Artificial intelligence (AI) is no longer a futuristic concept; it is a transformative force reshaping industries, redefining customer experiences, and streamlining operations across the globe. For organizations, the potential of AI is immense—offering opportunities to drive efficiency, unlock new revenue streams, and gain a competitive edge. However, implementing AI successfully is far from straightforward. Many organizations struggle to balance the cost of implementation, the risk of failure, and the value AI can create.
For non-technical executives tasked with leading their organizations into an AI-powered future, the challenge is clear: how can you adopt AI in a way that minimizes risk while maximizing long-term impact? The answer lies in taking a phased approach to AI implementation—a strategy often referred to as “crawl, walk, run.” This blog will explore how organizations can use this framework to introduce AI incrementally and strategically.
The “crawl, walk, run” framework is a structured approach to adopting new technologies like AI. It emphasizes starting small with manageable projects before gradually scaling up to more complex and transformative initiatives. This phased strategy allows organizations to build foundational capabilities, learn from early projects, and align their efforts with broader business goals.
• Crawl: In this phase, organizations focus on small-scale projects that are low-cost and low-risk. These initiatives are often internally focused and designed to build familiarity with AI tools and processes. This phase allows you to build the foundations for the right technology, infrastructure, processes and skills.
• Walk: Once the organization has gained confidence and built foundational capabilities, it can move on to medium-complexity projects that involve moderate investment and risk. This phase may include both internally focused and customer-facing initiatives.
• Run: The final phase involves high-cost, high-risk projects with transformative potential. These are game-changing initiatives that can significantly impact the organization’s competitive position or customer experience.
The crawl, walk, run framework works because it reduces risk while enabling organizations to learn incrementally. By starting small and building on early successes, companies can avoid costly mistakes that often arise from overambitious or poorly planned AI projects. This approach also helps secure buy-in from stakeholders by demonstrating tangible progress at each stage.
Not all AI projects are created equal. To implement AI effectively, organizations must carefully evaluate potential initiatives based on their impact, focus area, cost of implementation, risk of failure, and value creation.
AI projects can be broadly categorized as either game-changing or enhancement-focused. Game-changing projects aim to radically transform business models or customer experiences. For example, using AI to develop a personalized product recommendation engine could revolutionize how customers interact with your brand. On the other hand, enhancement projects focus on improving existing processes or efficiencies—such as automating routine data entry tasks.
While game-changing projects are more exciting and potentially more valuable in the long term, they also come with higher risks and costs. For this reason, organizations should prioritize enhancement projects during the crawl phase. These initiatives provide quick wins that build momentum while laying the groundwork for more ambitious efforts later.
AI initiatives can also be classified based on whether they are internally focused or customer-facing. Internally focused projects aim to streamline operations or improve internal processes—for example, optimizing supply chain management or automating HR workflows. Customer-facing projects directly impact customers by enhancing their experience with your organization—for instance, deploying an AI-powered chatbot for customer support.
Internally focused projects are typically less risky because they do not directly affect customers. As such, they are ideal candidates for early-stage (crawl) initiatives. Customer-facing projects can be introduced during the walk phase once the organization has gained confidence in its AI capabilities.
When evaluating potential AI projects, it is essential to consider three key factors:
1. Cost of Implementation: How much will it cost to develop and deploy the solution? This includes expenses related to data collection and preparation, purchasing tools or platforms, and hiring skilled talent.
2. Risk of Failure: What is the likelihood that technical or operational challenges will derail the project? Early-stage initiatives should prioritize low-risk options to minimize setbacks.
3. Value Creation: What benefits will the project deliver? While early initiatives may offer modest value (e.g., small efficiency gains), later phases should focus on high-value opportunities like revenue growth or improved customer satisfaction.
By balancing these factors carefully at each stage of implementation, organizations can ensure they allocate resources wisely while minimizing risks.
The crawl phase is all about starting small with low-cost and low-risk initiatives that build foundational capabilities for future success. At this stage, the primary goal is not necessarily to generate significant value but rather to establish basic infrastructure and gain hands-on experience with AI technologies.
Examples of Crawl Phase Projects
1. Automating Routine Tasks
One of the simplest ways to get started with AI is by automating repetitive tasks using robotic process automation (RPA). For example, RPA tools can handle tasks like invoice processing or data entry with minimal human intervention. These solutions are relatively inexpensive to implement and deliver immediate efficiency gains.
2. Data Cleaning and Preparation
High-quality data is critical for successful AI implementations. During the crawl phase, organizations should invest in organizing and labeling their data to prepare for more complex machine learning models later on. While this work may not deliver immediate ROI, it creates a strong foundation for future success.
• Focus on measurable outcomes that demonstrate progress without overwhelming resources.
• Invest in training employees on basic AI concepts to build internal expertise gradually.
Once your organization has built confidence during the crawl phase, it’s time to move into the walk phase by tackling medium-complexity projects that involve moderate investment and risk.
Examples of Walk Phase Projects
1. Predictive Maintenance
3. Predictive maintenance uses machine learning algorithms to predict when equipment is likely to fail so that repairs can be scheduled proactively. This approach reduces downtime and maintenance costs while improving operational efficiency.
2. AI-Powered Chatbots
Customer-facing chatbots powered by natural language processing (NLP) can handle common inquiries efficiently while freeing up human agents for more complex issues. These solutions improve response times and enhance customer satisfaction at a relatively modest cost.
• Leverage insights from crawl-phase projects to refine strategies.
• Ensure collaboration between technical teams (e.g., data scientists) and business units (e.g., operations or marketing).
The run phase represents the pinnacle of your organization’s AI journey—where high-cost and high-risk projects deliver transformative value.
Examples of Run Phase Projects
1. Personalized Customer Experiences
3. Advanced recommendation systems powered by machine learning can deliver highly personalized experiences for customers—for instance, suggesting products based on browsing history or purchase behavior. These systems drive customer loyalty while increasing revenue per user.
2. AI-Powered Decision Support Systems
AI can assist executives in making strategic decisions by analyzing vast amounts of data quickly and accurately—for example, identifying market trends or forecasting demand patterns. These tools enhance agility while reducing decision-making risks.
• Establish robust governance frameworks to manage risks effectively.
• Continuously monitor performance metrics to ensure maximum ROI.
While the crawl-walk-run framework provides a clear roadmap for implementing AI incrementally, there are several pitfalls organizations must avoid:
1. Skipping Phases
3. Jumping directly into complex “run” projects without building foundational capabilities often leads to costly failures.
2. Underestimating Data Challenges
Many organizations overlook the importance of high-quality data as a prerequisite for successful AI implementations.
3. Neglecting Change Management
Introducing AI requires cultural change within an organization—executive sponsorship and employee engagement are critical throughout every phase.
Adopting artificial intelligence is not just about deploying cutting-edge technology; it’s about transforming your organization thoughtfully over time. By following the crawl-walk-run framework—starting small with low-risk initiatives before scaling up—you can minimize risks while maximizing long-term impact.
For non-technical executives leading this journey, patience and strategic planning are key ingredients for success. With careful execution at every stage of implementation, your organization can unlock AI’s transformative potential while staying aligned with its broader business goals—one step at a time!
Note: The opinions expressed here are mine and do not necessarily reflect the views of Gartner.