Individual teams execute well. They ship features, close deals, resolve tickets, and hit their goals. Their internal processes work. Their coordination is smooth. Their accountability is clear.
Then the organization looks at outcomes and finds that execution failed. The product launched but customers don’t want it. The feature shipped but sales can’t sell it. The deal closed but operations can’t deliver it. The project completed but doesn’t solve the problem.
The failure didn’t happen inside any team. It happened at the interfaces between teams.
Interfaces are where different teams meet, coordinate, and hand off work. They’re where product meets engineering, engineering meets operations, operations meets sales, sales meets support. They’re where assumptions collide, context evaporates, and responsibility diffuses.
Most execution failures trace back to interface problems. Not team incompetence. Not individual mistakes. But coordination breakdown at organizational boundaries.
What Interfaces Look Like
An interface exists anywhere two teams must coordinate to produce an outcome.
Product defines requirements. Engineering builds features. That handoff is an interface.
Engineering deploys code. Operations runs infrastructure. That boundary is an interface.
Sales promises capabilities. Product decides what to build. That negotiation is an interface.
Marketing generates leads. Sales qualifies prospects. That transition is an interface.
Support receives complaints. Engineering fixes bugs. That feedback loop is an interface.
Each interface has similar properties:
Different goals. Each team optimizes for different outcomes. Product wants features that sell. Engineering wants maintainable code. Operations wants stability. Sales wants closed deals. The goals create tension at every interface.
Different information. Each team has context the other lacks. Product knows customer requests but not technical constraints. Engineering knows implementation complexity but not business priority. Neither has complete information for optimal decisions.
Different timelines. Product plans in quarters. Engineering ships in sprints. Operations thinks in uptime hours. Sales operates on deal cycles. The timeline mismatch creates coordination friction.
Different language. Each team uses domain-specific terminology. What product calls “simple” engineering calls “complex.” What engineering calls “done” operations calls “deployed.” What sales calls “committed” product calls “discussed.” Translation failures create misunderstanding.
Unclear ownership. Individual teams own their work. No one clearly owns the interface. When interface execution fails, both sides blame the other. Accountability disappears.
The Requirements Translation Problem
Product writes requirements. Engineering implements features. The translation between requirement and implementation is an interface where execution fails.
Product writes: “Users should be able to search across all their data.”
Engineering reads this and must answer questions product didn’t specify:
- What counts as “all their data”? Current data? Historical data? Deleted data?
- How fast should search be? Instant? Seconds? Minutes?
- What happens if data is too large to search quickly?
- Should search work offline?
- What search syntax is supported?
- How are results ranked?
Engineering makes assumptions to fill gaps. The assumptions are reasonable but not validated. Implementation proceeds based on interpreted requirements, not actual requirements.
The feature ships. Product reviews and realizes it’s wrong. Engineering built exactly what the requirements said but not what product meant. The requirement specification interface failed.
This happens because:
Product assumes shared understanding. They think “search” is obvious. It’s not. Search has dozens of design decisions.
Engineering assumes minimal viable interpretation. When requirements are ambiguous, engineering builds the simplest thing that technically satisfies the requirement. This is often not what product wanted.
Clarification is expensive. Going back to product for every question slows engineering. So engineering guesses. Most guesses are wrong.
Requirements can’t be complete. Specifying every detail makes requirements unreadably long. But incomplete requirements guarantee implementation divergence.
The interface fails not because anyone is incompetent, but because perfect requirement translation is impossible. The gap between specification and implementation is fundamental.
The Deployment Handoff
Engineering builds features. Operations deploys and runs them. The handoff is an interface where execution fails.
Engineering builds a service that works perfectly in test environments. They deploy to production. It fails.
Operations investigates and finds:
- The service assumes specific network topology that doesn’t exist in production
- Resource requirements exceed what’s allocated
- Dependencies aren’t available in production configuration
- Logging format is incompatible with monitoring systems
- Security policies block necessary connections
- Scaling behavior doesn’t match production traffic patterns
Engineering tested thoroughly. In their environment, everything worked. But the production environment is different in ways engineering didn’t know about.
This happens because:
Engineering and operations have different contexts. Engineering knows the code. Operations knows the infrastructure. Neither has complete picture.
Test environments don’t match production. Creating perfect production replicas is expensive. So test environments are simplified. Features that work in test fail in production.
Production constraints aren’t visible to engineering. Security policies, network topology, resource limits, and operational procedures are managed by operations. Engineering learns about them through failure.
