This data was originally featured in the April 9th, 2025 newsletter found here: INBOX INSIGHTS, April 9, 2025: AI Integration Strategy Part 1, AI Prompting Frameworks
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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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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