The Reality Gap in AI Implementation

Everyone talks about using AI to solve problems or build something new. Tech newsletters are filled with companies claiming AI has transformed their operations. But here's what's missing: practical examples of how businesses are actually implementing AI to deliver measurable results.

If you're running a business—especially as a non-technical founder—you need more than theoretical use cases. You need a framework that works regardless of your industry or technical expertise.

The Input-Output Framework for AI Implementation

At its core, every business follows a similar pattern:

1. Your team performs a set of activities

2. These activities create a delivery process

3. The delivery process results in something your customers receive

What many business owners miss is that within this flow exists numerous "inputs" that can be transformed through AI into valuable "outputs"—often replacing manual processes that consume time and resources.

Existing Business Inputs

Take a moment to identify the existing inputs in your business. These might include:

• Team allocation management

• HR processes

• Ideas from meetings

• Documents and project briefs

• Content topics

• Budgets and quotes

• P&L statements

• Feature ideation sessions

• Metrics, goals, and KPIs

• Company manuals

These inputs exist regardless of whether you're using AI. The question is: how can AI transform them into more valuable outputs?

Real-World Example: How We Implemented AI at Jams

As a product design agency, our key activities include building websites, apps, low-code development, product design, and strategy. Our deliverables go to non-technical founders in B2B SaaS, gaming, education, and other industries.

Before implementing AI, our workflow included:

• Client and internal meetings

• Writing and designing product scopes

• Creating user journeys

• Developing wireframes

• UI design

• Low-code and no-code development

The Transformation Process

We identified two critical inputs that could be enhanced through AI:

1. Meetings - Previously random conversations

2. Briefs/Pitches - Often poorly written documents or hundreds of Slack messages

Through AI implementation, we transformed these into structured one-pagers and facilitated ideation sessions. This created a new process where:

Recorded, AI-facilitated meetings → Generated outputs including:

• User stories

• Product documentation

• Website/app copywriting

• Benchmark research

• Client testimonials

• Action point lists

The magic happened when we realized these AI-generated outputs could themselves become inputs for further processes. For example, AI-created user stories and product documentation now feed into:

• Wireframes

• Product roadmaps

• Visual assets

• Functional prototypes and proof-of-concepts

From Designs to Functional Prototypes

Previously, we would design multiple screens with high-fidelity visuals but non-functional prototypes in Figma. Now, we use tools like V0 to build functional proof-of-concepts for landing pages, dashboards, or product features.

The result? What used to take weeks now takes days—and clients love it.

Beyond Delivery: Operational Transformation

The input-output framework extends beyond client deliverables to internal operations:

• Our P&L: Transformed from a massive, manually-updated Google Sheet into an AI-powered app using V0 and Supabase

• Team time allocation: Converted from a complex Gantt-based Google Sheet to an AI application that provides real-time insights into how team members allocate time across projects

How to Implement the Framework in Your Business

1. Identify your key activities - What core functions drive your business?

2. Map your existing inputs - What information, documents, or processes already exist?

3. Determine potential AI outputs - How could AI transform these inputs?

4. Create feedback loops - How can AI-generated outputs become inputs for other processes?

5. Start small - Select one high-impact process to transform

Remember, this framework works regardless of your industry. Whether you run a service business, e-commerce store, or manufacturing operation, you have existing inputs that AI can transform.

The Real Benefits

Implementing AI through this framework delivers three key benefits:

1. Faster delivery - Processes that took weeks now take days

2. Cost reduction - Automation reduces labor costs on repetitive tasks

3. Quality improvement - Structured approaches lead to more consistent outputs

Getting Started Today

Look around your business. What meetings, documents, or processes could AI help transform? Start with one input-output transformation and measure the results.

The businesses succeeding with AI aren't just experimenting with the technology—they're systematically identifying where it fits within their existing workflow.

What existing business input will you transform first?