Becoming an AI-First Company: From Chaos to Clarity

Struggling to integrate AI across your organization without adding more confusion and complexity? Wondering how successful businesses are aligning teams and workflows to truly embrace AI-first operations?

In this article, you’ll discover a practical framework for transforming your company into an AI-first organization—so you can cut through the chaos, unlock clarity, and lead with confidence in a rapidly evolving business landscape.

Why an AI-First Mindset Matters for Teams and Businesses

If your team uses ChatGPT in an ad hoc manner, daily tinkering with it through random acts of artificial intelligence (AI), research shows they're probably about 30% more productive than they are without it. While this improvement is significant, it's not the full potential of what AI can unlock.

Systematizing your AI usage—where you define something, and AI does it—could improve productivity by 300% or more. 

Adopting an AI-first mindset across your business is the bridge that will get you there.

What does an AI-first mindset look like? Instead of immediately diving into manual work, asking a colleague for help, or using traditional methods to complete a task or project, you pause and ask yourself: “How could AI help me with this?”

If your teams lack this mindset, you'll struggle to teach them to use any AI system you build because they aren't comfortable with using AI in the first place.

Why AI System Implementation Fails Without an AI-First Mindset

Rachel Woods worked with an agency that had a comprehensive onboarding process for any new client they brought into their business. 

Her team systematized the onboarding with an extensive AI process that ran for two hours.

The system saved massive amounts of time, but the only people who understood how to use it were the initial team that had contacted Woods in the first place—those who had already been using AI. The broader team, who hadn't adopted an AI-first mindset, didn't understand how to use and engage with the system effectively.

The solution required backtracking. Instead of teaching the team how to use the system, Woods taught them about writing prompts and using AI for basic tasks. Once that foundational familiarity was established, the team easily understood how to use the sophisticated AI process for onboarding.

#1: Assign 3 Critical AI-First Roles

The businesses making the fastest progress with AI projects in their operations have figured out they need to staff three roles to lead the internal AI operations function. Once in place, these roles remove a lot of friction and help you move forward quickly.

Role 1, AI Visionary or AI Leadership: This person leads strategy conversations, ensures you're working on the highest-impact opportunities, and keeps business goals in mind.

This role often falls to the owner, founder, or COO—leaders with executive authority.

People often assume that this person should also be responsible for driving AI progress, but someone else needs to manage implementation because the AI visionary kicks off a series of time-intensive functions when they say, “We're going to use AI for our client onboarding process.” It's better to hand off those functions to the following two roles so the visionary can continue to lead with strategy.

Role 2, AI Operator: Think of this person as the AI operations project manager who bridges business processes with AI requirements and drives the AI-ification of your business processes. Properly supported with time to focus on AI transformation, this role represents the biggest differentiator between businesses implementing AI quickly and those struggling to figure out how to move forward.

The ideal AI operator is a project manager or process-oriented team lead who's excited about AI and naturally excels at detailed work.

Role 3, Implementor: This dedicated role handles the technical building, testing, and improving of your AI systems. They make changes based on feedback, fix problems when systems break, and upgrade systems as new capabilities emerge.

#2: Train Your Team for AI-First Thinking

Before you can create complicated systems for your teams to use, you'll need to help them develop the instinct to think, “How could AI help with this?” as a first response.

Here are specific strategies for helping your team develop an AI-first mindset:

Set Clear Expectations: Require everybody to use AI in their work regularly and share what they've learned with others.

Focus on Real-World Examples: Giving your team hands-on experience with how AI can change real work they've already done will help them understand how to work with it. Walk through how they completed a project or stand-alone task without AI, and then redo the work with them in ChatGPT.

Implement Habit Stacking: Inserting AI into existing workflows rather than creating entirely new processes builds familiarity and comfort with the technology. Find a process your team is already doing regularly, like onboarding a client, and figure out where you can insert a single prompt or AI-run task. Make it super easy by providing copy-and-paste prompts for the AI model you're using with specific instructions.

