Not long ago, artificial intelligence (AI) seemed like the stuff of sci-fi movies: flying cars, personal robots, and information injected directly into one’s brain.
And when we previously looked at reality, you and I might even feel that AI is a futuristic technology reserved for tech giants or research labs.
But today, AI is no longer a distant dream; it’s a force reshaping industries and changing how we work, communicate, and create.
Heck! You can access that intelligence at a price lower than your weekly grocery expenses, or your monthly telco bill!
From automating repetitive tasks to uncovering deep insights, AI has made its way into the workflows of countless organizations. And while the potential is massive, so are the challenges.
Where do you even begin?
How do you identify the right opportunities for AI?
What if the investment doesn’t pay off?
These questions echo the same uncertainties businesses faced in the early days of the internet, cloud computing, and mobile technology.
The key lies in preparation. Successfully integrating AI isn’t just about implementing tools—it’s about creating an environment where AI can thrive.
At Radiant Institute, we call this AI Enablement: a strategic, continuous process of equipping your organization to leverage AI for long-term growth and innovation. Enablement goes beyond technology. It touches culture, workflows, training, and most importantly, clarity.
This article will guide you through the 4 Stages of Organizational AI Enablement: Exploration, Experimentation, Examination, and Expansion. Together, these stages form a cycle of learning and growth, fueled by the 4 Cs: Curiosity, Courage, Commitment, and Clarity.
By the end, you’ll have a framework to approach AI with confidence and purpose. Before we jump into this article, here’s a thought: adopting tools is just the beginning. Enablement is where the real transformation happens.
Quick Note:
“While this article focuses on AI Enablement, it’s worth noting that implementing AI tools is just one part of the process. Enablement ensures your organization is prepared to maximize AI’s potential through strategy, culture, and alignment. We’ll explore the difference between AI Adoption vs. AI Enablement further in next week’s article.”

Learning from the great teacher called “History”.
The Internet Revolution: A Story of Reluctance and Transformation
Let’s rewind back to 1995. The world is abuzz with a curious new invention—the internet. Most people think of it as a niche hobby for tech enthusiasts or an expensive experiment for research institutions.
Businesses, especially small ones, are skeptical. Why spend money on a website when newspaper ads, brochures, and word-of-mouth have worked just fine for decades?
At the time, fewer than 1% of the world’s population had internet access. To connect, you had to endure the screech of a dial-up modem (remember that iconic dial tone?), only to load pages so slowly you could brew coffee in between clicks.
And yet, some forward-thinking companies saw potential.
Amazon launched in 1995—not as the global behemoth we know today but as an online bookstore run out of Jeff Bezos’ garage. His gamble? That people would buy books online instead of visiting their local bookstore.
By 1998, the internet started gaining momentum. Google was born, changing how we find information forever. Businesses slowly began to experiment. Some built basic websites with their contact details—a modern version of the Yellow Pages. Others ventured into e-commerce, often facing ridicule. “Who would buy clothes without trying them on?” skeptics asked.
But then, the data rolled in. Companies noticed that their websites were attracting customers they’d never have reached otherwise. Those who experimented with email marketing realized they could communicate with thousands of people at once for a fraction of the cost of traditional mail.
Fast forward to the early 2000s, and the internet became indispensable. When the internet is down even for 5 minutes, you can see the chaos in the office! Businesses that had hesitated to adopt it now faced an uphill battle. Those who embraced it early had an edge—they’d already learned how to optimize their websites, rank higher on search engines, and, most importantly, build trust with their online customers.
By 2010, even traditional industries like real estate and healthcare had fully integrated online tools. The internet was no longer optional; it was the foundation of modern business.
Here’s the hindsight wisdom: The internet didn’t just change how we worked; it changed what we valued. Speed, convenience, and connection became the new currency. Businesses that recognized these shifts early didn’t just survive—they thrived. They understood that innovation isn’t just about adopting technology; it’s about rethinking how you serve your customers, how you scale your operations, and how you stay relevant.
Now, in 2025, AI is following a similar trajectory. Much like the internet in its early days, AI feels experimental, even intimidating. But if history has taught us anything, it’s that early curiosity, experimentation, and refinement pave the way for exponential growth.
The question is: Will you take the first step before it becomes a necessity?

