Where should AI change your marketing work next?
Some opportunities are already visible: a recurring bottleneck, a useful AI experiment, or work that takes too long.
Others emerge when you examine the decisions and expertise behind the work, or consider what AI makes possible that wasn’t practical before.
We help B2B marketing teams find those opportunities, redesign the work, and build AI-enabled workflows that put the improvements into practice.
We start by investigating how the work actually happens. Then we determine what AI should take on, where people need to apply judgment, and what a better workflow could look like.
From there, we build and test the solution with your team, using the tools and systems that make sense for the work.
What you leave with
A working AI-enabled workflow, tested against real work, with clear responsibilities for people and AI and a practical plan for putting it to use.
From Understanding to Building
01
Understand the Work Before Building
AI development often starts with what the technology can do. We start with what your people are trying to accomplish.
We talk with the product marketer developing a launch brief, the demand gen lead running a campaign, the researcher analyzing customer interviews, and the leaders reviewing their work.
We look at actual examples, decisions, handoffs, and the context people rely on without always thinking to explain it.
We also look beyond the individual task.
Making one step faster may not help if the next step is still the bottleneck. That gives us a clearer view of what should change, what AI needs to know, and where a different approach could create more value.
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02
Redesign, Build, and Test
Once we understand the work, we help your team decide what a better approach should look like.
Which steps could disappear? What can AI take on? Where should people review, intervene, or make the final decision? And what could your team accomplish that wasn’t feasible before?
Then we build.
Depending on the problem, that might mean a reusable AI skill, a specialized agent, a connected workflow, or a small application.
We test the solution using representative work, examine where it falls short, and refine it with the people who will use it.
The result is a working workflow with defined quality standards, clear responsibilities, and an agreed path toward everyday use.
What You Leave With
Depending on the engagement, your sprint can include:
A research-backed understanding of the existing workflow.
Identification of the decisions, expertise, and context the workflow depends on.
A redesigned workflow defining the roles of AI and people.
A working AI skill, agent, connected workflow, or application prototype.
Testing against representative tasks and real examples.
Defined quality standards and human oversight requirements.
Measures for evaluating performance and business value.
Documentation explaining how the workflow works and how to use it
A practical handoff and recommendations for broader deployment or future development.
AI Makes It Easier to Build. It Doesn’t Make It Easier to Know What to Build.
AI can now take on work that would have required considerable time, specialized software, or additional people only a short time ago.
That creates an opportunity to do more than automate existing tasks.
A monthly competitive update might become an ongoing intelligence workflow. Customer research that once informed a single project might become a resource the entire marketing team can use. Work that never justified the time or expense might become practical for the first time.
But building something quickly doesn’t mean you’ve built the right thing.
The value comes from understanding the work, choosing what should change, and testing whether the new approach actually makes it better.
That’s where we focus.
What Makes Us Different
Built for Marketing Teams
Marketing work depends on customer understanding, market context, judgment, and collaboration. We develop AI workflows around those realities, not a generic list of automation opportunities.
Grounded in Research
Our research practice is built on asking useful questions, examining real behavior, and uncovering what an initial explanation leaves out. We bring that discipline to understanding the work before building a solution.
One firm, from evidence to execution
We don’t stop at identifying opportunities or recommending what your team should build. We help turn the work into a functioning AI-enabled workflow, test the results, and establish what comes next.
Guided by Fluid Intelligence®
Good AI workflows require judgment: what to delegate, what context to provide, how to evaluate the result, and when a person needs to step in. We design with those decisions in mind.
Master the Art of AI Collaboration
As AI gets more capable, the quality of the result depends less on knowing the right prompt and more on knowing how to direct the work. The Art of Asking is our framework for doing that well.
- How to effectively delegate to AI — use the 5-element framework to manage AI like a capable colleague.
- Workflows and agentic use cases — learn to meaningfully manage AI-driven workflows and agents.
- Techniques to create AI champions — build internal champions who help AI adoption stick across teams.
Sean Campbell
Co-founder & CEO, Cascade Insights®
Research. Teaching. Real World Experience.
Sean has spent his career where technology, business, and teaching meet. He has taught at Purdue University and Willamette University’s MBA program, and today teaches at George Fox University’s College of Business, where he is Director of AI Integration for the MBA program. He has written technology books for Microsoft Press and Intel Press, delivered more than 150 conference sessions and workshops, and appeared on more than 100 podcasts.
That teaching background shapes how we approach AI work. The material is not built from vendor demos. It comes from watching how organizations adopt AI, interviewing hundreds of professionals a year about how their work is changing, and using the tools ourselves every day.