We keep hearing about how AI is transforming everything—from coding and copywriting to customer service. But while the headlines celebrate success, the reality for most businesses is far less impressive. In 2025, AI has hit a wall—but this is expected. It’s a common part of new technology adoption patterns.
Below are the core reasons why most AI projects are underperforming, stalling, or being scrapped altogether—along with direct links to recent reporting.
The Reality Check
95% of generative AI projects fail to scale beyond experimentation according to this popular MIT report. It may be overstated but there’s some useful data in the report.
42% of enterprises have abandoned most of their AI initiatives, up from just 15% two years ago.
Why It's Happening
Lack of Alignment with Business Objectives: Projects often chase novelty instead of solving real operational problems. Too many pilots, not enough production.
Infrastructure and Deployment Gaps: Organizations underestimate the cost and complexity of AI infrastructure. Models may be powerful—but can’t run without pipelines, data quality, or MLOps.
Workforce Resistance and Readiness Gaps: Executives are bullish. Staff? Less so. Lack of training, poor communication, and fear of displacement undermine internal adoption.
Missing Metrics and Accountability: AI gets deployed without a clear definition of success. No ROI tracking, no feedback loop, no accountability.
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AI Isn’t Failing. It’s Maturing—Just Not at Hype Speed.
Why the "trough of disillusionment" is a sign of progress, not collapse
What Is the Trough of Disillusionment? Coined by Gartner, it's the stage after peak hype when reality sinks in. Expectations nosedive. Pilot projects underdeliver. Headlines scream failure.
But per Amara's Law, we tend to overestimate tech in the short run and underestimate it in the long run. The trough isn't a verdict—it's a filter.
Why AI Fails (and What You Can Do About It)
88% of AI pilots fail to scale. Or insert your favorite AI stat here. Not because the technology doesn’t work—but because the organizational scaffolding around it does. The specific numbers may matter less than the details around why failures occur.
1. No Executive Ownership
AI initiatives often stall because no single leader is accountable. Like any strategic project, AI needs a clear owner with the authority to drive adoption, allocate resources, and push past resistance. If no one’s responsible, no one’s motivated to ensure results—or be blamed if it flops.
2. No Defined Business Value
Many AI pilots begin with vague aspirations. Think back to the early days of corporate websites: before e-commerce, most were just digital brochures. Similarly, AI is often pitched as transformational, but without clear ROI targets, it becomes just another dashboard. If you can’t measure the value, you won’t prioritize the investment.
3. No Workforce Enablement
Even the best models fail if no one knows how to use them. Most employees haven’t been trained to collaborate with AI. They see tools like ChatGPT or Copilot as novelties, not as strategic assets. Upskilling is the unlock. It’s not optional—it’s the multiplier.
4. Poor Pilot Design
According to the MIT Sloan Management Review, most AI pilots fail because they are either:
Poorly scoped (solving the wrong problem), or
Poorly embedded (bolted on, not built in).
This isn’t a technology failure—it’s an integration failure. Or more accurately, it’s organizational friction. Friction that can be removed with the right playbook.
How to Implement
Survive the trough by flipping the script:
Start with non-customer-facing, high-friction use cases
Build adoption into workflows—don't expect behavior change
Set KPIs based on time saved, errors reduced, or throughput increased
Train teams before, not after, rollout
Build guardrails to ensure security and compliance
Common Missteps
Launching for PR, not process
Overpromising timelines
Ignoring culture and change management
Confusing novelty with utility
Business Value
Goldman Sachs got it right: AI was embedded into existing workflows and tied to measurable gains in output and efficiency across 10,000 employees. No fanfare. No fizzle. Just operational lift.
Normally I share new apps, but personally I’ve been experiencing AI app sprawl. This edition I am providing those low-risk, high-reward apps that I think can make a big difference without sacrificing your personal success. Though admittedly not the most exciting use cases, these apps provide small boosts in productivity for discrete tasks that compound over time.
Microsoft 365 Copilot - I don’t love Microsoft 365 but if you are an office worker and you have access it’s beneficial. Embedded across Word, Excel, and Teams, it reduces manual effort and boosts document productivity.
Gemini for Google Workspace - I am primarily a Google Workspace user and I find that Gemini embedded in the workspace is a time saver even for tasks where I’d rather use ChatGPT. However, I do appreciate it’s ability to create drafts, summarize, and automate tasks inside Docs, Gmail, and Sheets with generative AI.
ChatGPT with Custom GPTs: Whenever I see something I can automate, I use Custom GPTs configured for my role or workflows to automate internal research, draft content, or prep strategy docs. It’s a great precursor to hone your instructions and prompts for AI agents.
SaneBox - I’ve been using Sanebox for years. It prioritizes important emails and summarizes the rest, reducing inbox overload without needing AI expertise.
