What could you accomplish by 10x-ing your productivity?

Productivity is a form of ROI: output generated from time invested. 

That means improving productivity comes from either improving outputs with the same inputs (effectiveness) or reducing inputs (efficiency).

So in a world where:

• Less than 20% of our professional research questions can be fully answered by AI, but more than 80% of them can be delivered more efficiently with the help of AI
  & 

• Study after study shows that AI on its own generates more ideas, not necessarily better ones — but AI and humans intelligently combined generate 5-7x more “really good” ideas vs. humans alone

...this large but “messy middle” presents a goldmine of opportunity for us to do our jobs much more efficiently and effectively.

How can we benefit from technology's tailwinds to improve both parts of the productivity equation – doing more, better, faster?

Let’s break it down, using research productivity as an example. 

1. Every company follows some version of this innovation pipeline to go from early ideas to commercialized solutions.

2. At every stage, there are knowledge gaps which need to be closed to hit the appropriate level of fidelity for making smart decisions at that stage. Because as we know: decisions without data are at best baseless assumptions and, at worst, dangerous misfires. 

3. Those knowledge gaps turn into questions, which become the starting point for the knowledge assembly line. Here, the classic DIKW model transforms knowledge gaps into knowledge nuggets that power better, smart decisions.

4. The challenge? The data, information, knowledge, and wisdom needed to reach the desired confidence level vs. the time required to get it. On one extreme, moving too quickly risks low fidelity (i.e. poor) decisions. On the other extreme, you hit diminishing returns and waste time trying to chase elusive “perfect information.”

5. Now, tapping the best of technology AND of humans, we can not only start farther ahead, but leave more of the heavy lifting to tech. And with lower overall effort exerted on our part when we receive the baton, we can focus our energy on generating a much stronger result than previously possible. (Not to mention getting hours back in our days.)

So our prediction for this new era of tech-enabled services?

Whether starting from the tech-first or talent-first end of the spectrum, we'll see a mass migration toward a hybrid offering that better equips today’s professionals to get the raw inputs, who / what / when / where, why / how, and “so what” faster, so that the “now what” can become truly 10x.

At Wonder, we’re taking it one step further: 

For each type of research project being done (very manually) at each stage of the innovation pipeline (think market mapping, competitive intelligence, white space analysis, audience insights, etc.), we’re 10x-ing the tech-enabled solution we deliver. 

By tapping the speed, horsepower, and breadth AI enables AND the rigor, relevance, and depth of our expert researchers, strategy and innovation leaders are starting farther along the curve with more robust, higher quality knowledge to base important decisions on.  

In other words, they’re getting more, better, higher fidelity information (⬆️ outputs) in exponentially less time and with minimal effort (⬇️ inputs).

What could you accomplish with that type of boost to your productivity…?! 🤯

Victoria

PS. Our head of product, Ainesh Ravi, regularly shares his thinking on topics like this and the behind-the-scenes on what we’re building on LinkedIn. Give him a follow or grab some time with us to learn more about how we’re leveraging the best of AI and humans to supercharge professional research.