Launching the first artificial intelligence project with a grain of rice: access to access, influence, confidence and effort to create your road map

Companies know that they cannot ignore artificial intelligence, but when it comes to building with it, the real question is not. What can artificial intelligence do – that it, What can you do reliably? And most importantly: Where do you start?

This article offers a framework to help companies give priority to artificial intelligence opportunities. Inspired by project management frameworks such as rice The registration form to determine the priorities, balances the value of business, the ability of the market, and the ability to expand and risk to help you choose The first artificial intelligence project.

As artificial intelligence succeeds today

Artificial intelligence does not write accounts or manage companies yet, but where they are still valuable. It increases the human effort, and does not replace it.

In coding, AI tools improve the speed of completing the task by 55 % and increased symbol quality by 82 %. Through industries, artificial intelligence works to automate repeated tasks-e-mail messages, reports and data analysis-people implement to focus on higher value work.

This effect does not come easily. All artificial intelligence problems are data problems. Many companies are struggling to reliably work artificial intelligence because their data is stuck in silos, or is badly integrated or simply not ready for intelligence. If the data is made on hand and can be used in an effort, which is why it is important to start small.

Ai Generalism works better as a collaborator, not an alternative. Whether this is the formulation of e -mail messages, reports or refining code, AI can reduce productivity and cancel productivity insurance. The key is a small start, solving real problems and construction from there.

A frame to determine the location of the start with obstetric artificial intelligence

Everyone gets to know Artificial intelligence capabilitiesBut when it comes to making decisions about the starting place, they often feel paralyzed due to the huge number of options.

For this reason, there is a clear framework for evaluating opportunities and setting opportunities for opportunities. It gives a structure for the decision -making process, which helps companies balance the terms of work, time to the market, risks and expansion.

This framework depends on what you learned from work with business leaders, and combines practical ideas and installed curricula such as rice records, cost analysis and benefits, to help companies focus on what really concerns: providing results without unnecessary complication.

Why is a new frame?

Why not use current frameworks like rice?

Although it is useful, it does not completely explain the random nature of artificial intelligence. Unlike traditional products with predictable results, artificial intelligence is inherently unconfirmed. “AI Magic” quickly disappears when it fails, leading to bad results, strengthening biases or bad faith. For this reason, the time of market and risk is very important. This framework helps in prejudice against failure, successfully defining projects priorities and controlled risks.

By allocating your decision -making process to calculate these factors, you can put realistic expectations, give priority effectively and avoid the restaurants of chasing ambitious projects. In the next section, I will dismantle how the frame works and how to apply it to your work.

Frame: Four basic dimensions

1. Work value:
   • What is the effect? Start by determining the potential value of the application. Will revenues increase, reduce costs or enhance efficiency? Do you comply with strategic priorities? Projects with high value directly address the basic work needs and provide measuable results.
2. Market time:
   • How quickly is this project implementation? Evaluation of the speed that you can move from the idea to post. Do you have the necessary data, tools and experience? Is technology mature enough to implement efficiently? Faster applications reduce risks and provide value sooner.
3. risk:
   • What can make a mistake?: Evaluating the risk of failure or negative results. This includes technical risks (Will artificial intelligence achieve reliable results?), Adoption risks (Will users embrace the tool?) And the risks of compliance (is there a privacy of data or organizational concerns?). Projects with low risk are more suitable for initial efforts. Ask yourself if you can achieve only 80 % resolution, is this good?
4. Expansion (long -term feasibility):
   • Can the solution grow with your business? Evaluating whether the application can expand to meet future work needs or deal with higher demand. Consider the long -term feasibility of maintaining the solution and its development as your requirements grow or change.

Registration and prioritization

Each possible project is recorded through these four dimensions using a simple 1-5 scale:

• Work value: What is the impact of this project?
• Market time: How realistic and fast implementation?
• risk: How to control the risks concerned? (Low risk degrees are better.)
• Expansion: Can the application grow and develop to meet future needs?

For simplicity, you can use the scaling of shirts (small, medium, large) to record dimensions instead of numbers.