Prompt of the Week: The Prompt Architect
I’ve been singing the praises of meta prompting lately. But I think the ultimate hack is the meta prompt for meta prompting.
This meta prompting framework is designed to help you systematically construct high-quality prompts for any LLM task. It functions as a "prompt for creating prompts" - guiding you through the essential components needed to generate clear, effective, and reproducible AI instructions.
By following this structured approach, you can transform vague requests into precise, well-defined prompts that consistently produce desired outputs.
Use this framework when you need to:
Create reusable prompt templates for recurring tasks
Translate complex requirements into clear AI instructions
Ensure consistency across multiple prompt iterations
Debug and improve underperforming prompts
Teach others how to write effective prompts
How to Use This Meta-Prompt:
Copy the meta-prompt into your preferred LLM.
Fill in the “REQUIREMENTS GATHERING” section with your specific needs.
Run the prompt to generate your custom prompt.
Test the generated prompt with sample inputs.
Iterate and refine requirements then regenerate if needed.
Best Practices:
Be specific in your requirements. Vague inputs create vague prompts.
Include examples of desired outputs when possible.
Test edge cases before deploying the prompt.
Save successful prompts for reuse.
Document context and note why certain decisions were made.
Common Use Cases:
Content Generation - Blog posts, documentation, creative writing
Data Analysis - Structuring analysis tasks, report generation
Code Development - Code review, refactoring, documentation
Educational Content - Lesson plans, explanations, tutorials
Business Tasks - Email templates, proposals, summaries
This framework is designed to be LLM-agnostic and can be adapted for use with any large language model (GPT-4, Claude, Gemini, etc.).
The Meta Prompting Prompt
You are an expert prompt engineer. Your task is to generate a highly effective prompt based on the requirements provided below. Follow this structured approach to create a comprehensive, clear, and actionable prompt.
Interview the user to gather requirements.
## REQUIREMENTS GATHERING
### Core Objective
**What is the main goal?**
[Describe the primary outcome you want to achieve]
### Target Audience/Use Case
**Who will use this prompt and in what context?**
[Specify the end user and typical usage scenario]
### Input Parameters
**What information will be provided to the prompt?**
- Input type: [text/data/code/image description/etc.]
- Input format: [structured/unstructured/template]
- Variable elements: [what changes between uses]
### Output Requirements
**What should the response look like?**
- Format: [paragraph/list/JSON/markdown/etc.]
- Length: [word count/tokens/pages]
- Style: [technical/casual/academic/creative]
- Structure: [sections/components required]
### Constraints & Guardrails
**What boundaries must be respected?**
- Must include: [essential elements]
- Must avoid: [prohibited content/approaches]
- Edge cases: [how to handle ambiguous situations]
- Fallback behavior: [what to do when uncertain]
### Quality Criteria
**How will you measure success?**
- [ ] Accuracy: [specific accuracy requirements]
- [ ] Completeness: [all required elements present]
- [ ] Clarity: [readability and comprehension level]
- [ ] Actionability: [practical and implementable]
- [ ] Consistency: [reliable across multiple uses]
### Examples (if applicable)
**Provide input/output examples:**
## PROMPT GENERATION INSTRUCTIONS
Based on the requirements above, generate a prompt that:
1. **Opens with clear role definition** - Establish the AI's identity, expertise, and perspective
2. **States the task explicitly** - Use action verbs and specific language
3. **Provides structured instructions** - Break complex tasks into numbered or bulleted steps
4. **Includes format specifications** - Show exact output structure expected
5. **Sets quality standards** - Define what "good" looks like
6. **Handles edge cases** - Specify behavior for ambiguous inputs
7. **Uses examples when helpful** - Include few-shot examples for complex tasks
8. **Ends with confirmation** - Option to ask for clarification if needed
## OUTPUT FORMAT
Generate the prompt in this structure:
[TITLE OF PROMPT]
Role: [Define the AI's role and expertise]
Task: [Clear, specific description of what needs to be done]
Instructions:
[Step 1]
[Step 2]
[Step 3]
...
Format Requirements:
[Specification 1]
[Specification 2]
...
Quality Standards:
[Standard 1]
[Standard 2]
...
Examples: (if applicable)
[Provide examples]
Note: [Any additional context or edge case handling]
## REFINEMENT CHECKLIST
After generating the prompt, verify:
- [ ] Is the objective crystal clear?
- [ ] Can someone else use this prompt without additional context?
- [ ] Are all ambiguities addressed?
- [ ] Is the desired output format unambiguous?
- [ ] Have edge cases been considered?
- [ ] Is the language concise yet complete?
- [ ] Would examples improve clarity?
Now, generate an optimized prompt based on the requirements provided.
I appreciate your support.
Your AI Sherpa,
Mark R. Hinkle
Publisher, The AIE Network
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