AI Consulting Process

▶️ Here is an entire overview of the personal AI consulting process I use with each client:

AI Business Audits: Discovery Phase

Every AI project starts with a discovery phase that revolves around a complete audit of a business—a deep dive into a company's current workflows, tools, and pain points. It is an ‘AI health check’ to separate hype from actionable opportunities.

During this audit, a consultant should be going over things like your team’s daily operations, software stack, and data systems to pinpoint inefficiencies.

Workflow Mapping

A major component of the audit process is identifying every manual process (e.g., copying data between Excel sheets), outdated tools (legacy CRM systems), and repetitive tasks (like invoice approvals).

Example: A manufacturing client was manually tracking inventory across 4 spreadsheets. I flagged this as a “low-hanging fruit” for automation, saving over 15 hours/week by simply using n8n paired with an API request to the client's inventory software.

Prioritize Audit Results by Impact:

Now that we've got our audit results, we want to prioritize tasks that most closely align with the client's goals. These goals would have been discussed during the onboarding process.

There are two main groups we can put the results into that we want to focus on.

🏆 Quick wins: Automating basic day-to-day data entry and customer service tasks (Quick and easy implementation that saves time immediately).

☄️ Moonshots: Building custom AI models trained on a company's entire documentation base, onboarding documents, confidential data, and creating a permanent full-time “AI employee” also known as an “agent” that can execute tasks that a regular employee would do. (Major upfront investment of both money and time, Massive long-term ROI).

Build Phase: Tools + Ethics

Tool selection:

I'm not going to waste any time. There are a few popular automation solutions, but in my opinion, the best one by far is a self-hosted instance of n8n paired with an open-source self-hosted AI model like DeepSeek-R1.

With this stack, you can have unlimited automation (you might know these as Zaps if you’re coming from Zapier) and you can self-host it on a $10/month VPS.

If you’re a fan of serverless systems, you can host the front end on something like Fly.io, Railway, or Vercel with the Postgres database hosted on Supabase or Neon.

Or even better, install Coolify (open-source) onto a VPS and host your version of platforms like Vercel. Take control over your data and privacy and support the open-source development community.

If you're looking to get started with n8n, I suggest checking out their official Beginner Course below:

For Control + Compliance:

If your main concern is having absolute control over your data, keeping things on-premises, and being completely compliant with privacy and data regulations then definitely go with the n8n + DeepSeek-R1 stack.

⚙️ Tools: n8n (self-hosted) + open-source models (DeepSeek-R1).

💵 Cost: ~$10/month VPS + internal hosting (no internet exposure).

✨ Use Case: HIPAA/SOC 2 compliance (e.g., healthcare client’s on-premise setup).

🏆 Why It Wins: Unlimited "automations", full data ownership, no vendor lock-in.

For Speed + Simplicity:

If you want to get started as soon as possible, I highly suggest taking a look at the automation SaaS (Software as a Service) providers like Zapier, Make.com, and n8n cloud.

⚙️ SaaS Tools: Zapier (plug-and-play), Make.com (visual workflows).

💵 Caveat: Costs scale with usage; limited customization.

☁️ Note: n8n cloud is a managed hosting offering, as opposed to the self-hosted version where you have to provide and manage the hosting yourself.

Custom Solutions:

Finally, if one of the automation platforms doesn't support a feature you want, you'll have to develop a custom solution to sync the data.

💵 Caveat: This is an expensive solution that needs to be integrated and maintained by a developer.

APIs for niche systems (manufacturing ERPs) built with Python/JS.

Pro Tip: Use AI coding tools (Cursor + Claude Sonnet) to speed development.

Example: I helped a law firm automate their client onboarding flow with an on-premise hosted instance of DeepSeek + n8n, saving them over $10k, which another agency quoted them for a custom app. Overall consulting fees are a fraction of that cost. Also, since I’m hosting the data on a server that doesn’t connect to the internet and only runs on their internal network - we can ensure HIPAA and SOC 2 compliance across all industries.

Ethical Stress-Testing:

Bias checks: “Does your hiring tool penalize resumes from community colleges?”

Compliance: Ensuring patient data in healthcare AI follows HIPAA.

Ethical stress testing includes workforce impact reviews: “Will this automation displace 30% of the warehouse team? If yes, what’s our reskilling plan?”

Example: A fintech startup’s loan approval system unfairly flagged rural applicants. I discovered this discrepancy through AI analysis of their CRM data, which presented an accurate view of their customers. I then retrained the model, reducing bias by 70%.

Scale Phase: Team Adoption + ROI Measurement

Training that works:

AI moves fast—and if your team isn’t actively learning, they’re slowing your business down. Consultants don’t just hand over tools and vanish; they train your team to wield AI as a competitive weapon. The stakes are high: Companies that skip training face operational bottlenecks, inefficiency spikes, and competitors who automate them out of the market.

⚠️ TRAINING ISN'T OPTIONAL

Employees who avoid AI tools create workflow logjams. Teams clinging to manual processes waste hours on tasks that could be solved in minutes—costing you contracts, customers, and credibility. Effective training dismantles resistance by focusing on three pillars:

All companies should be striving to teach teams to use AI tools in their daily work.

AI Return on Investment (ROI) Reports:

This is the most important part. This is how a company can tell if their AI consultant is doing a good job. AI consultants measure ROI with one rule: If you can’t quantify it, it doesn’t count. Here’s the no-BS breakdown:

What Goes Into an AI ROI Business Report?

