Connecting the dots: The missing piece in your AI Roadmap

Hi there,

Many organizations I talk to face a common challenge when building their AI roadmaps: they approach each use case in isolation.

Teams are often quick to jump on "quick win" projects or tackle ambitious goals without considering how these efforts connect across the business. This narrow focus can leave valuable opportunities on the table—missed chances to streamline processes, reuse infrastructure, and multiply impact.

A better approach is to connect the dots between use cases before deciding which one to build.

That's what we're going to explore today!

The Problem with Isolated AI Projects

Take a look at this use case prioritization matrix for a hypothetical sales department looking to improve its Request for Proposal (RFP) process with AI:

What we see is that Use Case 1, an RFP Chatbot, in the upper right corner stands out as the best starting point. Quick context: this RFP chatbots is a chat-with-your-documents use case that helps sales teams answer RFP-related questions quickly and accurately. It promises to deliver high value quickly and also ranks quite well in terms of expected feasibility.

That's where most organizations would jump in and start building that chatbot.

But by jumping in immediately, they miss a larger opportunity: the chance to connect the dots. Sure, the chatbot might make immediate progress, but here's the problem: as the project moves forward, things are likely to get more complex, as AI initiatives often do. And when those inevitable challenges arise, the AI project can stall, people lose interest, and momentum fades. Without broader support and integration into other use cases, the initiative might halt entirely.

That's why it's critical to tie this use case back to something larger:

• What other benefits do you unlock by working on this?

• What capabilities are you building that can support other, potentially even more impactful, projects?

• What learnings from this chatbot can be applied elsewhere?

Connecting the dots is about finding links and synergies between different AI use cases. Instead of treating each initiative in isolation, this approach identifies and leverages connections between projects.

To do this, focus on four key areas:

1. Common Data Sources: Look for AI projects that rely on similar data inputs, like customer profiles, transaction histories, or, in our case, RFP documents.

2. Technology Overlaps: Identify use cases that share a technological foundation, such as large language models, or similar data pipelines.

3. Similar Processes: Recognize when different teams or departments are solving similar problems, like making sense of complex documents or responding to client requests.

4. Same Customer Journey: Find out which use cases appear at the same time in a given customer journey. Are you working on three AI use cases that all occur during the checkout process? Good, then there's your connection!

By focusing on these areas, you can uncover synergies that allow AI projects to share infrastructure and create greater value across the board. This creates a strong foundation for future projects and sets the stage for sustained impact.

The Power of Sequencing

Why is connecting the dots so powerful? The answer lies in the compound effects you generate by building AI capabilities that work together rather than in isolation.

When you connect the dots between AI use cases strategically, you can:

• Leverage shared data pipelines and infrastructure: Centralizing efforts eliminates redundancies and improves efficiency.

• Maximize technology overlap: Advances in one project - like improving a model or tool - can benefit others, accelerating progress.

• Enable smart sequencing: Once synergies are identified, you can sequence AI initiatives to optimize resources and results.

Sequencing is the process of determining the order in which AI projects should be executed. By sequencing projects strategically, you can tackle foundational, quick-win use cases first – those that offer immediate value, but also pave the way for more complex initiatives. This creates a roadmap where each project builds on the success of the previous one.

For example, foundational projects might create a data pipeline or implement key technology, which later projects will rely on. Sequencing ensures that the necessary groundwork is in place, reducing duplication of efforts, minimizing risks, and shortening time to implementation for subsequent use cases. It also balances quick wins with longer-term strategic bets, maintaining momentum while laying the groundwork for future AI capabilities.

By connecting the dots, you not only increase the overall efficiency of your AI roadmap, but you also gain the ability to unlock real transformational gains in the long run, rather than getting bogged down in delivering many independent quick wins.

How This Transforms Your AI Strategy

Let's revisit the RFP process as a practical example of connecting the dots and sequencing use cases. Let's say our backlog consists of the following use cases:

When we look at this, starting from our prioritized Use Case 1 (RFP Chatbot), we can identify three use cases that share a clear connection in terms of their data inputs (the RFP documents submitted by customers) and similar technologies (large language models). These use cases are:

• Content Generator: Helps draft sections in response to the RFPs.

• Call Transcriber: Analyzes calls related to the RFP and enriches its data source.

• Domain Avatar: Emulates an internal subject matter expert to provide further answers or clarify concepts mentioned in the RFP documents.

By connecting the dots between these use cases, we can now sequence their execution and align them on our roadmap:

1. Start with a Quick Win: The RFP Chatbot
   The RFP Chatbot is a clear, feasible quick win. This tool will immediately help clarify questions about the RFP, using the provided documents. By significantly reducing manual effort, it delivers significant business value in a relatively short time. More importantly, the chatbot serves as the foundation for the entire document pipeline that other projects will rely on. By setting up this infrastructure first, you lay the groundwork for future use cases like the Content Generator and Domain Avatar.

2. Next, Build on That Foundation: The Content Generator
   Once the document processing pipeline is established for the RFP Chatbot, you can extend its use to the Content Generator. This tool leverages the same data but goes further by drafting sections of the RFP based on predefined templates and client specifications. By reusing the infrastructure already set up for the chatbot, there's no need to reinvent the wheel. This approach allows you to implement the Content Generator efficiently while maintaining consistency across both tools.

3. Phase in Strategic Bets: The Domain Avatar
   The Domain Avatar represents a more ambitious, strategic use case. It serves as a virtual expert, advising teams on complex RFP-related questions or even engaging in live conversations with clients. Since it shares the document pipeline and language model technology with the previous two use cases, there is no need to build everything from scratch. You can phase this project in gradually, adding advanced features as needed. Each step builds on the foundation created by the earlier use cases, reducing development risk and shortening time to deployment.

By following this phased, sequenced approach, your AI roadmap delivers both immediate value and long-term impact. The RFP Chatbot provides a fast return, the Content Generator builds on that momentum, and the Domain Avatar becomes a strategic bet that becomes increasingly feasible over time.

Each project strengthens the others by sharing resources, infrastructure, and insights. This approach doesn't just create isolated wins, but it delivers compounded benefits across your AI initiatives. And that's exactly what we want!

Conclusion

The takeaway here is clear: your AI roadmap should never be a collection of individual, isolated projects. Instead, try to connect the dots – find areas where use cases share similar technology, data, or process overlaps to unlock synergies that multiply the impact of your AI efforts.

This integrated approach ensures that you're not just meeting short-term goals, but also setting the stage for a more advanced, scalable AI future – one that could transform your entire organization.

So, as you plan your next AI initiatives, take a step back and look for those synergies.

The dots are already there, now it's your time to connect them.

See you next Friday,
Tobias