Gaps in Prompt Design Derail the User Experience
What happens when gaps in prompt design or user intent recognition turns everyday AI interactions into a dead‑end waste of time?
Have you ever experienced this type of situation?
For example, I recently tried to claim a rebate for a kitchen appliance, but the company’s AI assistant misinterpreted my request and left me stuck with irrelevant FAQs. The issue was poor prompt design. With proper prompt optimization, the assistant could have recognized my rebate request and guided me to the correct, enabled form. A smarter workflow would have turned a dead end into a faster way to get my rebate money.
What fun!
This experience underscores a critical truth: when prompts fail to capture real‑world users’ intent, the customer experience collapses.
For business and IT users working with AI, this isn’t just a consumer inconvenience, it’s a wake‑up call. Prompt optimization has become a strategic discipline the bridge between user intent and AI execution. It ensures AI systems can interpret nuance, adapt to context, and deliver meaningful results.
Prompt optimization extends this discipline, helping organizations scale reliably across large language models (LLMs) and multiple AI systems so that business goals and IT execution remain aligned.
Whether in customer service troubleshooting, agentic AI-driven enterprise workflows, or customer engagement, the quality of prompts directly determines the quality of the user experience.
In this age of AI agents and assistants, refining prompts is no longer optional; it is essential for building trust, driving efficiency, enabling scalable automation, and achieving successful outcomes.
Reducing AI Project Failures – Understanding Prompt Engineering and Optimization
AI projects don’t fail because the models are weak, they fail because communication breaks down. When business intent isn’t translated into clear instructions, even the most advanced systems deliver poor results. Prompt engineering and prompt optimization have emerged as the disciplines that close this gap, turning human goals into AI assistant ready guidance.
Prompt engineering shapes the initial interaction, ensuring AI systems can interpret nuance and context. Prompt optimization builds on that foundation, refining prompts for accuracy, efficiency, and scalability across large language models (LLMs) and multiple AI systems. They enable organizations to move beyond ad hoc experimentation toward reliable, enterprise‑grade outcomes.
Together, prompt engineering and prompt optimization form the bridge between business and IT: business teams articulate goals and context, while IT ensures technical precision and scalability. When aligned, they transform user intent into reliable AI execution.
According to a RAND report, 80% of AI project failures stem from poor human to AI communication. Misunderstandings between business and IT teams about goals, data, and capabilities are the most common cause of failure. Companies that master prompt engineering achieve significantly higher ROI compared to those relying on basic or ad hoc prompting (1).
In short: success doesn’t come from the technology alone; it comes from how we communicate with it.
Why It Matters
Prompt engineering, with prompt optimization as a key subset, is no longer a fringe skill. It is becoming a critical organizational capability embedded into everyday business functions.
According to Market Research Future (MRFR), the prompt engineering market [which includes prompt optimization] was valued at $2.195 billion in 2024 and is projected to grow to $32.78 billion by 2035, with a staggering 27.86% CAGR (2).
SAP Prompt Optimization Adoption
Prompt optimization transforms vague queries into precise instructions, guiding AI toward accurate, consistent, and actionable outputs. With AI agents and assistants like SAP’s Joule, Microsoft Copilot, Google Gemini, and OpenAI’s ChatGPT, organizations recognize that prompt optimization is essential to maximize AI’s potential and align outcomes with business goals.
SAP has embedded prompt optimization into Joule, powered by the Generative AI Hub in SAP AI Core and Launchpad (part of the SAP Business Technology Platform) and supported by SAP’s AI Foundation, delivering enterprise‑grade accuracy and trust with Large Language Models (LLMs).
At SAP Sapphire 2025, SAP announced a proprietary Prompt Optimizer built with the company, Not Diamond. This optimizer enables enterprises to migrate and adapt prompts across multiple LLMs, supporting consistency, accelerating adoption, and improving the user experience across diverse AI systems (3).
Not Diamond envisions “a multi‑model AI future, with thousands of foundation models, millions of fine‑tuned variants, and billions of custom inference engines running on top of them.” Their infrastructure routes, governs, and scales prompts across these systems, ensuring flexibility and trust.
This partnership confirms prompt optimization as an important, core enterprise requirement, bridging business strategy with technology capabilities for responsible AI.
From Hard‑Coded Prompts to Agentic AI Orchestration
Currently, there is a larger shift from static prompt engineering toward dynamic, context‑aware orchestration. Agentic AI is evolving as the inevitable evolution.
In this rebate example handling with prompt optimization means hard‑coding keywords such as “rebate” and product numbers and writing scripts to manually enable the routing to the appropriate form. It requires constant upkeep.
