Unlocking the Power of AI for Your Business: How to Build AI Products That Deliver Real Value

AI is everywhere – but how do you know which AI opportunities will truly benefit your business? With the ever-growing hype, it’s easy to get swept up in the excitement and end up investing in projects that don’t move the needle. The question is: how do you make sure your AI initiatives are the ones that actually create value?
As an entrepreneur or business leader, you’re probably hearing a lot about AI. The media tells us it’s transforming industries, but the reality is that it can be overwhelming. You're likely bombarded with a laundry list of AI ideas, but how do you know which ones will deliver the results you're looking for?
The key lies in strategy. AI is a tool, not a magic solution, and it can either drive real change or become just another shiny distraction. So how do you decide which AI products to build and implement? By using frameworks that align your projects with your business goals and make sure you're solving real problems.
In this article, I’ll share two powerful frameworks that will help you identify, prioritize, and successfully implement AI products, ensuring that you focus on what will actually move the needle for your business.
The Reality of Building AI Products: Strategy is Everything
AI is often presented as the next big thing, and with over $3.9 billion in venture capital flowing into AI projects in just one quarter, it’s clear that everyone is eager to get in on the action. But here’s the catch: AI isn’t just a shiny toy, it’s a tool. Like any tool, it can be powerful or useless depending on how it’s used.
The reality is that AI can deliver incredible value, but only if you approach it strategically. The risks are twofold:
1. Moving too quickly without a strategy: Jumping on the AI bandwagon without a clear plan can result in flashy solutions that don’t solve any real problems.
2. Moving too slowly: Delaying action can leave you behind, as competitors who’ve embraced AI take the lead.
Building AI products requires a strategic roadmap. And that’s where the right frameworks come in.
Avoiding Common Pitfalls: Aligning AI with Business Objectives
We read every day about businesses make the same mistakes when building AI products. These mistakes often arise from a lack of alignment with business objectives or poor leadership structure. The two biggest pitfalls are:
• Misalignment with business goals: Companies dive into AI because it’s trendy, but they forget to align their AI initiatives with core business objectives. This often leads to wasted time and money on projects that don’t solve real problems.
• Lack of cross-functional leadership (wrong team): Many AI projects are led by tech teams with little involvement from business stakeholders. But AI is not just a tech project, it’s a business initiative. For AI to succeed, it must be aligned with strategic goals, requiring leadership from decision-makers at every level.
With these pitfalls in mind, let's dive into two powerful frameworks to ensure your AI investments drive real value.
Identifying High-Value AI Use Cases: The D.E.W. Framework
So, how do you choose which AI use cases will truly impact your bottom line? The answer lies in the D.E.W. Framework: Data, Expertise, and Workflows.
1. Data: Do you have the right data to support your AI initiative? Without quality, relevant data, your AI project won’t perform as expected. For example, if you want to build an AI-powered recommendation engine but don’t have enough customer transaction data, you’ll be stuck.
2. Expertise: AI isn’t just about the technology; it’s about solving business problems. Does your team have the necessary domain expertise to ensure your AI solution is effective? If your company lacks knowledge in the specific business processes you're trying to automate, your AI will lack context and effectiveness.
3. Workflows: AI excels in optimizing existing workflows. Whether it’s automating repetitive tasks or improving decision-making, high-value AI projects integrate seamlessly with your existing operations.
Real-World Application: The D.E.W. Framework in Action
Let’s say you run a retail business. How can you apply the D.E.W. framework?
• Data: Your company has transactional data, customer preferences, and inventory data, perfect for AI-driven recommendations or inventory forecasting.
• Expertise: Your marketing team understands customer behavior, and your product team knows how to manage stock. But your data science capabilities are still developing.
• Workflows: You already have clear processes for handling customer orders and managing inventory, but these processes are largely manual and could benefit from automation.
Given this, a perfect starting point might be building an AI-powered personalized recommendation system or an AI-driven demand forecasting tool—both of which can significantly improve revenue and operational efficiency.
Prioritizing AI Projects: The V.V.V. Framework
Now that you have potential AI projects on the table, how do you decide which to tackle first? The V.V.V. Framework—Viability, Value, and Velocity—will help you prioritize your initiatives.
1. Viability: Is the project technically feasible? Do you have the resources to implement it? For example, the company might have the data for personalized recommendations, but implementing the necessary AI tools could require additional technical expertise or resources.
1. Value: What’s the return on investment (ROI)? Value isn’t just about cost savings, it’s about how AI can make a tangible impact on your business. For instance, automating inventory management might save hundreds of labor hours, but personalized recommendations could drive significant sales.
2. Velocity: How quickly can the project be developed and deployed? Time matters. AI development can be complex and time-consuming. By assessing how quickly a project can be built, you can ensure that you don’t get stuck on a long, drawn-out project that risks becoming irrelevant before it’s even finished.
Using the V.V.V. framework, you might decide to start with a recommendation engine, a feasible, high-impact project that can be implemented relatively quickly. Inventory optimization can come next once you’ve scaled your AI expertise and resources.
Final Tips for Building AI Products That Actually Work
To make sure your AI initiatives deliver the most value, here are a few tips I’ve gathered over the years:
1. Involve subject matter experts at every stage: AI development isn’t just about algorithms, it’s about solving real business problems. Subject matter experts (SMEs) are crucial throughout the entire process. AI models need to be trained with the right context and understanding, and engineers alone can’t evaluate the quality of the AI's output.
2. Adapt to new ways of working: AI is different from traditional software development. Models evolve, and their output can be unpredictable. Collaboration between business and tech teams is crucial, releasing early versions and refining the model based on feedback.
3. Focus on incremental wins: Start small. AI development can be complex, so begin with manageable projects that deliver quick wins. This lets you learn and iterate before taking on larger-scale initiatives. For instance, automating a small, repetitive task could lead to quick improvements and provide insights that help you scale up later.
Why You Need a Partner to Navigate AI
Building AI products that drive real business value isn’t easy. It requires a clear strategy, expert knowledge, and a tailored approach. That's where I come in. I specialize in helping businesses identify AI opportunities, align them with business goals, and implement them effectively.
If you're ready to unlock the power of AI for your business—avoiding common pitfalls and creating solutions that deliver tangible results, I’d love to help. Let's talk Together, we can create a roadmap to leverage AI for your business in ways that will save time, reduce costs, and increase profits.