Operations discovers problems late. They see code at deployment time. By then, architectural decisions are fixed. Fundamental incompatibilities can’t be changed without rebuilding.
No one owns the environment gap. Engineering owns code quality. Operations owns infrastructure stability. The gap between code assumptions and infrastructure reality belongs to neither.
The deployment interface fails because production complexity exceeds what can be communicated through documentation and standards. The handoff requires knowledge transfer that doesn’t happen.
The Sales-Product Boundary
Sales sells. Product builds. The boundary between what’s sold and what exists is an interface where execution fails.
Sales is on a call with a major prospect. The prospect asks: “Can your product do X?”
X isn’t currently supported. But sales knows:
- This deal is large
- The prospect will buy if X exists
- Product has talked about building something similar
- Competitors probably can’t do X either
Sales says “yes” or “we can do that” or “it’s on our roadmap.”
The deal closes. Sales celebrates. Then they tell product about the commitment.
Product learns they’re now committed to building X by a deadline they didn’t set, for a customer they didn’t talk to, with requirements they don’t understand.
What happens:
Product is surprised. They didn’t know this commitment was being made. Their roadmap is now overridden.
Requirements are unclear. Sales knows the customer wants X but doesn’t know the technical details product needs to build it.
Timeline is unrealistic. Sales committed to a timeline based on urgency to close the deal, not engineering estimates.
Trade-offs weren’t considered. Building X means not building other things. Product would have made different decisions if consulted. They weren’t.
Customer expectations are wrong. Sales described X in terms that make sense to the customer. Product will build X as technically feasible. These might not match.
The sales-product interface fails because:
Sales is incentivized to close deals. Their compensation depends on it. Saying “no” loses deals.
Product is incentivized to protect roadmap. They have a strategy and technical debt to manage. Custom commitments disrupt plans.
Real-time decisions can’t wait for product approval. Sales calls happen live. Asking permission to make commitments would kill deals.
After-the-fact coordination is conflict-prone. Sales already promised. Product must deliver. There’s no negotiation, only tension.
No one is wrong. Sales did their job. Product is doing theirs. The interface between selling and building is structurally broken.
The Marketing-Sales Handoff
Marketing generates leads. Sales closes deals. The transition from lead to qualified prospect is an interface where execution fails.
Marketing runs campaigns. Traffic increases. Form submissions grow. Lead count goes up. Marketing reports success.
Sales receives these leads and finds:
- Most leads are students, competitors, or people exploring
- Contact information is often incomplete or wrong
- Leads don’t match target customer profile
- Interest level is “downloaded a white paper,” not “ready to buy”
- Lead source tracking is missing or inaccurate
Sales calls the leads. Conversion is terrible. Sales complains to marketing that lead quality is bad. Marketing responds that sales isn’t working the leads properly.
The interface failed because marketing and sales measure different things:
Marketing measures lead volume. More leads look like better performance. Quality is secondary to quantity.
Sales measures deal closure. They need qualified prospects, not raw leads. Volume without quality is noise.
Qualification criteria differ. Marketing considers someone a lead if they engaged with content. Sales considers someone qualified if they have budget, authority, need, and timeline. These are different standards.
Feedback loops are broken. Marketing doesn’t see which leads convert. Sales doesn’t explain why leads are unqualified. Neither adjusts based on the other’s information.
Attribution is contested. When deals close, marketing wants credit for generating the lead. Sales wants credit for closing. When deals don’t close, each blames the other.
The marketing-sales interface fails because their goals aren’t aligned. Marketing succeeds by generating volume. Sales succeeds by closing quality deals. The interface between volume and quality creates permanent tension.
The Engineering-Support Feedback Loop
Customers report problems. Support handles tickets. Engineering fixes bugs. This loop is an interface where execution fails.
Support receives a customer complaint. They investigate and find:
- The customer’s problem is real
- The problem might be a bug or might be user error
- Reproducing the issue requires technical knowledge support doesn’t have
- Escalating to engineering requires information support doesn’t know how to gather
- Engineering has fixed similar issues before but support doesn’t know if this is the same
Support escalates to engineering with incomplete information. Engineering responds:
- Can’t reproduce the issue
- Needs more diagnostic data
- Wants to talk to the customer directly
- Questions whether this is actually a bug
- Asks why support didn’t check obvious things first
The ticket bounces between support and engineering. The customer waits. Resolution is delayed. Everyone is frustrated.