As you continue to encourage AI-first thinking, each team member will begin to instinctively see AI opportunities. This organic recognition is the foundation you need before you ask the whole team to tackle complex AI implementations.

#3: Identify and Prioritize Strategic AI Edge Opportunities

Once you've established an AI-first mindset across your team, you can start thinking about applying AI to your existing processes and systems.  

As you list use cases, you'll notice they fall into two categories: Commoditized use cases, where you can leverage someone else's tool or process instead of building one from scratch, and high-leverage use cases that benefit from your unique expertise and processes. 

The latter is what Woods refers to as your AI edge–specific use cases that not only save time but also help you build a competitive edge in the market. These use cases are where you should prioritize implementation and invest in custom development.   

If you had unlimited time, what would you add to your existing services or launch a new service to gain a competitive edge in your market? 

Now, consider how AI can help bring those things to life.

#4: Systematize Your Business Processes With AI: The CRAFT Cycle Framework

Once you have the right people in place and your entire team consistently uses an AI-first mindset, your AI Operator and Implementor can use the CRAFT framework to transform business processes into AI-powered systems.

C: Clear Picture

You need a clear picture of the current process. Collecting process information from teams can happen through questionnaires asking about inputs, steps, outputs, or deliverables, links to existing materials, and any templates currently being used.

Once you understand the current process, you need to map and convert it into a structured format with:

• Clear inputs or prompts for AI
• Defined steps for AI execution
• Specified outputs

This structured documentation becomes the playbook for executing the realistic design phase.

R: Realistic Design

Realistic design involves figuring out what's achievable. If you're considering something that feels like a multiple-month process, you should pare it down to an initial version you can build and implement in a week.

When you have the first version set, you can expand it.

When initially evaluating what's realistic, focus on processes that convert information into other types of information. For example, a content process that takes a transcript from a talk and converts it into multiple social media posts represents an ideal process because it involves clear transformation AI can handle effectively.

A: AI-ify and Automate

Most people naturally want to jump into creating demonstrations or testing whether AI can handle specific tasks first thing. While this experimentation is valuable, waiting until this third step to build makes you much faster at creating what you actually need because you've completed the planning work upfront.

Implementation tasks can range from simple–such as writing out all the prompts to use in the process, to complex–plugging prompts into automated systems or setting up AI agents.

The Implementator typically handles this step.

F: Feedback

Many organizations skip this stage in their rush to move on to the next project, but without intentional feedback collection, you risk not realizing the full ROI of your AI implementation. You want people to evaluate the quality of AI output and flag anything that makes the system less valuable before full deployment.

Rather than pushing a newly built AI system live for everyone, Woods recommends rolling it out to a small set of people and tasking them with quickly providing high-volume feedback.

There are two types of critical feedback to collect. 

Substance feedback focuses on whether the AI creates an output or executes processes that feel completely off-target. For example, suppose you're running a content process, and all the post topics are terrible. In that case, that's a substance issue, usually indicating you need to spend more time on instructions and breaking down what you're trying to get AI to accomplish.

Style feedback addresses formatting and usability issues that are easier to fix. For instance, you create an AI system that generates sales briefs for team members before they make client calls, then find that nobody uses the reports because they can't easily find basic information like company size. Adjusting the formatting to move the company size to the top of the report solves the problem.

Each of these improvements ensures your AI outputs are genuinely useful for your team rather than technically correct but practically unusable.

T: Team Rollout

The team rollout phase ensures that your AI system becomes a natural part of your organization's operations rather than a sophisticated tool that sits unused because it's not properly integrated into daily workflows.

Training represents the most critical component of the team rollout. Everyone who will use the AI system needs to understand how to use it, how to provide feedback, and what their responsibilities are within the new process. Consider recording live training sessions, then linking to the recordings wherever the process is used.

Making it convenient and natural for people to access the system dramatically increases usage. This can be as simple as ensuring links to your AI system appear where they are executed in your project management tool.

Other Notes From This Episode

• Connect with Michael Stelzner @Stelzner on Facebook and @Mike_Stelzner on X.
• Watch this interview and other exclusive content from Social Media Examiner on YouTube.

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