5 Steps to Building With AI: What It Can Do Reliably (and How to Start)

This article first appeared on VentureBeat.

Businesses know they can’t ignore artificial intelligence (AI)—but when it comes to building with it, the real questions aren’t What can AI do? It’s What can it do reliably? And more importantly, Where do we start?

This post introduces the VISTA Framework, a structured approach to prioritizing AI opportunities. Inspired by project management models such as RICE (Reach, Impact, Confidence, and Effort), VISTA focuses on four dimensions—Business Value, Implementation Speed, Scalability, and Tolerance for Risk—to help you confidently choose your first AI project.

Where AI Is Succeeding Today

AI isn’t writing novels or running businesses just yet, but where it succeeds is still valuable. It augments human effort; it doesn’t replace it.

In coding, AI tools improve task completion speed by 55% and boost code quality by 82%. Across industries, AI automates repetitive tasks—emails, reports, data analysis—and frees people to focus on higher-value work.

This impact doesn’t come easily. All AI problems are data problems. Many businesses struggle to get AI working reliably because their data is stuck in silos, poorly integrated, or simply not AI-ready. Making data accessible and usable takes effort, which is why it’s critical to start small.

Generative AI works best as a collaborator, not as a replacement. Whether it’s drafting emails, summarizing reports, or refining code, AI can lighten the load and unlock productivity. The key is to start small, solve real problems, and build from there.

The VISTA Framework: A Smarter Way to Prioritize AI Projects

Everyone recognizes the potential of AI, but when it comes to making decisions about where to start, they often feel paralyzed by the sheer number of options.

That’s why having a clear framework to evaluate and prioritize opportunities is essential. The VISTA Framework provides a structured way to balance business value, implementation speed, scalability, and tolerance of risk, ensuring that you focus on AI projects that are both practical and high impact.

This framework draws on what I’ve learned from working with tech leaders, combining practical insights with proven approaches like RICE scoring and cost-benefit analysis to help businesses focus on what really matters: delivering results without unnecessary complexity.

Why a New Framework?

Why not use existing frameworks like RICE?

While useful, they don’t fully account for AI’s stochastic nature. Unlike traditional products with predictable outcomes, AI is inherently uncertain. The “AI magic” fades fast when it fails, producing bad results, reinforcing biases, or misinterpreting intent. That’s why implementation speed and risk are critical. This framework helps bias against failure, prioritizing projects with achievable success and manageable risk.

By tailoring your decision-making process to account for these factors, you can set realistic expectations, prioritize effectively, and avoid the pitfalls of chasing overly ambitious projects. In the next section, I’ll break down how to use the framework and how to apply it to your business.

The VISTA Framework: Four Core Dimensions for AI Adoption

1. Business Value

   • What’s the impact? Start by identifying the potential value of the application. Will it increase revenue, reduce costs, or enhance efficiency? Is it aligned with strategic priorities? High-value projects directly address core business needs and deliver measurable results.

2. Implementation Speed

   • How quickly can this project be implemented? Evaluate the speed at which you can go from idea to deployment. Do you have the necessary data, tools, and expertise? Is the technology mature enough to execute efficiently? Faster implementations reduce risk and deliver value sooner.

3. Scalability (Long-Term Viability)

   • Can the solution grow with your business? Evaluate whether the application can scale to meet future business needs or handle higher demand. Consider the long-term feasibility of maintaining and evolving the solution as your requirements grow or change.

4. Tolerance of Risk

   • What could go wrong? Assess the risk of failure or negative outcomes. This includes technical risks (Will the AI deliver reliable results?), adoption risks (Will users embrace the tool?), and compliance risks (Are there data privacy or regulatory concerns?). Lower-risk projects are better suited for initial efforts. Ask yourself: If I can achieve only 80% accuracy, is that okay?

Scoring With the VISTA Framework: Prioritizing AI Investments

Using the VISTA Framework, each potential project is scored across four dimensions on a 1-5 scale, ensuring a structured way to prioritize AI investments.

1. Business Value: How impactful is this project?

2. Implementation Speed: How realistic and quick is it to implement?

3. Scalability: Can the application grow and evolve to meet future needs?

4. Tolerance of Risk: How manageable are the risks involved? (Lower risk scores are better.)

For simplicity, you can use T-shirt sizes (e.g., small, medium, large) instead of numbers to score dimensions.

