Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When I was working with a B2B startup on their website revamp, something unexpected happened. Their team spent more time in Slack discussing who was supposed to do what than actually doing the work. Sound familiar?
This wasn't a technology problem—they had project management tools, communication platforms, and dashboards coming out of their ears. But here's what struck me: everyone was solving coordination as a tool problem when it's actually a clarity problem.
While the market is flooding with AI platforms promising to coordinate your cross-departmental chaos, I've discovered something counterintuitive through my client work. Most of these solutions are addressing symptoms, not causes. They're building sophisticated systems to manage confusion instead of eliminating the confusion itself.
Here's what you'll learn from my contrarian take on this AI coordination trend:
Why most AI coordination platforms create more noise, not less
The simple automation approach that actually worked for my clients
How to identify when you need coordination vs. when you need clarity
My framework for choosing between Zapier, Make, and N8N for real coordination wins
Why treating AI as digital labor beats treating it as intelligence
If you're considering an AI platform for cross-departmental coordination, this might save you from an expensive mistake. And if you're already drowning in coordination tools, this will show you a different path—one that focuses on simple automation rather than complex AI orchestration.
Industry Reality
What the coordination platform vendors won't tell you
Walk into any SaaS company today and you'll hear the same complaints: "Marketing doesn't know what Sales is doing," "Engineering is building features nobody asked for," "Customer Success is the last to know about product changes." The knee-jerk solution? Let's buy an AI platform that coordinates everything!
Here's what the industry typically recommends for cross-departmental coordination:
AI-powered project orchestration platforms that use machine learning to predict bottlenecks and automatically assign tasks
Intelligent notification systems that supposedly know exactly who needs to know what, when
Cross-departmental dashboards powered by AI insights that promise a single source of truth
Automated workflow engines that route information and approvals based on department rules
AI meeting assistants that coordinate schedules and follow up on action items across teams
This conventional wisdom exists because coordination problems are visible and painful. When a product launch fails because Marketing, Sales, and Engineering weren't aligned, executives immediately think: "We need better coordination technology."
The platforms promise to be the intelligent nervous system of your organization—automatically routing information, predicting conflicts, and keeping everyone in sync. It sounds revolutionary, and frankly, it sells really well.
But here's where this falls short in practice: these platforms assume your processes are worth coordinating. They'll efficiently orchestrate chaos, intelligently route confusion, and beautifully dashboard your dysfunction. What if the real problem isn't coordination—it's that your departments are doing too many unnecessary things that require coordination in the first place?
Most coordination platforms solve the symptom (poor communication) while ignoring the disease (unclear roles and unnecessary complexity). This leads me to a completely different approach.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Here's the situation I found myself in: a B2B startup came to me for a website revamp, but their real problem became obvious within the first week. Every simple decision required a coordination meeting.
"Should we update the pricing page?" required input from Sales (for objection handling), Marketing (for messaging), Product (for feature accuracy), and Finance (for approval). A 10-minute task became a week-long process involving four departments, three meetings, and two follow-up Slack threads.
The client's immediate instinct was to find an AI platform that could coordinate these decisions better. "We need something that routes approval requests intelligently and keeps everyone in the loop automatically," the CEO told me. Sound reasonable, right?
We looked at several AI coordination platforms. They promised automated workflows, intelligent task routing, and cross-departmental visibility. The demos were impressive—beautiful dashboards showing real-time project status across departments, AI-powered notifications that supposedly knew exactly who needed to be involved in each decision.
But here's what happened when I dug deeper into their actual workflow: they had no clear decision-making authority. Every department felt they needed input on everything. The coordination problem was actually a clarity problem.
The root issue wasn't that they needed better coordination tools—it's that they had never defined who owns what decisions. They were trying to coordinate their way around fundamental organizational confusion.
This is when I started questioning the entire premise. Instead of finding smarter ways to coordinate four departments on a pricing page update, what if we eliminated the need for coordination altogether? What if we clarified ownership so decisively that most decisions didn't require cross-departmental input?
Rather than implementing an AI coordination platform, I proposed something completely different: automation that eliminates coordination needs instead of managing them.
Here's my playbook
What I ended up doing and the results.