In the future, agentic AI changes the flow entirely. Instead of static prompts, the agent recognizes, in the rebate example, the intent dynamically, queries the product database directly, and routes requests to backend systems without manual intervention.
Responses adapt in real time, errors trigger self-correction cycles, and product updates sync automatically with the catalog.
This rebate scenario shows the shift from today’s manual prompt engineering to tomorrow’s agentic orchestration moving from scripted rules to adaptive intelligence.
Best of Prompt Optimization Capabilities
Prompt optimization is the discipline that converts AI potential into measurable business outcomes, delivering benefits such as:
- Clarifies Intent – Ensures AI understands user goals, reducing miscommunication and aligning outputs with business needs.
- Customer Engagement & Personalization – Crafts prompts that drive tailored chatbot interactions, outreach, and recommendations, fostering loyalty and satisfaction.
- Structures Outputs – Guides AI to deliver usable formats (tables, lists, reports, narratives) for clarity and consistency.
- Improves Efficiency & Operational Automation – Minimizes unnecessary token usage, accelerates turnaround, and empowers agentic AI to automate repetitive workflows such as reporting, summarization, and FAQ handling.
- Adds Domain Context – Embeds industry‑specific language, governance and compliance rules, approved product names, synonyms, and brand tone for relevance.
- Supports Scalability Across Teams – Builds reusable prompt libraries and frameworks that allow consistent enterprise‑wide adoption without reinventing workflows.
- Content Quality & Consistency – Ensures outputs are brand‑aligned, accurate, and emotionally resonant across all channels.
- Optimizes Across Models – With tools like Not Diamond’s Prompt Optimizer, prompts can be migrated and adapted across Copilot, Gemini, ChatGPT, Joule, and other LLMs.
- Enhanced Accuracy & Decision‑Making – Produces precise, consistent outputs and extracts actionable intelligence quickly to support better business decisions.
By uniting these capabilities, prompt optimization becomes the foundation for trustworthy, scalable, and business‑aligned AI success.

Short on Time? Who Manages Prompt Engineering
The shift from prompt engineering to agentic AI isn’t just the domain of IT or developers. It is opening up a wave of business‑friendly, low‑code/no‑code opportunities.
Consultants, contractors, and employees can create value by helping organizations design, deploy, and optimize AI workflows without deep computer science level programming.
Examples include:
- Prompt Engineer (Business‑oriented) – Designs and optimizes prompts for customer service, marketing, and enterprise workflows.
- Agentic AI Workflow Designer – Uses low‑code tools to connect AI agents with backend systems, automating rebate claims, reporting, and customer engagement without heavy coding.
- AI Solutions Consultant – Advises companies on integrating agentic AI into existing processes, bridging IT and business teams.
- No‑Code AI Developer – Builds AI‑powered applications using drag‑and‑drop platforms and reusable prompt libraries.
- AI Product Owner / Business Analyst – Guides adoption by mapping workflows, defining requirements, and ensuring AI agents align with business goals.
- AI Governance & Compliance Specialist – Establishes rules for prompt libraries, agentic workflows, and data handling.
In fast‑paced, short‑handed work environments, these specialists provide focus and discipline, coach key stakeholders with standard operating procedures (SOPs), monitor performance, and refine prompts as business needs evolve ensuring AI systems remain accurate, consistent, and trusted across the enterprise.
Final Thoughts
Prompt optimization and its refinement through prompt optimization evolving into agentic AI is no longer a niche skill or technical curiosity; it has become a core business capability that drives efficiency, compliance, and innovation.
Organizations that embrace it gain a competitive edge, while those that neglect it risk wasted investments and inconsistent results.
By embedding prompt optimization as a business standard, integrated into workflows through enterprise‑wide libraries, governance, and automated refinement, companies can optimize AI strategies to keep pace with evolving models, regulations, and customer expectations.
The future of enterprise AI will not be defined by those who merely explore prompts and agentic AI, but by those who master prompt optimization and agentic AI, transforming raw model potential into trusted, scalable business value.
And as a result, users who want to get their rebates processed can save time by getting answers versus gaps and dead ends.
—
Footnotes
- RAND Corporation, “The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti‑Patterns of AI,” (August 2024)
- Market Research Future, “Prompt Engineering Market Size, Share, Trends, Analysis 2035.” Published (October 2025)
- Press Release, “Not Diamond Launches Prompt Adaptation, an Agentic System for Multi-Model Enterprise AI” (May 2025)
Note: This article is also available in the SAP Community | LinkedIn.



















Designing an intuitive user experience will increase efficiencies and generate higher value by enabling site visitors to quickly navigate the interface and find essential information, therefore optimizing businesses processes.At the same time, the interface must maintain the organization’s brand and messaging throughout the experience.