Decision Debt: AI Is Creating A New Kind Of Backlog

Lori Schafer is CEO of Digital Wave Technology, an AI-native platform delivering AI, GenAI, and Agentic AI on governed master data.

getty​Technical debt is a well-known concept: the accumulation of software solutions for short-term or quick-fix problems that ultimately weigh down an enterprise when the tech becomes obsolete. IT leaders can spend years measuring, managing and paying down solutions.​

Companies need to be aware of tech debt when exploring AI-powered tools, but they also need to be aware of another rising concern that comes with it: decision debt.​

By adding a stream of AI tools and AI-driven decisioning, a fault can open within operations, a widening gap between the number of decisions that AI systems surface and the capacity to evaluate, entertain, prioritize and act on them. This is decision debt.​

Just as there can be a backlog of solutions taking up time and space, AI can create a backlog of insights and ideas to respond to, but enterprises can manage the debt.​

AI is supposed to make decisions faster, but some organizations are overwhelmed by the sheer volume of decisions generated, making it harder to keep up and fact-check AI. Machine learning and generative AI tools produce a steady stream of recommendations, alerts, opportunities, scenarios and considerations. In retail, these decisions can surface across multiple functions: pricing, merchandising, supply chain, marketing and more.​

To be clear, the problem isn’t with the AI itself. The solution is designed to generate keen, business-centric insights. The issue struggles inside organizations that don’t have an infrastructure and process in place to keep pace with the AI.​

This partly falls under change management, as companies need to train teams in order to rely on them to accurately prioritize and override AI decisions as needed. Without a process or system in place, AI decisions pile up and create a backlog that behaves like technical debt. Worse, a business’s performance drops due to unmonitored or uncontrolled AI decisions.​

Managing decision debt is identifying priorities, avoiding organizational paralysis and empowering teams to turn intelligence into action with confidence.​

Building infrastructure to support AI decisions doesn’t mean overloading organizations with oversight, such as a slew of dashboards, committees and signoffs before a decision gets made. Infrastructure requires coordinated AI and connected systems.​

For example, AI outputs can be frequently generated by disconnected systems, each optimized for its own function, with no shared mechanism for reconciling competing recommendations or routing them to the right owner and the right time. Without embedding a connective layer, every AI capability adds to the pile of decisions rather than reducing the effort required to act on them.​

Weak or fragmented data foundations can stall systems, too. When recommendations are built on inconsistent or unverified data, leaders spend additional cycles validating outputs before they can trust them enough to act, adding yet another layer to the backlog.​

Companies also need to avoid adding AI without having a clear strategy in place. AI solutions or layers that get added without rethinking how decisions are coordinated and executed increase decision debt rather than eliminate it.​

The goal isn’t to generate more recommendations; it is to design a stable infrastructure that helps close the loop faster around decisions, with less manual reconciliation, so that decision volume translates into action rather than backlog.​

AI decisions require discipline. And while the goal is to have an operating layer that connects insight to decision to action, rather than relying on people to manually bridge that gap across disconnected tools, AI agents should be working with manual teams.​

Business teams need to have discipline in how they work with decisions, coordinating recommendations across functions, applying guardrails and priorities set by the business and routing decisions to the right owner, or in defined cases, executing them directly.​

Essentially, teams should triage AI decisions to develop a durable path to decisioning that’s incremental. As decisions come in, identify the highest-friction decision points first and connect those specific workflows to an execution layer capable of acting on trusted data. For a retail organization, that may be markdown timing, replenishment triggers or promotional exceptions.​

Meaningful reductions in decision debt tend to show up in weeks, not months, because the goal is targeted coordination.​

As companies grow more comfortable with how they implement AI systems, decision debt deserves the same disciplined attention that technical debt receives.​

• Tracking Decision Latency: Hesitation in decisions could highlight decisions that are less important and can be removed or handed off to AI agents.

• Tracking Override And Abandonment Rates: Recommendations that are consistently ignored might signal a problem with the underlying data.

• Tracking Manually Resolved Decisions: Specifically, if decisions are being manually resolved across disconnected tools versus routed through a coordinated execution layer, the ratio indicates the level of friction in a system.​

Resolving these issues and improving the efficiency of AI decisions can lead to better business performance, including faster markdown decisions and replenishment issues for retailers. Reducing decision debt not only clears a backlog but also shortens the distance from insight to action to revenue. This is the ultimate measure: not how many AI recommendations a system generates but how quickly and consistently those recommendations lead to results on the P&L.

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