10 Steps for Developing an AI Strategy

As we think about artificial intelligence (AI) these days, two things come to mind immediately: vast potential and an almost equal amount of hype. Certainly, the current and emerging AI tools are transformative; they are revolutionizing businesses, enhancing decision-making, and driving unparalleled efficiencies.  However, determining how to cut through the ever-present hype and use these tools to create actual value for your business requires a pragmatic AI strategy to identify what to build, how to build it, and how to manage it once it is built. This strategy ensures that AI initiatives are aligned with your organizational goals, effectively managed, and ethically deployed.  

So how do you begin building an AI strategy, one tailored to your business? How do you incorporate your goals and your capabilities?  

At RevGen, we’ve developed a process to help you build an AI strategy specifically tailored to your organization. We start with understanding your business goals and finish with measuring how each AI project AI impacted those goals. Along the way, we identify and prioritize use cases, consider how to build or skill-up your team, select technologies, and establish a governance framework. 

Before You Begin

One note before you begin crafting your organization’s AI strategy: please start playing with AI now. As with any emerging technology, you can’t create a strategy without understanding the tools and how they apply to your business.  

Start with ChatGPT and CoPilot and branch out from there. Watch YouTube videos and spend some time browsing the “AI Services” portions of the AWS, Azure , and GCP sites. There are thousands of AI tools out there and you need to start to be familiar with what AI can (and can’t) do.  

If you wait until your AI strategy is “complete” before getting your hands on the tools, your strategy is going to be both less relevant and more likely to require rework. 

Step 1: Start with the Fundamentals

Any solid strategy is grounded in the goals of the business and guided by the challenges that business faces. It will require buy-in from key stakeholders and need executive sponsorship. Your AI strategy needs to support the goals of your business — successful AI projects will either directly support those goals or will address specific challenges the business is facing. You need to understand this to identify, prioritize, build, and measure AI initiatives.

Begin by documenting those business goals and challenges. Discuss them with executive leadership and key stakeholders in the business and IT to make sure you understand how they plan on accomplishing the goals and addressing the challenges. Identify specific pain points that AI might be able to solve. Look for inefficiencies and manual processes. Ask about the biggest expenses. Listen for statements that begin, “If we could only…”

The goal of this first step is to build the understanding necessary to create an executive vision and secure executive alignment. The vision creates a clear, compelling picture of how AI will transform your business while securing leadership’s commitment needed to drive AI initiatives. This will help you develop alignment throughout the organization. 

Step 2: Understand Your Starting Point

Next, work on understanding the current state of AI and data capabilities within your business.  You are trying to understand items such as: 

• Current AI projects, tools, and expertise 
• Data sources, experts, and capabilities, including data sources, reports, core applications, data warehouses, etc. 
• Cloud platforms currently used, such as AWS, Azure, or GCP 
• Individuals or teams with experience with (or a passion for) AI 

Knowledge of the capabilities, skills, and data that you to currently have allows you to understand the projects you can tackle now versus where your organization needs to grow before addressing more complicated challenges. 

Step 3: Define your AI Vision and Objectives

A little over 15 years ago, we entered the mobile era, and every company had to have a mobile app. It didn’t matter whether the app was helpful to the user or brought value to the company. Consequently, millions of dollars were wasted on the development of mobile apps that were rarely used. 

To avoid this, your company needs to be able to clearly articulate a vision of what AI means for your organization and how it will help you reach the business goals documented in Step 1. Will you use AI to transform your industry? Will it make your employees more efficient? Will it help you attract new customers or increase sales to existing customers? Will it help you reduce operational costs?  

By clearly defining how AI can help you meet your goals and establishing clear, measurable objectives aligned with those business goals, you can ensure that your projects are providing clear value. 

After you have a solid draft of your AI Vision, socialize it with stakeholders and executives. You’re looking for feedback and alignment. 

Step 4: Develop Use Cases

Once you’ve defined where you want to go, you and your colleagues get to brainstorm about how to get there.  Conduct a workshop (RevGen can help!) where you get all those great ideas down. For each idea, evaluate how feasible it is, what the cost of implementation might be, what benefit the project would have, and how you would measure that benefit. You don’t have to get too specific as this stage; T-shirt sizing for cost and value is fine at this point.

Next, you’ll want to analyze each item.  Prioritize high-value, low-cost projects with easily measurable benefits for quick wins. These projects typically have the highest ROI and clear business cases.

