[Free Post] Lean AI Framework: 80% of AI Projects Will Fail

Advanced ChatGPT Prompt to Apply the Framework

Hey AI Productivity Explorer,

Do you know that 80% of the AI projects fail? That’s twice the rate of failure of traditional IT projects. (Source: Rand.org and Gartner)

The Internet is filled with AI buzz and the rosy future as AI will solve almost all of mankind’s problems. The reality is far from it.

Why?

AI isn’t magic and implementing it without a clear strategy often leads to wasted time, money, and effort. Most organizations fail to understand the fundamentals of AI and dive right into the solutioning.

Let’s review the reason for the staggering failure rate.

1. Undefined Problems: Many AI projects start without a clear problem to solve, leading to irrelevant or overcomplicated solutions. If you ask your tech team “Let’s do something with AI” then you are asking the wrong question.

2. Data Issues: Poor quality or insufficient data often derails AI systems, resulting in inaccurate predictions or insights. Garbage in is garbage out and that has not changed. If you put bad data into AI and expect great results it ain’t gonna t gonna happen.

3. Overcomplication: Companies build overly complex models that don’t align with their needs or infrastructure. Some companies are obsessed with the buzzword “Enterprise AI”. Billions will be spent to manifest Enterprise AI without a longer term vision and purpose.

4. Poor Integration: AI tools fail when they don’t fit into existing workflows or lack actionable outputs for users. Operating AI in a silo is not going to cut it. You need to have AI as part of your tech ecosystem.

5. Lack of Iteration: AI systems degrade without updates, and failing to iterate leads to irrelevant or outdated results. One time prompts without any fine tuning or model update are a recipe for failure.

These challenges paint a clear picture: AI’s potential is enormous, but without the right approach, it’s easy to burn time, money, and resources chasing results that never materialize.

The common thread in most failures?

Complexity, misalignment, and lack of focus on solving real-world problems. Businesses need a way to simplify AI development, focus on what truly matters, and ensure solutions are practical and scalable from day one. That’s where Lean AI comes in.

Lean AI is about stripping things down to the essentials to solve specific, meaningful problems efficiently and effectively.

Think of it as a smarter, faster approach to innovation with no fluff, no wasted effort, just results. It applies the principles of the Lean Startup methodology (build, measure, learn) to the world of artificial intelligence.

Lean AI isn’t just for big tech giants or billion-dollar corporations. It’s designed for you irrespective of the type of service business you are running. Lean AI is a framework that works for all types of service businesses.

How?

Let me show you what I mean by diving into one of the most universal challenges in the service industry: customer onboarding.

Imagine you’re running a thriving service business maybe it’s marketing, consulting, or financial planning. You’re attracting new clients, but every onboarding process feels like pulling teeth. You’re bogged down in repetitive tasks, answering the same questions over and over, and chasing down forms that never seem to come back on time.

It’s draining, right?

Now, what if you could streamline that entire process by using a proven framework instead of jumping on a tool or off the shelf solution?

Introducing the Solve with AI Lean AI Framework

Lean AI takes the principles of the Lean Startup methodology, simplicity, rapid experimentation, and iterative learning applies them to artificial intelligence. It’s a strategic approach designed to strip away the unnecessary complexity of traditional AI projects, ensuring that every step is purposeful and aligned with real-world business goals.

Think of Lean AI as a smarter, faster way to implement AI. Instead of aiming for perfection from the start, it emphasizes starting small, testing quickly, and refining based on feedback. The framework provides a roadmap for businesses to build effective AI solutions without overextending resources or wasting time on features or capabilities that don’t add value.

At its core, Lean AI revolves around six key steps:

1. Define the Problem: Start with a specific, clearly defined problem your business needs to solve. AI is only as valuable as the problem it addresses. This step ensures the project has a focused goal from the beginning.

2. Build an AI MVM (Minimum Viable Model): Instead of developing a full scale AI system, create a small scale solution to test your hypothesis. This could be a simple chatbot, a basic prediction model, or a straightforward automation tool.

3. Measure Impact and Gather Feedback: Deploy the MVM to real users in a controlled environment. Measure its performance against defined success metrics, like time saved, cost reduced, or customer satisfaction improved. Feedback from users will guide the next steps.

4. Iterate Based on Insights: Use the data and feedback collected to refine the AI solution. This iterative cycle ensures that the system evolves to meet actual needs, not just assumptions.

5. Scale Gradually: Once the solution proves effective, scale it up strategically. Integrate it into your broader workflows or expand its capabilities to address additional use cases.

6. Continuously Improve: AI is not static and it needs to evolve as data changes, customer behaviors shift, or business requirements grow. Regularly monitor performance and make updates to ensure long-term success.

This structured, iterative approach ensures that businesses avoid the common pitfalls of AI development, such as overcomplicating solutions, wasting resources on irrelevant features, or deploying tools that don’t deliver tangible results.

