ELUXR

The Question I Ask Myself Before I AI

In working with AI, I’m stopping before typing anything into the box to ask myself a question: what do I expect from the AI?

2×2 to the rescue! Which box am I in? On one axis, how much context I provide: not very much to quite a bit. On the other, whether I should watch the AI or let it run.

Understanding Context and Control

If I provide very little information & let the system run: ‘research Forward Deployed Engineer trends,’ I get throwaway results: broad overviews without relevant detail.

Running the same project with a series of short questions produces an iterative conversation that succeeds – an Exploration:

• Which companies have implemented Forward Deployed Engineers (FDEs)?
• What are the typical backgrounds of FDEs?
• Which types of contract structures & businesses lend themselves to this work?

When I have a very low tolerance for mistakes, I provide extensive context & work iteratively with the AI. For blog posts or financial analysis, I share everything (current drafts, previous writings, detailed requirements) then proceed sentence by sentence.

Defining the Task

Letting an agent run freely requires defining everything upfront. I rarely succeed here because the upfront work demands tremendous clarity – exact goals, comprehensive information, & detailed task lists with validation criteria – an outline.

These prompts end up looking like the product requirements documents I wrote as a product manager.

The answer to ‘what do I expect?’ will get easier as AI systems access more of my information & improve at selecting relevant data. As I get better at articulating what I actually want, the collaboration improves.

I aim to move many more of my questions out of the top left bucket – how I was trained with Google search – into the other three quadrants. I also expect this habit will help me work with people better.

Business Benefits of Improved AI Interaction

By refining the way we interact with AI, businesses can anticipate a variety of benefits:

• Enhanced Decision-Making: Clear expectations lead to more precise outcomes.
• Increased Efficiency: Streamlined processes save time and resources.
• Improved Collaboration: Better integration of AI insights fosters team alignment.

Measuring ROI

The average benefits’ ROI can vary, but consider these examples:

1. Time Savings: Businesses that streamline decision-making processes with AI can cut project completion times by up to 30%.
2. Cost Reduction: Improved efficiency can lead to resource savings, with estimates suggesting reductions of up to 20% in operational costs.
3. Enhanced Output Quality: Higher-quality insights and decisions could lead to a substantial increase in client satisfaction, boosting retention rates and lifetime value.

Actionable Steps for Implementation

To implement these benefits effectively, businesses should:

• Invest in training staff on effective AI interaction techniques.
• Facilitate an iterative feedback loop where employees can share AI insights and results.
• Set clear goals and metrics to measure AI effectiveness and outcomes.

Conclusion

In summary, the question of what to expect from AI is foundational in transforming how businesses harness technology. By engaging in thoughtful and iterative dialogues with AI, companies can unlock unprecedented efficiencies and insights.

Take the next step in optimizing your AI processes. Schedule a consultation with our team today to explore how we can help you maximize AI’s potential in your organization!

Source