The interface fails because:
Support lacks technical depth. They can’t debug code or understand system internals. So they can’t diagnose root causes.
Engineering lacks customer context. They don’t know how customers use the product or what problems matter most. So they undervalue support escalations.
Information loss in translation. The customer explains the problem to support. Support summarizes for engineering. Engineering sees a filtered version. Critical details are lost.
No standard escalation format. What information does engineering need? Support doesn’t know. So escalations are incomplete.
Priority misalignment. Support wants fast resolution for the customer. Engineering wants to fix root causes, not symptoms. These aren’t the same goal.
Feedback doesn’t propagate. When engineering fixes a bug, support doesn’t always learn what the fix was or how to explain it to customers. The same questions get escalated repeatedly.
The support-engineering interface fails because support needs quick answers and engineering needs accurate diagnostics. The interface doesn’t bridge the gap.
The Design-Engineering Coordination
Design creates interfaces. Engineering implements them. The translation from design to code is an interface where execution fails.
Design delivers mockups. Engineering reviews and finds:
- The design shows states that can’t exist in the system
- Interactions assume instant responses but operations are async
- Edge cases aren’t designed (what happens when there’s no data?)
- Designs assume features that don’t exist yet
- Visual polish requires engineering effort design didn’t account for
Engineering builds an approximation of the design. Design reviews and finds it doesn’t match. Engineering explains the constraints. Design is frustrated that implementation “doesn’t match the vision.”
This happens because:
Design works in ideal space. They design the best user experience assuming technical flexibility. Reality has constraints design doesn’t see.
Engineering works in constraint space. They implement what’s technically feasible given time, architecture, and dependencies. The feasible set is smaller than the ideal set.
Communication happens through artifacts, not conversation. Design produces mockups. Engineering writes code. The translation happens with minimal interaction. Assumptions don’t get challenged.
Design review happens after implementation. Engineering has already built something. Changes are expensive. So design accepts compromises they wouldn’t have made if consulted during implementation.
No one owns the translation. Design owns experience. Engineering owns implementation. The gap between experience and implementation is no one’s responsibility.
The design-engineering interface fails because design and code are different mediums with different constraints. Perfect translation is impossible.
The Data Science-Product Integration
Data science builds models. Product integrates them into features. The boundary between model and product is an interface where execution fails.
Data science trains a model. Performance in testing is good. They hand it to product for integration.
Product integrates the model and finds:
- Model latency is too high for production use
- Model requires data that’s not available at decision time
- Model outputs are hard to explain to users
- Model needs retraining but there’s no production pipeline
- Model performance degrades on edge cases
- Model assumptions don’t match product context
Data science tested rigorously. The model worked. But the model isn’t the product. The model is a component. Integration surfaces mismatches between model assumptions and product requirements.
This fails because:
Data science optimizes for model accuracy. They measure performance on test datasets. Production performance involves different constraints.
Product needs more than accuracy. They need latency, explainability, robustness, maintainability. Model accuracy is one dimension.
Integration requirements aren’t specified upfront. Data science doesn’t know production constraints. Product doesn’t know model limitations. Each discovers the other’s constraints during integration.
Testing environments differ from production. Models perform differently on real data, real latency requirements, and real user behavior.
Model assumptions aren’t validated. Data science assumes data distributions. Product discovers distributions change over time or vary by customer.
The data science-product interface fails because model development and product deployment have different success criteria. The gap surfaces during integration.
Why Interfaces Are Fragile
Interfaces fail reliably because they have properties that make coordination hard:
Ownership ambiguity. Each team owns their domain. The interface belongs to neither. When problems occur, both teams think the other should fix it. Nothing gets fixed.
Context loss. Information that’s obvious to one team is invisible to another. The handoff loses context. Decisions made with full context look wrong without it.
Asynchronous communication. Teams don’t work in real time together. They communicate through tickets, documents, and scheduled meetings. Questions take hours or days to answer. Decisions proceed based on assumptions rather than waiting for clarification.
Incentive misalignment. Each team optimizes for local goals. Interface quality is a shared responsibility that neither team is primarily measured on. It gets deprioritized.
Compounding assumptions. Each team makes assumptions about the other. Assumptions compound through the interface. Small mismatches become large failures.
No rapid feedback. Interface failures surface late. By the time product sees engineering’s implementation, or operations deploys engineering’s code, or sales closes deals based on product commitments, correcting course is expensive.
