🚀 AI Implementation for Business Growth

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We are doing something a little different this week. We are going to get practical with AI and how to implement it for business growth.

Not so long ago "implementing AI" meant hiring a data scientist who'd disappear into a back room for six months burn a pile of cash and emerge with "a model" that nobody knew how to use.

Thank God! Those days are officially over. 🙏🏽

Today we're kicking off a three-part series on AI Implementation for Business Growth - because a majority of our subscriberes want to know more about this. Stay tuned for all three parts - And I promise, no computer science degree required.

The AI Implementation Paradox 🤔

Here's the thing about AI in 2025: Everyone (almost) knows they need it, but most businesses still don't know where to start.

It's like being handed the keys to a Ferrari when you've only ever driven a golf cart. You know it's powerful, you know it could get you places faster, but you're a little worried you might end up crashing into a wall at 200mph.

According to McKinsey, while 92% of companies plan to increase their AI investments over the next three years, only 1% consider themselves "mature" in AI deployment. That's a whole lot of businesses stuck in the "we should do something with AI" phase.

Why This 3-Part Series Will Actually Help You 💪

Over the next three weeks, we're going to break down AI implementation into digestible, actionable chunks:

Part 1 (Today): Finding Your Starting Point - How to identify high-value AI use cases in your business
Part 2 (Next Week): From Pilot to Production - Turning AI experiments into business results
Part 3 (Final Week): Scaling Success - Building an AI-powered organization

No jargon. No hype. Just practical steps that work for real businesses.

The "Gold Rush" Problem with AI Implementation 💰

Many businesses approach AI like prospectors during the gold rush - frantically digging holes everywhere hoping to strike it rich, with no real plan or strategy.

The result? A lot of wasted time, money, and enthusiasm.

OpenAI's research with their enterprise customers revealed something fascinating: The most successful companies don't start with the technology - they start with their business problems.

Think of it this way: You wouldn't buy a hammer and then walk around your house looking for things to hit. You'd identify something that needs fixing first, then reach for the right tool.

The Impact/Effort Framework: Your New Best Friend 📊

One of the simplest yet most powerful tools for identifying AI use cases comes from OpenAI's guide on "Identifying and scaling AI use cases." It's called the Impact/Effort Framework, and it looks like this:

The framework divides potential AI projects into four categories:

1. Quick Wins (High Value/Low Effort) ✅

These are your "no-brainer" starting points - projects that deliver significant value without requiring massive resources.

Real-world example: Morgan Stanley equipped their financial advisors with AI assistants to summarize market analyses and generate research reports. Implementation was straightforward, but the impact on productivity was immediate.

2. Strategic Projects (High Value/High Effort) 🎯

These projects might take more time and resources, but they can be transformational for your business.

Real-world example: Indeed built an automated system to explain to jobseekers why specific openings were being recommended to them. It took months of testing and iteration, but resulted in a 20% increase in job applications.

3. Fill-In Projects (Low Value/Low Effort) 🔄

These are "nice-to-haves" that you can tackle when resources are available.

Real-world example: Tinder created an AI tool that helped their product team quickly test new ideas without needing technical skills. It let anyone on the team experiment with features that would normally require a developer, making innovation faster and more inclusive.

4. Avoid (Low Value/High Effort) ⛔

These are projects to deprioritize or avoid altogether.

An example: Building a custom AI assistant to generate web forms when your team already uses a reliable, tried and tested, well integrated tool for this purpose.

Finding Your "Quick Wins": 3 Places to Look 🔍

So where do you find these high-value, low-effort use cases in your business? Here are three places to start looking:

1. Repetitive Tasks That Eat Time

Look for tasks that your team performs frequently that follow a consistent pattern. These are prime candidates for AI automation.

Questions to ask: What tasks do your team members complain about doing? What work feels like "groundhog day" - the same thing over and over?

2. Information Bottlenecks

Identify where information gets stuck or takes too long to process in your organization.

Questions to ask: Where do people wait for answers? What knowledge is trapped in specific departments or with specific people?

3. Customer Friction Points

Find moments where customers have to wait, repeat themselves, or abandon their journey with your business.

Questions to ask: When do customers get frustrated? Where do they drop off in your sales or service process?

The Department Workflow Mapping Technique 🗺️

Once you've identified potential areas, the next step is to map out the entire workflow. This helps you see not just individual tasks, but how AI could transform entire processes.

Here's a simplified example for a marketing team:

1. Research: Use AI to analyze market trends and competitor activity

2. Analysis: Process customer data to estimate opportunity size

3. Strategy: Brainstorm campaign approaches using AI as a thought partner

4. Execution: Generate initial messaging, assets, and translations

5. Optimization: Automate content localization and channel optimization

By mapping the entire workflow, you can identify multiple integration points rather than just isolated tasks.

Real Talk: Why Most AI Projects Fail ⚠️

Before you rush off to implement AI everywhere, let's talk about why so many AI projects fail to deliver value:

1. Starting with the Technology, Not the Problem

The most common mistake is asking "How can we use AI?" instead of "What problems do we need to solve?"

2. Unrealistic Expectations

AI isn't magic. It won't fix broken processes or replace human judgment entirely.

3. Poor Data Quality

AI systems are only as good as the data they're trained on. Garbage in, garbage out.

4. Lack of Integration

AI tools that don't connect to your existing systems create more work, not less.

5. No Clear Success Metrics

Without defining what success looks like, you can't tell if your AI implementation is working.

Your Action Plan for This Week 📝

1. Identify 3-5 potential "quick win" use cases using the Impact/Effort Framework

2. Map one complete workflow in your highest-priority department

3. Define clear success metrics for each potential AI implementation

4. Inventory your data sources related to your priority use cases

Coming Next Week: From Pilot to Production 🔮

Once you've completed this week's action items, you'll have a solid foundation for Part 2 of our series. 

Next week, we'll build directly on your identified use cases and workflow maps to show you:

• How to turn your "quick win" candidates into working AI pilots without breaking the bank

• The "Minimum Viable AI" approach that gets results faster with the data you've inventoried

• How to translate your success metrics into measurable pilot outcomes

• Real-world case studies of successful implementations that followed this exact process

• Common pitfalls and how to avoid them when moving from planning to execution

The work you do this week will directly feed into next week's implementation strategies and perfectly position you to move from planning to action.

Speaking of growing your business - check out Masters in Marketing. The newsletter to help you master marketing for your business.

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