Expertise Multiplied: How AI Finally Solves the Scaling Challenge

Of all the things in business you can scale, expertise is not one of them. Or at least, it was not.

Anyone who has ever hired will tell you that you cannot magically scale expertise by adding more people. Traditionally, there have been only three ways to try.

The Three Flawed Ways to Scale Expertise

1. Work More Hours

2. Hire People

3. Raise Your Prices

These approaches simply redistribute the pressure rather than solving the underlying challenge.

The Real Bottleneck: The Translation Layer

The true problem is the gap between knowing the answer and documenting it for someone else.

Consider an experienced consultant who can diagnose a systems problem rapidly. Yet, the post diagnosis documentation takes hours. That ratio is the problem. This has always been the bottleneck. A senior Architect can whiteboard a design in a single session, but validating the solution, complete with creating the output documentation, can take many days or weeks.

Your brain works fast. Documentation works slow.

The Breakthrough for Scaling Expertise with AI

There is a game changing breakthrough for scaling expertise with Artificial Intelligence. This is not just for Architects and Engineering contractors. The principles apply to anyone, whether you work in technology or not.

Key Principles of AI-Driven Expertise Scaling:

• Expertise Compounds, Documentation Does Not: Your deep knowledge remains your competitive edge, but the documentation burden is dramatically reduced.

• You Retain Quality Control: The AI provides the foundation, but the crucial final sign off remains with the expert.

• Embrace the 80/20 Threshold: Focus your finite time on the most valuable 20 per cent of the work, allowing AI to handle the initial 80 per cent draft.

• Context is the Multiplier: The power of AI lies in the context you provide. To generate a high quality first draft, you need to define four critical areas:

  • The Task: What you need.

  • About You: The context of your role and expertise.

  • The Audience: Context about the client, patient, or reader.

  • Your Expectations: The specific goal for this draft.

The clearer you are, the better the AI's 80 per cent draft will be, and the more efficiently you can apply your expertise to the final 20 per cent.

How Orion Data Analytics Solves the Challenges

We use our extensive knowledge to solve the biggest barriers blocking organisations from fulfilling successful AI solutions. We focus on the major contributing factors required to produce accurate designs and rapid documentation:

1. Architecting the Solution

This involves the human expertise and strategic work:

• AI Business Strategy

• Data and AI Business Requirements

• Design Thinking (Workflow Optimiser)

• Architecting (Stack and Integration)

2. Automating the Translation

We then utilise AI tools throughout our process to collect the data at scale and speed up the process, synthesise the data (using techniques like RAG, Azure Search, and LLMs), and finally present the findings by creating the pre-execution phase artefacts:

• Project Plan and Scope

• Architecture Diagrams and Design (HLD, LLD)

• Security Requirements and Policies

• Azure DevOps Setup and Access Controls and Permissions

  
  

Hopefully, your chosen consultancy is leveraging AI, otherwise you are at a disadvantage of speed, longer wait times, and missing out on cost efficiencies and scalability.

What is the one thing this week where you spend hours translating your expertise? How can you practice letting AI lift that bottleneck for you?