AI Starts with Human Expertise – Here’s How to Capture It

You can’t effectively automate what you don’t fully understand. That’s what most teams run into when trying to scale expertise with AI. The workflow seems simple…then you start documenting them and then nuance, judgment calls, and micro-decisions start to surface.

Let’s lay out how to capture that “unspoken” expertise, collaborating with subject matter experts (SMEs) and design internal documentation that easily translates into decision logic that AI can actually use. 

You’ll be equipped with actionable techniques, like:

• Turning subjective, gut-feel decisions into clear, objective criteria
• Using shadowing sessions to uncover the micro-decisions your SMEs make instinctively
• Building documentation that actually enables AI to replicate expert thinking
  
  

Scaling Human Expertise by Capturing Micro-Decisions

To design agentic workflows, you first need to deconstruct human expertise. This is at the root of building the systems that can make nuanced, context-rich decisions the same way your experts do. Your goal? Take those small, intuitive choices that happen in the moment and turn them into something consistent, repeatable, and reliable.

Search marketing, for example, isn’t just about plugging keywords into tools. Search marketers make hundreds of small, nuanced decisions. Which websites are ranking? What features show up on the SERP? What is the breadth or depth of content? How is the searcher’s intent shifting? What do they need to solve their problem or find the right solution? When you review all of these datapoints together, what problem or solution does it point to?

Just like training a new team member, you can’t assume that AI agents will just "get it." You have to approach teaching them the same way you’d teach a human. Breaking down the thought process and making those intuitive decisions explicit. It’s not enough to hand over directional documentation and expect accurate results. You wouldn’t fault an intern for using the wrong approach when you never gave them direction. AI is no different.

You have to capture the logic behind each decision and make it clear, consistent, and repeatable. That way, your automated workflows are as functional as they’re reliable and grounded in real expertise.

We’re Seeing Faster Insights, Better Results

By automating key aspects of our own workflows, we’re giving our team the freedom to focus on what they do best: developing creative, impactful strategies for clients. AI won’t replace the human element but will augment it, allowing us to:

• Speed up research and recommendations by automating repetitive tasks
• Eliminate individual bias and deliver consistent insights across industries
• Align more closely with searcher intent to improve rankings, conversions, and revenue

Our automation initiatives are about unlocking our team’s full potential. Don’t slow down your innovation because of the commentary about replacing humans – use the below to scale your team’s expertise. By embedding our collective knowledge into repeatable frameworks, we leverage diverse insights from across the team. AI amplifies human expertise, delivering smarter, more consistent results at scale.

How can you start automating your most complex workflows?

So, how do you get started when the most important workflows feel complex or high-stakes? Don’t jump straight into prompts or tooling. Start by understanding and mapping the thinking behind the processes that power your best work. 

1. Conduct a Disruption Analysis

The first step to automating your expertise is to complete an AI disruption analysis — a review of all of the work your organization or department executes with each individual deliverable, workflow, or process scored against the same rubric:

• How generative is the output
• How predictive is the output
• How data-driven is the output
• How repetitive is the process

You should also include some value or effort metrics like associated revenue, time spent, or complexity.

After scoring, you’ll be able to see which processes have the greatest opportunity. Either low-hanging fruit (items with the highest propensity for disruption) or by value (by revenue or time savings with automation).

2. Capture Unspoken Expertise

Building effective AI solutions requires meticulous research, detailed planning, and a thorough understanding of the processes we aim to replicate or enhance. You might be surprised that even the most detailed documentation often falls short!

This is because most internal documentation is designed assuming a baseline of knowledge. We often see this in practice at Seer. While we have documentation or requirements across workflows or deliverables, that documentation is designed for our team of subject matter experts, strategists, and consultants. People who have the experience and are already familiar with the nuances and micro-decisions that drive great outcomes.

When we completed user shadowing and asked our team members to describe their actions and thought processes aloud, we discovered just how complex and nuanced their processes really are. What seemed straightforward on the surface quickly unraveled into dozens of micro-decisions, choices that live in our team’s heads, built from years of experience. These aren’t things you can easily explain or replicate without some serious effort, and they’re part of what makes our work so valuable.

TIP: Record transcripts of users describing their actions and thought processes aloud during user shadowing sessions

3. Translate subjective decisions into objective logic

SMEs rely on their experience to make fast decisions. What this often looks like is an internal definition of good/bad, built up internally over time - a personal strategy playbook.

During user shadowing, make sure you hone in on these types of decisions, which are often flagged with subjective statements. You can use the 5 Whys technique to get beyond the surface-level opinions and better understand all of the factors they are using for their internal definitions.

Here’s an example of this in action:

TIP: Keep an ear out for subjective statements and use the 5 Whys to turn opinions into actionable rules

💡 This subjective vs objective is when I usually see people get frustrated with AI. They give subjective rules ... and then AI had a different definition for "good or bad", etc. — and the person never defined what "good" meant from the start. For instance, if you told me something was "low effort" in terms of turnaround time, my definition of low effort might be 1 day. But yours might be 1 week. If I don't ask you what low effort means to you, we're not going to be on the same page for delivery time.

4. Improve your documentation

Now that you’ve documented the process, improve the existing documentation with your research for a quick win while you’re working through the long-term solution. You may see some incremental efficiencies from more robust documentation, especially when training your next generation of experts.

Those subjective rules? You can turn those into sights & sounds sections or decision trees in internal resources so that the innate knowledge and expertise is shared more widely.

TIP: Don’t just use your research for training AI - improve your documentation for incremental efficiencies

What’s Next? Building the Future, One Prototype at a Time

You don’t need to automate everything tomorrow. Start with one high-impact workflow. Build a prototype. Pressure test it. Does the AI output match your team’s best thinking? If not, what’s missing?

We believe the future of search marketing belongs to companies that can blend technology with human expertise – we’d love to show you how we’re helping teams like yours. Let’s chat!