For years, companies have been concerned about technical debt, the long-term cost of cutting corners in software development.
Now, thanks to the proliferation of generative AI applications in the workplace, they have to worry about a new kind of debt: decision debt.
According to McKinsey’s State of AI in 2025 According to the report, 88% of organizations are using AI in at least one business function, up from 78% just one year ago. With the increased use of AI systems to prioritize tickets, recommend responses, route customer inquiries, summarize incidents, suggest operational actions, and more, organizations are making decisions faster than ever before. But while that speed has undeniable benefits, it can come at the hidden cost of a loss of context.
In the future, many companies that use these AI systems to support business decisions and support operations may struggle to answer simple questions such as:
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Why was this customer escalated?
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Who actually approved this decision?
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What did we follow that recommendation?
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Why was this case given priority over others?
As AI moves from information generation to impacting business operations, AI-related decision debt is emerging as a new governance issue.
A faster decision is not necessarily a better decision
Both technical debt and decisional debt increase future costs by taking shortcuts now. For decision debt, these shortcuts include using AI to accelerate operational decisions without understanding or storing the reasoning behind the decisions.
“Decision debt is the gap between the speed at which decisions are made and the quality of records left behind,” said Sai Joshita Katari, a senior site reliability engineer based in Austin. In production, decision debt accumulates when teams act on recommendations generated by AI without maintaining the reasoning behind the recommendations or the resulting decisions. The job gets done quickly, but over time the context behind that choice is lost.
Organizations may be deploying AI solutions to optimize the speed of decision-making, but they are not necessarily improving the quality of decisions in the process. Many companies recognize that the risks of integrating AI into their business environments stem from the mistakes the technology sometimes makes. However, the long-term risk arises when companies are unable to understand why AI-assisted decisions were made and whether they were correct.
This problem will become more acute as AI recommendations influence hundreds or even thousands of operational decisions every day. gartner predicts that by 2028, the average Fortune 500 company will manage approximately 150,000 AI agents, up from less than 15 in 2025. “Speed without traceability can cause confusion later on,” says Kathari. In many cases, incorrect AI recommendations are easier to deal with than missing decision-making context, she argues. When organizations have inputs, recommendations, human reviews, and results, they can understand whether failed decisions are due to bad data, lack of oversight, or the AI itself. However, when that information is missing, responsibility can quickly become unclear.
When decision-making debt appears in daily work
As AI-enabled technology is increasingly integrated into a wide range of business operations, decision debt can also appear in different parts of an organization. Decision debt can create risks in ticket routing, alert prioritization, escalation recommendations, AI-generated summaries, customer operations, and incident response, to name a few examples that are already relevant in many workplaces.
Taken individually, none of the AI-assisted decisions related to the above areas seem particularly important. However, these decisions collectively constitute the company’s operational history and should be treated as operational records. Unfortunately, while organizations often have a record of the final actions taken based on operational decisions, the same is not necessarily true for AI recommendations, human review, and supporting context and outcomes. This is where decision debt accumulates.
Imagine a series of operational decisions being made and six months later, with the help of AI-enabled technology at various points in the process, those decisions need to be reviewed. For example, the department may have been audited or the subject of a compliance review. Without proper documentation of these AI-supported decisions, it becomes difficult to ask important questions related to them.
In these situations, teams often need more information than they can to make a final decision, such as metrics, logs, alerts, runbook references, deployment history, approval records, and even chat conversations. That depth of information allows us to reconstruct why that decision seemed reasonable based on what was known at the time. “The most important thing is understanding what the team knew at the time,” Katari says.
Even months later, poor documentation can make it difficult to answer questions like “What information did the AI use to make this decision?” or “Did a human approve of this decision at some point?” It can also be difficult to determine who is ultimately responsible for a particular decision.
