Digital Marketing hasn’t seen a shake-up this big since the evolution of mobile.
2024 feels like a rollercoaster: AI-driven search, new platforms, and algorithm shake-ups hitting faster than we can blink. Exciting? Absolutely. Chaotic? You bet.
The evolution of artificial intelligence (AI) has sowed doubt and uncertainty for many. New platforms for consumer search are turning heads and raising questions. And of course, all this has occurred amidst some serious changes, with core algorithm updates also happening this year.
If you’re a team of one, or even on a small marketing team, I can imagine the decision paralysis you’re feeling right now. Where do you even start?
Training your department or organization on how to safely and effectively use artificial intelligence is a valuable use of time. Right?
Not so fast. You can’t train people on tools they don’t have access to, which requires capital investment and opens questions around ROI. Okay, surely picking a tool is simple…
Or is it?
- OpenAI emerged as an early enterprise favorite
- Google Gemini is quickly catching up
- Meanwhile, Anthropic’s Claude has emerged as a great option for enterprise
While you’re making a decision that feels akin to Bounty vs Brawny paper towels, you’re likely also being asked to track what’s up with these new AI Overviews everyone is talking about.
Unfortunately, Google won’t supply us with data there, so we’re heads down once again to figure out how to track all of this.
Meanwhile, your boss’s boss’s boss thinks your audience is searching for your services on ChatGPT more than Google and wants to know how to optimize over there.
Exciting? Yes! Exhausting? Also yes!
We feel it too. At Seer, that’s where our AI Council comes in.
We don’t have the R&D war chest of venture-backed giants, but what we lack in funding, we make up for with an almost unhealthy desire to win for our team and our clients. To learn more about what drives us, check out our most recent impact report.
And because we’re always looking for opportunities to help, we’re also eager to share: what we’re seeing, what we’re doing, and what we think is coming.
So, as VP of AI and SEO, I’m going to start sharing our notes so we can work together to figure out what’s next for marketing and AI, and in hopes that even more organizations will share theirs. Let’s push beyond LinkedIn clickbait and get real about what is an opportunity today, in Q4 of 2024, and when we should apply the ‘Wait Calculation.’
AI disruption isn’t slowing down, and neither are we.
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Follow along below in this first set of field notes as we outline what we’ve learned from our Disruption Analysis, and how we’ve made progress on this initiative over the past six months.
What You’ll Get From This Blog
In this post, you’ll learn how Seer is uncovering opportunities to transform workflows across SEO, Paid Search, Analytics, and Creative teams. Explore actionable insights, real-world examples, and practical strategies to prioritize AI integration.
Jump to a Section
- Step 1: How to Identify AI Opportunities
We reviewed 170 deliverables and workflows to uncover opportunities for AI integration with a Disruption Analysis - Step 2: How to Prioritize AI Investments
Our approach to prioritizing scaled AI solutions without overwhelming the team - Step 3: Rolling out the Basics
We identified three base-level GPTs that impact 80% of our deliverables - Step 4: Planning Where We Go From Here
How we are preparing for the future of AI in marketing, and how you can, too
Step 1: How to Identify AI Opportunities
Our first step was a disruption analysis. A disruption analysis is an A-Z 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, focusing (for now) on text generation
- How predictive is the output
- How data-driven is the output
- How repetitive is the process
The Marketing AI Institute can be credited with the concept, as well as the initial guidance in how to set up this effort.
At Seer, we put our own spin on it from there.
How We Conducted Our Disruption Analysis
At Seer, we analyzed ~170 deliverables and workflows that our team handles regularly. Here’s what we looked at:
- Revenue Impact
How much revenue is tied to these deliverables?
Why it matters: This indicates business priority. - Time Investment
How much time did we spend on these deliverables in the last 12 months?
Why it matters: This highlights the biggest opportunities to drive value. - Process Complexity
How many steps are involved in each deliverable?