Power asymmetry. Often one team depends on another. The dependent team can’t force cooperation. Interface problems persist because neither team can unilaterally fix them.
The Integration Testing Gap
Organizations test within teams. They test across teams rarely or never.
Engineering tests their code. It works.
Operations tests their infrastructure. It works.
But engineering’s code running on operations’ infrastructure fails.
Why wasn’t this tested? Because cross-team integration testing is expensive:
Requires coordination. Both teams must allocate time simultaneously. Scheduling is hard.
Environment complexity. Creating test environments that match production integration points is complex and costly.
Ownership unclear. Who runs integration tests? Who’s responsible when they fail? Neither team naturally owns cross-team testing.
Slows individual teams. Teams want to iterate quickly. Cross-team testing requires waiting for the other team. It’s faster to skip integration testing and fix production issues.
Success criteria differ. What does “passing” mean for an integration test? Engineering and operations might have different standards.
So integration testing doesn’t happen. Teams test their components in isolation. Integration happens in production. Production is where interface failures surface.
Organizations that care about interfaces run integration tests. They assign ownership. They invest in test environments. They accept that coordination slows individual teams but prevents interface failures.
Most organizations don’t invest in this. They accept interface failures as the cost of team autonomy.
The Specification Precision Paradox
Interfaces could be specified precisely. Contracts could define exactly what each team provides and expects. This would prevent many interface failures.
But precise specifications have costs:
Time to create. Writing detailed interface specifications takes longer than doing the work. Teams want to move fast, not document perfectly.
Rigidity. Precise specifications are hard to change. When requirements evolve, specifications become obstacles rather than guides.
False precision. Many interfaces involve judgment that can’t be specified precisely. “Good enough” performance, “acceptable” quality, “reasonable” behavior. Specifications that try to quantify these create compliance games rather than real quality.
Implementation doesn’t match specification anyway. Teams write specifications then deviate during implementation because reality is more complex than anticipated. The specification becomes documentation debt.
So organizations have a choice:
- Loose specifications that allow flexibility but guarantee interface mismatches
- Tight specifications that create coordination overhead and rigidity
Most choose loose specifications. They accept interface failures as preferable to specification overhead.
The organizations that maintain good interfaces invest in specification. They write contracts, create interface documentation, and enforce standards. They pay the coordination cost upfront to avoid execution failures later.
The Authority Gap at Interfaces
When interface execution fails, who has authority to fix it?
Product and engineering disagree about whether a feature meets requirements. Who decides? Neither team has authority over the other.
Sales committed to something product won’t build. Who resolves this? Sales has authority to sell. Product has authority to plan roadmap. The conflict has no owner.
Engineering and operations disagree about production readiness. Who decides when to deploy? Engineering controls code quality. Operations controls infrastructure stability. Neither can override the other.
Design and engineering can’t agree on what’s feasible. Who has final say? Design owns experience. Engineering owns implementation. The decision falls between them.
Interface failures persist because:
Authority is distributed. Each team has autonomy in their domain. Autonomy means no one can force interface decisions.
Escalation is slow. Resolving interface conflicts requires escalating to shared leadership. Shared leadership is higher in the organization, farther from details, and slower to decide.
Escalation creates perverse incentives. Teams learn that escalating conflicts gets them overruled. So they avoid escalation. They make local decisions that protect their team even if it hurts the interface.
Consensus is expensive. Teams can try to build consensus. Consensus requires extended negotiation. Most interfaces aren’t worth the time investment. So they remain unresolved.
Organizations with healthy interfaces create explicit interface ownership. Someone has authority to make binding decisions when teams can’t agree. This person or team focuses on interface quality, not team autonomy.
Most organizations don’t do this. They treat interfaces as spaces between teams rather than things requiring active management. The interfaces fail predictably.
The Synchronous Communication Cost
Interface problems could be solved through real-time communication. If product and engineering talked through requirements live, translation failures would decrease. If sales consulted product before making commitments, misalignment would reduce.
But synchronous communication is expensive:
Schedule coordination. Getting multiple teams in a room requires aligning calendars. This creates delays.
Opportunity cost. Time spent in interface coordination meetings is time not spent on primary work.
Doesn’t scale. If every interface decision requires synchronous communication, the organization spends more time coordinating than executing.
Interrupts flow. Deep work requires focus. Synchronous coordination creates interruptions that fragment attention.
Not all questions are urgent. Most interface questions don’t need immediate answers. But distinguishing urgent from non-urgent is itself coordination work.
