How to implement AI in your business: A simple guide to get started

AI has advanced rapidly. 

But how do you implement AI in your business to stay competitive, impress investors, and boost productivity? 

It’s simpler than you think. Follow our three-step guide to start using AI effectively today.

3 steps to start with AI

At BRACAI, we make things simple. Here’s a three-step guide to help you start using AI:

1. Explore AI use cases

2. Develop a practical AI strategy

3. Implement and iterate

Let’s break it down.

Step 1: Explore AI use cases

Implementing AI into your business starts with identifying where it can add the most value. 

Start with an AI readiness assessment to uncover areas with the highest potential. Look for opportunities where AI can solve real problems—whether it’s automating routine tasks, improving customer service, or analyzing data faster. Keep it practical and focused. 

At BRACAI, we recommend keeping things simple: find a few high-impact opportunities rather than trying to tackle everything at once.

By the end of this step, you should have a clear list of actionable AI ideas for your business. This is your foundation for success.

Step 2: Develop a practical AI strategy

A good AI strategy does not need to be long or complex. But it must be explicit.

At a minimum, it should answer three questions:

• Where AI should take the company

• Which initiatives matter most

• How progress will be measured

Below is a simple and practical way to structure it.

2.1 Create a clear vision for AI

A good AI strategy starts with a vision.

This is a clear view of how your company should look once AI is part of everyday work.

Not in theory. In practice.

Without a vision, organizations end up with isolated pilots that never scale.

A good AI vision:

• Connects directly to your corporate strategy

• Explains why AI matters for your business

• Provides guardrails for what to pursue and what to ignore

We recommend taking both a top-down and bottom-up approach:

• Top-down: start from your strategic priorities and business goals

• Bottom-up: collect ideas and pain points from teams on the ground

AI initiatives should connect corporate priorities with real operational needs. Ideas come bottom-up. Direction comes top-down. Value is created where the two meet.

The real value is created where these two meet. That is how you avoid standalone AI projects and instead build momentum around the right themes.

2.2 Prioritize the right AI initiatives

Many organizations jump into AI with good intentions, but poor prioritization.

A common mistake is starting with:

• Ambitious use cases

• High technical complexity

• Unclear business impact

This often leads to slow progress and limited P&L impact.

To avoid this, we recommend using a simple prioritization matrix to evaluate AI initiatives before committing resources.

Each potential use case should be assessed across three dimensions.

Business value

• Impact. Will this meaningfully improve customer or employee outcomes?

• Alignment. Does it support our core business objectives?

• Reuse. Can this capability be reused or extended later?

Actionability

• Usability. How accurate does the AI need to be to be useful?

• Adoption. Can this fit naturally into existing workflows?

• Speed. How quickly can we generate real value?

Feasibility

• Technical fit. Is AI actually the right solution?

• Data readiness. Do we have access to reliable data?

• Risk tolerance. What happens if the AI is wrong?

Plotting use cases this way helps you focus on high-impact, achievable initiatives. Avoid over-engineering early efforts. Build confidence through quick wins. This does not mean avoiding ambition. It means sequencing it correctly.

2.3 Measure your generative AI progress

What gets measured gets improved. Yet many AI initiatives fail because success is never clearly defined.

Measuring AI progress does not require complex dashboards. It requires a small number of meaningful signals.

At a minimum, you should track:

• Adoption. Are people actually using the solution?

• Productivity. Is time or effort being reduced?

• Quality. Are outputs improving or becoming more consistent?

• Scalability. Can this be reused across teams or processes?

These metrics should evolve over time.

Early on, learning speed and adoption matter more than automation rates.

The goal is not perfection. The goal is momentum and learning.

Step 3: Implement and iterate

Now that you have your AI roadmap, it’s time to bring it to life. But sending out a memo or hosting a long online training session won’t cut it.

You can do much more to make this process effective.

This step addresses critical questions: How do you onboard your team effectively? How do you ensure processes improve over time? And who is responsible for what?

AI onboarding training sessions

Effective onboarding is key to making AI work. But not all training sessions are created equal.

Some common pitfalls that we see a lot:

• Too dense seminars. These often bore participants and fail to engage

• Online sessions. People tend to multitask or lose focus

• Sessions may be too generic or overly technical

How to improve:

• Conduct in-person training sessions tailored to your roadmap

• Record sessions for easy future access. Keep them concise and clear

• Train key team members directly managing AI use cases. If roles aren’t clear, revisit Step 2

In-person sessions encourage interaction and questions, making them more effective. While remote work is valuable, face-to-face training builds better engagement and ensures understanding.

SOPs and AI guidelines

SOPs help execute the AI use cases from Step 1 and the roadmap from Step 2.

Some best practices we recommend:

• Keep it simple: Long, complex SOPs are ignored. Stick to clear, concise documents

• Enable iteration: Use dynamic tools so SOPs can evolve as processes improve

• Assign ownership: Each AI use case manager should maintain and update their SOPs based on real performance

If this sounds straightforward, it’s because it is. But company documentation is often messy, so keeping it simple and organized is key.

A one-pager with AI guidelines is also essential. These guidelines should include what’s legal and what’s not at your company. They should be short and clear, providing recommendations on how staff should—and shouldn’t—use AI. Without clear guidance, employees may misuse AI, increasing risks such as data leakage.

But be careful: simply forbidding everything can cause your company to fall behind competitors. Strike a balance that enables innovation while managing risks.

Conclusion: How to implement AI in your business

Getting started with AI doesn’t have to be complicated. With our simple three-step approach, you now have a clear roadmap to explore use cases, develop a strategy, and implement AI in your business.

Remember, the key is to start small, focus on real value, and build from there. Taking the first step today can put your business ahead of the curve tomorrow.

If you’d like expert guidance or support at any stage, we’re here to help. Just drop us an email, and we’ll be happy to assist you in making AI work for your business.