Full Context Operations: The Final Automation Priority

TL;DR: We just released the final episode of our automation series featuring Tree Lindemann-Michael from the WWT AI Ops team. He breaks down why "full context operations" is the capstone priority - and why you can't just jump to AI without building the scaffolding first. Watch the full episode or check out the key takeaways below.

Tree is a practice lead for AI Ops and observability at World Wide Technology, helping field sales teams tackle the big, hairy questions in the tumultuous world of IT operations. And honestly? He's the perfect person to close out our automation series.

We saved full context operations for last because there's a reason - you need scaffolding. You need pipeline. You need to understand how bits and bytes actually translate to dollars and cents before you can even think about marrying all that organizational context into something your operations team can actually use.

Here's what Tree taught me about getting your IT house in order:

The Simple Definition That Changes Everything

When I asked Tree to define full context operations, he gave me the best business card explanation I've heard: "The entire IT organization working in lockstep."

Right now, most of us are stuck in the traditional monolith. Network does network things. Storage does storage things. Cloud does cloud things. And they're not talking to each other at a high level. The context about how all these pieces actually relate to each other? It's missing from our view.

We understand our business from a hardware perspective - are the devices up, are they reporting data - but we don't really know if that network device over there is causing our application to lose capacity. That's the context we're missing.

Why This Matters Now More Than Ever

Two things are driving this urgency. First, the sheer scale of operations. Hyperscalers have hundreds of thousands of devices in the wild, creating a need for a real source of truth about what's actually deployed and how it's being managed.

Second, AI is burgeoning into our industry. If you're not looking at some AI solution, your competitor is. That nimbleness - being able to pivot when you need to - is incredibly valuable. But you can't get there without understanding what you have first.

The Four Steps That Actually Work

Tree walked me through four practical steps outlined in our research:

1. Start with a Critical User Journey

Traditional monitoring focuses on up/down status, but that doesn't tell you if your application is doing its job - supporting the business mission and generating revenue.

Take a checkout button on an e-commerce app. If I can't pay because of a 404 error, how does the business know that technical hiccup just cost them money? You really can't answer that with traditional monitoring because we're all stuck in bits and bytes.

The critical user journey gets engineers thinking like the business: Why are we paying someone to build this application? Because it makes money. And that's measurable.

2. Attribute Business Value to IT Systems

This is dependency mapping - identifying each link in the chain and understanding its relative weaknesses. Software is really just handing off data between systems, and these serial events can be measured as discrete units.

The hard part? You usually lack the required data to answer these questions initially. You spend time putting measuring sticks in place. But here's the key: you're doing this with automation so that once you do it for one system, you can roll it out to others quickly.

3. Establish a Baseline for System Coverage

This is hypothesis-based experimentation for monitoring systems. Instead of operating on feelings and best guesses, you create controls, make experiments, and validate with data.

Tree's software analogy hit home: When you inherit a monolith project without tests, it's dangerous. You do black box testing, gather baseline outputs, and suddenly you can have data-backed conversations instead of emotional ones.

4. Foster Collaboration

Here's where it gets interesting. Tree shared a story about a large oil and gas company rolling out zero trust laptop images. When campus IT calls spiked, they got 35 people in a room to map the 15 different gates between end users and the internet.

Turns out only six of those 15 gates were actually monitorable. The rest were "ethereal things people just hoped worked." This discovery phase - getting all your verticals together to understand what actually needs monitoring - is essential.

The Practical Advice That Stuck

Start with your network team. They see everything but don't always know why, making them perfect for building initial momentum. They're the glue between all services and can identify patterns even if they don't understand the business context.

Don't skip the scaffolding. Tree was clear: throwing AI at bad data only accelerates bad outcomes. You need clean, accessible data and full understanding of your operations before AI can help.

Think in terms of onboarding. Tree's challenge: "Most businesses take two weeks or longer to onboard a new employee. That should be a one-day activity that's fully automated." That's dollars and cents right there.

The Reality Check

Tree's parting wisdom centers on three critical questions:

1. What physical IT assets exist that prop up your business?
2. How is data from all of that being combined to make business insights easier to answer?
3. How ready are you for AI to consume that data?

Because here's the thing - AI is happening whether you're ready or not. The question is whether you'll be throwing it at a foundation that can actually support what you're trying to build.

Why This Episode Matters

This isn't just another automation discussion. It's about understanding that good automation looks like AI - it appears intelligent and responsive to dynamic changes. But in reality, it's thoughtful architecture that understands how your business consumes technology to meet its mission.

The stakes are too high now. Tool costs are high. The cost of not being able to do business is high. You can't just throw people at problems anymore. You need systems that work together, and you need to be able to measure whether they're actually working.

Check out the full episode to hear Tree's complete breakdown of full context operations and how WWT can help you build the scaffolding your organization needs.