So organizations default to asynchronous communication. Teams work independently, communicate through documents and tickets, and coordinate at scheduled intervals.
Asynchronous communication is efficient but lossy. Context doesn’t transfer completely. Decisions get made with partial information. Interfaces fail.
The trade-off is fundamental: synchronous communication improves interface quality but reduces execution speed. Asynchronous communication enables speed but degrades interfaces.
Different organizations make different choices. Organizations that prioritize interface quality invest in synchronous coordination. Organizations that prioritize speed accept interface failures.
The Responsibility Diffusion
When work is owned by one team, accountability is clear. When work crosses teams, responsibility diffuses.
A feature requires design, engineering, data science, and operations. It fails. Who is responsible?
- Design says they delivered mockups
- Engineering says they implemented the design
- Data science says the model met accuracy targets
- Operations says they deployed what engineering provided
Each team did their part. The integration failed. No one is individually responsible because the failure happened at the interfaces between teams.
This happens because:
Success requires all teams. If any team underperforms, the feature fails. But individual team performance can be good even when integrated outcome is bad.
Interface failures are invisible to individual teams. Each team sees their own work. They don’t see how it integrates with other teams until late.
No one owns integration. Individual contributors own their tasks. Managers own their teams. Integration across teams is no one’s primary responsibility.
Accountability systems measure team output. Performance reviews, team goals, and success metrics focus on what teams deliver, not how well teams integrate.
Organizations with good interface execution assign integration ownership. Product managers, program managers, or technical leads explicitly own cross-team coordination. Their success is measured by integrated outcomes, not individual team performance.
Most organizations don’t do this explicitly. Integration is everyone’s responsibility, which means it’s no one’s responsibility. Interfaces fail and no one is clearly accountable.
The Implicit Assumptions
Interfaces fail because teams make incompatible assumptions.
Engineering assumes operations has standard deployment infrastructure. Operations assumes engineering follows deployment best practices. Neither assumption is validated. Deployment fails.
Product assumes sales understands what features exist. Sales assumes product is building what customers need. Neither assumption is validated. Commitments and capabilities diverge.
Design assumes engineering can implement designs as mocked. Engineering assumes design accounts for technical constraints. Neither assumption is validated. Implementation diverges from design.
These assumptions are implicit. Teams don’t state them. They don’t validate them. They operate as if assumptions are facts. When assumptions collide, execution fails.
This happens because:
Making assumptions explicit is awkward. Stating “I assume you know X” implies the other team might not know X, which feels like questioning their competence.
Validating assumptions is time-consuming. Every assumption becomes a question requiring an answer. Questions slow progress.
Assumptions feel obvious. To the team making the assumption, it seems like common knowledge. They don’t realize the other team has different context.
Assumption mismatches surface late. Teams discover incompatible assumptions when work integrates. By then, correcting course is expensive.
Organizations with healthy interfaces create spaces to surface and validate assumptions. Design reviews where engineering challenges design assumptions. Pre-deployment reviews where operations validates engineering assumptions. Sales-product syncs where commitments are aligned with capabilities.
Most organizations don’t create these spaces systematically. Assumptions remain implicit until they fail.
The Documentation Decay
Interface documentation could solve many problems. If interfaces were well-documented, teams would know what to expect from each other.
But documentation has problems:
Expensive to create. Writing good documentation takes significant time.
Expensive to maintain. Implementations change. Documentation must be updated to match. This rarely happens systematically.
Documentation drift. Over time, documentation and reality diverge. Teams stop trusting documentation. They don’t update it because they don’t trust it. The cycle continues.
Reading is work. Teams would rather ask questions than read documentation. Documentation exists but doesn’t get used.
Implicit knowledge isn’t documented. The things most important for interface coordination are often implicit. Teams don’t know what to document because they don’t know what the other team doesn’t know.
So documentation exists but doesn’t prevent interface failures. Teams maintain API documentation, deployment guides, and requirement templates. The interfaces still fail because documentation can’t capture the tacit knowledge required for coordination.
Organizations that maintain good interfaces invest heavily in documentation. They enforce documentation standards, allocate time for documentation work, and create processes to keep documentation current. This is expensive but prevents interface failures.
Most organizations treat documentation as optional. It gets created during initial projects then abandoned. Interface failures persist.
What Actually Prevents Interface Failures
Organizations with reliable cross-team execution don’t solve interface problems through better communication or clearer documentation. They solve them structurally:
Explicit interface ownership. Someone owns each critical interface. This person’s job is ensuring coordination between teams. They have authority to make binding decisions when teams disagree.