In a world where speed and efficiency are critical, Lean AI provides a blueprint for businesses to harness the power of AI without getting bogged down in unnecessary complexity.

Whether you’re a service business looking to streamline customer onboarding or getting a handle on customer service, the Lean AI framework empowers you to focus on what matters most i.e. solving problems, delivering value, and scaling intelligently.

Let’s expand this a bit further and apply the 6 step Lean AI framework to a common use case.

Use Case: Applying Lean AI to Service Business Customer Onboarding

Customer onboarding is a critical process for service businesses. It’s the moment when first impressions are made, expectations are set, and relationships are built. Yet, many businesses struggle with inefficient, manual onboarding workflows that waste time, frustrate clients, and delay the path to revenue.

I have worked, led, and consulted with many service businesses. The onboarding issues typically fall into these categories:

• Service delivery expectation

• Onboarding documentation

• Customer setup

• Billing and payments

• Training and enablement

• Internal coordination between operations, sales, and support

• Customization and special requirements

• Compliance and regulatory issues

To transform a chaotic onboarding process into a smooth, efficient workflow, you need to start with clarity.

The first step in the Lean AI Framework is defining the problem because if you don’t know what’s broken, you can’t fix it. This step will make sure you’re not wasting time automating unnecessary tasks or addressing symptoms instead of the root cause. Let’s break down how to identify and define the specific challenges in your onboarding process.

Step 1: Define the Problem

Start by identifying the specific pain points in your onboarding process. For most service businesses, the problems include repetitive tasks like collecting customer information, chasing missing documents, answering the same FAQs, or handling onboarding delays caused by manual follow-ups.

For example, clients often delay providing required documents or information, causing bottlenecks that slow down project initiation.

Focus on streamlining document collection and communication during onboarding. Create a detailed onboarding questionnaire paired with an onboarding form that feeds your CRM or ERP database. This step ensures you’re solving a meaningful problem instead of automating unnecessary tasks.

Step 2: Build an AI MVM (Minimum Viable Model)

Instead of creating a comprehensive AI system, build a simple, focused solution to address the core problem. For onboarding, this could be a basic chatbot or workflow automation tool that handles repetitive tasks.

Example MVM:

Develop an AI onboarding chatbot that:

• Guides clients through the onboarding process step by step.

• Collect key information such as contact details, project requirements, and preferred timelines.

• Provide instant answers to FAQs about pricing, timelines, or required documentation.

Simple MVM Tech Stack:

• Use ChatGPT for conversational AI.

• Combine with tools like Zapier or Airtable for automation.

The goal is to deploy something functional quickly, gather feedback, and iterate rather than perfecting it upfront.

Step 3: Measure Impact and Gather Feedback

Once the MVM is live, test it with a small group of clients. Collect quantitative and qualitative data to measure its effectiveness.

Key Performance Indicators (KPIs):

• Time taken to complete onboarding.

• Reduction in manual follow-ups.

• Onboarding satisfaction scores.

To gain feedback, you can ask your customers questions like, “Was the process clear and easy to follow?” or “What additional support or features would have improved your experience?”

Step 4: Iterate Based on Insights

Use the data and feedback from Step 3 to refine the AI solution.

• Add a feature that sends automated reminders for incomplete steps.

• Include personalized messaging based on the client’s industry or project type.

• Train the chatbot with more specific FAQs based on real client queries.

By continuously tweaking the MVM, you can ensure the solution evolves to meet real needs rather than relying on assumptions.

Step 5: Scale Gradually

Once you’ve validated the AI solution and refined it through iterations, scale it to all new clients. It’s not one and done.

You can integrate the chatbot with your CRM (e.g., Salesforce or HubSpot) to automate data entry and ensure seamless handoffs to account managers. You expand the chatbot’s capabilities to handle more complex onboarding steps, such as sending contracts for e-signatures or scheduling kickoff calls.

Scaling gradually allows you to monitor the system’s performance and address any unforeseen issues as the client base grows.

Step 6: Use Data to Constantly Improve

Onboarding needs and client expectations will evolve, so your AI solution must adapt. Regularly monitor performance metrics and collect ongoing feedback to keep the system relevant and effective.

Use analytics to identify bottlenecks, such as frequently skipped steps or delays in response times. If your business spans across multiple regions then add multilingual support. Think of adding predictive models to proactively address client concerns based on past onboarding data.

This step ensures your onboarding process remains optimized as your business scales.

ChatGPT Lean AI Framework Prompt

Now let’s use a detailed ChatGPT prompt to make the application of the entire Lean AI framework easy.

I have created this prompt to help you navigate through the above steps. The prompt is designed to ask you specific questions in each part of the process.

The end result? A blueprint for AI success irrespective of your service business sector.

