Episode 7.5: AI Marketing Action Plan Workbook & Toolkit

The AI Marketing Action Plan Workbook & Toolkit is a comprehensive guide for creating a personalized and actionable AI Marketing Plan. It serves as your roadmap to AI marketing success. The workbook aims to help you identify key challenges, strategically select AI tools using the STRIVE Framework, design a manageable pilot project, and ensure that your implementation is both effective and ethical. The core philosophy guiding the process is to “Start small, measure, learn, and iterate”... The workbook is the final assignment for “The AI Marketing Advantage Course”

Transcript

Welcome to the deep dive. Today we’re getting really practical about AI and marketing.

Yeah, we are.

The learner has shared some, real world AI marketing action plan ideas they seem to be wrestling with.

Mhmm.

And our goal here is to unpack what separates the pilot projects likely to succeed from those that well, might just stumble.

Right. Think of it as a kind of fast track maybe, helping you understand the nuts and bolts of making AI actually work in your marketing. Exactly.

The learner wants to get smart about this stuff quickly, you know, effectively without the overwhelm.

And that’s what we’re aiming for, those key insights, those moments. We’ll be drawing from the AI marketing action plan workbook and toolkit and also the AI marketing advantage course to keep things grounded.

Okay, great. So let’s unpack this. We’re going look closely at these scenarios through a few specific lenses.

Okay.

First up, pilot project scope. How well defined is it? Is it focused, achievable or maybe a bit too ambitious? Then we’ll really scrutinize the SMART metrics. Are they, you know, specific and measurable or are they more like wishful thinking?

Crucial difference there.

We’ll also look at how the STRiVE framework can be applied or maybe wasn’t applied to justify the AI tool choices. Mhmm. And of course, the ethical review side of things.

Absolutely. Can’t forget that.

So let’s dive into our first scenario. This one really highlights the power of, well, keeping things tight.

Yeah. It’s fascinating, isn’t it? Right out of the gate, how a seemingly small difference in just defining the project scope Okay. Can have this massive ripple effect on whether it’s likely to succeed or not.

Absolutely. So scenario one presents a pilot with a real laser focus. Increase efficiency in social media content creation using an AI writing tool.

Okay.

And it gets specific drafting initial text posts just for Instagram and LinkedIn.

See, you can immediately feel the clarity there. The objective, the platforms, the content type all clearly stated.

Right.

And that kind of focused approach, it allows for really targeted experimentation. You get clear feedback. Well, by concentrating on just those platforms, Instagram and LinkedIn, and that specific type of content, text posts, you can really dig into how the AI performs in that context.

Ah, okay. Like what prompts work best?

Exactly. What prompts give you the best results? How does the output actually align with the, you know, the different nuances of Instagram versus LinkedIn?

And I guess how you measure the efficiency gains too.

Crucially, yes. How do you accurately measure those gains if you’re trying to do too much at once?

Okay. So now let’s contrast that. Picture another scenario. Implement AI across all marketing channels to improve overall performance.

Yeah.

Sounds great on the surface maybe? Ambitious.

Ambitious, yes, but practical. That’s where it gets tricky. How do you even begin to tackle something that broad?

Right. Overall performance. Yeah. What does that even mean in concrete terms?

It’s so nebulous. And trying to implement AI across every single channel all at the same time, it feels a bit like trying to boil the ocean.

Yeah, it really does. It raises so many practical questions.

Like,

well, how do you allocate resources effectively across all those different channels? And what single metric or even set of metrics could possibly give you a meaningful read on overall performance?

And even if you do see some positive movement somewhere, do you confidently say, okay, that was the AI in this specific area versus something else entirely?

Yeah. Becomes a real challenge to pull out any meaningful learning, doesn’t it?

Totally. You can’t isolate the variables easily.

And that ties directly back to that core principle from the AI marketing action plan workbook and toolkit. Mhmm. That idea of start small, measure, learn, and iterate.

