Decoding the digital age
The Value Puzzle
As organisations build ever-larger SaaS stacks, software that enables coordination across systems, tools and teams is accruing the most value – while also adding risks.
Games & impact
The Value Puzzle
How enterprise coordination layers are accumulating value – but also concentrating risk.
Written by James Richards
Over the past decade, enterprise software has become remarkably good at helping individuals perform specific tasks. Writing, design, coding, accounting, customer support, analytics. For almost any function, there is now a specialised tool with polished UX, deep features and a clear persona in mind.
In many organisations, there are hundreds of them.
Yet as the quality of individual tools has improved, a different constraint has quietly emerged. In modern organisations, friction and inefficiencies are more likely to be found in the connections between tools, rather than within the tools themselves.
Work no longer lives inside a single application. A product change might begin in a design file, move through a ticketing system, trigger a CI pipeline, update documentation, notify a customer success team and generate compliance artefacts. Each step may be efficient in isolation. The delays, errors and costs accumulate in the handoffs.
The same pattern repeats across functions, with departments such as finance and marketing tasked with reconciling data from multiple systems. With AI agents increasingly inserted into these workflows as additional actors, there are even more nodes that must be monitored, authorised and aligned.
Friction and inefficiencies are more likely to be found in the connections between tools, rather than within the tools themselves.
The main pinch-point in modern software environments is therefore no longer task execution, but task coordination. This shift is significant because it changes how stakeholders perceive software value within an organisation.
Task Tools vs Optimisation Layers
To understand the nature of this transition, it helps to identify the two broad software categories.
The first improves local tasks. These tools are designed for a specific persona performing a defined workflow: editing a document, generating a report, scheduling meetings, analysing a dataset or creating a mock-up. Their success depends on speed, usability and feature depth. The value proposition is straightforward: help someone do a job faster, better or with fewer errors.
Because the benefits are tied to a specific role, adoption typically spreads within a function rather than across the organisation. A design team standardises on one tool, finance adopts another and customer support selects a platform tailored to its needs. Each decision makes sense in isolation, improving productivity within its own domain.
The second category operates at a different level. Rather than optimising a single workflow, it improves how multiple actors and systems work together. These products manage shared state, align processes across teams, control identity and permissions, and orchestrate actions across multiple services.
This is system optimisation.
In practice, these tools operate at a distinct layer in the software stack; task tools operate at the edge, serving individual workflows. Coordination platforms sit above or between them, forming a connective layer that governs shared state, identity and cross-system orchestration.
In this category, a coordination platform is defined less by the number of features it offers than by where it resides. Positioned between functions, systems or organisations, it reduces the cost of handoffs, synchronisation and oversight. Instead of helping one person work faster, it improves the performance of the network they operate within.
The interactions between individual tools and the connective layers now define organisational dynamics.
The distinction echoes broader shifts in how SaaS business models have evolved over the past decade, from feature-centric tools to infrastructure-level control points.
This shift is especially significant because modern organisations increasingly behave like distributed systems. Work crosses team boundaries, decisions trigger downstream effects in other tools, and data must remain consistent across multiple environments. At the same time, permissions have to be enforced across an expanding identity surface. When coordination fails, the impact propagates across the organisation.
The difference is therefore especially clear in failure modes: if a task tool goes offline, a team is inconvenienced and work slows, but alternatives may exist, and the disruption is usually local. When a coordination layer fails, however, workflows stall across functions, data diverges, access breaks and compliance risks increase - the operational blast radius is much larger.
Coordination Services in Practice
A cross-section of platforms that now function as connective layers inside modern enterprises.
| Platform | Coordination Role | What It Connects |
|---|---|---|
| Stripe | Payments orchestration and compliance layer | Merchants, banks, card networks, regulators |
| GitHub | Code, identity and workflow coordination | Developers, repositories, CI pipelines, security tooling |
| Atlassian (Jira) | Cross-team workflow management | Product, engineering, support and documentation teams |
| Okta | Identity and access coordination | Users, applications, devices and services |
| ServiceNow | Enterprise workflow and service coordination | IT, HR and finance systems |
| Salesforce | Commercial system of record | Sales, marketing and customer data systems |
Therefore, it's reasonable to say that the interaction between individual tools and the connective layer now defines organisational dynamics. As software ecosystems become more modular and API-driven, the number of high-quality task tools continues to grow, representing another integration point, and another identity context.