Integrated testing. Integration tests run continuously. Both teams are responsible for keeping integration tests passing. Failures block deployment.
Co-location of authority and responsibility. Teams that must coordinate share a manager close enough to context to make informed decisions quickly.
Standard interfaces with enforcement. Interfaces have standard contracts. Automated tools enforce compliance. Teams can’t deploy non-compliant implementations.
Shared incentives. Teams are measured on integrated outcomes, not just individual output. Compensation and performance reviews include interface quality metrics.
Embedded liaisons. Representatives from one team sit with another team. They provide real-time context and catch coordination problems early.
Synchronous alignment rituals. Teams meet regularly in real-time. These aren’t status updates. They’re working sessions where interface decisions get made.
Simplified interfaces. Reduce the number of interfaces by merging teams or creating clean abstraction boundaries. Fewer interfaces mean fewer coordination points.
Redundant communication. Critical interface information is communicated multiple times through multiple channels. Redundancy fights information decay.
These interventions are expensive. They require investment in coordination mechanisms, staffing, and process. Most organizations don’t invest this way. They assume autonomy and loose coordination will work. The interfaces fail predictably.
Why Organizations Don’t Fix Interfaces
If interface failures are predictable and preventable, why don’t organizations invest in fixing them?
Interface problems are invisible. Individual teams meet their goals. Projects appear on track. Interface failures surface late, often after individual team work is complete. The problem looks like bad luck or unexpected integration issues rather than predictable structural failure.
Coordination is expensive. The mechanisms that prevent interface failures require time, staffing, and process overhead. Organizations optimize for speed. Coordination slows teams down.
Autonomy is valued. Modern management practices emphasize team autonomy and loose coupling. Interface coordination mechanisms feel like micromanagement or bureaucracy.
Success cases don’t prove the need. Some cross-team projects succeed despite poor interfaces. Success happens because talented people worked around the structural problems. Organizations generalize from successes and assume interfaces aren’t really a problem.
Failure attribution is local. When interface execution fails, organizations blame the specific teams involved rather than the interface structure. They replace people or reorganize teams instead of fixing interface mechanisms.
No one owns interface quality. Individual managers own team performance. No one’s performance review depends on interface quality. The problem persists because it’s organizationally invisible.
So interface problems remain unsolved. Organizations repeatedly execute poorly across team boundaries, attribute failures to specific circumstances, and continue with unchanged interface structures.
The Cost Accumulation
Interface failures create costs that compound:
Rework. Teams build things that don’t integrate. Work gets redone. This is pure waste.
Delays. Projects stall at integration points. What could take weeks takes months.
Opportunity cost. Resources spent on rework and delay can’t be spent on new value. The organization moves slower than capability suggests.
Morale damage. Teams work hard and deliver, only to discover their work doesn’t integrate. Repeated interface failures create cynicism and disengagement.
Customer impact. Interface failures often surface as customer-facing problems. Features don’t work as expected. Promised capabilities don’t exist. Support can’t resolve issues. Customer trust erodes.
Compounding technical debt. Interface failures get patched with workarounds. Workarounds accumulate as technical debt. Future changes become more expensive.
These costs are substantial but diffuse. No single interface failure is catastrophic. But organizations with poor interfaces spend enormous aggregate resources on coordination failures.
The Structural Reality
Execution fails at interfaces because interfaces are structurally difficult:
- Ownership is ambiguous
- Context doesn’t transfer completely
- Assumptions remain implicit and unvalidated
- Communication is asynchronous and lossy
- Authority is distributed without clear resolution mechanisms
- Incentives optimize for local rather than integrated outcomes
- Testing happens within teams, not across interfaces
These aren’t solvable through better communication or documentation. They’re fundamental properties of how teams coordinate across boundaries.
Organizations that execute well across teams don’t have better communication. They have better interface structures. They assign ownership, create integration testing, align incentives, standardize contracts, and invest in coordination mechanisms.
Organizations that execute poorly across teams assume autonomy and loose coordination will work. The interfaces fail. The organization blames specific people or circumstances rather than recognizing the structural problem.
Execution doesn’t fail inside teams. It fails at the boundaries where teams meet. Fixing execution means fixing interfaces, not improving individual team performance.
Most organizations don’t recognize this. They invest in team capability and wonder why cross-team execution remains poor. The problem isn’t the teams. It’s the interfaces between them.