Why it matters: Complexity often signals where AI can simplify and streamline workflows with a 1:1 replacement. - Business Priority
How critical is each deliverable or workflow to our goals?
Why it matters: Not all work is created equal, and alignment with business leaders is key.
We scored all of the above to arrive at a prioritized list of opportunities.
Perhaps one question this leads to is “Who should be responsible for the disruption analysis?” and to answer that, we’ll cover more details of how we have created our AI Council in a further post.
Step 2: How to Prioritize AI Investments
Once we had our full list of opportunities, we had further decisions to make. We are but a small team, and starting at #1 on the list of 170 opportunities didn’t make much sense.
Picture this: We spend weeks working on that opportunity.
Weeks spent, one shiny outcome at best. Or nothing at all.
That’s not a strategy—it’s a gamble.
Further, does the broader team have the stamina to witness one-by-one changes, every other week, for the next several years?
Even if we were able to update these deliverables once per week (an unrealistic target to be sure when you factor in R&D, validation testing, user acceptance testing, change management, and impact measurement), the most we could get through is about 50 per year.
If you know Wil, do me a favor and think about what his reaction might be if you told him you’d have AI integrated at Seer by ~2030 at the earliest. To put it mildly, even that “best-case scenario” is unpalatable.
Identifying single steps found across many processes
Since a ~6 year evolution won’t do it for us, we had to find another way. This brings us to where we are today.
Seer’s AI Council identified 3 ‘base level GPTs’ that can impact over 80% of our deliverables at Seer. Before I get to those base-level GPTs, I want to ensure it’s clear how you can follow this approach.
By identifying a few opportunities that impact many workflows, we ensure we are thinking about scaled solutions.
We may not get it right, but if we do, the impact is immense.
Equally important, the ability to implement change management is much easier. We can teach the team how to do one thing and instruct them to do it across 60 different deliverables. Sounds better than learning 60 new processes, right?
Across your organization, there are likely dozens of things you and your team do. They’re varied, they’re unique, and presumably, they are valuable.
Once you take inventory of these opportunities and identify potential opportunities for AI integration, it’s critical to break down the steps taken within these workflows.
Hopefully, you already have this mapped and documented as part of your operations process. If not, it’s a worthwhile activity to do so.
Prioritizing the right areas of focus for your organization
Once you’ve got all of those process steps documented, you’ll likely immediately start to see some themes emerge.
Your next step is to identify a short list of themes you can prioritize impacting.
The data you have collected should guide you, but ultimately your recommendation should be made by identifying your most critical need.
Here are a few examples of what that might look like:
Team Buy-in: Perhaps your team is *not* bought in on the merits of AI, and reluctant to change. In this instance, I would prioritize initiatives that will help them see the light. It needs to click for each individual. Yes, there are all kinds of things AI gets wrong. But there’s power there, and you need to see it to believe it.
Financial Performance: Maybe you’re getting squeezed on pricing and you need to make some big changes, now. In that case, I would focus on opportunities that you feel most confident will have a big impact on efficiency. Review your tasks and themes and create some hypotheses.
Innovation: Lastly, maybe you’re an organization trying to disrupt an industry. You need to make some big bets and create something that will turn heads. It’s high risk and high reward, but the potential for domain experts to innovate has never been greater.
At Seer, we’re in a fortunate position. Our team is bought in, our financial performance is strong, and we continue to innovate rapidly. Our approach models one we observed from an unlikely source: Moderna.
In this case study, Moderna shares how they were able to integrate AI into all parts of the business and drive innovation from within. They had me at ‘drive innovation from within.’ The team at Seer is brilliant, and we know if we can upskill them effectively they will build the right solutions for the right problems.
As such, our approach to AI integration at Seer is guided primarily by a desire to help the team evolve into next-gen marketers and, in doing so, ensure we’re delivering next-gen performance to our clients.
