Expanding on using the Decision Story tool to help people make decisions. I want to explore the decision making process of business executives, and how this tool could help that target audience. By talking to people who are in the executive role and interacted with CEOs, I found one popular way for which CEO uses to make decisions. For a decision that may change the direction of the company, CEOs would like to ask the alternatives, and how these alternative changes the financial bottom line, as well as departments of the company. They want to know what would change if they make the decisions in as many aspect of the company as possible. So they could evaluate if those changes is good for the company overall.
So I though, what if the decision making tool could incorporate those aspect and ask the large language models (LLM) to evaluate it through the lens of an Econ. An Econ is often referred to in the economy academics as someone who always make the most logical decisions with the lack of emotion and bias. I have tried to use this approach and got some reasonable answers from the LLM. And I have found the answer covered some aspect of the decision that I haven’t realized. There are definitely CEOs who are smarter than me, but it probably doesn’t hurt for people to know that they already covered the basics. Here are some of aspected worth mentioning when asking the LLM for alternatives.
Here is an example in which I asked the tool to help me make a decision to whether to use a in-house model, 3rd Party model, or a combination approach to improve an existing product.
Here is what I put in:
“I’m an CEO of a mid size company with 300+ employees. We are working on speech analytics and helping customer to understand call center conversations. Large language models are getting big. I have some decisions about which model to use. We developed some simple binary classification model in house. We can use Openai 3rd party models. Or we can use our existing rule based solution. Or we can use a combination of some of the above. Please provide the pros and cons about each and provide other alternatives for long term growth. Specifically aim at IPO in next few years.”
And here is things I’m putting into the models.
1. Objective Definition:
Define the goal you’re trying to achieve. In your case, it might be “select the best AI approach to enhance our speech analytics service.”
2. Information Gathering:
Gather as much information as possible relevant to your decision. This includes:
- Business Requirements: Specific needs of your business and customers.
- Technical Requirements: Scalability, data volumes, security, etc.
- Market Trends: What your competitors are doing, trends in AI technology.
- Legal and Regulatory Requirements: Compliance to data privacy laws, etc.
- Financials: Budgets, potential ROI, etc.
3. Decision Criteria:
Establish the criteria you will use to evaluate your options. This could include factors like cost, time to implement, ease of use, scalability, potential return on investment, etc.
4. Generate Alternatives:
Generate a list of potential options. In your case, these are the AI technologies you’re considering: In-house model, third-party model, rule-based solution, or a hybrid.
5. Evaluate Alternatives:
Use your decision criteria to evaluate each alternative. This may involve financial modeling, technical assessments, consulting with experts, and other forms of analysis.
6. Select the Best Alternative:
Based on your evaluations, select the option that best meets your criteria and aligns with your business objectives.
7. Action Plan:
Develop an action plan for implementing your decision. This should include key milestones, resources required, risks and mitigation plans, and a timeline.
8. Review Decision:
After a certain period of implementation, review the decision to see if it’s delivering the expected results. If not, understand why and adjust as needed.