Moving from Exploration to Expansion.
The 4 Stages of Organizational AI Enablement
The story of the internet’s rise teaches us an invaluable lesson: major technological shifts don’t happen all at once.
They unfold in phases, with each stage laying the foundation for the next. Early adopters who explored, experimented, and refined their strategies were the ones who gained the most, setting themselves apart from those who hesitated or rushed without a plan.
AI is no different.
To harness its full potential, organizations must approach it with a structured process—one that builds on curiosity, rewards calculated risks, learns from feedback, and culminates in sustainable growth. At Radiant Institute, we call this the 4 Stages of Organizational AI Enablement.
Here’s a quick overview of these stages:
1. Exploration: Curiosity Sparks Discovery
Every journey begins with curiosity. In this stage, organizations analyze their workflows to uncover opportunities where AI can add value. It’s about asking questions like:
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Where are we facing inefficiencies or bottlenecks?
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Which processes feel repetitive or data-heavy? Exploration is the foundation—it sets the direction for everything that follows.
2. Experimentation: Courage to Test and Learn
The next step is to test the waters. This involves designing small-scale pilot programs and experimenting with AI solutions to validate hypotheses. It’s about taking calculated risks, measuring outcomes, and learning quickly—embracing failures as data points rather than setbacks.
3. Examination: Commitment to Refine
Once the initial experiments are complete, it’s time to step back and review. This stage focuses on analyzing results, gathering feedback, and refining processes. Commitment is key here—refinement takes time and requires the discipline to iterate until the solution is truly effective.
4. Expansion: Clarity to Scale
With proven solutions in hand, the final stage is scaling AI across the organization. This involves more than just rolling out tools. It’s about creating clarity around goals, aligning teams, and ensuring the infrastructure can support long-term growth. Clarity ensures that scaling is deliberate, not chaotic.
These stages aren’t a one-and-done process.
Like the infinity loop we mentioned earlier, AI enablement is a continuous cycle. Each iteration builds on the last, pushing the organization toward deeper integration, innovation, and impact.
Now, let’s jump further into each stage, starting with Exploration, where curiosity lights the way.

We don’t know what we don’t know. (Yet)
AI Enablement Stage ONE – Exploration – Curiosity Sparks Discovery
Every journey begins with a question: So… what can I do with this?
And of course, it doesn’t help when the answer is “sky’s the limit”.
Personally, I think that’s why most organizations are stuck with AI.
Yes, I know I’m guilty of that as well when I say something like “AI is like MSG. What dishes, or what processes, you want to spice up?”
Dishes are easier because we know the outcome of a good dish. But with something unknown like AI, it can be one step forward, two steps back!
Which is why we usually give the AI Advantage Chart to our clients:

The AI Advantage Framework
Note: I’m already planning a video on this, so drop me a DM or email (mav@radiant.institute* with the subject like AI Advantage Video), and I’ll send it to you once it’s out of the oven.*
Armed with the AI Advantage Chart, the Exploration stage is where curiosity takes the lead. It’s the moment organizations step back and examine their workflows, processes, and customer interactions, asking, “Out of the six core areas here, where can AI make the biggest difference?”
But exploration isn’t just about dreaming big—it’s about being intentional.
Successful exploration requires a clear focus on identifying opportunities and challenges within your current operations. It’s less about jumping on trends and more about discovering where AI can solve real problems.
What Happens in Exploration?
Exploration involves taking a magnifying glass to your organization’s operations. Here are the key elements:
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Analyzing Bottlenecks: Look for processes that slow your team down. Are there repetitive tasks eating up time and resources?
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Spotting Data-Rich Processes: AI thrives on data. Identify workflows that already generate or rely on significant amounts of information.
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Engaging Stakeholders: Talk to teams at every level. They often know exactly where the pain points are but haven’t been asked how to solve them.
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Asking the Right Questions: Start with curiosity.
Challenges of Exploration
Exploration sounds exciting, but it’s not without its hurdles:
- Overwhelm: The sheer scope of AI can leave organizations feeling paralyzed. With so many possibilities, where do you start? (Again, that’s why we have the AI Advantage Chart as a main guiding tool)
- Data Silos: Data silos, which occur when information is fragmented or restricted to specific departments, can significantly limit the scope of exploration. These silos prevent teams from accessing the full picture, leading to missed opportunities where AI could have made a substantial impact.
- Skepticism: Teams may be resistant to change, especially if they view AI as a threat rather than a tool. I wrote about this issue at length in this article, where I talk about the concept of Primary and Secondary Task, and Meaningful and Mundane Work.
How to Succeed in Exploration
Here are practical steps to navigate this stage with clarity and purpose:
- Conduct an AI Readiness Assessment: Identify processes that are repetitive, time-consuming, or prone to error. Map out areas where data is already being collected (e.g., CRM, sales, or customer service).
- Start Small: Focus on one department or process to explore. For example, HR might examine how AI could streamline candidate screening.
- Create a Cross-Functional Task Force: Bring together employees from different teams to brainstorm and identify areas where AI could make a difference.
- Leverage External Resources: Consult AI specialists or use tools like AI-readiness checklists to guide your exploration.

