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. When it comes to building with it, the real question isn’t, What can AI do — it’s, What can it do reliably? And more importantly: Where do you start?

My fellow innovators, let’s get real. The AI revolution demands strategic thinking, not just enthusiasm. Here, we’ll introduce a framework to prioritize AI opportunities. It’s inspired by project management frameworks like the scoring model for prioritization. It balances business value, time-to-market, scalability, and risk for choosing the right path forward.

Where AI is succeeding today

AI isn’t writing the next great American novel or running Fortune 500 companies just yet. However, where it succeeds, it’s a game-changer. It augments human effort without replacing it. In coding, AI tools improve task completion speed significantly. AI automates those pesky repetitive tasks—emails, reports, data analysis—freeing us to focus on importance.

Here’s the thing—it’s not easy. Most AI challenges are data challenges. Many businesses can’t get AI working reliably because their data isn’t ready. It takes effort to make data accessible and usable. Start small to make manageable progress.

Generative AI works best as a helper, not a replacement. Drafting emails, summarizing reports, or refining code can free up time. Moving step-by-step, you can unlock productivity in ways not yet fully understood. The key? Start small. Solve actual problems. Build momentum from there.

How can AI improve business efficiency?

AI can vastly improve business efficiency by automating repetitive tasks, allowing employees to focus on strategic activities. For instance, AI can handle data entry, report generation, or customer inquiries, which often take up a chunk of time. This frees up employees to focus on more value-driven tasks, like strategy and customer engagement.

In a small business, AI could improve customer service. A chatbot can answer common questions, freeing up customer service reps for more complex queries. This not only enhances customer service but also boosts employee productivity. Over time, businesses recognize patterns, allowing AI to learn and adapt. This results in smarter, faster decision-making.

A framework for deciding where to start with generative AI

Everyone sees AI’s potential, but the choices can feel overwhelming—like standing before a buffet on an empty stomach.

A clear framework for prioritizing AI opportunities is crucial. It structures decision-making, balancing trade-offs between business value, time-to-market, risk, and scalability.

This framework draws from real-world business experiences, mixing practical insights with approaches like RICE scoring and cost-benefit analysis. It helps focus on what really counts: results without unnecessary complexity.

Why a new framework?

Existing frameworks like RICE are useful, but they miss AI’s unpredictable nature. AI comes with inherent uncertainty. The “magic” fades when it fails, producing bad results, bias, or misinterpretation. Time-to-market and risk are critical.

This framework biases against failure, prioritizing achievable success. By considering these factors, you can set realistic expectations and avoid overreaching. Let’s unpack how it works for your business.

The framework: four core dimensions

Business Value:
- What’s the impact? Look at the potential value. Does it boost revenue, cut costs, or enhance efficiency? Does it align with strategic goals? High-value projects tackle core needs and deliver results.
Time-to-market:
- How quickly can you implement it? Speed is key. Do you have the data, tools, and expertise? Is the tech mature? Quick implementations reduce risk and deliver value fast.
Risk:
- What could go wrong? Evaluate technical risks. Will AI deliver reliable results? What are the adoption and compliance risks? Lower-risk projects are better starters. Consider if 80% accuracy is acceptable.
Scalability:
- Can it grow with your business? Assess if the application can scale or handle higher demand. Can you maintain and evolve it as needs change?

Scoring and prioritization

Score each potential project across these four dimensions on a 1-5 scale:

Business Value: How impactful is it?
Time-to-market: Is it realistic and quick?
Risk: Are the risks manageable?
Scalability: Can it grow with needs?

For simplicity, use T-shirt sizing—small, medium, large—for dimensions instead of numbers.

Calculating a prioritization score

Size or score each project across the dimensions, then calculate a prioritization score:

Prioritization score = (Business value × Time-to-market × Scalability) ÷ Risk^α

Adjust α to change risk’s influence:

α= 1 (standard risk): Equal weighting, balancing risk and reward.
α> 1 (risk-averse): More weight to risk, ideal for new to AI or regulated environments.
α< 1 (high-risk, high-reward): Less influence on risk, pushing ambitious projects.

This formula highlights projects with high business value and scalability but manageable risk.

Applying the framework: a practical example

Let’s see how this works in practice.

Imagine you’re a mid-sized e-commerce company keen on leveraging AI for operational and customer experience improvements.

Step 1: Brainstorm Opportunities

Identify inefficiencies and automation opportunities, internal and external:

Internal:
1. Automate meeting summaries.
2. Generate new product descriptions.
3. Optimize inventory forecasts.
4. Sentiment analysis for reviews.
External:
1. Personalized marketing campaigns.
2. Customer service chatbot.
3. Automated responses for reviews.

Step 2: Build a Decision Matrix

Use the four dimensions to evaluate each opportunity: Business value, time-to-market, risk, scalability.

Application	Business value	Time-to-market	Scalability	Risk	Score
Meeting Summaries	3	5	4	2	30
Product Descriptions	4	4	3	3	16
Optimizing Restocking	5	2	4	5	8
Sentiment Analysis for Reviews	5	4	2	4	10
Personalized Marketing Campaigns	5	4	4	4	20
Customer Service Chatbot	4	5	4	5	16
Automating Customer Review Replies	3	4	3	5	7.2

Step 3: Validate with Stakeholders

Share the matrix with stakeholders—marketing, operations, customer support. Align priorities with their input.

Step 4: Implement and Experiment

Start small but define clear metrics at the beginning. This helps track and identify needed changes.

Start small: Begin a proof of concept (POC) with product descriptions. Use existing data for training.
Measure outcomes: Track key metrics—efficiency, quality, business impact.
Monitor and validate: Keep an eye on ROI, adoption and error rates. Make adjustments if outcomes don’t align.
Iterate: Learn from the POC to refine. If successful, scale to more campaigns.

Step 5: Build Expertise

Most start without AI expertise—it’s built through experimentation. Begin small, build trust, and ensure reliability before taking on larger initiatives.

Building trust with AI solutions is key. Successes develop the expertise needed to handle more complex AI projects.

Wrapping up

Don’t try to boil the ocean. Like cloud adoption, start small, experiment, and scale as the value becomes apparent.

AI needs the same treatment: start small, learn, scale. Aim for quick wins with minimal risk. Use those successes to build the expertise and confidence to take on ambitious projects.

Gen AI can revolutionize business, but success takes time. Thoughtful prioritization, experimentation, and iteration are vital. Build momentum to create value beyond what anyone’s imagined.