The 275-app problem: Why your tech stack is killing productivity in 2026

The stakes are higher than a bad chat experience.

We’re talking about the millions wasted annually on "ghost" SaaS licenses and the chaos of disconnected "agent silos" that don't talk to each other. It’s time to move toward a system that puts your strategic goals, not just your apps, at the centre of your business.

This is where agentic AI comes into its own.

We’re moving past the era of reactive chat into the era of agentic apps: the difference between a bot that suggests a solution and a system that actually executes it.

When you transform AI from a smart writer into a reliable, autonomous worker, you close the loop on your most complex workflows.

The "toggle tax" is more than a nuisance, it’s a line item

It sounds like a minor annoyance: clicking from Slack to Salesforce, then over to a project tracker, then to a Google doc.

But according to the Harvard Business Review, the average digital worker toggles between apps 1,200 times a day. This adds up to nearly four hours a week in lost time simply re-orienting.

The real cost isn’t just minutes. It’s attention, continuity and decision quality, which is why teams often describe modern work as exhausting, even when they have more tools to ‘help’.

When you scale that across a mid-sized enterprise, a few minutes here and there quickly becomes weeks of productive capacity (the very weeks you thought you were “buying back” when you digitised).

The second leak: wasted spend

You don’t just pay the "toggle tax" in time. You pay it twice, once in focus and once in spend.

SaaS sprawl is notoriously hard to govern. Unused and underutilised licences are common, with figures often cited that around half of licences sit idle in large organisations.

The app silo isn’t merely a tech headache. It’s a compounding drag on ROI, particularly when budgets tighten and leaders start asking the question nobody enjoys answering: “If we’ve bought all this software, why aren’t we more productive?”

The wasted spend leaks out in small, daily increments. The average business faces nearly 247 SaaS renewals every year—almost one for every business day. When renewals happen this frequently, they often slip into "autopilot." Without a centralised AI operating system to track actual usage, companies find themselves renewing:

• Zombie licenses: Paying for seats for employees who left months ago.
• Shadow AI: Duplicate tools purchased by different departments for the same task.

Tier bloat: Paying for "Pro" features that only 5% of the team actually touches.

Why "more AI" isn't always the answer

By the end of 2026, Gartner predicts that 40% of enterprise applications will feature task-specific AI agents. This is smart.

But adding a new AI agent into an existing silo runs the risk of creating "automated chaos." If your AI isn't aligned across all your apps, it’s just another disconnected tool.

This is what we mean by automated chaos: lots of activity, plenty of output, not nearly enough alignment. Each system starts making “helpful” moves based on incomplete context, which creates more exceptions, more reconciliations and more human clean-up.

For example, your CRM agent spots a renewal risk and creates a “save” task. The marketing agent, seeing low engagement, drops the same customer into a reactivation campaign. The customer success agent, working off support tickets, schedules an “escalation call”.

Individually, each move is defensible. Together, you’ve just spammed a frustrated customer with mixed messages, assigned three people to do overlapping outreach and nobody can tell which action actually worked because the context is split across tools.

The solution: The AI operating system (AIOS)

The fix isn't another app; it’s a new layer of intelligence.

Decidr moves beyond individual tools toward a unified AI operating system.

An operating system doesn’t replace every application. It sits beneath them, providing the foundations that stop everything from fragmenting: shared identity, permissions, memory, orchestration and governance.

It’s the layer that allows different capabilities to behave like one coordinated organism rather than a swarm of disconnected limbs.

Decidr’s approach is to move beyond isolated automation toward an AIOS that can:

• Eliminate context loss by linking data and workflows across platforms so teams aren’t forced to rebuild the story every time they switch tools.
• Align actions to goals so automation reflects how your business defines success, risk, and priority rather than what a single app happens to optimise for.
• End the "toggle tax" by giving your people a consistent interface to coordinate work across a multi-app world.

This requires structure.

We often talk about a “schema”, which is a precise description of how work, data and decisions connect inside your business. If you want a deeper explanation, this is the core idea.

The short version is that AI apps become useful at scale when they have a reliable blueprint to reason over, otherwise they’re improvising in the dark.

We use the phrase “agentic apps” deliberately. The goal isn’t to unleash autonomy everywhere and hope for the best. The goal is to build applications that can evaluate, coordinate and act within clear constraints and with governance that reflects how your business actually runs.

What changes when you put structure underneath the stack

Once you have an operating system layer, a few practical outcomes follow.

First, context stops evaporating. Decidr maintains a shared, governed view of what’s happening and why. That’s the foundation for better handovers, faster onboarding, and fewer “can you catch me up?” meetings.

Second, automation becomes aligned rather than random. Without a shared goal structure, automations become a grab bag of scripts that trigger actions without understanding trade-offs. With an AIOS, actions can be evaluated against objectives, permissions and risk constraints before they happen.

Third, new capability doesn’t create new silos.

In a typical stack, every new tool adds another interface, another data model, another set of permissions and another learning curve. In an operating system model, new “agentic apps” can plug into the same underlying structure, which is how you scale capability without scaling complexity.

Finally, governance gets easier, not harder. When decisions and actions flow through a consistent layer, you can observe what’s happening, audit it, and improve it. That matters for risk, compliance and plain old operational sanity.

How to start without ripping everything out

Most organisations don’t need a dramatic “replace the stack” program. They need a redesign of the layer that connects the stack to the way work actually happens.

A sensible starting path looks like this:

• Map workflows, not appsIdentify the 10–20 workflows that move revenue, reduce risk, or materially improve customer outcomes.
• Define what “good” meansSet goals, constraints and decision rights. Agentic apps are only safe when success and limits are explicit.
• Find the seams where humans are acting as glueLook for copy-pastes, reconciliation steps, shadow spreadsheets and manual approvals in chat.
• Build the schema layerTreat this as infrastructure. Once it exists, your automation and AI efforts stop being brittle.

The future is fewer seams, not more software

The promise of 2026 isn’t that you can buy more tools, faster. The promise is that you can design an organisation that behaves like one system, where intelligence flows through work the way electricity flows through a grid: structured, governed, and dependable.

If you’re still paying the "toggle tax," it isn’t because your people aren’t trying hard enough. It’s because your stack wasn’t built to cooperate.

An AI operating system is how you make it play nice.