#Role
You are an experienced AI consultant guiding me, a service business owner, through the Lean AI Framework to create an AI-driven solution for my business. 

#Tasks
Your goal is to help me develop a detailed, actionable blueprint by asking step-by-step questions at each stage of the framework. Use the following structure to guide the process:

	1.Define the Problem
	2.Build a Minimum Viable Model (MVM)
	3.Measure Impact and Gather Feedback
	4.Iterate Based on Insights
	5.Scale Gradually
	6.Continuously Improve

For each step, ask detailed, thought-provoking questions to uncover my needs, resources, and priorities. Ensure the interaction is clear and focused on my business challenges. At the end of the process, summarize everything into a blueprint for launching the AI solution.

Step-by-Step Questions for the Prompt:

1. Define the Problem
	•What specific challenge or inefficiency do you want to solve with AI? (e.g., onboarding delays, repetitive tasks, customer complaints)
	•How does this problem impact your business operations, customer experience, or profitability?
	•Are there repetitive tasks or bottlenecks in your workflows that consume too much time or resources? If yes, describe them.
	•What is your ultimate goal for addressing this problem? (e.g., saving time, improving accuracy, enhancing customer satisfaction)

2. Build a Minimum Viable Model (MVM)
	•What is the simplest version of an AI solution that could address this problem?
	•Which of the following AI capabilities could help? (e.g., chatbots, predictive analytics, automation tools, recommendation systems)
	•What specific tasks would this solution automate or simplify?
	•Do you have any existing tools, data, or workflows that can support this solution?
	•What is the expected outcome of this MVM? (e.g., reducing onboarding time by 50%, automating follow-ups)

3. Measure Impact and Gather Feedback
	•How will you measure the success of your AI solution? (e.g., time saved, error reduction, customer satisfaction)
	•Who are the key users or customers who will interact with this solution?
	•What metrics will you track during the initial testing phase?
	•How will you collect feedback from users to identify areas for improvement?

4. Iterate Based on Insights
	•Based on feedback, what adjustments could make your solution more effective?
	•Are there additional features or capabilities that users need?
	•Is there any new data or functionality that could improve accuracy or performance?
	•What steps will you take to ensure the next iteration solves the identified gaps?

5. Scale Gradually
	•Once the MVM is validated, how will you expand its use?
	•Will you integrate the AI solution with other tools or systems (e.g., CRM, billing software)?
	•Are there additional workflows or processes that could benefit from scaling this solution?
	•What precautions will you take to ensure scalability doesn’t compromise performance or user experience?

6. Continuously Improve
	•How will you monitor the long-term performance of your AI solution?
	•What feedback loops will you establish to keep the system updated?
	•Are there potential changes in your business, industry, or customer base that the AI solution should adapt to?
	•How often will you review and refine the AI system to maintain its relevance and effectiveness?


#Blueprint Summary

At the end of the interaction, summarize the user’s inputs into a clear blueprint:
	•Problem Definition: [Insert user’s description of the problem]
	•MVM Design: [Describe the AI solution, its tasks, and expected outcomes]
	•Success Metrics: [List metrics and user feedback plans]
	•Iteration Plan: [Describe how improvements will be made]
	•Scaling Strategy: [Summarize plans for expanding the solution]
	•Continuous Improvement: [Outline plans for ongoing monitoring and updates]

#Example Output (for a Customer Onboarding Use Case)

Problem Definition:
Manual customer onboarding delays the start of projects by an average of two weeks due to repetitive tasks and missed document submissions.

MVM Design:
An AI-powered chatbot to automate document collection, answer FAQs, and send reminders. Expected outcome: Reduce onboarding time by 50%.

Success Metrics:
Time saved per onboarding, completion rates for document submissions, and customer satisfaction scores.

Iteration Plan:
Enhance chatbot FAQ responses based on customer queries and add document upload tracking for better monitoring.

Scaling Strategy:
Integrate the chatbot with the CRM system to automate data entry and enable personalized messaging for different customer segments.

Continuous Improvement:
Monitor chatbot performance monthly and update based on new onboarding trends or customer feedback.

#Rules:
1. Do not go to the next step until the user has completed the each step. Check with the user first to get their confirmation.
2. Do not skip any steps

Summary and Next Steps

Ready to transform your business with Lean AI?

Start by identifying one area that’s slowing you down, like onboarding or customer support, and focus on creating a solution for it.

Use the interactive ChatGPT prompt provided to guide you step-by-step in building your AI blueprint. Begin small by developing a Minimum Viable Model, test it with real users, and refine it based on feedback. Share your progress or success stories by replying to this email or adding comments to the Substack post

I’d love to hear how Lean AI is making an impact for you. AI is no longer the future; it’s here. Take the first step today and watch your business grow smarter, faster, and leaner.

Best,

Sameer Khan

Creator of Solve with AI.