Calculating a Prioritization Score

Once you’ve sized or scored each project across the four dimensions, you can calculate a prioritization score:

Here, α (the Risk Weight Parameter) allows you to adjust how heavily risk influences the score:

• α=1 (Standard Risk Tolerance): Risk is weighted equally with other dimensions. This is ideal for organizations with AI experience or those willing to balance risk and reward.

• α> (Risk-Averse Organizations): Risk has more influence, penalizing higher-risk projects more heavily. This is suitable for organizations that are new to AI and those operating in regulated industries or environments where failures could have significant consequences. Recommended values: α=1.5 to α=2.

• α<1 (High-Risk, High-Reward Approach): Risk has less influence, favoring ambitious, high-reward projects. This is for companies that are comfortable with experimentation and potential failure. Recommended values: α=0.5 to α=0.9.

By adjusting α, you can tailor the prioritization formula to match your organization’s risk tolerance and strategic goals. 

This formula ensures that projects with high business value, reasonable time to market, and scalability—but manageable risk—rise to the top of the list.

Applying VISTA: A Practical Example of AI Prioritization

Let’s walk through how a business could use the VISTA Framework to decide which generative AI project to begin with. Imagine you’re a midsize ecommerce company looking to leverage AI to improve operations and customer experience.

Step 1: Brainstorm opportunities.

Identify inefficiencies and automation opportunities, both internal and external.

Here’s a brainstorming session output:

• Internal Opportunities

  1. Automating internal meeting summaries and action items

  2. Generating product descriptions for new inventory

  3. Optimizing inventory restocking forecasts

  4. Sentiment analysis and automatic scoring for customer reviews

• External Opportunities

  1. Creating personalized marketing email campaigns

  2. Implementing a chatbot for customer service inquiries

  3. Generating automated responses for customer reviews

Step 2: Build a decision matrix.

Evaluate each opportunity using the four dimensions: Business Value, Implementation Speed, Scalability, and Risk. In this example, we’ll assume a risk weight value of α=1. Assign scores (1-5) or use T-shirt sizes (small, medium, large) and translate them to numerical values.

Step 3: Validate with stakeholders.

Share the decision matrix with key stakeholders to align on priorities. This might include leaders from marketing, operations, and customer support. Incorporate their input to ensure that the chosen project aligns with business goals and has buy-in.

Step 4: Implement and experiment.

Starting small is critical, but success depends on defining clear metrics from the beginning. Without them, you can’t measure value or identify where adjustments are needed.

1. Start small: Begin with a proof of concept (POC) for generating product descriptions. Use existing product data to train a model or leverage prebuilt tools. Define success criteria—such as time saved, content quality, or the speed of new product launches—up front.

2. Measure outcomes: Track key metrics that align with your goals. For this example, focus on the following.

   • Efficiency: How much time is the content team saving on manual work?

   • Quality: Are product descriptions consistent, accurate, and engaging?

   • Business Impact: Does the improved speed or quality lead to better sales performance or higher customer engagement?

3. Monitor and validate: Regularly track metrics such as return on investment (ROI), adoption rates, and error rates. Validate that the POC results align with expectations and make adjustments as needed. If certain areas underperform, refine the model or adjust workflows to address those gaps.

4. Iterate: Use lessons learned from the POC to refine your approach. For example, if the product description project performs well, scale the solution to handle seasonal campaigns or related marketing content. Expanding incrementally ensures that you continue to deliver value while minimizing risks.

Step 5: Build expertise.

Few companies start with deep AI expertise, and that’s okay. You build it by experimenting. Many companies start with small internal tools, testing in a low-risk environment before scaling.

This gradual approach is critical because businesses often have trust hurdles that must be overcome. Teams need to trust that the AI is reliable, accurate, and genuinely beneficial before they’re willing to invest more deeply or use it at scale. By starting small and demonstrating incremental value, you build that trust while reducing the risk of overcommitting to a large, unproven initiative.

Each success helps your team develop the expertise and confidence needed to tackle larger, more complex AI initiatives in the future.

Wrapping Up

You don’t need to boil the ocean with AI. Like cloud adoption, start small, experiment, and scale as value becomes clear.

AI adoption should follow a structured approach. That’s why the VISTA Framework helps businesses prioritize AI investments strategically. Focus on projects that deliver quick wins with minimal risk. Use those successes to build expertise and confidence before expanding into more ambitious efforts.

Generative AI has the potential to transform businesses, but success takes time. With thoughtful prioritization, experimentation, and iteration, you can build momentum and create lasting value.

To learn more, download the ebook: A Guide to Event-Driven Design for Agents and Multi-Agent Systems.