The purpose of this article is to provide organizations with a comprehensive guide to strategically implementing AI while addressing AI-readiness gaps. By introducing the FastTrack AI approach, the article aims to help organizations experiment with AI in a structured and proactive manner. This approach ensures that organizations can leverage AI to drive innovation, improve efficiency, and gain a competitive edge, even if they are not fully prepared initially.

The objective of this article is to outline a clear, actionable framework for organizations to follow in their AI journey. This includes:

1. Identifying AI-Readiness Gaps: Assessing the current state of AI readiness in terms of data, culture, skills, infrastructure, and budget.
2. Spotting TOP-AI Opportunities: Recognizing high-impact AI initiatives that align with long-term business goals.
3. Crafting a Short-Term AI Strategy: Developing a plan to address readiness gaps while executing AI initiatives.
4. Monitoring, Adapting, and Iterating: Continuously tracking progress, adapting strategies, and iterating to improve AI-readiness and implementation success.

1. FastTrack AI: Your AI Journey

1.1 Overview

The FastTrack AI approach lets you strategically experiment with AI while bridging AI-readiness gaps in your organization. This method is intuitive, innovative, and proactive.

Four Steps to the FastTrack AI Approach:

1. Spot AI-Readiness Gaps
2. Identify TOP-AI Opportunities
3. Craft a Short-Term AI Strategy
4. Monitor, Adapt, and Iterate

Start by forming an AI strategy team, including business leaders, innovators, AI experts, data engineers, and database or data warehouse engineers. This team will develop and implement an AI strategy for your department or organization.

1.2 Diagram

1.3 Spot AI-Readiness Gaps

The first step in the FastTrack AI approach is to assess your current state culturally, technologically, and financially to identify potential gaps. Consider the five key aspects of AI readiness: Data Readiness, Culture Readiness, Skill Readiness, Infrastructure Readiness, and Budget Readiness. Use these aspects to create a survey-style checklist for identifying gaps.

Sample Survey Questions:

1. Do we totally know our data assets? (Yes/No/Some/Maybe)
2. Are we storing all the cool data we generate? (Yes/No/Some/Maybe)
3. Can we access all our data like a boss? (Yes/No/Some/Maybe)
4. Are we logging key customer interactions like pros? (Yes/No/Some/Maybe)

Create a similar checklist to pinpoint your gaps. By the end, you’ll know where your gaps are, and that will be your starting point. All “No” and “Some” answers indicate gaps to be filled. “Maybe” answers need further clarification before being mapped to “Yes,” “No,” or “Some.

1.4 Identify TOP-AI Opportunities

The goal of Step 2 is twofold: first, to identify AI initiatives that will be most beneficial to your organization, and second, to develop AI skills for leaders and members of your AI strategy team in a hands-on manner, filling part of the skill readiness gaps. This happens before addressing the gaps from the previous steps.

There are several ways to surface AI opportunities in your organization:

1. Brainstorm with Your AI Strategy Team: Identify areas where AI would be most applicable based on existing domain knowledge or known inefficiencies.
2. Examine Long-Term Goals: Determine if there are problems that AI can solve to help achieve your organization’s long-term objectives.
3. Gather Input from Business Leaders: Ask leaders from different units to submit their top problems that could benefit from AI. Ensure they understand where AI would be most helpful.

Once you have a series of AI ideas, turn them into concrete initiatives by combining them with the TOP-AI discovery framework. This will help you shortlist the most feasible and impactful initiatives. Depending on how deep you go into the framework, by the end of this step, you should have a handful of TOP-AIs or at least shortlisted promising AI initiatives.

Attempting to find TOP-AIs could also reveal that you don’t have good problems that AI will solve efficiently. In such cases, you can set AI aside but continue closing the critical gaps in your preparation. Alternatively, you can use several low-impact AI initiatives to build your organization’s AI experience. Either way, if better AI opportunities arise down the road, you will be prepared in terms of knowledge, experience, and possibly even data.

1.5 Craft a Short-Term AI Strategy

Once you’ve identified several TOP-AI opportunities, it’s time to craft a short-term AI strategy. This involves creating a plan to fill the gaps identified in Step 1 while simultaneously executing several AI initiatives to improve your organization’s AI-readiness.

Start by defining a clear long-term goal for AI. A specific long-term vision will provide purpose and motivation to push initiatives forward. Avoid broad goals like “We want to become AI ready,” as they can seem complex and unattainable.