Calculate the degree of priority

Once you get the size or registration of each project over the four dimensions, you can calculate the degree of priority:

Here, α (the Risk weight teacher) It allows you to control the extent of the risk effect on the result:

• α = 1 (Standard Risks): The risks are equal to other dimensions. This is ideal for organizations with the experience of Amnesty International or those who want to balance the risk and reward.
• α> (Organizations that spend risks): The risks have a greater impact, and the project of projects with a higher risk is more. This is suitable for new institutions on artificial intelligence, work in organized industries, or in environments that failure may have severe consequences. Recommended values: α = 1.5 to α = 2
• α <1 (High -risk approach, highly rewarding): The risks have a lower effect, in favor of ambitious and highly controlled projects. This is for comfortable companies with experimentation and potential failure. Recommended values: α = 0.5 to α = 0.9

By setting α, you can customize the priority setting form to match the tolerance of the risks in your institution and its strategic goals.

This formula guarantees that projects of high value in the field of business, a reasonable time for time, and the expansion-but controlled risks-rise to the top of the list.

Frame application: practical example

Let’s go through how the company uses this framework to determine which of Gen Ai Project To start. Imagine that you are a medium -sized electronic trade company looking to take advantage of artificial intelligence to improve operations and customer experience.

Step 1: Opportunities for brainstorming

Determine incompetence and automation opportunities, internal and external. Here is the result of the brainstorming session:

• Internal opportunities:
  1. Automation of the internal meeting summaries and business elements.
  2. Getting the descriptions of the product for a new stock.
  3. Improving inventory re -storage predictions.
  4. Conducting feelings and automatic registration for customer reviews.
• External opportunities:
  1. Create personal marketing email campaigns.
  2. Chatbot implement customer service inquiries.
  3. Getting automatic responses for customer reviews.

Step 2: Building a decision matrix

to request	Work value	Market time	Expansion	risk	a result
Meeting summaries	3	5	4	2	30
Product descriptions	4	4	3	3	16
Improving re -storage	5	2	4	5	8
Feelings analysis of reviews	5	4	2	4	10
Personal marketing campaigns	5	4	4	4	20
Customer Customer Service	4	5	4	5	16
Customer review responses	3	4	3	5	7.2

Evaluate each opportunity using the four dimensions: work value, lack of market, risk and expansion. In this example, we will assume the value of risk weight α = 1. Set the grades (1-5) or use shirt sizes (small, medium, large) and translate them into numerical values.

Step 3: Check the health of the stakeholders

The decision matrix participated with the main stakeholders to agree on priorities. This may include leaders of marketing, operations and customer support. Merging their inputs to ensure the alignment of the chosen project with the work objectives and has a purchase.

Step 4: Implementation and experience

The small start is very important, but success depends on setting clear standards from the beginning. Without it, you cannot measure the value or locate the need for adjustments.

1. Start smallStart by proving the concept (POC) to generate the descriptions of the product. Use the current product data to train a model or take advantage of the tools that have been previously built. Determine success criteria in advance – such as preserved time, content quality, or speed of launching new products.
2. Measurement of results: Track the main scales that are in line with your goals. For example, focus on:
   • efficiency: How long does the content team provide manual work?
   • quality: Are the descriptions of the product consistent, accurate and attractive?
   • Work effect: Does improved speed or quality lead to better sales performance or high customer participation?
3. Monitor and verify: Tracking standards regularly, such as the return on investment, adoption rates and error rates. Verify the health that POC results are in line with expectations and make adjustments as needed. If some areas are lower than performance, improve the form or adjust the workflow to process these gaps.
4. RestoreUse POC lessons to improve your approach. For example, if the product description project works well, expand the solution to deal with seasonal campaigns or related marketing content. The expansion of your expansion ensures that the value of providing the value while reducing the risks to the minimum.

Step 5: Building experience

A few companies start with deep experience of artificial intelligence – this is good. You adopt it by experiment. Many companies start with small interior tools, and test them in a low -risk environment before scaling.

This gradual approach is very important because there is often a confidence obstacle for companies that must be overcome. The teams need to trust that artificial intelligence is truly reliable and useful before they want to invest in a widespread or use. By starting a small job and showing a gradual value, you can build that confidence while reducing the risk of excessive increasing to a large, unprotected initiative.

Each success helps your team to develop the expertise and confidence needed to address the largest and most complicated artificial intelligence initiatives in the future.

conclusion

You do not need to boil the ocean with artificial intelligence. Like adopting the cloud, start a small, experience and size the value becomes clear.

Artificial intelligence should follow the same approach: Start small, learn, and size. Focus on projects that offer rapid victories with minimal risks. Use these successes to build experience and confidence before expanding more ambitious efforts.

Gen AI has the ability to transfer companies, but success takes time. With the priorities of studied, experimenting and repetition, you can build momentum and create a permanent value.

Sean Waloner is an Amnesty International businessman in residency in crispy.