The real risk: loss of organizational memory
Organizational memory or organizational memory is the accumulation of experience, knowledge, and data that an organization has acquired over its lifetime. This includes a wide range of information. Databases, implicit cultural norms, and standard operating procedures are just a few of the many examples of organizational memory artifacts.
Traditionally, when an operational decision was made, someone (often more than one person) within the organization would remember why it was made. Managers and incident leaders were able to recall the event and see what influenced the decision. Long-time staff members remembered the decisions made and their impact.
But today, organizational memory is changing as AI increasingly influences business decisions. Decisions are made and approved faster. When AI can be used to inform decisions, people spend less time thinking and talking about them. That speed also means the number of decisions made can increase. As a result, no one remembers why that decision was made or what assumptions it was based on. Additionally, if AI-enabled decision-making causes problems in the future, you may not know where to turn.
Decision debt doesn’t just affect accountability in an organization. It also limits an organization’s ability to learn from experience. Frank Meltke, CEO of contraco, says that often the problem is not a lack of technical ability. Organizations often deploy AI systems that can record inferences and actions, but fail to establish processes to store that information and assign responsibility. As a result, the team ends up asking questions months later that should have been answered from the beginning, Meltke said.
“Why did that system do what it did six months ago? Are those reasons still applicable today? The answer usually doesn’t come. Not because the AI was opaque by design, but because no one assigned ownership of the decision trail at the time of deployment,” he said.
Governance needs to maintain context, not just control
In many organizations, governance discussions focus on access, authorization, security, and privileges. While these areas are important, given the growing prevalence of decision debt, governance discussions need to be expanded to consider inputs, recommendations, and preservation of results. Human review should be clearly delegated. And the final decision must be documented.
Meltke argues that governance also needs to establish who owns authority over AI systems. Organizations often approve the deployment of AI without explicitly defining what the system can decide or who is responsible for reviewing those decisions. He recalled working with a global logistics company that was using an AI optimization agent to manage vendor contracts. Executives were asked about the agency’s spending authority and contract renegotiation duties, but no one could answer. “They had approved a dashboard of projected savings. They never issued a mandate to the agency,” he said.
Organizations should retain not only the AI-enabled recommendations themselves, but also the information that informed those recommendations, the state of the AI system, human reviews during the process, the actions ultimately taken based on the recommendations, and the results of those actions, Katari recommends. These elements work together to create an audit trail that allows your team to revisit decisions in the future.
It would be harmful for organizations to withdraw potentially useful applications of evolving technology out of concern that every AI-powered recommendation requires too much human approval. But lack of action is also harmful when the decision debt problem is already growing. Meltke said a Contraco survey of 247 organizations across 12 industries found that fewer than one in five organizations could state exactly what their AI agents were authorized to do. “Debt is not technical; it is systemic,” he said.
The way to combat this is to increase the organization’s confidence that its most important business will be accountable in the future. Overall, Katari says organizations need to be able to answer four questions after making critical AI-assisted decisions: What information did you use? Who accepted, modified, or rejected the recommendation? And what happened as a result?
AI requires memory and intelligence
As AI-related technologies develop, it is inevitable that the application of AI will improve the speed of decision-making. But the organizations that benefit the most from integrating AI into their business operations aren’t necessarily the ones that will be the fastest.
Rather, the companies that excel in the current environment will be those that can explain why decisions were made, who authorized them, what assumptions existed when those decisions were made, and whether those assumptions were valid. Rather than adding new documentation requirements, organizations should build decision capture directly into the tools they already use. “Being explainable doesn’t slow down a team,” Katari said.
Many organizations have learned through technical debt that moving quickly without maintaining code quality will somehow slow things down. In this era of rapid adoption of AI, organizations may have to learn difficult lessons due to decision debt. It is possible to use AI to act quickly, but it is only sustainable if the reasoning behind business decisions is acknowledged.
“The goal is to allow AI to speed up work around decision-making, while humans remain responsible for decisions that impact reliability, customers, and production systems,” Katari said.
