How to Align Technical Strategy With AI Goals

One reason AI initiatives stall? Few execs use AI in their own work. In 3 hours, I take leaders from “I don’t know” to a POV (co-developed with AI!) on how AI can support key strategic initiatives. To crack the code on exec adoption we: >> Focus on Strategic Use Cases that Click with Execs << To get experience with high value use of AI, we dive into cases that directly enhance executive decision-making and strategic thinking. This tends to be a major eye-opener—most leaders don't realize AI can elevate their highest-level work. Once executives experience immediate personal value, they better understand how AI can have immediate impact across the organization. >> Reframe Mental Models << Generative AI operates fundamentally differently from anything we've seen before, so we need to identify why and how digital change playbooks must shift to leverage this moment. I go straight to the heart of the silent organizational barriers that prevent productive adoption, and how to navigate a path forward. >> Start with the Business, Not the Tech << We don’t begin with AI—we begin with your business. We anchor the process with the breakthroughs that will drive real impact—and to get there, we go analog with brainstorming, whiteboards, and post-its, working to envision what advancement could look like. What could be possible if cognitive limits were lifted? What long-standing friction could finally be overcome? This surfaces a library of meaningful, business-driven opportunities. Then, using proven filters and frameworks, we zero in on the highest-impact places to start applying AI. >> Use AI to Develop AI Strategy << We then—on the spot—collaborate with AI to develop executive viewpoints on how AI can accelerate those strategic priorities. This is hands-on work with AI tools to co-create a path forward, often culminating in each group sharing a lightning talk (co-developed with AI) with the broader team. This approach fast tracks execs to: 1️⃣ Build readiness: Gain deep understanding of the new landscape of use cases today’s AI offers, and the organizational structures needed to effectively harness it. 2️⃣ Map use cases: Develop a prioritized library of strategic use cases ready for immediate collaboration with technology and data teams. 3️⃣ Accelerate alignment: Establish common language and jump-start cross-functional alignment on tackling high-impact opportunities. 4️⃣ Hands-on understanding: Acquire hands-on experience with AI tools they can immediately apply to their most challenging strategic work. What do my clients say about this approach? That their teams shift from skepticism to enthusiasm—hungry for more, and from uncertainty to clarity about the next steps. It’s a remarkable change, especially in a few hours. ➡️ Want to learn more? Let’s talk. #AIworkshop

AI is only as powerful as the strategy behind it. When I first started using AI in content marketing, I made the same mistake I see a lot of marketers make—I jumped in without a clear plan. I had all these AI tools at my disposal, but without defined objectives, I wasn’t maximizing their potential. That changed when I started treating AI like a strategic partner, not just a tool. Here’s how I approach AI integration in my content marketing workflow: 📍 Set clear marketing goals – Before touching AI, I define the business outcome I want. More traffic? Higher engagement? Improved efficiency? AI needs direction. 🎯 Create SMART AI objectives – Vague goals like "use AI for content" don’t work. Instead, I aim for something measurable: "Increase our blog’s average time on page by 20% in three months using AI-driven headline optimization." 🔗 Align AI with strategy – If AI isn’t helping me scale content, improve quality, or enhance personalization, it’s not the right fit. I focus on AI that amplifies what’s already working. 🤖 Use AI where it makes sense – I let AI handle repetitive tasks like keyword research, content outlines, and SEO recommendations, so I can focus on high-level strategy and creativity. 📊 Measure AI’s impact – AI should drive real results. I track performance metrics, analyze what’s working, and tweak my AI settings accordingly. 🚀 Iterate and improve – AI isn’t set-it-and-forget-it. I review performance regularly and adjust my approach to keep improving. AI works best when it’s guided by strategy. If you’re using AI in content marketing, how do you ensure it’s actually moving the needle? 

Where is the ROI From AI? Glenn Hopper, Hyoun Park and I hashed out where and how to improve the return on your investment. Some excerpts: ➡️ On Goals: One of the most common ways of measuring the ROI of AI is through efficiency gains. Automation and faster task processing hold the potential to free up time and resources. But AI can also lead to new activities and insights beyond what we are doing today. “If we just treat AI as a massive productivity enhancer, then we’re missing the point,” said Hopper. “Can AI process these thousands and tens of thousands and millions of manual checks?” said Park. “Where are you trying to find needles in a haystack? That’s where AI can provide some real value.” ➡️ On Alignment: It’s critically important to make sure that the goals of your AI project match the company’s goals. At a strategic level, “tie AI initiatives to business goals and prioritize the impactful use cases,” said Hopper, and build C-suite support to ensure focus and cross-team alignment. Then, at a process level, take a step back and figure out where AI fits into the workflows. Data integration, application integration solutions that may already have workflows in place, and closed automation solutions are areas that lend themselves well to the use of AI. ➡️ On Scaling: When scaling AI, it's crucial to consider your classic computing or IT aspects in terms of storage and network to avoid overprovisioning. “You don't want too many duplicate resources all doing the same thing,” said Park. “You don't want to use a model that’s overkill for the type of use case you’re employing.” Smaller, custom-built agents are often more cost-effective than using large models (200-300 billion parameters) for simple tasks. Also, be sure to consider storage costs for AI outputs, as necessitated by governance and compliance rules. “You need to take care of the storage because you're probably going to have to maintain the outputs,” said Park. “It's important to do that due diligence and just make sure that your AI approach does not lead to massive overruns on your intended budget.”