Launching your first AI project with a grain of RICE: Weighing reach, impact, confidence and effort to create your roadmap

Businesses know they can’t ignore AI, but when it comes to building it, the real problem is not What can AI do – It is, What can it do reliably? More importantly: Where did you start?

This article introduces a framework to help businesses prioritize AI opportunities. Inspired by the project management framework rice Scoring model for priority, it balances business value, time to market, scalability and risk to help you choose The first AI project.

Where AI is successful today

AI has not written novels or run a business yet, but the places where success is still valuable. It increases human efforts rather than replaces it.

When encoding, the AI ​​tool passes 55%, improve code quality by 82%. Throughout the industry, AI can automate repetitive tasks – email, reports, data analytics – to keep people focused on high-value work.

This effect is not easy. All AI problems are data problems. Many businesses have difficulty getting AI to work reliably because their data is trapped in silos, underintegrated or not ready at all. Make data accessible and available, which is why startups are small.

The generated AI is best suited as a collaborator, not a replacement. Whether it’s drafting an email, summarizing reports or refining code, AI can reduce load and unlock productivity. The key is to start solving the real problem small and build it from there.

A framework that decides to start with generating AI

Everyone recognizes The potential of AIbut they often feel paralyzed by a large number of choices when making decisions about where to start.

This is why it is essential to have a clear framework to evaluate and prioritize opportunities. It provides structure for the decision-making process, helping businesses balance tradeoffs between business value, market time, risk and scalability.

The framework draws on what I learned from working with business leaders, combining practical insights with proven approaches such as rice rating and cost-benefit analysis to help businesses focus on what really matters: delivering results without unnecessary complexity.

Why have a new framework?

Why not use existing frameworks like rice?

Although useful, they do not fully explain the random nature of AI. Unlike traditional products with predictable results, AI is inherently uncertain. When “AI Magic” fails, “AI Magic” disappears quickly, producing adverse results, enhancing bias or misunderstanding intentions. This is why time to market and risk are crucial. The framework helps bias failure and prioritizes projects with achievable success and manageable risks.

By tailoring the decision-making process to illustrate these factors, you can set realistic expectations, prioritize effectively, and avoid the pitfalls of chasing overly ambitious projects. In the next section, I will break down how the framework works and how it can be applied to your business.

Framework: Four core dimensions

1. Business Value:
   • What is the impact? First determine the potential value of the application. Will it increase revenue, reduce costs or increase efficiency? Is it aligned with strategic priorities? High-value projects directly meet core business needs and provide measurable results.
2. Time of listing:
   • How to implement this project quickly? Evaluate how fast you can go from idea to deployment. Do you have the necessary data, tools and expertise? Is this technology mature enough to be effectively executed? Faster implementation reduces risks and realizes value early.
3. risk:
   • What’s wrong?: Risk of failure or negative results for assessment. This includes technical risks (will AI bring reliable results?), adoption risks (will users accept the tool?) and compliance risks (whether there are data privacy or regulatory issues?). Low-risk projects are more suitable for initial efforts. Ask yourself, do you only achieve 80% accuracy, okay?
4. Scalability (long-term feasibility):
   • Can the solution grow with your business? Evaluate whether an application can be scaled to meet future business needs or meet higher needs. Consider the long-term feasibility of maintaining and developing solutions as demand grows or changes.

Ratings and Priority

Each potential item is scored on these four dimensions using a simple 1-5 scale:

• Business Value: How influential is this project?
• Time of listing: Realistic and fast implementation?
• risk: How can the risks involved be managed? (Lower risk scores are better.)
• Scalability: Can applications grow and grow to meet future needs?

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

Calculate the priority score

Once you have sized on four dimensions or scored each item, you can calculate the priority score:

Here, α( Risk weight parameters) Allows you to adjust the impact of risk on scores:

• α= 1 (standard risk tolerance): Risk is equally weighted with other dimensions. This is ideal for organizations with AI experience or willing to balance risks and rewards.
• α> (Risk Avoiding Organization): Risks have greater impact and the punishment for high-risk projects is even more serious. This applies to new organizations in AI, in regulated industries or environments where failures can have significant consequences. Recommended value: α= 1.5 to α= 2
• α<1 (high risk, high return method): The risk has less impact, favoring ambitious advanced projects. This is aimed at companies that are experimenting and potentially failing. Recommended value: α= 0.5 to α= 0.9

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

This formula ensures that items with high business value, reasonable time to market and scalability (but manageable risks) are improved to the top of the list.