Real-World Example: UPS’s Exploration of AI in Logistics
UPS, a global leader in logistics, recognized inefficiencies in their delivery routes, leading to increased fuel consumption and operational costs. During their exploration stage, they analyzed their delivery processes and identified that optimizing routes could significantly enhance efficiency. By implementing an AI-powered fleet management system, UPS was able to optimize delivery routes, resulting in a reduction of fuel costs by $50 million per year. (Source)
The Key to Exploration: Curiosity
Exploration is the foundation of AI Enablement, and curiosity is the fuel that drives it. It’s about asking “What if?” and being open to the possibilities that AI can bring.
But curiosity alone isn’t enough—it needs to be paired with focus and structure to uncover actionable insights.
So, where in your organization could curiosity spark the next big opportunity? As we move to the next stage, Experimentation, we’ll explore how to test these insights with purpose and courage.

Adopt the Experimenter’s Mindset
AI Enablement Stage TWO – Experimentation – Courage to Test and Learn
Exploration sparks ideas, but experimentation puts them to the test. As a matter of fact, back in January 2020 when I had the opportunity to meet and learn from Tom Kelley of IDEO, he mentioned how adopting an experimenter’s mindset helped them become one of the most innovative companies in the world.

Meeting Tom Kelley, founding team member at IDEO
Stage two is where courage becomes essential. Experimentation isn’t about perfection—it’s about starting small, embracing uncertainty, and turning insights from the exploration stage into actionable pilots.
In this phase, organizations validate their AI hypotheses by running controlled experiments. These experiments help answer crucial questions:
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Does this solution work in practice?
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What’s the measurable impact?
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Are there any unexpected challenges or opportunities?
Successful experimentation builds the foundation for a long-term AI enablement.
What Happens in Experimentation?
Experimentation focuses on testing AI solutions in real-world settings without committing to full-scale implementation. Here’s what it involves:
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Defining Hypotheses: Based on exploration, organizations identify specific areas to test AI. For example, “Can AI reduce manual errors in our invoice processing system by 50%?”
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Designing Pilots: Small-scale implementations or sandbox environments are created to test AI solutions.
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Setting KPIs: Clear, measurable outcomes are established to evaluate success.
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Iterating Quickly: Experiments are designed for rapid iteration, allowing teams to refine or pivot based on results.
Challenges of Experimentation
While experimentation is exciting, it’s not without its challenges:
- Fear of Failure: Teams may hesitate to experiment due to a fear of wasting resources or looking bad if the pilot doesn’t succeed.
- Resource Constraints: Limited budgets, time, or expertise can restrict the scope of experiments.
- Unrealistic Expectations: Overhyping AI’s capabilities without realistic KPIs can lead to disappointment.
How to Succeed in Experimentation
To navigate experimentation with confidence, consider these steps:
- Start Small, Scale Fast: Choose one department, process, or product line for your pilot program. For example, test an AI-powered chatbot in customer service rather than across the entire organization.
- Involve Cross-Functional Teams: Collaboration between IT, operations, and end-users ensures diverse perspectives and better problem-solving.
- Leverage Existing Tools: Use affordable, accessible AI platforms like Google Cloud AI, Microsoft Azure or OpenAI Platform Playground to minimize upfront investment.
- Track and Learn: Focus on KPIs such as time savings, error reduction, or improved customer satisfaction. Treat every failure as a data point, not a setback. Insights from a failed pilot are invaluable for future iterations.

