AI Consulting: What Businesses Actually Need (Not What They Think)

Every week we talk to businesses that want to "integrate AI." Most of them have the wrong idea about what that means.

The AI Hype Gap

Here is what businesses think AI consulting means: a team of data scientists building a custom machine learning model trained on their proprietary data, deployed on custom infrastructure, with a flashy dashboard.

Here is what they actually need: someone to look at their workflows, identify where an LLM API call would save 10 hours a week, and build that integration in two weeks.

The OpenSphere Framework

We use a simple framework for every consulting engagement:

1. Workflow Audit (Week 1)

We spend the first week understanding how work actually flows through the organization. Not the org chart — the actual day-to-day. Where do people copy-paste between systems? Where do they write the same email template? Where do they manually classify or categorize things?

These repetitive, text-heavy tasks are where AI creates immediate value.

2. Opportunity Scoring (Week 2)

We score every opportunity on three dimensions:

• **Impact:** How much time or money does this save?
• **Feasibility:** Can current AI models do this reliably?
• **Risk:** What happens if the AI gets it wrong?

A customer support email classifier is high impact, high feasibility, and low risk. An autonomous medical diagnosis system is high impact, questionable feasibility, and extremely high risk. We start with the first kind.

3. Rapid Prototype (Weeks 3-4)

We build a working prototype of the top opportunity. Not a proof of concept. Not a slide deck. A working tool that actual employees can use the next day.

This usually means: - A Next.js web app with a simple interface - Claude or GPT API integration on the backend - Connection to their existing data sources (email, CRM, documents) - Basic error handling and logging

4. Measure and Expand (Ongoing)

Once the first integration is working, we measure actual time saved, error rates, and user satisfaction. Then we move to the next opportunity on the list.

Common AI Use Cases That Actually Work

Based on our consulting work, these are the use cases with the highest success rate:

1. **Document analysis and extraction** — CRA letters, contracts, invoices
2. **Email drafting and response** — customer support, sales follow-ups
3. **Content classification** — tagging, routing, prioritization
4. **Internal knowledge search** — RAG over company documents
5. **Report generation** — turning data into readable summaries

Notice what is not on the list: chatbots on your website. That is usually the first thing businesses ask for, and it is rarely the highest-value use case.

What It Costs

AI consulting does not have to be a six-figure engagement. Our typical project:

• **Strategy audit:** 2 weeks, fixed price
• **First integration:** 2-4 weeks, fixed price
• **Ongoing support:** monthly retainer, optional

We have seen companies save 20+ hours per week with a single well-placed AI integration. The ROI is usually obvious within the first month.

Ready to Start?

If your team is spending hours on repetitive text-heavy work, there is probably an AI integration that pays for itself in weeks. Email us at shivi@opensphere.ca for a free initial assessment.