Ensuring we are starting a fire, not filling a bucket
This concept was borrowed directly from Moderna’s case study. We don’t want to build a system that has the team living hand-to-mouth with change management.
In other words, we can’t just position a small team to build everything. There is immense nuance in performance marketing across different industries, businesses, and marketing strategies. There will be a need for customization.
This is another advantage of approaching the problem with base-level GPTs. We are basically building open-source frameworks for AI workflows (in this case, largely custom GPTs).
This means the team can take what we release and customize it as they see fit. Perhaps there is a significant amount of nuance requested for a certain client’s content review. In that case, the team can start with our base-level build and add to it.
This also helps the team use AI solutions with some training wheels. Directly prompting an LLM like ChatGPT is extremely powerful and useful. And, it can be a bit intimidating. It’s easy to go down the wrong path and get an output that isn’t helpful at all. We want to help our team avoid that as much as possible.
Step 3: Rolling out the Basics
With all that background out of the way, let’s get into the base-level GPTs we identified as opportunities at Seer.
Remember, we landed on three base-level GPTs that will impact 80% of our deliverables at Seer. The work certainly doesn’t stop there. We don’t want to lightly insert AI into our processes, we want to transform the way we work.
And while transformation is exciting, it comes with responsibilities—keeping our client data safe and secure tops the list. While we feel confident in the securities provided by our tool providers, we understand some organizations do not.
- For organizations comfortable using AI with nonpublic data, we’re building solutions that make it easy.
- For organizations that aren’t? We’re building options for them, too.
We’re also realistic about the nuances that come with client work. What looks great on paper often shifts in practice. And that’s fine. This is about incremental change, small wins that add up to big shifts over time. And not for nothing, the Wait Calculation continues to be on our minds.
Seer’s consideration set for base-level GPTs
Before I highlight the base-level GPTs we are pursuing, I thought it would be helpful to share the full consideration set we reviewed.
We still plan to attempt to build all of these, and if others in the industry have built their own, we would love to see what you’ve done.
Internal Reviews & Revisions
At Seer we’re committed to delivering polished, QAed, and impactful work to our clients. It’s important, and it’s also very time-consuming.
One of our most frequently executed steps includes an individual getting feedback from their manager or project lead. There are many different flavors of this.
- Analysis Pause Points: Built-in checkpoints during projects to catch errors early and avoid rework.
- QA Checklists: Balance quality standards with creativity for all deliverables.
- R1/R2 Revisions: Embrace feedback to adapt to shifting client needs and improve outcomes.
Revisions are a natural part of getting it right. At Seer, we lean into feedback to make the final output even stronger.
Presentations
At Seer, we make a lot of presentations. Why? Because stakeholder buy-in moves the needle—and stakeholders love a good deck.
But a deck isn’t just slides. It’s an opportunity to create something effective, compelling, and influential. Done right, it wins support. Done poorly? It’s just another set of boring slides collecting digital dust.
Here’s how we get it right:
- Craft a Strong Narrative: Ensure presentations tell a compelling, impactful story.
- Slide QA: Validate data accuracy, clean design, and alignment with the core message.
- Address Objections: Anticipate stakeholder concerns and proactively resolve them within the deck.
The result? A narrative that feels airtight and a team ready to move the needle.
Data Manipulation
Data manipulation happens across all roles and teams at Seer. It’s par for the course when being data-driven is part of your ethos.
And while AI might not always be the perfect tool for the job, it’s proving useful in helping us identify what the right tool actually is.
Here’s how data manipulation breaks down at Seer:
- Data Pulling: Go beyond basic exports to uncover deeper insights.
- Data Cleaning: Ensure accuracy with reliable, optimized datasets.
- Data Organization: Contextualize data to fit goals, use cases, and audiences.
As my colleague Christina Blake often points out to us, cleaning and organizing are two very distinct steps.
- Cleaning Data: Ensures you’re working with a reliable, optimized dataset. It’s purely objective.
