Bridging Coordination Gaps: Using AI to Connect the Dots and Unlock Hidden Business Value
- David Golub
- Oct 30
- 5 min read
Updated: 4 days ago
Many companies approach AI by identifying tasks they can automate. They scan workflows, find repetitive work and deploy tools to handle it faster. This optimization thinking leaves money on the table.
The bigger opportunity is not making current processes cheaper. It's recognizing that AI enables coordination across fragmented parts of organizations that couldn't align before. In other words, agentic AI opens new growth possibilities that might only have been imagined previously.
Sales and legal couldn't coordinate on custom contracts because review cycles made non-standard deals uneconomical.
Customer service and product development couldn't route feature requests into roadmap prioritization because manual handoffs didn't scale.
Operations and finance couldn't pull real-time data from five systems for scenario modeling because the coordination cost was prohibitive.
These aren't just automation opportunities to have robots do repetitive tasks. They're coordination gaps, misaligned gears in the enterprise. Revenue sits trapped in them.
According to a 2025 IBM CEO Study, 61% of CEOs are actively seeking to adopt AI agents, with investment expected to double over the next two years.
Yet most struggle with prioritization questions. Which coordination problems to solve first, in what sequence and why?
Unlocking coordination potential is the path to ROI with agentic AI. We see three types of coordination failures that typically hinder growth, each of which suggests different gaps to address and new ways to imagine the future of your business.
What is a Coordination Gap?
Coordination gaps are places where valuable interactions don't occur because making them happen has either been too costly or has, up to now, simply not been possible.
Consider these examples: Sales can't dynamically price based on delivery constraints.
Product can't systematically incorporate usage patterns from customer success. Operations can't adjust capacity based on sales pipeline velocity.
It should be evident that these challenges are not merely processes that need optimization. They're coordination impossibilities, blocking revenue.
As Pascal Bornet and his co-authors have written in Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work and Life, the real opportunity for AI isn’t just automating tasks — it’s solving messy coordination problems. We’ve blogged about this impressive book elsewhere.
Across most organizations, work gets delayed not by slow steps, but by handoffs, unclear ownership, and tool sprawl. These coordination gaps slow progress and bury business value.
Extending our examples, we understand how this plays out in the real world.
Your sales process exists the way it does because humans couldn't simultaneously track customer signals, check inventory availability and adjust pricing in real time.
Your product cycle is structured around the impossibility of continuously integrating feedback from support, usage data, and market trends.
Your procurement workflow assumes manual reconciliation of supplier quotes, inventory needs and budget approvals.
It’s obvious that these scenarios describe compromises built for a world where coordination was expensive or impossible. We believe leaders should not be tempted merely to fix what’s broken today, but recognize opportunities to think bigger than process automation.
Bornet’s rule that “every workflow with friction is a use case” translates into businesses that rely on syncs, manual updates or reminders to move work forward. This is where agentic AI’s power to plan, act, and adapt becomes real
Agentic AI offers a new approach to close coordination gaps, orchestrate workflows and get things done.
The Gaps You're Not Seeing
Your customer service team hears product issues daily. Your product team makes quarterly roadmap decisions. Between them sits a coordination gap: routing customer signals into product priorities requires manual effort that doesn't scale.
The task automation question asks: "Can we automate ticket routing?" The coordination question asks: "Can we enable continuous coordination between customer signals and product decisions?"
Task automation makes ticket routing faster. Coordination enablement changes the relationship between customers and product strategy. Customer input shapes development in real time instead of quarterly.
That's the difference. One optimizes existing workflows. The other enables new capabilities.
Coordination Without Integration
Traditional coordination requires integration first. Before systems work together, teams must agree on data formats, process standards and approval hierarchies. This is also why most integration projects stall.
Getting alignment across business units takes longer than the technical work. Endless meetings, tooling updates and last-minute check-ins. Traditional integration requires systems to agree on protocols before they communicate.
But AI agents can bypass this entirely or mostly through coordination without prior data homogenization. They do things that have never been possible.
For example: An agent (and its sub-agents) extracts customer intent from unstructured emails, matches it against inventory data in your ERP, checks margin requirements in your finance system and routes pricing approvals through your communication tools. The systems don't speak the same language. The agent translates and coordinates.
Historically, such alignment demanded months of alignment and might not have even been feasible. Agent-enabled coordination works differently. The agent extracts intent from System A in its native format, translates it into requirements in System B's context, and executes it through System C's interface. Systems never integrate.
The result? Coordination without integration happens anyway and timelines drop from months to weeks.
Decision Support for Spotting Gaps
Recognizing that your organization has coordination gaps is not the same as knowing how to close them. You can’t see what you can’t see, right? Even motivated teams may struggle with deciding where to place their agentic bets.
We offer a 3-part decision-support model for categorizing opportunities, a progressive matrix of likely addressable gaps with things to look for and steps to take according to each type of gap. Decision-makers can think of this as a kind of maturity model for agentic enablement, for finding hidden business potential.
Documented Gaps: You know where coordination fails, but not which failures block the most revenue.
You know where alignment is missing, but you don't know which failures cost most. Coordination means moving structured data across incompatible systems. The approach is less "which processes are inefficient" and more "which gaps block the most revenue opportunity." You are designing the workflow for new kinds of collaboration, working iteratively with security and governance in mind.
Recommendation: Rapid Definition & Prioritization
Stakeholder Gaps: Stakeholders experience different gaps while systems absorb unstructured inputs from stakeholders with competing priorities.
Customer service can't route insights to the product. The product can't access customer patterns from the service. You need to map conflicting perspectives before prioritization. Cognitive and causal mapping can reveal not "which tasks are repetitive" but "which key actions don't happen because coordination is expensive."
Recommendation: SODA Cognitive and Causal Mapping
Cross-System Gaps: You don't know where value is trapped by invisible disconnects. Need to discover gaps across the enterprise and align stakeholders.
When you don't know where valuable coordination isn't happening, we suggest you discover cross-system opportunities through SSM Rich Picture and/or systems dynamic mapping. Here we seek not to reveal "which processes are slow" but "which new business capabilities can we unleash."
Recommendation: Enterprise-level Agentic Strategy
Coordination on the Horizon
The shift from task automation to coordination represents the start of agentic enablement. The gaps we’ve seen as inevitable aren't permanent features of business. They're solvable problems.
When AI agents translate between systems and stakeholders in real time, old constraints disappear. Customer insights can flow directly to product teams. Pricing can respond to supply chain changes instantly. Departments can see how their choices affect others. This isn't about being 15% faster. It's about enabling interactions that weren't possible before.
Start with one high-impact gap. Perhaps it's between customer feedback and product development, or between sales promises and delivery capacity. Build a pilot that demonstrates new capabilities, not just efficiency gains. Then expand from there.
Agentic AI creates new models of collaboration that often weren't thinkable before. Today’s coordination problems are tomorrow's competitive advantages.
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