An age old question of diminishing returns…
Knowing when to continue searching for better solutions and when to start extracting value from what you’ve got can mark the difference between success and failure. A multitude of theories and algorithms have been leveraged to help locate the tipping point of explore/exploit, and it’s been the subject of studies in psychiatry, behavioral ecology, computational neuroscience, computer science and business.

The explore/exploit trade-off conundrum gets a little stickier in the realm of hyper-automation in that, as an organization rounding the sharp edges off of your ecosystem, you’re always exploring. With right process and tools in place you’ll be able to iterate quickly on solutions making the balance of exploration and exploitation a more fluid process, with a faster cadence. Pioneers in the realm of hyper-automation don’t often have the opportunity to survey a wide variety peers; other organizations in the hyper-automation trenches. Not only are there few great examples, but any examples (great or not) are highly competitive cards held extremely closely to the chest. What you can use to your advantage, however, is your ecosystem itself.

Ideally you’ll have lots of people across your organization continually trying out new solutions, putting you in a better position to conduct lots of exploration, and discovering many solutions that bear fruit. With accessible no-code tools for creating and analyzing solutions, iterating is faster and easier for teams. This collaborative approach can also foster success in spotting trends in what works well so that your exploits can bear ample fruit. All said, the explore-exploit conundrum doesn’t go away even as you’re able, ready and in the full swing of rapid creation, iteration and analysis of solutions. You will still find yourself questioning, “how do we know when to continue improving our solution, and when is the right time to exploit the solution we have?”

There are many fascinating philosophies and some useful theories, formulas and algorithms for trying to answer this question. Our experience with creating and analyzing over 10,000 conversational applications has taught us to value two factors in particular that tend to drive the length or effort for exploring: volume and value.

We’ve drafted a digital book that provides some philosophies and useful tools for getting the most value out of hyper-automation. The findings are based in years of research and experience building thousands of conversational applications.

Our hope is that sharing the best practices and insights we’ve gleaned can make the crucial difference for enterprise companies struggling to balance the problems that come with those random acts of bot-building. A strategy that fosters co-creation with a conversational interface can be the road away from collections of disparate bots that disappoint companies and their users alike, leading towards the creation of an intelligent ecosystem.

Get the digital book that aims to equip problem solvers and leaders for going beyond with a strategy for building an intelligent, coordinated ecosystem of impactful automation—a network of skills shared between intelligent digital workers.