Exactly. A tightly defined scope makes that whole iterative process so much more manageable. You can actually isolate the impact.

Gather concrete data.

Make informed adjustments. Learn something useful quickly.

The workbook really emphasizes that these first pilot projects, they’re about targeted learning. They’re not about wholesale transformation overnight.

No, definitely not. You’re testing a specific hypothesis ideally in a controlled setting. A narrow scope just facilitates quicker learning cycles.

And minimizes the risk of the whole thing getting bogged down by its own complexity.

Precisely. Keeps things moving.

Okay, that makes a lot of sense for scope. Let’s shift gears now to scenario two and the critical role of SMART metrics. Metrics.

Right, this is where we move beyond just having good intentions.

Yeah, and define exactly how we’ll know if we’re succeeding with our AI pilots.

Robust smart metrics, they give you that essential framework for evaluating any pilot project, really, but especially when you’re introducing something dynamic like AI.

Without them. You’re just guessing.

Pretty much. You’re guessing if you’re making progress, it’s hard to prove value.

So the AI marketing action plan workbook and tool kit gives us a solid example, right, in that social media context again.

It does. It suggests a metric like reduce the average time spent by the marketing team on drafting initial social media posts. Okay. Specifically text only posts for Instagram and LinkedIn, and then it adds the targets.

Ah, the measurable part. Like where

Like decreasing that drafting time from say an average of forty five minutes down to twenty minutes.

That’s quite specific.

And within a defined time frame, say four weeks, and measure it across a set number of posts, maybe posts, to get a reliable average.

Okay. Let’s break that down. You can see how precisely defined that is.

Exactly. It’s specific drafting text for those two platforms.

Measurable from forty five down to twenty minutes.

Achievable. Well, that’s the hypothesis. A significant but potentially realistic reduction Yeah. With AI help.

Relevant, it directly addresses the goal of efficiency.

And time bound within those four weeks. Smart. There’s really no room for ambiguity in figuring out if you hit that goal or not.

Right. Now let’s think about some, less effective alternatives, things you might hear.

Oh, yeah. Like what?

Improve social media engagement with AI or maybe increase content creation speed using AI.

Okay. They sound directionally positive.

Yeah. But how do we actually quantify that improvement or that increase?

That’s the absolute crux of the issue. Improve engagement, improve it how? How are you tracking it? Is it likes, shares, comments, clicks back to the website?

By how much?

By what percentage are you aiming to improve? Over what period? It’s just too fuzzy.

And increase content creation speed. Same problem.

Same problem. Increase it by what factor? For which specific types of content? Videos, blog posts, social snippets. Without those concrete details, you lack the benchmarks.

You just can’t objectively assess the AI’s impact.

And as the toolkit points out, establishing baselines first is absolutely crucial.

Oh, totally. Before you even introduce the AI, you need to know where you stand right now.

So in our example, knowing the team currently averages forty five minutes per post manually. That’s the vital reference.

That baseline gives you the necessary context. Without it, any changes you see later might be because of the AI.

Or

Or they could just be normal fluctuations in your team’s output or maybe wider market trends or seasonality. Understanding your starting point lets you make a much more accurate assessment of the AI’s actual contribution.

Makes sense. Clear metrics need a clear starting line. Okay. Let’s move to our third scenario. This one zooms in on how we strategically choose an AI tool and how a structured approach like the Strive framework from the toolkit can guide that decision.

Yeah. The Strive framework offers a really methodical way to evaluate AI tools. It helps ensure your selection is driven by, you know, actual strategic considerations.

Not just the latest hype.

Exactly. Not just the hype or a superficial feature list. It forces a much more rigorous analysis.

Okay. So let’s imagine our team is looking at an AI writing tool for that social media pilot. Maybe one of those mentioned in the AI marketing advantage course like Jasper or Copy.AI or Writesonic.

Okay.

Applying Strive would mean first looking at strategic fit and alignment. How well does this specific tool actually support our goal of boosting content creation efficiency? Does it fit our brand voice needs?

