INBOX INSIGHTS, April 9, 2025: AI Integration Strategy Part 1, AI Prompting Frameworks - Trust Insights Marketing Analytics Consulting

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INBOX INSIGHTS: AI Integration Strategy Part 1, AI Prompting Frameworks (2025-04-09)

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Approaching AI Integration Strategically – Part 1

The pressure to adopt AI is everywhere, but rushing in without a strategy is like me trying to walk my 125 lb dog without a leash or cheese. In that scenario, she’s off and running, and I have no way to recall her back to me. Needless to say, not a great approach.

I keep hearing from people stating they have been told by their leadership to “implement AI across the organization” by the end of the quarter. When I ask what problem they are trying to solve with AI, there is that uncomfortable silence I’ve come to recognize all too well.

“I don’t know,” they will finally admit. “Leadership just read something about how everyone needs AI now or they will fall behind.”

Sound familiar?

What an AI Strategy Is and Isn’t

Let’s get something straight: AI is not a strategy. AI is a tool – a powerful one, yes, but still just a tool. Your strategy explains why you’re implementing AI and what business problems you’re trying to solve.

An AI strategy statement might look something like: “We are integrating AI capabilities to improve customer service response times and accuracy, reducing resolution time by 50% while maintaining our high satisfaction scores.”

Notice how specific that is? It’s not just “let’s do AI” – it explains why AI makes sense for your business goals.

Here’s what an AI strategy is NOT:

It’s not a list of AI tools you plan to purchase
It’s not “because our competitors are doing it”
It’s not a shiny object to impress stakeholders
It’s not a replacement for human expertise

I can’t tell you how many times I’ve seen companies throw money at expensive AI tools without first understanding what they need those tools to accomplish. That’s a recipe for wasted resources and frustrated teams.

The STEM Framework for AI Integration

When approaching AI integration, I find it helpful to use our STEM framework to organize thinking and create an actionable plan:

Strategy: Why are we implementing AI?
Tactics: What specific AI applications will we use?
Execution: How will we implement and manage these tools?
Measurement: How will we know if our AI implementation is successful?

Let me break this down with a real example from a client who wanted to use AI for content creation:

Strategy (Why): To scale our educational content production to reach more potential clients while maintaining quality and reducing the burden on our subject matter experts.

Tactics (What):

Implement AI writing assistant for first drafts
Use AI for content optimization and SEO
Create AI-powered content templates for consistent quality

Execution (How):

Train content team on effective AI prompt engineering
Establish editorial workflow that combines AI and human expertise
Create content governance guidelines for AI usage

Measurement (How we’ll know):

Track content production volume (aiming for 3x increase)
Measure time saved for subject matter experts
Monitor content quality scores and engagement metrics
Compare ROI of AI-assisted content vs. previous methods

The framework helps ensure that every part of your AI implementation links back to your core strategy. Without this connection, you’re just chasing technology for technology’s sake.

Why Starting with Measurement Matters

I’m going to tell you something that might seem backward at first: with AI implementation, I always recommend starting with the measurement piece. Here’s why:

Many AI initiatives fail because organizations can’t actually quantify their impact. They “feel” like the AI is helping, but they can’t prove it—which makes it nearly impossible to justify continued investment.

I learned this lesson the hard way. At a previous company, we implemented an AI tool for customer segmentation that the vendor promised would “revolutionize” our marketing. Six months and many thousands of dollars later, we couldn’t definitively say whether it had improved anything.

The problem wasn’t the tool itself – it was that we hadn’t clearly defined:

Our current baseline metrics
What specific improvements we expected
How we would measure success

Now, I always start AI strategy discussions with these questions:

What metrics are we trying to improve?
What are our current numbers?
What improvement would make this investment worthwhile?
How and when will we measure the impact?

Where to Start with Your AI Strategy

If you’re feeling overwhelmed about creating an AI integration strategy, here’s a simple approach to get you started:

Identify pain points – Where are your teams spending the most time on repetitive tasks? Where do you have bottlenecks? These are often good candidates for AI assistance.
Define clear objectives – What specifically do you want AI to help you achieve? Be concrete (e.g., “reduce document processing time by 40%” rather than “improve efficiency”).
Start small—choose one well-defined use case for your first AI implementation. Success here will build confidence and knowledge for bigger projects.
Create a skills plan – What skills will your team need to work effectively with AI? This might include prompt engineering, data preparation, or AI governance.
Establish ethical guidelines – How will you ensure your AI use aligns with your values and maintains trust with customers?
Set up measurement systems – How will you track the impact of your AI implementation on your business metrics?