Once you’ve got a small handful of high-priority use cases, go back and flesh out the business case for each one. Evaluate the difficulty of implementing each one. How much will it cost? Do you have the right technologies? The data? The skills? Refine the cost, moving from t-shirt sizing to real numbers.

What will it take to build, deploy, and maintain the project? How many additional sales will it drive?  How much cost can it reduce? How will you measure the benefits?

These business cases will give you a clear picture of which projects fall in the sweet spot of technically feasible with a reasonable cost to develop and substantial impact to the business. 

Step 5: Grow Your AI Team

Once you know the type of projects you will be working on, it’s time to think about your team. Identify the skills needed and plan for hiring or training existing employees. Think about when you need those skills so that you can hire or train at the right time. Consider partnerships with AI vendors and consultants that can help you quicky move you towards your goals.   

Finally, think about how you want to foster knowledge sharing and excitement about AI within your organization. We recommend implementing one of two methods: 

• AI Center of Excellence: These are “top-down” groups within a company, typically aimed at education and standardizing delivery through best practices. They are sponsored by the organization and typically organized by one or two designated individuals. 
• AI Community of Practice: Less formal, these groups typically are self-organized and focus more on generating excitement. They usually have more “show and tell” and “did you hear?” type of content than Centers of Excellence. 

Step 6: Design Your Supporting Technical and Data Infrastructure

Choosing the right AI tools, platforms, and technologies can have a significant impact on your AI development. Factors such as the expertise of your team, tools or platforms currently used, and the types of initiatives you want to accomplish will drive these decisions. Bring in technical architects to assist with these evaluations.

Keep in mind that all AI initiates are dependent on data. You should identify other initiatives, especially around data quality and availability, to support your AI development. Is your data easily accessible? Is your data quality high? 

Step 7: Create Your AI Strategy Roadmap

Now, you get to take all the work you’ve done and create a roadmap for AI. Depending on where you’re starting, the roadmap can cover as little as a few months or as much as a couple of years. Your roadmap should contain the following elements: 

• Quick wins: Map out the top use cases developed in step 5. Be sure to think about dependencies between initiatives, as the same people will typically be involved in most of your first AI projects and it’s easy to overload them. 
• Longer-term projects: Add in some longer-term, strategic projects with a high projected ROI.   
• Governance: Include a project to ensure that governance and ethics are considered as a part of every AI initiative, as described in the next step. 

Step 8: Establish Governance and Ethics

No one wants to end up in the news because of an unintended consequence of their AI project. To prevent this, establish a governance and ethics framework early in your AI journey. A governance framework, complete with processes, guidance, and a cross-functional team provides the double (and triple) check needed to keep your AI projects aligned with your corporate values.  

For each proposed project, the governance committee should consider:  

• Is the project ethical and aligned with your organizational values? 
• Does the project have value for the organization that outweighs the cost? Value should include not just financial return but learning and capability creation while cost should include not just development and ongoing maintenance cost but opportunity cost. 
• How does the project compare with other AI proposals? Which will provide the most value for the company based on the investment? 

Step 9: Plan for Change Management

 AI is a new technology, and an effective change management plan will dramatically reduce resistance across your organization. You want to make sure you incorporate a communication plan to keep stakeholders informed as well as training and support for employees who will be asked to adapt to AI driven changes. 

Step 10: Monitor and Evaluate

 As mentioned earlier, the best AI initiatives provide demonstrable value to the organization. You need to measure and continue to monitor that value as AI projects are deployed. KPIs should be defined as part of the use cases as they are being developed. Ensure that deployment plans include monitoring and checkpoints where that ROI data is calculated.

This also means that you need to track the actual development cost. This rigorous tracking of cost and benefit helps you more efficiently develop use cases in the future and demonstrates a track record of accountability and success with AI. 

Additionally, encourage a community of continuous improvement. Whether you elected for a Community of Practice or a Center of Excellence, you should be collecting and disseminating lessons learned and feedback. 

Conclusion

Developing an AI strategy is essential for companies to harness the full potential of AI, driving innovation, efficiency, and building a competitive advantage while managing the risks inherent in any new technology. An effective AI strategy not only ensures successful AI initiatives but positions companies to cut through the hype and see sustained success in an increasingly AI-driven world. 

Interested in how RevGen can help define your AI Strategy? Contact us today to speak to an expert.

Noah Benedict leads RevGen’s Digital Enablement practice.  He is passionate about using technology to advance business and empower his clients to embrace new opportunities.