- Organizing Data: Shapes the data based on context—your goals, your use case, your audience. This can be both objective and subjective.
Analysis
Analysis is the engine that drives our insights and recommendations. It’s critical, it’s scientific, and it’s fun. Ask anyone on the Seer team what their favorite part of the job is, and you’ll likely hear:
“Digging into the data, performing analysis, and uncovering insights.”
But, those same people also fast follow with some opportunities where we can improve.
There are two big opportunities for us here:
- Multi-Format Audits: Deliver insights in spreadsheets, documents, and presentations for various audiences.
- Forecasting: Project outcomes to build trust and align with client expectations.
When done right, forecasting builds trust by ensuring we’re speaking the same language as our clients. When done wrong? That trust can erode fast.
Insight & Recommendation Generation
Data is only as valuable as what you do with it. At Seer, our job is to help clients make the best possible bets with their resources and funds. That means moving from analysis and research to actionable insights and clear recommendations.
This has many shapes and forms. This is a small sampling of our opportunities at Seer.
- State of Search Reports: Provide clients with timely industry updates relevant to their strategies.
- Project Briefs: Align teams with clear processes, goals, and deliverables.
- Action Items: Turn meeting discussions into organized, actionable tasks that drive impact.
At Seer, action items aren’t just a list of to-dos, they’re the bridge between ideas and impact.
Seer’s Priority Base-Level GPTs
Without further ado, I can share the base-level GPTs we prioritized:
Presentation Narratives, Deliverable Reviews, and Forecasting.
Beyond all of the rationale I included above, here are a few more reasons these three opportunities made our priority list.
General Purpose Technology to the max. These three opportunities represent three very different ways we can use this technology.
- Presentation Narrative GPT: a thought partner, helping an individual craft the right outline for the story they’re trying to tell.
- Deliverable Review GPT: leverages LLMs’ ability to reference and review content.
- Forecasting GPT: will tap into our ability to perform data analysis and create/manipulate code.
Fail fast and move on. The inverse angle to those advantages is that we’ll quickly know which concepts have legs and which don’t.
- For example, we’re hitting some major speed bumps with deliverable reviews. Our R&D builds are hallucinating at a concerning rate, forcing us to think about the problem differently. This is all part of the process.
Accessibility for the win. Remember our core priority at Seer. We are enabling next-gen marketers that can drive next-gen performance for our clients. Our goal is to light a fire, not fill a bucket.
- These base-level GPTs are accessible for all skill levels and will serve as a great training ground for building confidence and improving the team’s technical skills.
Buying time for the Wait Calculation. I’m a risk-averse person. That’s made this year especially challenging for me. I have a really hard time stomaching the idea of our team spending hours and hours to solve a problem that may be achievable ‘out of the box’ in the next evolution of LLMs.
As such, the initiatives we are focused on are wide-ranging and varied enough that we’re learning a truly staggering amount with every new research project and MVP.
- Even if 3 months from now all of these base-level GPTs can be accomplished natively with ChatGPT 5, we should still be able to consider the time spent productive.
Step 4: Planning Where We Go From Here
I was most inspired to write this for those working on small teams with big initiatives.
At Seer we have so much going for us. We have top-down buy-in on all things AI. We have budget allocated for R&D. We have (in my opinion) the smartest team in the industry. And it’s still hard for us. It’s still easy to second guess if the path we’re on is the right one and if we’re investing the right amount of time and resources into the right initiatives.
If you work within an in-house marketing team you likely have far less resources and support to navigate all of this. It’s a great time to have a partnership in place that can take some of the onus of research, training, and change management off of your plates.
Maybe your team is on the cutting edge but you need support getting buy-in from stakeholders or other internal departments. Ultimately there is a lot of opportunity, which means there is a lot of work to be done!
More than anything we offer solidarity and a call for more collaboration. And if you want to be a part of what we are building at Seer, keep your eyes on our careers page for open roles.
Want to learn more about how we manage AI disruption?
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