Where to Start With AI: The Question Most Teams Skip
Not sure where to start with AI? You're not alone. The answer isn't picking a vendor. It's naming your workflow pattern first.
Name Your AI Workflow Before You Build It
You wouldn't start a journey without knowing two things: where you are and where you're trying to go. The vehicle you need depends entirely on the answers. Ocean crossing, mountain climb, daily commute....different journeys, different requirements.
AI implementation works the same way. The way humans and AI work together falls into distinct patterns, and each pattern has different requirements, different failure modes, and different context needs. But most companies skip the map entirely. They start with "we need AI," then chase vendors, run pilots, and wonder why the outputs feel generic or miss the point.
We're past the experimentation phase now. The companies that spent 2024 and 2025 running pilots are now trying to operationalize AI across real workflows, and they're discovering that capability isn't the bottleneck. Knowing what you're actually building is.
Before your next AI implementation, consider the trailhead question: Which workflow pattern is this, and what context does it need?
If you can't answer clearly, you're not ready to build. Here are five patterns to help you figure out where you are.
Five Patterns of Human-AI Work
The Watch
Bounded AI autonomy with no human in the loop.
Customer service agents. Pricing optimization. Content moderation. Fraud detection. These are domains where AI operates continuously within defined constraints, making decisions without human review of each action.
The question to ask: What tells the AI what's happening outside its task?
A CX agent that doesn't know a competitor just launched a lower-priced alternative will fumble objections. A pricing algorithm that doesn't know the market shifted last week will optimize for conditions that no longer exist. The AI does its narrow job fine, but nobody told it the world changed.
The failure mode: Perfect execution of an outdated playbook.
The Current
AI acceleration of existing manual processes.
Document processing. Code review. Data cleaning. Report generation. These are known processes with known outcomes where AI removes friction and increases velocity. The human is still doing the work; the AI makes it faster.
The question to ask: What makes sure faster is actually better?
Moving faster in the wrong direction isn't efficiency. If you're speeding up a process that competitors have already leapfrogged, or optimizing a workflow that shouldn't exist at all, you're just getting to the wrong place quicker.
The failure mode: Running faster toward the cliff.
The Relay
Handoffs between human and AI team members toward a fixed goal.
This is the most common pattern in enterprise AI today: a human starts something, an AI continues it, another human reviews it, an AI refines it. Marketing strategies, sales proposals, research synthesis. Anywhere work passes through multiple hands (human and artificial) on the way to a defined outcome.
The question to ask: What carries meaning across the handoffs?
Every relay lives or dies by its baton. When context gets lost at each pass, when the AI doesn't know what the human was really trying to accomplish, when the next human doesn't know what constraints the AI was operating under, you get work that checks the boxes but misses the point.
The failure mode: Each handoff produces something technically correct but strategically off.
The Expedition
Joint human-AI exploration toward uncertain outcomes.
R&D. Market discovery. Scientific research. Strategic planning under uncertainty. This is work where neither human nor AI knows the destination, where the goal is to find what you didn't know you were looking for.
The question to ask: What shows you where others have already been?
The worst expedition outcome isn't failure. It's "discovering" what competitors found years ago. It's getting excited about territory that looks like white space but is actually a graveyard of failed attempts. You need to know what's already known to recognize what's actually new.
The failure mode: Reinventing the wheel. Mistaking your own blind spots for genuine opportunity.
The Deep
Autonomous AI exploration with uncertain outcomes.
Emergent strategy generation. Unsupervised pattern discovery. Autonomous research agents. This is the frontier: AI operating with minimal human involvement in domains where the outcomes can't be predicted.
The question to ask: What keeps the AI connected to what actually matters?
Without some grounding, autonomous exploration becomes aimless drift. The AI finds patterns nobody cares about, optimizes for metrics that don't matter, surfaces discoveries disconnected from any real business need.
The failure mode: Impressive but useless. A lot of sophisticated work that doesn't connect to anything you're trying to accomplish.
The Common Denominator
Here's what becomes clear when you map these patterns: every single one requires external context (competitors, markets, strategic positioning) to produce value instead of noise.
- Watches need context to know when the world outside their task has changed
- Currents need context to make sure speed is pointed the right direction
- Relays need context to keep the original goal intact across handoffs
- Expeditions need context to tell new discoveries from old news
- The Deep needs context to stay connected to real business priorities
This isn't a capability problem. The models are plenty capable. It's a context problem. We've built powerful AI systems and connected them to internal data, but we haven't connected them to the external reality they need to operate intelligently.
The Implementation Sequence Most Companies Get Backwards
The typical AI implementation sequence:
- Select a use case
- Choose a model/vendor
- Connect internal data
- Deploy
- Wonder why outputs feel generic or miss the point
The sequence that actually works:
- Name the workflow pattern (Watch? Current? Relay? Expedition? Deep?)
- Identify what context that pattern needs
- Build or acquire that context layer
- Select a use case
- Choose a model/vendor
- Deploy with the context in place
The second sequence is the difference between AI that executes tasks and AI that executes strategy.
The Trailhead Question, Revisited
You asked it at the start. Ask it again now: Which workflow pattern is this, and what context does it need?
The companies that win in 2026 won't be the ones with the most AI deployments. They'll be the ones who knew the answer before they started.
How Strata Helps
Strata builds the context layer that makes each of these workflow patterns actually work.
We transform external intelligence (competitors, markets, positioning) into structured Context Shells that plug into your AI workflows in under two minutes. Think of it as Layer 0: the foundational context that sits beneath your agents, your automations, and your human-AI teams.
For the Watch: Context Shells tell autonomous agents what's happening outside their task, so they don't execute yesterday's playbook in today's market.
For the Current: Context Shells make sure that when you're moving fast, you're moving toward the right goal and away from competitive threats.
For the Relay: Context Shells keep strategic intent intact across handoffs, so every leg (human or AI) knows not just what to do, but why and against whom.
For the Expedition: Context Shells show where competitors are, where they've been, and where they're heading, so your team can spot actual white space instead of well-trodden ground.
For the Deep: Context Shells keep autonomous exploration connected to what your business actually needs.
The pattern doesn't matter if the context isn't there. We make sure it is.