Breaking the Cycle: How Organizations Can Sidestep Doomprompting and Deliver Success

Since its theoretical concept in the 1950s, artificial intelligence (AI) paved the way for businesses to experience enhanced opportunities and productivity through various techniques, especially machine learning systems. These tools/technologies improved forecasting and decision-making, laying the groundwork for future technical advancements. In recent times, Generative AI has promised to upend everything we know about working and has democratized AI experience. Users now engage with AI models like ChatGPT, through “prompting”, where one interacts back and forth with an AI model. However, these benefits also come with a new challenge: Doomprompting. This is equivalent to doom scrolling on online content, without a defined goal, trapping users in rabbit holes. With AI though, the rabbit hole talks back.  This act of continuous AI prompt refinement for both generative and agentic models, driven by the ambition to acquire the perfect output (and sometimes by prompting without any specific goal in mind), leads to increased costs and diminishing returns. It creates a major roadblock to success and defeats the purpose of using the AI technology itself.

As businesses increase their AI-related budgets, decision makers need to understand the path to real returns on their investments and what is the value it is generating. A 2025 report by IEEE, ‘The Hidden Costs of AI: How Small Inefficiencies Stack Up,’ demonstrates how minor adjustments can accumulate into significant economic burdens. To avoid becoming part of this costly struggle, organizations must refine their training of employees using LLMs to achieve the full potential of their AI investments.

Generative AI brings the promise of optimization and efficiency. However, when teams get trapped in the cycle of endless refinement (or radar-less wandering), inefficiency undermines this foundation.

Cleaning up the “Workslop”

One of the reasons teams continuously refine outputs to generate a perfect response is workslop. First described in Harvard Business Review, workslop encompasses ‘AI-generated work content that masquerades as good work but lacks the substance to meaningfully advance a given task.’

This AI-produced ‘slop’ is the first domino in a long line that creates the doomprompting cycle. While modifying the subpar content through iterations or edits is important, one needs to understand when to stop, before it gets into the slope of diminishing returns. Organizations must approach their time investment in AI training with a delicate balance. On one side, teams should be cognizant of the required quality; on the other hand, they should know when it is too much. The training of employees in the smarter use of AI models through optimal prompting and clear goals would also come in handy.

Leveraging Agentic AI to Avoid Doomprompting

In recent years, businesses significantly increased their interest and investment in agentic AI, which is recognized for its ability to improve operational effectiveness. Agentic AI can take complex tasks, orchestrate with multiple agents (including RAG and action agents) to decide the course of action, and execute the tasks to complete the overall task autonomously.

These qualities may help AI mitigate doomprompting, or sidestsep it altogether. This can take away the need for instructing GenAI interfaces through multiple prompts to complete the task. An example of this can be found in AI-powered IT operations, or AIOps, which are modernizing IT by threading AI into daily tasks. Traditionally, teams spend their time manually adjusting systems. 21st-century departments are ones that leverage AI to autonomously handle critical functions like troubleshooting, incident response, and resource allocation.

Another fitting example is how agentic AI systems can handle a complex incident autonomously. These agents, along with ITOps, are capable of understanding the issue contextually, orchestrating with reasoning agents to decide course of action, using action agents to do the last mile fixes on IT systems and finally, employ learning agents to comprehend the resolution and apply it more effectively in future incidents.

Agentic AI’s intelligent automation helps in less human interaction and does tasks autonomously. To meet evolving business demands, repetitive tasks and operations should be turned over to autonomous AI. This delegation eliminates the cycle of re-prompting and repetitive refinement that often fuels doomprompting. Autonomous operations allow AI models to continuously optimize and respond to changing variables without manual input, leading to faster results with minimal human intervention.

While trained professionals will still play an instrumental role within day-to-day operations via the human-in-the-loop approach, their time will be better utilized in scanning for result verification. This approach minimizes the risk of introducing errors or over-adjustment.

The Role of Governance in Preventing Doomprompting

In a recent McKinsey survey, 88% of respondents reported leveraging AI in at least one business function. This was a 10% jump from 2024 and an astonishing 33% increase since 2023. For Agentic AI, this jump was even more profound. From only 33% in 2023 to nearly 80% in 2025.

This widespread adoption is driving businesses to find new solutions to doomprompting. One such tool is robust governance frameworks. These should be carefully crafted to ensure AI projects remain aligned with business objectives and do not fall victim to the endless waltz of optimization. When teams develop these frameworks, they should consider:

• Guideline establishment: Data streams to and from AI models are becoming increasingly complex. To simplify this, AI guidelines should create a framework for teams to handle data, make decisions, and manage AI outputs responsibly.
• Training the users: Proper training in prompt usage can help towards optimal productivity
• Use of specialized models: Industry and purpose-specific AI models are likely to provide contextual and meaningful outputs faster
• Training the AI models: Training the AI models with industry/task/organization specific data (wherever possible) can lead to less workslop and more suitable outputs faster.
• Rule development: Drafting and implementing a clear set of rules is essential for guiding AI development and deployment. When teams establish operational boundaries, they ensure that adopted systems align with organizational goals, ethical standards, and regulatory requirements.

While the adoption rate of AI solutions is increasing, governance has not. According to the 2025 PEX Industry Report, less than half have an AI governance policy in place. Meanwhile, only 25% were in the process of implementing one, and nearly a third did not have any AI governance policy in place. These frameworks can be the defining factor in helping businesses set clear boundaries on what constitutes acceptable performance.

Escaping the Doomprompting loop

To sidestep falling into the cycle of doomprompting, businesses must embrace AI strategies that prioritize results over perfection. Use of prompt training, purpose specific AI models, and models trained on contextual enterprise data can reduce the need for extensive re-prompting. Businesses that harness agentic AI, autonomous IT operations, and strong governance frameworks can reallocate critical resources towards achieving their business goals without getting bogged down by endless optimization cycles. Success will come when teams shift their mindset from constant refinement to one of focused execution and measurable outcomes.