. Adoption reinforced itself.
SharePoint and Teams were already core to how the firm worked. We embedded the AI assistant into their current workflow, not asked them to use a new system. Friction was near zero.
Bring the problem. We'll come back with a written brief: what to build, what to defer, and where AI actually moves the number. No deck pitches.