How to make AI Work for Us

This question drives our exploration of AI’s potential to amplify human performance. Here’s our current thinking and we’re curious to hear yours.

Mapping AI Capabilities and Outputs

Imagine a simple grid. On one axis, AI capabilities range from ‘dumb’ to ‘smart,’ and on the other, outputs span from ‘generic’ to ‘personal.’ This visualization aids in pinpointing where our tools and efforts currently stand and where they could improve.

The Ideal Scenario: Smart AI with Personalized Outputs

In the optimal quadrant of our grid, we find smart AI producing personalized outputs—the holy grail of technology integration. This scenario represents our target, where technology genuinely augments human capabilities, making each process more intuitive and every decision more informed.

Evaluating Current AI Tech

Taking examples like GPT-3.5 and GPT-4, these technologies would approximately land in the middle in terms of intelligence but lean towards providing generic outputs. They exemplify significant advancements yet highlight the ongoing need for more tailored solutions.

Your company’s internal AI, trained specifically on your data, knows your business needs better and delivers more personalized outputs. It sits closer to the ideal middle of our grid but reminds us that even this tailored approach requires continual evolution to remain effective.

Consider this: What we consider ‘smart’ today might be ‘dumb’ tomorrow. AI evolves rapidly, pushing older models toward obsolescence. Building your own tools here is signing up for a strategic approach which invovles anticipating these shifts and adapting to ensure your solutions do not become outdated.

The Human Element

Let’s go back to humans. Ideally humans be at the top right of our grid. However, without proper training, motivation, and rest, we can slide towards producing generic and less effective outputs. Recognizing and enhancing human conditions is crucial for maintaining high engagement and productivity.

We’ll then split the average human in green, yellow, and red states. Where sometimes we’re super productive and smart, and sometimes we’re bored and lazy. How do we plot these states?

This really becomes the baseline that AI Copilots/assistants are trying to improve upon.

AI-Copilots: Lift Performance Baseline

At best, AI-assistant applications that utilize both personal and company data can significantly enhance our baseline performance. These tools help elevate us from average to high-performing, pushing closer to our ideal scenario.

Meanwhile, at the worst these applications can provide us with tireless decision making energy freeing our brain for better uses.

Designing AI-Enhanced Workflows

How do we design AI-enhanced workflows, user interfaces, and experiences that gather personalized data with minimal effort? The key lies in creating systems that understand and anticipate our needs. This approach moves everyone closer to the ideal scenario of smart, personalized AI solutions.

Imagine a workplace where AI seamlessly integrates into our daily routines, enhancing each process and informing each decision. That is the future we strive for at Hypercontext.