Real-World Example: Michelin’s Experimentation with AI in Manufacturing
Michelin, a global leader in tire manufacturing, sought to improve efficiency and quality control in their production processes. During the experimentation phase, they implemented AI-powered data analytics tools to monitor factory performance and detect anomalies. The pilot program focused on a single production line, allowing them to refine the AI solution before scaling. The result? Improved production efficiency and a significant reduction in quality control issues. (Source)
The Key to Experimentation: Courage
Experimentation requires boldness—an openness to try, fail, and learn. It’s about seeing each test as a stepping stone, not a final destination. Courage is what turns abstract AI ideas into tangible outcomes.
As we move to the next stage, Examination, we’ll explore how to analyze the results of experimentation and refine AI solutions for maximum impact.

There is much value in “Evaluated” Experience.
AI Enablement Stage THREE – Examination – Commitment to Refine
Experimentation might validate an idea, but it’s in Examination that the real value emerges.
This stage is all about taking a step back to review, analyze, and refine. It’s where organizations commit to digging deeper into the data, learning from feedback, and making the necessary adjustments to ensure AI solutions deliver sustainable results.
In many ways, Examination is the bridge between testing and scaling. It requires discipline, patience, and a commitment to continuous improvement—because in AI, the first implementation is rarely the best one.
What Happens in Examination?
The Examination stage focuses on learning and fine-tuning. Here’s what it involves:
- Data Analysis: Reviewing performance metrics from the pilot program. Did the AI solution meet its KPIs? What patterns or anomalies emerged?
- Stakeholder Feedback: Engaging with teams and users to gather qualitative insights. How did the solution impact their workflows or experiences?
- Identifying Gaps: Pinpointing areas where the AI solution underperformed or where unexpected challenges arose.
- Iterating: Using insights to refine the solution—adjusting algorithms, retraining models, or redesigning workflows.
Challenges of Examination
This stage requires a shift from action to reflection, which comes with its own set of challenges:
- Data Overload: Sifting through large volumes of data can feel overwhelming without the right tools or focus.
- Resistance to Change: Teams may struggle to embrace the need for further refinement, especially if they view the pilot as “good enough.”
- Lack of Clarity: Without clear KPIs or structured feedback processes, it can be hard to determine what needs improving.
How to Succeed in Examination
Commitment is the key to making this stage successful. Here are some practical tips:
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Focus on KPIs: Revisit the goals set during Experimentation. For example, if the pilot aimed to reduce processing times by 30%, did it succeed? If not, why?
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Use Dashboards: Leverage analytics tools to visualize and track performance metrics in real-time, making it easier to identify trends and gaps.
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Engage End-Users: Host feedback sessions with employees or customers who interacted with the AI solution. Their insights often reveal blind spots.
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Iterate Quickly: Treat Examination as a dynamic process. Adjustments should be implemented and retested promptly to maintain momentum.

Real-World Example: IBM Watson’s Evolution in Healthcare
IBM’s Watson, an AI system designed to assist in medical decision-making, underwent significant refinement after its initial deployment. Early versions faced challenges in accurately diagnosing certain cancer cases, leading to critical feedback from medical professionals. In response, IBM committed to an extensive examination phase, analyzing performance data and incorporating clinician feedback. This process involved retraining Watson’s algorithms with more diverse and comprehensive datasets, enhancing its ability to provide accurate treatment recommendations. Through this commitment to refinement, IBM improved Watson’s performance, making it a more reliable tool in the healthcare industry. (Source)
The Key to Examination: Commitment
Examination isn’t a “set it and forget it” phase—it’s where the hard work of refining takes place. Commitment is what turns a good solution into a great one, ensuring AI tools are optimized for both performance and user experience.
As we move to the final stage, Expansion, we’ll explore how to take these refined solutions and scale them across the organization with clarity and confidence.

