How to Actually Use AI in Consulting (Starting with an AI Audit)

Introduction

Consulting is undergoing a seismic shift. While most consultants talk about AI, few actually implement it effectively. In this post, I’ll walk you through exactly how I use AI in consulting, starting with the most critical step—an AI Audit.

This isn't about adding flashy tools. It’s about using AI to solve real bottlenecks, save time, and drive measurable results for your clients.

Step 1: Conducting a Strategic AI Audit

Before implementing any AI solution, I always start with a full AI Audit.

What’s an AI Audit?

An AI Audit is a deep dive into your client's operations, tech stack, workflows, and bottlenecks to discover where automation and AI can save the most time or generate the most value.

What I Analyze:

• Repetitive tasks across departments (customer service, lead gen, operations)

• Internal processes (CRM usage, task management, reporting)

• Communication patterns (email, chat, meetings)

• Data readiness (structured data, integrations, API access)

• Decision bottlenecks (Where people make slow/manual decisions AI can assist)

Deliverable:

A clear AI Opportunity Map that prioritizes tasks based on:

• ROI potential

• Automation readiness

• Implementation complexity

Step 2: Choosing the Right AI Use Cases

Once the audit is complete, I present 3–5 high-impact AI use cases tailored to the client’s business.

Examples from Past Projects:

• For a consulting firm: Automating proposal generation using GPT-powered templates

• For a cleaning equipment manufacturer: Integrating AI to extract insights from CAD files and ERP data

• For a Shopify brand: Deploying a GPT-powered chatbot for customer service and upselling

Step 3: Tool Selection & Stack Setup

No two clients are the same. So instead of pushing one-size-fits-all tools, I design an AI Stack tailored to their workflows.

Tools I Often Combine:

• OpenAI / Claude – Text generation & summarization

• Zapier / Make / n8n – Automation glue

• Voiceflow / Custom GPTs – Conversational AI

• Notion AI / ClickUp AI – Internal productivity

• LangChain / Pinecone – If vector search or advanced retrieval is needed

Step 4: Quick Wins First — Implementation in Sprints

I roll out the AI stack in small sprints, focused on quick wins.

What This Looks Like:

• Week 1: Auto-generate weekly reports

• Week 2: AI assistant drafts client emails

• Week 3: Automate lead intake + qualification with a chatbot

This phased approach ensures the team sees results early—building confidence and adoption.

Step 5: Train the Team & Measure ROI

AI is not plug-and-play. Training the client’s team to use the tools properly is critical.

I help with:

• Video SOPs

• AI command templates

• Weekly check-ins for the first month

Then I track:

• Time saved (hours/week)

• Response speed

• Output volume/quality

• Revenue increase or cost savings

What Makes This Different from Other AI Consultants?

Most AI consultants stop at ideas or tools.

I go further:

• I diagnose (via audit)

• I customize (AI stack + workflow fit)

• I implement (and iterate)

• I train the team (so it sticks)

• I measure ROI (so they know it’s working)

Want to See If AI Could Save You 10+ Hours a Week?

Start with a custom AI Audit. Book a strategy call and get a roadmap of how AI can scale your consulting business without hiring more people:

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