Start with Baselines (The “Before AI” Snapshot)

📊 Labor Costs:

“How many hours does your team waste on [task]?”

Example: If invoicing takes 40 hours/month at $30/hour, that’s $14.4k saved/year

🛑 Error Rates:

“What’s the cost of manual mistakes?”

Example: A shipping company lost $8k/month on freight invoice typos.

🔮 Opportunity Costs:

“What could your team do with freed-up time?”

Example: Sales reps spending 10 hours/week on data entry = 50+ lost deals/year.

📈 Track Tangible Metrics (The “After AI” Payoff)

• Hours Saved → Dollars Saved:

  • “Automation cut 1,200 hours/year. At $50/hour, that’s $60k back in your pocket.”

• Error Reduction → Cost Avoidance:

  • “AI slashed invoice errors by 90%, saving $25k/month in refunds/rework.”

• Revenue Growth from Efficiency:

  • “Faster order processing boosted sales by 12% ($240k/year).”

✅ Build Live Dashboards (No Trust Issues)

• Real-Time Tracking:

  • Consultants set up tools like Looker Studio or Power BI to show:

    • Hours saved per department.

    • Error rates pre/post-AI.

    • Revenue tied to freed-up capacity (e.g., sales teams closing more deals).

• Example: A logistics client’s dashboard showed AI cut freight errors by 90% in real-time. CFOs love seeing dollar signs, not jargon.

☠️ Kill What Doesn’t Work (Fast)

• Sunset Rule: If a tool doesn’t pay for itself in 6 months, scrap it.

  • Example: A client’s “predictive inventory AI” only boosted accuracy by 2%. We nuked it, rebuilt it with open-source models, and tripled ROI.

🚀 Post-Deployment Audits

• 3/6/12 Month Check-Ins:

  • “Is the tool still saving $5k/month, or did your team revert to spreadsheets?”

  • Adjust workflows based on new pain points (e.g., holiday demand spikes).

If your AI consultant isn’t doing these 4 things, FIRE them immediately:

✅ Setting Transparent Goals & Baselines:
“Pre-AI, your team wasted 80 hours per month on invoicing. That’s $57k/year.”

✅ Live Monitoring:
Live dashboards showing real-time savings.

✅ Kill Fast:
Sunset any tool that doesn’t pay for itself in 6 months. Example: A “$30k AI inventory system” got axed when open-source models outperformed it.

✅ Post-Launch Audits:
3-month check-in: “Is the tool still saving $5k/month, or did your team revert to spreadsheets?” What about at 6 months? Or 12 months?

Industry-Specific Wins: How AI Consultants Deliver Real-World Results

AI isn’t one-size-fits-all. Here’s how we adapt strategies across sectors:

🏥 Healthcare: Smarter Scheduling, Safer Patients

Problem: A clinic had 3-hour patient wait times due to chaotic staff rotations.
Solution: My team and I built a predictive scheduling AI using historical patient data + seasonal illness trends.
Result: 40% shorter wait times and nurses gained 8 hours/week for actual patient care (not paperwork).

🛍️ Retail: Inventory That Anticipates Demand

Problem: A boutique chain kept overordering seasonal items (35% ended up clearance-racked).
Solution: AI-powered demand forecasting linked to local weather patterns and Instagram trends.
Result: Stockouts are down 60%, and clearance waste is cut in half.

🏭 Manufacturing: Quality Control Without the Overtime

Problem: Defect detection required nightly manual inspections (120 overtime hours/month).
Solution: Added computer vision AI to existing CCTV cameras.
Result: 90% of defects caught in real-time – $18k/month saved on overtime pay.

The Bottom Line: Whether you’re tracking stethoscopes or SKUs, AI consultants convert your chaos into your competitive advantage.

AI Consultants vs. In-House Teams: When to Call in the Pros

Let’s get real: Not every company needs a full-time AI engineer. Here’s how to decide if outsourcing to a consultant beats building internally:

🚩 When You Need an AI Consultant

Does this sound like you?

“We’re stuck in proof-of-concept purgatory” - Your team built a cool AI prototype… that’s been gathering dust for 8 months. Consultants ship production-ready solutions.

“Compliance keeps me up at night” - GDPR, HIPAA, or industry-specific rules? Consultants bake compliance into workflows upfront (no retroactive fixes).

“We need results yesterday” - Example: A startup hired me to automate investor reporting. Their dev team quoted 6 months; we used no-code tools to deliver in 3 weeks.

🛠️ When to Keep It In-House

Hey, sometimes an in-house team makes the most sense. I'm not hating. This is probably you right?

“We’re scaling a proven model” - If your AI chatbot handles 80% of customer queries, hire engineers to optimize it further.

“Data is our crown jewel” - Sensitive R&D? A pharma client kept AI training in-house but hired me for ethical auditing only.

“We have unicorn-tier talent” - If your CTO previously led AI at Google or OpenAI, you’re probably covered.

The Sweet Spot - 79% of my clients use consultants to jumpstart AI adoption, then transition to hybrid internal/external teams.

Common AI Consulting Myths:

❌ Myth: “Audits are just fancy reports.”

✅ Reality: Audits expose hidden costs. Example: My team and I discovered a client was overpaying for an “AI” CRM that was just a rebranded database—switching tools saved them $12k/year.

❌ Myth: “AI consultants are only for tech teams.”

✅ Reality: AI consultants work with HR, marketing, operations, development teams—and any department drowning in repetitive tasks. A hotel chain’s housekeeping team used AI scheduling to reduce staff burnout by 25%.