The paradox of modern software is that as local execution becomes easier, maintaining system coherence becomes harder. And that is where coordination cost begins to accumulate. After all, automation does not eliminate coordination cost; in some cases, it increases oversight and governance demands.
Why Coordination Costs Are Rising
To be clear, the growing importance of coordination is not a cultural shift or a consequence of remote work. It reflects a structural change in how software environments are built.
Over the past decade, organisations have assembled their technology stacks incrementally. New tools are adopted to solve specific problems, often by individual teams with their own budgets and priorities. The result is a landscape that is powerful but fragmented. Large enterprises now operate with dozens, sometimes hundreds, of SaaS applications, each optimised for a narrow function.
Individually, these systems work well. Collectively, they create a dense web of dependencies.
As complexity compounds, the limiting factor is no longer the capability of individual tools. It is the organisation’s ability to keep them aligned.
Each new application introduces additional integration requirements. Data must move between systems, events in one tool trigger actions in another, and records must remain consistent across environments that were never designed to share a common model. APIs make these connections possible, but they do not remove the underlying complexity. They simply make it easier to add more connections.
Over time, this produces architectural sprawl. What begins as a clean set of integrations evolves into a network of links, middleware and custom workflows, where changes in one system create downstream effects and the operational burden shifts from building features to maintaining coherence.
At the same time, work itself has become more cross-functional. Decisions increasingly span multiple teams, and the systems that support them must reflect shared ownership. Processes that once lived inside a single function now cross organisational boundaries, increasing the cost of misalignment.
Security and compliance add another layer. Modern environments must manage identities across applications, enforce access controls and maintain auditability. Each additional system introduces another permission model and another potential gap.
The common thread across these trends is accumulation: more tools, more integrations, more actors and more shared state. Each addition delivers local value, but the overall system becomes harder to reason about and more expensive to operate.
As complexity compounds, the limiting factor is no longer the capability of individual tools. It is the organisation’s ability to keep them aligned. That alignment has a cost. In modern software environments, that cost is rising.
Value and Risk in the SaaS Stack
If coordination costs are rising, the economic consequences for software vendors are profound. In software markets, the most durable value tends to concentrate at the points organisations cannot easily bypass. Coordination platforms often sit directly in that path.
Unlike task tools, which improve a specific workflow at the edge of the organisation, coordination layers manage shared state across multiple functions or systems. They become the place where information is reconciled, permissions are enforced and actions are triggered. Once a product occupies that position, it is no longer evaluated as a convenience. It becomes part of the operational infrastructure, which changes both pricing power and competitive dynamics.
Coordination tools have four main sources of value leverage:
- The first source of leverage is critical-path dependency. When a coordination layer fails, the disruption is system-wide rather than local. Workflows stall across teams, data integrity is questioned and operational risk increases. Organisations become more conservative about changes, prioritising stability and continuity over incremental feature gains elsewhere in the stack.
- The second is control of shared state. Coordination platforms often become the system of record for key relationships: identities and access rights, workflow status, transaction histories or cross-team artefacts. Once multiple systems depend on that shared state, replacing the platform requires unwinding dependencies across the environment rather than migrating a single team.
- The third is identity and permissions. Modern organisations operate across an expanding set of users, services and automated actors. A platform that governs who or what can access which resources, and under what conditions, effectively controls the operational boundary of the organisation. That layer is both sensitive and difficult to reconfigure, which increases switching friction.
- Finally, there is ecosystem gravity. Once a coordination platform becomes widely adopted, other tools optimise around it. Integrations are built, workflows assume its presence and partners treat it as a default layer. Over time, the surrounding ecosystem reinforces its position, not because it offers the best individual features, but because it has become the place where everything connects.
The Cost and Risk of Switching
The contrast with local task tools is stark. A team can replace a writing assistant, analytics dashboard or design utility with limited disruption beyond the immediate workflow. Even if migration is inconvenient, the operational impact is limited. Competition therefore centres on features, price and user experience, and differentiation erodes quickly as alternatives proliferate.
