Future-Proof Your Business: The AI Readiness Handbook

This is a follow-up to our AI Readiness Quiz. If you have not attempted it, I'll strongly recommend you to do so! It can help to prompt some reflection on how you can better implement AI in your business. In addition, the figures in this handbook are derived from a McKinsey report.

Many organisations today lack coherent and structured AI adoption strategies, employee training and leadership alignment. This is not a surprise, because AI development has been fast and furious and business leaders are racing to catch up. This is why we wrote this AI Readiness Handbook- it will help you identify key areas for improvement and build a robust AI adoption framework.

Step 1: Defining your AI Roadmap

Why it matters

Without a clear AI adoption roadmap, organizations risk inefficiencies, missed opportunities and potential security risks. A structured blueprint ensures alignment across teams, maximises ROI, and can help to protect your company's reputation.

Action steps for readiness

1. If you are new to adopting AI, start with a bottom-up approach. Identify employees already leveraging AI, document best practices, and host peer learning sessions.
2. If your organisation has already begun implementing AI, continue on with a top-down survey. Have leaders of each department in the same meeting to understand the workflow of the business. This can help to spark ideas on how AI can be implemented at scale across the whole organisation.
3. Define clear AI priorities, success metrics, and phased implementation plans. When you implement AI, employees need to understand why they are doing it. In addition, having clear, measurable goals ensure that you can continually evaluate progress.

Step 2: Enhancing Employee AI Training and Upskilling

Why it matters

48% of employees rank training as the most critical factor for AI adoption, yet nearly half feel unsupported. AI readiness requires structured training in key areas.

Core AI Training Areas for Employees

1. Prompt engineering- Writing effective prompts can lead to vastly superior outputs. Understanding how a model works- that it is a general purpose tool that performs better as you provide it with more context- can help users better craft their prompts.
2. Multimodal model usage- While most users are familiar with ChatGPT, they may not be aware that AI has drastically improved in the few years since the launch of ChatGPT. Models can now take in text, images, PDFs etc. This means that AI can be applied to many more tasks.
3. Model selection- AI is improving so fast that it's hard to keep up with all the models. However, different models from different labs have their own strength- GPT for writing, Claude for coding, Gemini for affordable long context tasks. Such advantages will obviously change over time, so it's important to have AI experts (internal/external) helping the company.

How to implement AI Training

1. Develop an internal AI best practices library.
2. Conduct AI boot camps and hands-on workshops for employees. Collect questions from employees before these training sessions so that their specific workflow/queries can be addressed.
3. Set up an AI mentorship program, where power users in your organisation can help those who are weaker.

Step 3: Gain leadership buy-in and support

Why it matters

Successful AI adoption depends on leadership engagement. The McKinsey report found that executives are 2.4x more likely to cite employee AI readiness as a barrier—but employees are already using AI more than expected.

How to Drive Leadership Adoption

• Educate leaders on practical AI use cases and business impacts
• Encourage leaders to use AI tools firsthand- a great use case is querying reports (PDFs) instead of reading it from cover to back.
• Create a top-down + bottom-up AI culture. Leaders have the ability to see how AI can transform their workflow across the organisation, while employees can increase their daily productivity through the usage of AI tools.

Step 4: Establish custom AI evaluation and benchmark

Why it matters

39% of US C-suite leaders rely on general benchmarks (e.g., accuracy, precision) instead of customized AI performance metrics. Custom metrics improve AI effectiveness.

How to build strong AI benchmarks

1. Define use case specific evaluation metrics- e.g. response relevance for customer support AI agent
2. Create an AI evaluation dataset with real-world (or synthetic) data to test performances
3. Implement continuous monitoring of AI inputs and outputs to refine model quality

Example: AI Benchmarking for Press Release

• Objective: Ensure AI written press release includes key information in appropriate tone.
• Metrics:
  • Relevance: Did the AI include all the relevant information?
  • Tone: Did the AI adopt a tone appropriate for the press release?
  • Process: Track input and AI outputs and conduct regular performance reviews.

Step 5: Address AI Explainability and Trust

Why it matters

AI is often perceived to be a "black box". However, enterprises need explainable AI systems to ensure decision transparency, compliance and user trust.

How to improve AI Explainability

• Implement reasoning models that provide transparent decision making traces.
• Develop AI audit logs to track AI behaviour and decision making.
• Adopt human in the loop, especially in the early stages of AI deployment.

Now what?

I hope the 5 steps outlined in this handbook can give you more confidence in the roadmap that you need to take.

AI deployment is an ongoing process- while its deployment may involve a fair amount of uncertainty, your competitors are unfortunately not sitting around. The best time to start was yesterday. The next best time is today.

All the best!

P.S.: If you are looking for custom AI adoption consulting, reach out to me. I'll be happy to help!