Right. Then technoecnic efficacy and performance, what do independent reviews, case studies, maybe even your own small tests, suggest about the quality and reliability of this tool’s output for your specific use case.

Okay. S t then r.

R is ROI and scalability. What’s the cost? Not just the sticker price, but maybe training time too. And what kind of return on investment can we realistically expect? Mostly time savings here, maybe some improvement in content performance.

And scalability. Can it grow with us? Can we test it easily first, like with a free trial or a freemium option?

Good points. Then RE’s integration and usability. How easily does this thing actually plug into our current marketing workflows? Our tech stack, is it user friendly? Will the team actually use it?

That’s a big one. And finally, v and e.

Right. V is vendor viability and support. Is the company behind the tool stable? Reputable. Do they offer decent support if we run into trouble?

Good documentation.

And ethical and compliance alignment. We touched on this.

Yeah. This loops back to the ethical review piece mentioned in part four of the workbook and discussed in the course too. Data privacy is huge here.

What data does the tool need? Where does it go?

What about potential bias in the AI’s output? We need to think about that as module eight in the course highlights. Transparency, accountability, these all fall under EAT.

So by systematically hitting each of those STRIVE points, you build a much stronger case for why this tool is the right choice for this pilot.

Exactly. It helps you avoid that common pitfall of just picking a tool because it a flashy demo or someone else said it was cool.

Yeah. We’re just trying a free version and liking the first couple of outputs.

Right. And then deciding to roll it out without thinking through the long term costs or the integration challenges or the vendor’s track record or the ethical side.

That’s where you introduce pretty significant risks down the line. Definitely.

You might find later it doesn’t scale or it won’t talk to your other key platforms or the support is terrible when you actually need help. Strive is designed to front load that critical thinking.

Mitigate those downstream problems before they happen.

That’s the idea.

Okay. So as we start to wrap up this deep dive, what are the really crucial takeaways here for the learner and, well, anyone else starting out with AI marketing pilots.

I think the key differentiator, again and again, between a pilot that flies and one that fizzles, it often boils down to clarity and rigor.

Clarity and rigor.

Okay. Define your project scope with precision. Keep it tight, especially at first.

Start small.

Set measurable goals using that smart framework so you can track progress objectively. Know what success actually looks like.

And don’t just grab the first shiny AI tool you see.

Right. Apply a structured evaluation, something like Strive, to make sure the tool aligns with your strategy, offers real potential for return, and meets your ethical standards.

Exactly. And for the learner who’s really looking for those clear insights without getting overwhelmed, seeing these principles applied across different scenarios hopefully provides a useful roadmap. Mhmm. Because a well structured approach to AI pilots, it’s not actually about stifling innovation, is it?

Not at all. It’s about focusing your efforts so you get and crucially, the maximum learning from each step.

So you can approach these AI initiatives with more confidence. Build a stronger foundation for actually integrating AI meaningfully into your overall marketing strategy down the road.

Absolutely. It’s about building capabilities step by step.

So here’s a final thought for you, the learner. How can these core principles we’ve talked about, that focus scope, the smart metrics, the justify tool choices via Strive, the ethical check-in, how can they become your internal checklist?

Like a filter.

Yeah. A filter for evaluating any AI marketing initiative you encounter or maybe you’re considering planning yourself. Yeah. Because that well thought out small scale test that’s really the groundwork for successful, sustainable AI adoption.

Well said. And of course, for anyone wanting to go deeper, the AI marketing action plan workbook and toolkit and the AI marketing advantage course offer much more detailed guidance and frameworks.

Definitely worth checking out. So the learner, our final question back to you is this. Thinking about everything we’ve discussed, what specific narrowly defined marketing challenge are you now considering as a potential starting point for your own first AI pilot project? Nailing that focused starting point, that could be your real key to unlocking the power of AI in your marketing efforts. Thanks for diving deep with us today.