Coordination platforms compete on a different axis. Their advantage comes less from what they enable individuals to do and more from the cost and risk of removing them. In economic terms, they benefit from structural switching costs rather than functional superiority alone.
This does not guarantee defensibility. Integration layers can be replicated, and incumbency alone is not a moat. But when a product sits at the intersection of systems, identities and shared state, it occupies a position where organisational inertia works in its favour. In a fragmented, API-driven environment, that position increasingly determines where value accumulates.
Structural Leverage in Practice
The coordination dynamic becomes clearer when viewed through specific examples, not as success stories, but as illustrations of where these platforms sit.
Take Stripe. It is often described as a payments company, but its enduring value lies elsewhere. Stripe coordinates merchants, banks, card networks, regulators and tax authorities, abstracting away not just transaction processing but compliance, identity verification and cross-border complexity. Individual payment flows matter, but the platform’s real leverage comes from managing shared state across a fragmented financial system. Once embedded, removing it requires re-engineering relationships with multiple external actors, not simply swapping an API.
A similar pattern appears with GitHub. At a surface level, it hosts repositories. In practice, it coordinates developers, version control, CI pipelines, security tooling and enterprise governance. It becomes the shared layer where code, identity, review and automation converge. Teams may argue about editors or testing tools, but the coordination layer is treated with far more caution.
Design collaboration offers another example. Figma did not win by offering marginally better drawing tools. Its advantage came from coordinating designers, feedback loops, stakeholders and engineering handoff in a single shared environment. The value was less about pixels and more about synchronisation. Once design becomes a shared, live artefact rather than a file passed between tools, the coordination layer becomes difficult to displace.
Finally, consider Atlassian. Products like Jira and Confluence coordinate work across teams, functions and planning horizons. Their persistence inside large organisations reflects not love for any individual interface, but the cost of unwinding shared workflows, permissions and institutional memory once they are embedded.
These platforms differ in market and function, but they share a structural role. They reduce coordination cost across systems, not just effort within a task. That role, more than feature depth, explains their durability.
AI and the Stack
However, none of this guarantees that today’s coordination platforms will hold their position. Integration layers have a history of commoditisation, in the sense that what begins as proprietary orchestration often becomes standard infrastructure. If APIs converge, data models stabilise and identity frameworks become more uniform, some of the complexity that gives coordination platforms their leverage may disappear.
AI introduces a second uncertainty. Much of what organisations experience as coordination friction involves translation, reconciliation and oversight: mapping fields between systems, interpreting state changes, routing information and identifying inconsistencies. These are precisely the kinds of tasks large models and agents are beginning to perform.
If AI can observe multiple systems, reason over their state and act across them, coordination may become less visible as a distinct product category (a similar dynamic is emerging in the AI interface competition, where where control of the orchestration layer is a key strategic question).
In SaaS stacks, rather than a central platform enforcing alignment, orchestration could emerge as adaptive behaviour running across the entire organisation. In this case, the control point may shift, with value accruing to the workflow layer, the identity layer, or the model layer itself.
But intelligent agents could also introduce more fragmentation, with each new agent becoming another actor with access, permissions and operational impact. Therefore, automation may reduce effort within individual tasks, while increasing the need for oversight, governance and system-level control.
Fragmentation, Consolidation or Synthesis?
There is another possibility. Today’s coordination platforms may not represent a permanent layer of control, but a medium-term response to a period of rapid SaaS fragmentation. As organisations consolidate their tools and simplify their architectures, some of the complexity these platforms manage may disappear, reducing their strategic importance.
What appears more durable is the underlying economic pattern: are you helping someone execute a task more efficiently, or reducing the coordination cost of the system they operate within?
As execution becomes faster and cheaper, the second question is likely to matter more. But whether coordination remains a visible platform, dissolves into infrastructure or migrates into the model layer is, as yet, unresolved.
For now, the economic gravity of software is shifting away from individual tools and toward the points where systems come together. Whether those places become enduring control points - or temporary junctions in a stack still reorganising itself - remains an open question.
