How do you prioritize AI solutions?

1 Define your problem

Before you dive into the AI possibilities, you need to clearly define the problem you want to solve or the opportunity you want to seize. What is the pain point, gap, or challenge that your IS faces or can address? How does it affect your business performance, customer satisfaction, or competitive advantage? What are the desired outcomes and benefits of solving the problem or seizing the opportunity? By defining your problem, you can narrow down your AI scope and focus on the solutions that are relevant and impactful.

2 Assess your data

Data is the fuel of AI, and the quality and availability of your data will determine the feasibility and effectiveness of your AI solutions. You need to assess your data sources, formats, volumes, and quality. How much data do you have and where does it come from? Is it structured, unstructured, or semi-structured? Is it clean, consistent, and reliable? How often is it updated and how easy is it to access and integrate? By assessing your data, you can identify the data gaps, risks, and opportunities that affect your AI solutions.

3 Explore your options

Once you have a clear problem definition and a data assessment, you can explore the different AI options that can address your problem or opportunity. You can use various frameworks and tools to generate and compare AI ideas, such as design thinking, brainstorming, SWOT analysis, or PESTLE analysis. You can also look at the best practices and trends in your industry or domain, and learn from the successes and failures of other IS projects. By exploring your options, you can generate a list of potential AI solutions that match your problem and data.

4 Evaluate your criteria

To prioritize your AI options, you need to evaluate them according to criteria that reflect your IS goals and capabilities. A scoring system or matrix can be used to rank AI solutions based on desirability, viability, feasibility, and risk. Desirability looks at how well the AI solution meets the needs and expectations of users, customers, or stakeholders. Viability assesses how profitable, sustainable, and scalable the AI solution is for your business model and strategy. Feasibility examines how technically and operationally feasible the AI solution is for your IS infrastructure, resources, and skills. Risk considers ethical, legal, social, or environmental implications. By evaluating these criteria, you can prioritize the AI solutions that offer the most value and the least risk for your IS.

5 Validate your assumptions

Before you commit to any AI solution, you need to validate your assumptions and test your hypotheses. You can use various methods to validate your AI solutions, such as prototyping, piloting, experimenting, or interviewing. You can also use metrics and indicators to measure the performance, impact, and feedback of your AI solutions. By validating your assumptions, you can verify the validity and viability of your AI solutions and make adjustments or improvements as needed.