Remember: AI strategy doesn’t have to be complex. It just needs to connect the technology to your business goals and provide a framework for measuring success.

Next week, I’ll dive deeper into our 5P Framework (Purpose, People, Process, Platform, and Performance) and how it can be applied specifically to AI integration planning.

In the meantime, I’d love to hear: What’s your biggest challenge when it comes to implementing AI in your organization?

Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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This week, let’s revisit prompting frameworks. A long time ago in AI years, Trust Insights created the RACE framework for AI prompting:

Role: tell the model who it is
Action: tell the model what it’s doing
Context: tell the model the background information it needs to know
Execution: tell the model what you expect for outcomes

This framework worked really well.

Then as models evolved over time and got smarter, we switched to the RAPPEL AI Framework:

Role: tell the model who it is
Action: tell the model what it’s doing
Prime: ask the model what it knows about the task
Prompt: tell the model how to do the task
Evaluate: ask, clarify, and correct the results
Learn: tell the model to write a new prompt for future use based on the conversation

RAPPEL worked really well, especially for non-reasoning models. But last year, when reasoning models took AI by storm, our strategies had to change. Reasoning models like OpenAI’s o family (o1, o3, etc), DeepSeek’s R family (R1 and R2), and Google Gemini’s thinking models (Gemini 2 Flash Thinking and Gemini 2.5 Advanced) take away chunks of these prompt structures.

Why? Reasoning models can figure out how to do a task. We still have to tell them what to do and why to do it, but we now leave how to do it up to them.

So we created PRISM as a framework for reasoning models.

Purpose: tell the model what it’s doing and why
Relevant Information: give the model lots and lots of information about the task
Success Measures: tell the model what its expected outcome looks like

This worked really well for reasoning models.

The latest iteration of AI models as Deep Research tools, like Perplexity’s Deep Research, OpenAI Deep Research, Google Gemini Deep Research, etc. now do a phenomenal job of preparing – from grounded sources – lots of contextual information, something we’ve discussed at length on our podcasts and livestreams.

Which funny enough brings us back full circle to the RACE model. Today, if I want a reasoning model to generate the absolute best results for me, RACE 2.0 now looks like this:

Role: ask the model to choose a role for itself
Action: ask the model to think about what it’s doing and why
Context: give the model a Deep Research report on the topic
Execution: ask the model to think through the outcomes and match it to the Action

What’s changed, besides injecting Deep Research data, is that I don’t tell the model what to do any more. Instead, I ask it. I ask it because reasoning models are so intelligent, so capable, that they can often do a better job than I can of coming up with those pieces.

Let’s look at an example. Here’s a RACE 1.0 prompt:

“You’re a Google Analytics expert skilled at GA4, Google Tag Manager, Google Looker Studio. You’ll analyze my Google Analytics data and help me explain what’s going on with my marketing. {A few tables of data get pasted here}. Based on this data, return your results as an outline of my marketing funnel – awareness, consideration, evaluation, purchase – and how effective my marketing is at each stage.”

That worked really well back in the day. Here’s what a RACE 2.0 prompt looks like:

“Let’s analyze these 14 screenshots of Google Analytics data I’m providing from Google Analytics 4. What role should you take on to perform this task? Explain the role you select, and ensure the role contains superlatives. What do you see in the data? Think through your analysis of the data – what happened? Why, if you can tell, did it happen? What does it mean? I’ll provide a guide on best practices for Google Analytics data analysis. {Add a whopping big Deep Research report} What conclusions could you draw from this data, especially about our marketing funnel? Explain your conclusions for each stage – awareness, consideration, evaluation, and purchase.”

This doesn’t seem like a huge change, but when you run it, the change is MASSIVE.

As models evolve, our prompting frameworks have to evolve too. I’d love to hear how your frameworks have changed over time – pop by Analytics for Marketers to share your stories!

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