Instead of buying an AI coordination platform, we took a radical approach: we automated the routine and clarified the critical. Here's exactly what we implemented:
Step 1: Eliminated Coordination Through Simple Automation
Rather than coordinating approval processes, we automated the tasks that didn't need human input. Using a simple Zapier workflow, we set up automatic updates between their CRM and website. When Sales closed a deal, customer logos automatically appeared on social proof sections. When Marketing updated messaging in their content management system, website copy updated automatically.
This wasn't sophisticated AI—it was basic automation that eliminated 70% of their "coordination" needs. No more meetings about updating customer logos. No more Slack threads about syncing messaging. The systems just stayed in sync.
Step 2: Clarified Decision Ownership
For the remaining 30% that actually required human decisions, we created clear ownership rules. Marketing owns messaging decisions. Sales owns pricing objection responses. Product owns feature descriptions. Finance only gets involved for changes over $X impact.
We documented these rules in a simple automation workflow that routed decisions to the right person—not through AI prediction, but through clear organizational logic.
Step 3: Built Decision Triggers, Not Coordination Dashboards
Instead of a complex AI platform monitoring everything, we created simple triggers. When a pricing change exceeded a threshold, Finance got automatically notified. When a new feature launched, Sales received automated talking points. When a customer complaint hit certain keywords, Product got alerted.
This was just intelligent automation using existing tools—no AI platform required.
Step 4: Measured Coordination Elimination, Not Coordination Efficiency
While most coordination platforms measure how well they manage complexity, we measured how much complexity we eliminated. Fewer coordination touchpoints meant the business was running more smoothly, not the other way around.
The magic wasn't in coordinating better—it was in needing less coordination altogether.
Automation First
Focus on eliminating coordination needs through simple automation before adding coordination tools
Decision Clarity
Define clear ownership rules that reduce the need for cross-departmental input on routine decisions
Simple Triggers
Use basic automation triggers rather than complex AI prediction to route critical decisions
Complexity Elimination
Measure success by reducing coordination touchpoints, not by managing them more efficiently
The results spoke for themselves. Within 30 days, the team eliminated approximately 75% of their coordination meetings. Decision velocity increased dramatically—pricing updates went from one week to same-day execution.
More importantly, the team's stress levels dropped noticeably. Instead of constantly checking coordination dashboards and responding to AI-generated notifications, people could focus on their actual work. The operations manager told me: "I didn't realize how much mental energy we were spending on coordination until we stopped needing it."
The cost comparison was striking too. The AI coordination platform they were considering would have cost $200+ per user monthly. Our automation approach using Zapier cost under $100 total per month for the entire team.
The unexpected outcome? When coordination became effortless through automation, departments started collaborating more naturally on the work that actually mattered. Without the friction of constant coordination overhead, they had energy for genuine strategic collaboration.
This project taught me that the best coordination platform is the one you don't need. When processes are clear and routine tasks are automated, departments coordinate naturally around the work that requires human judgment.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from rejecting AI coordination platforms in favor of elimination:
Coordination problems are usually clarity problems in disguise. Before adding coordination tools, clarify decision ownership and eliminate unnecessary touchpoints.
Simple automation beats complex AI orchestration. Basic Zapier workflows that eliminate coordination needs outperform sophisticated platforms that manage coordination.
The best coordination metric is how little coordination you need. Measure elimination, not efficiency.
AI platforms often solve symptoms rather than causes. They'll efficiently coordinate chaos instead of eliminating the chaos.
Decision velocity matters more than decision visibility. Clear ownership with simple automation enables faster decisions than complex coordination dashboards.
Start with ownership rules, not coordination tools. Technology should enforce clear organizational logic, not compensate for organizational confusion.
The coordination platform market preys on complexity addiction. Many businesses mistake coordination sophistication for operational maturity.
I'd do things differently by starting with an even more radical audit: mapping every coordination touchpoint and asking "Why does this require coordination?" instead of "How can we coordinate this better?"
This approach works best for teams ready to simplify rather than optimize complexity. It doesn't work if leadership believes coordination sophistication equals business sophistication.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to avoid coordination platform complexity:
Start with clear product/sales/marketing ownership boundaries
Automate customer data flow between tools using simple workflows
Define feature launch triggers that eliminate approval meetings
Use basic automation for user feedback routing to product teams
For your Ecommerce store
For ecommerce businesses avoiding coordination overhead:
Automate inventory updates across marketing channels
Set clear pricing change authorities to eliminate approval chains
Use simple triggers for customer service escalation to product teams
Automate seasonal campaign coordination through scheduled workflows