An AI Manifesto for Business: How to Prioritize the Right AI Initiatives

It’s been nearly 25 years since the Agile Manifesto. While “Agile” now draws mixed reactions, its principles still shape how organizations work.

AI has the potential to drive even greater change, but the sheer number of possibilities makes it hard to know where to focus.

Enter our take on an AI Manifesto: a practical, people-first framework grounded in the same values we bring to every conversation about technology and business.

We are defining reliable ways to identify and pursue AI opportunities by doing the work and helping others do it. Through this work, we have come to value:

• Real needs over copying what’s trendy
• Some data over perfect data
• Responsibility over efficiency
• Proven tools over building from scratch

Let’s explore each of these principles, and why they matter now more than ever.

Principle 1: Real needs over copying what’s trendy

It’s tempting to chase what others in the industry are doing, but AI isn’t one-size-fits-all. It needs to fit your specific workflows, your data, and your goals. Instead of asking, “What’s the latest AI solution in our industry?” ask questions like:

• “Where are we bottlenecked?”
• “What slows our teams down?”
• “What insights are we missing that could improve our outcomes?”

As leaders face tight budgets, hiring freezes, and rising expectations to do more with less, these kinds of questions and constraints often reveal the best opportunities to reduce waste, boost efficiency, and improve the customer experience.

Side note on making things fit you: The cost savings and productivity gains from AI coding assistants are prompting organizations to reconsider the buy vs. build equation, as in-house development is now faster, cheaper, and more sustainable than ever before.

(For more on this, check out our blog: How AI-Based Tools Like ChatGPT Change the Buy or Build Software Conversation.)

Principle 2: Some data over perfect data

Nearly every organization today wants to make their data clean and comprehensive, and most assume this is a prerequisite for using AI effectively. While clean, structured data is critical for certain types of AI work, like unsupervised machine learning or forecasting, gen AI tools (like ChatGPT or Claude) excel at handling imperfect and incomplete inputs. 

Whether it’s email archives, form submissions, chat transcripts, or operational notes, you can often achieve big productivity gains without completing a big data-cleaning initiative first.

And don’t start your data cleanup or optimization as an abstract exercise. Let real business problems guide you. Start solving them, and the gaps in your data practices will quickly reveal themselves.

Principle 3: Responsibility over efficiency

We all know that AI can support incredible gains in speed and productivity, but it’s important to recognize that progress requires more than efficiency. It requires responsibility. 

AI works best when it enhances human potential, not when it blindly replaces it. It can process data quickly, reduce cognitive load, and eliminate repetitive tasks, but it doesn’t understand your customer relationships, your team dynamics, or the ethical implications of its output.

To approach AI responsibly and ethically, ask critical questions like:

• “What are the consequences of exposing this data to AI?”
• “What’s the downstream impact of letting AI make this decision?”
• “Are we making life easier for teams, or quietly burdening them with more uncertainty?”
• “Who may be harmed by this?”
• “What are the potential risks to our business?”

For AI to drive responsible outcomes, its decisions must be transparent and explainable, and humans must be in the loop. Without that, you risk more than bad results – you risk doing harm and losing trust. That means addressing bias, ensuring observability, and monitoring continuously.

Principle 4: Proven tools over building from scratch

You don’t need to train your own model to get value from AI. Organizations like OpenAI, Anthropic, Google and others are investing heavily in making their LLMs better every day and you can build on that progress directly. Here are some practical ways teams are using proven tools to get started on their AI journeys:

• Custom GPTs for internal use
• Document Q&A chatbots
• AI-powered analytics dashboards
• Intelligent routing or summarization for customer support
• Detecting anomalies in machine operations
• Open source solution accelerators

Tailoring solutions (see Principle #1) doesn’t mean reinventing everything. Many problems are already solved and existing AI tools can often be adapted quickly.

Take language: your org might use jargon or acronyms that generic AI won’t understand. Instead of building custom models, use approaches like Retrieval-Augmented Generation (RAG) to inject business context efficiently.

It’s a faster, lower-risk way to build relevant AI without overcomplicating the tech stack.

Closing Thoughts

AI has the potential to reshape how we work, but only if we adopt it thoughtfully. That means:

• Aligning solutions to real needs
• Leveraging the data you already have
• Building with proven AI tools 
• And doing it all responsibly – keeping people and ethics at the center

This AI Manifesto isn’t a final answer – it’s an invitation to explore, question, and discuss. Agree or disagree, we want your perspective. Let’s shape the path forward together.

Special thanks to several Lean TECHniques’ team members who contributed thoughts and ideas to this manifesto and article: Alec Harrison, Josh Angolano, Kelli Wyngarden, Sharie Trachsel, Barry Closser, Austin Suiter, Matt Trachsel, and Mike Clancy.