Use the results from Step 2 to answer the following questions:

Where do you see the most AI opportunities?

Do these opportunities align with your long-term business objectives?

Which domains should you focus on and why?

By answering these questions, you can develop specific long-term goals. Once you know your long-term goals, define your short-term strategy. Essentially, you’ll use short-term strides to work towards your longer-term goals.

For example, if most of your AI opportunities are in customer service, and you see this as a competitive advantage, you might want to make customer service a key differentiator for your organization over the next three to five years. You could aim for faster response times, improved first contact resolution, increased productivity among service agents, and reduced burnout. As a one-year goal, you might aim to have at least one AI solution in production and 30% of the gaps from Step 1 closed. This forms your short-term strategy.

Depending on what you achieve in the first year, you can revise your strategy for the following year. These short-term strides will push you closer to your long-term goals of setting your organization apart in customer service.

Executing Your Short-Term Strategy

To execute your short-term strategy while filling AI-readiness gaps and experimenting with AI, you can use two approaches: the Innovative Method and the Optimization Method. Both can proceed in parallel.

1.5.1 Innovative Method

The Innovative Method “forces” you to get started with AI. You will shortlist a few TOP-AIs relevant to your long-term goals and start planning and executing those initiatives. As part of this, you will form an AI development team, make hiring decisions, explore infrastructure options, and take a project from an idea to implementation and then to production. Along the way, you will be learning, experiencing, and formalizing processes and tools.

Why Choose the Innovative Method?

The Innovative Method provides you with the needed experience in implementing AI. Through execution, you will learn to manage cross-functional AI development teams, evaluate AI infrastructure options, assess the success of initiatives, and deploy models to production. All of this will make future iterations of AI more efficient and streamlined.

Maximizing Investments

To maximize investments in the Innovative Method, use the lessons learned while piloting AI to create more formalized processes. As a side effect, it will close some of the gaps identified in Step 1. For example, as you establish the best configuration for cross-functional team collaboration, you will close gaps such as cultural readiness. Furthermore, even though you are experimenting with AI in the Innovative Method, several of the initiatives can make it to production because you are executing TOP-AIs, not just random AI projects. Additionally, if you measure success, you will know if these initiatives are delivering on their promises.

Real-World Example

Consider a company that used the Innovative Method to improve customer service. They formed an AI development team, identified key AI opportunities, and implemented AI solutions that reduced response times and increased customer satisfaction. This hands-on approach not only filled skill gaps but also positioned the company as a leader in customer service.

1.5.2 Optimization Method

The Optimization Method requires you to examine all the AI-readiness gaps identified in Step 1 and attempt to fill the most pressing ones. While some gaps can be filled using lessons learned from the Innovative Method, others need active planning. For example, company-wide AI education, data asset discovery and warehousing, and AI ethics and accountability require careful planning and can be addressed proactively. Additionally, you would work with the implementation team using the Innovative Method to ensure that certain processes have been formalized.

Why Choose the Optimization Method?

The Optimization Method helps you systematically address AI-readiness gaps, ensuring your organization is well-prepared for AI implementation. By proactively planning and filling these gaps, you create a solid foundation for successful AI initiatives.

Maximizing Impact

To maximize the impact of the Optimization Method, prioritize the most critical gaps first. Think of it like building a house: you start with the foundation. Skipping over essential steps like education and diving straight into AI pilots can leave you unprepared to recognize the best AI initiatives or measure their success. Worse still, you might end up solving the wrong problem with AI.

Real-World Example

Consider a company that used the Optimization Method to enhance its data readiness. They started with company-wide AI education, followed by data asset discovery and warehousing. This proactive approach ensured that when they began implementing AI projects, they had a strong foundation and clear understanding of their data assets.

Guidelines for Closing AI-Readiness Gaps

The table below provides guidelines on which gaps to close and when. Note that some of these gaps can be filled using the experience from the Innovative Method.

1.6 Monitor, Adapt, and Iterate

Track progress. Each short-term stride may expose new gaps. Over time, more pilots go into production and deliver value.

Allow 6 to 24 months to see significant results, depending on your starting point. Use an AI-readiness assessment to evaluate progress.

1.7 Conclusion

FastTrack AI offers a structured way to build and grow your organization’s AI capabilities. By identifying gaps, launching meaningful pilots, and improving readiness over time, your team will be positioned for long-term AI success.