Double down on what works!
AI Enablement Stage FOUR – Expansion – Clarity to Scale
After thorough examination and refinement, the next step is Expansion—scaling the AI solution across the organization.
This stage requires clarity in vision and execution to ensure that the AI implementation delivers consistent value enterprise-wide.
Scaling AI is not merely about deploying technology; it’s about integrating AI into the fabric of the organization, aligning it with business objectives, and preparing the workforce for seamless adoption.
What Happens in Expansion?
Expansion involves broadening the AI solution’s application from pilot environments to full-scale deployment. Key activities include:
- Strategic Alignment: Ensuring the AI initiative aligns with organizational goals and delivers measurable business value.
- Infrastructure Scaling: Upgrading IT infrastructure to support increased data processing and AI workloads.
- Change Management: Preparing the workforce for AI integration through training and clear communication.
- Monitoring and Governance: Establishing frameworks to monitor performance, manage risks, and ensure compliance.
Challenges of Expansion
Scaling AI comes with its own set of challenges:
- Resource Allocation: Significant investment in technology and talent is required.
- Cultural Resistance: Employees may resist changes due to fear of job displacement or lack of understanding.
- Operational Complexity: Integrating AI into existing systems and processes can be complex and may disrupt operations if not managed properly.
How to Succeed in Expansion
Clarity in strategy and execution is crucial for successful scaling. Consider the following steps:
- Develop a Clear Roadmap: Outline the phases of AI deployment, including timelines, resources, and expected outcomes.
- Invest in Training: Equip employees with the necessary skills to work alongside AI tools, fostering a collaborative human-AI environment.
- Enhance Infrastructure: Ensure your IT infrastructure can handle the increased demands of AI workloads, including data storage and processing capabilities.
- Implement Governance Frameworks: Establish policies to monitor AI performance, ensure ethical use, and maintain compliance with regulations.

Real-World Example: ServiceNow’s AI Integration Across Enterprises
ServiceNow, a leading digital workflow company, has been instrumental in helping enterprises scale AI solutions across their operations. Under the leadership of CEO Bill McDermott, ServiceNow has integrated AI into its platform to automate workflows and enhance productivity. By providing AI-driven tools that streamline processes across various departments, ServiceNow enables organizations to achieve efficiency and scalability in their AI initiatives. (Source)
The Key to Expansion: Clarity
Expansion demands a clear vision and meticulous execution. Clarity ensures that AI solutions are not only deployed effectively but also embraced by the organization, leading to sustainable transformation and growth.
As we conclude this journey through the four stages of AI enablement, it’s evident that each phase—Exploration, Experimentation, Examination, and Expansion—requires a distinct mindset and approach. Embracing curiosity, courage, commitment, and clarity will empower organizations to harness the full potential of AI, driving innovation and competitive advantage in the ever-evolving business landscape.

The future of your organization lies in your ability to AI-enable your team
The Journey Ahead
AI enablement is not a one-time project or a simple checkbox on your digital transformation list—it’s a journey. Like the early days of the internet, it requires vision, patience, and the courage to take the first step, even when the path forward feels uncertain.
The 4 Stages of Organizational AI Enablement—Exploration, Experimentation, Examination, and Expansion—aren’t just a framework; they’re a guide to navigating this journey with purpose and clarity.
Each stage requires a unique mindset:
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Curiosity to uncover opportunities.
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Courage to test and learn from failures.
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Commitment to refine and improve.
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Clarity to scale with confidence.
The real power of AI lies not in the tools themselves, but in how you prepare your organization to embrace them. It’s about fostering a culture of innovation, empowering your teams, and aligning AI initiatives with your broader business goals.
So, where do you begin?
Start by asking one simple question: What’s possible?
Whether you’re identifying inefficiencies, testing a small pilot program, or scaling proven solutions, every step forward matters.
We’ve all heard too often of the proverb of the thousand miles, so I’ll not repeat it here. The important step is to begin—and to keep learning, refining, and growing along the way.
And just like the infinity loop that is the 4 Stages of AI Enablement, the journey never truly stops. And I know whe rewards along the way will keep you spurring on.

Maverick Foo
Lead Consultant, AI-Enabler, Sales & Marketing Strategist
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