Application framework: A practical example

Let’s explain how businesses use this framework to decide which one Gen AI Project first. Imagine you are a mid-sized e-commerce company looking to leverage AI to improve operations and customer experience.

Step 1: Brainstorm Opportunities

Identify internal and external inefficiency and automation opportunities. Here is the brainstorming session output:

• Internal Opportunities:
  1. Automated internal meeting summary and action items.
  2. Generate product descriptions for new stocks.
  3. Optimize inventory replenishment forecasts.
  4. Perform sentiment analysis and automatic rating for customer reviews.
• External Opportunities:
  1. Create personalized marketing email campaigns.
  2. Implement chatbots to query customer service.
  3. Generate an automatic response to customer reviews.

Step 2: Create a decision matrix

application	Business Value	Time of listing	Scalability	risk	Fraction
Conference Summary	3	5	4	2	30
Product Description	4	4	3	3	16
Optimized replenishment	5	2	4	5	8
Sentiment analysis of comments	5	4	2	4	10
Personalized marketing activities	5	4	4	4	20
Customer Service Chatbot	4	5	4	5	16
Automated customer comments and replies	3	4	3	5	7.2

Evaluate each opportunity using four dimensions: business value, time to market, risk, and scalability. In this example, we assume that the risk weight value is α=1. Assign scores (1-5) or use a t-shirt size (small, medium, large) and convert it to a numeric value.

Step 3: Verify with stakeholders

Share the decision matrix with key stakeholders to maintain priority. This may include leaders from marketing, operations and customer support. Consolidate their inputs to ensure that the selected project is aligned with the business objectives and has a buy.

Step 4: Implementation and Experiment

Startups are rare, but success depends on clearly defined metrics from the outset. Without them, you will not be able to measure value or determine where to adjust.

1. Begin small: First, with a proof of concept (POC) used to generate product descriptions. Use existing product data to train models or leverage pre-built tools. Predefined success criteria – such as time savings, content quality or speed at which new products can start.
2. Measurement results: Track key metrics that are consistent with your goals. For this example, highlight:
   • efficiency: How much time has the content team saved?
   • quality: Is the product description consistent, accurate and engaging?
   • Business Impact: Will increasing speed or quality lead to better sales or higher customer participation?
3. Monitoring and Verification: Regularly track ROI, adoption rate and error rate indicators. Verify that the POC results are consistent with expectations and adjust as needed. If some areas are not performing well, refine the model or adjust the workflow to resolve these gaps.
4. Iteration: Complete your approach using lessons learned from POC. For example, if the product description project is running well, expand the solution to handle seasonal events or related marketing content. Scaling step by step ensures that you continue to deliver value while minimizing risk.

Step 5: Build expertise

Few companies start with deep AI expertise – it doesn’t matter. You build it through experiments. Many companies start with small internal tools and test in low-risk environments before scaling.

This gradual approach is crucial because there is often a trust barrier for businesses. Teams need to believe that AI is reliable, accurate and truly beneficial until it is willing to invest more deeply or use it at a large scale. By launching small amounts and demonstrating incremental value, you can build trust while reducing the risk of overcommitment to achieve a large unproven plan.

Each success can help your team develop the expertise and confidence needed for larger, more complex AI plans in the future.

Summarize

You don’t need to boil the ocean with AI. Like adopting the cloud, with clear value, small starts, experiments and scale.

AI should follow the same approach: from small, learn and scale. Focus on fast winning projects with minimal risk. Utilize these successes to build expertise and confidence before expanding into more ambitious efforts.

AI Gen has the potential to change the business, but success takes time. With thoughtful priorities, experimentation, and iteration, you can build momentum and create lasting value.

Sean Falconer is an AI entrepreneur who lives in the place of residence Convergence.