Remember those Capital One ads? “What’s in your wallet?”
This was a crafty question because people were carrying wads of cards—one for gas, one for groceries, one for miles, another for cash back. Different cards for different reasons, but no real strategy. And their question made people pause and think.
We are at a similar moment for AI tools. So we ask: what is in your AI stack?
Here’s what’s happening in enterprises right now: Marketing saw a YouTube video and bought Jasper—they want to move fast and break things. Engineering quietly added GitHub Copilot to the budget. Sales is running Clay pilots that IT doesn’t know about. The CIO is blocking everything because “security and compliance.” Workers are updating their resumes because they think AI is coming for their jobs.
Too many providers, not enough clarity, and zero shared language for what AI should actually do in your organization. The result? Decision-making chaos. Governance nightmares.
When the CEO finally asks, “What’s our AI strategy?” everyone points to their favorite tools and calls it AI transformation.
The real problem isn’t that you lack AI tools. It’s that you lack an AI posture.
You need an AI posture—not a stack of AI solutions that do not support each other. You need clear thinking and a strategy for where AI adds value to your enterprise.
One of the challenges is that you can’t build strategy without language. We use frameworks to organize our world. “Cloud vs. on-premise.” “B2B vs. B2C.” “Waterfall vs. Agile.” These aren’t just labels—they’re shared mental models that let teams make coordinated decisions.
AI needs the same clarity. The way out of anarchy is organizing what’s out there. Between sensational evangelists and apocalypse preachers, it is a Tower of Babel out there. Hence, this framework—to help you discern and decide.
Here’s how to think about it. Every AI tool falls into one of six categories based on two questions:
These two axes give you a simple but powerful way to classify every AI solution in your organization and see where you are over-invested, under-invested, or misaligned with your risk profile.
Examples: ChatGPT, Claude, Gmail Smart Compose.
These tools suggest the next action, and you manually apply it. They are good for augmenting individual productivity without changing underlying workflows. They keep humans fully in control, which makes them a low-friction entry point for most teams.
Examples: LinkedIn Job Posting, Calendly AI.
These are AI features embedded in tools you already use. They draft or propose; you review and publish. This is low-friction adoption—you don’t “add a new tool,” you unlock more value from existing systems while keeping an approval step.
Examples: ElevenLabs voice agents, ClayGen research agents.
These solutions handle fully automated execution. You get the output; you’re not involved in the process. They are best suited for repeatable, low-stakes tasks, where speed and scale matter more than nuanced judgment.
Examples: Perplexity AI, Microsoft Copilot.
You ask; they answer. Copilots support research, analysis, and strategy work where human judgment matters more than speed. They are powerful for knowledge work but still place humans at the center of every decision.
Examples: Claude Code, AgenticPM.
AI Workers execute multi-step workflows but check in before major decisions. They are autonomous enough to save serious time, yet supervised enough to maintain control. This is the sweet spot for most enterprises—and especially for regulated, complex domains like private markets and financial services.
Examples: Clawdbot, Devin.
You set it and forget it. These agents self-direct through complex tasks with minimal intervention. They are high leverage, high risk. Most companies aren’t ready—organizationally, technically, or from a risk and compliance standpoint—to deploy these at scale in core workflows.
The mistake enterprises are making today: buying tools in all six quadrants without any discernible alignment on AI posture.
There is no coherent view of:
What is needed: you should adopt an AI posture informed by your team’s skills, enterprise risk tolerance, and operational maturity. Otherwise, AI becomes an unintended consequence of ad hoc purchasing decisions rather than a deliberate competitive advantage.
Your AI posture is the deliberate set of choices about where AI adds value, who controls it, and how it evolves with your organization. It’s not a tool list—it’s a strategic framework that answers four questions.
To make this concrete, here’s how different sectors might answer these questions and translate them into posture.
(Consulting, Legal, Accounting)
Notice what’s different in each example? The AI posture is a response to the business reality of the enterprise—its revenue model, risk profile, regulatory context, and talent base.
Without this clarity, you’re all over the place:
Companies that win won’t have the biggest AI budgets. They’ll have the clearest AI posture—knowing exactly what they need AI to do, who should approve it, and how to grow their capabilities over time.
So: What’s in your AI stack? And more importantly—should it be?
The next step is not to buy another AI tool. It’s to audit your current stack against this framework, define your AI posture, and then align spend, governance, and implementation accordingly.
We’re building AI Workers for Private Markets that execute complex workflows with human oversight. These workers are designed for the realities of private equity and private markets: multi‑step processes, high-stakes decisions, and non‑negotiable governance.
If you’re ready to move beyond assistants and into strategic automation—where AI Workers handle the heavy lifting while your team retains control—it’s time to talk.Try for free here.