Originally published by The AI Journal on April 26, 2025
Too many companies are racing to define their “AI strategy,” as if artificial intelligence is some new business function that needs a dedicated lane. But the real opportunity isn’t about what you can build for AI—it’s about what you can unlock with it. The smartest organizations aren’t rewriting their playbooks from scratch. They’re using AI to accelerate, enhance, and sharpen the strategies they already care about.
This isn’t about replacing fundamentals. It’s about getting more leverage on the things that already drive impact.
Here are four core pillars of business strategy that are being transformed—not replaced—by working with AI.
1. Keep a Live Pulse on the Market (and Make it Actionable)
Every company tries to track what’s happening around them—competitor moves, emerging customer needs, shifting cultural signals. The problem is, most of that happens sporadically, with a heavy reliance on manual analysis, anecdotal insight, or high-level macro indicators.
AI changes that. Today, intelligent systems can sift through thousands of unstructured sources—Reddit threads, local news, LinkedIn posts, investor decks, product reviews—and convert that chaos into structured, directional insights. You’re not just reading content or collecting data; you’re mapping the market in real time.
The added value? These insights aren’t buried in a quarterly report—they can be delivered to the right teams at the right time. Some companies are even building “living” models of their market environments–constantly refreshed, customized by audience, and embedded into everyday workflows. The outcome is a strategy that doesn’t just respond to change—it contextualizes and anticipates it.
2. Elevate the Value Proposition (Not Just the Toolset)
AI can certainly enhance tools and automate tasks. But its real power shows up when it prompts a deeper rethink of how you create and deliver value.
Take, for instance, a healthcare brand that initially set out to build a product recommendation chatbot—something smart and lightweight that could guide customers to the right supplement or service. As the project progressed, the team realized the same underlying personalization engine could support onboarding, behavior change, educational nudges, and even care team handoffs. The chatbot didn’t just improve customer support—it became a doorway to reimagining the entire experience.
That kind of pivot isn’t about chasing the next tool. It’s about looking at your business through a different lens: now that we can personalize at scale, how else might we create a deeper, better relationship with the people we serve?
3. Experimentation is King (and Now You Can Do It Smarter, Faster)
One of the most powerful shifts AI brings is speed—not just in output, but in learning. Traditional experimentation takes time. You come up with a new message or offer, build the assets, run a test, wait for results… and often learn too little, too late.
AI changes the rhythm. With synthetic data and intelligent agents, you can prototype narratives, simulate reactions across segments, and generate tailored campaigns at a pace that was unimaginable a year ago. (Personal note: I’m old-school in some ways—I still love hearing directly from real people out in the world. But AI can help with that too: transcribing interviews, summarizing themes, even surfacing sentiment you might have missed.)
This shift is already reshaping creative and go-to-market teams. We’re seeing the rise of “vibe marketing”—a parallel to the “vibe coding” movement that gave us platforms like Replit, Bolt, and Lovable. Just as one developer with the right tools can now build and ship a new product in hours, one marketer with the right AI stack can 100X their output– e.g. spin up landing pages, test angles, generate collateral across channels and automate end-to-end workflows with speed and precision.
Emerging tools like PhantomBuster, Jasper, and OpenChair are enabling highly specialized, niche automation for media testing, competitive tracking, and persona-driven messaging. The direction is clear: fast, lightweight, focused systems that do one thing really well. The agency of the future might be one smart person and a “room” full of purpose-built agents.
4. Execute Better, Faster (With Tools You Design)
Every organization wants to move faster and reduce friction. But it’s not just about automating more—it’s about customizing tools that work the way your teams do.
In some forward-leaning companies, teams are building internal libraries of GPT-style agents tuned to specific workflows—from customer service to product research to compliance. In one example, a growth-stage startup built over 100 internal agents, each supporting a specific business process. More importantly, the functional teams themselves drove the design—flagging tedious repetitive tasks, brainstorming better flows, iterating on what worked, and benefiting from the AI leverage.
The result? A culture of active optimization, where AI isn’t imposed top-down, but developed ground-up in service of the work that actually needs doing. Building smarter tooling became everyone’s job.
And the long-term effect? Less internal drag. Fewer handoffs. More time focused on creative and strategic thinking—the stuff humans are still uniquely good at.
Reality Check: You Still Have to Change (Just Not Everything)
Of course, working with AI doesn’t mean business as usual. Some shifts are non-negotiable:
- Teams need to build new muscles—prompting, interpreting results, and course-correcting rapidly.
- Strategy has to move from static planning to continuous, feedback-fed evolution.
- Data systems must become more integrated, so insight and execution aren’t siloed.
- Proprietary advantage will increasingly depend on how companies use, integrate, and learn from their own data. Closing the feedback loop—between what your AI outputs and what actually works—creates better results, better models, and better strategy.
In short: the fundamentals stay, but the game speeds up. The teams that win will be the ones that can adapt in-flight, not just in the offsite.
Conclusion: Build with AI, Not for AI
The companies getting ahead right now aren’t the ones spinning up isolated AI pilots or innovation labs off to the side. They’re the ones embedding AI into the heart of what they already do—understanding their market better, elevating the customer experience, iterating faster, and executing with less drag.
You don’t need an “AI strategy” that lives apart from the rest of your business. You need a strategy that uses AI to get sharper, faster, and more responsive. Don’t build something for AI. Build something better with it.