Optimizing Business Processes with Gen AI and Use Case Crowdsourcing

Gen AI is set to revolutionize business by automating numerous steps in enterprise processes. But where should one begin? We suggest establishing a robust cycle of Ideation, Prioritization, Testing, and Analysis. Here are some typical errors and best practices for the Ideation stage.

Numerous studies show that Gen AI can deliver dramatic productivity improvements across hundreds of use cases in professional services, financial services, healthcare, manufacturing, and other industries.

But where should one begin?

Going Lean and Agile

We recommend adopting lean thinking and the agile methodology by implementing the classical Ideate > Prioritize > Test > Analyze loop.

During the ideation phase, the goal is to map out the current business process and identify all the possible steps that can be enhanced or even eliminated with the help of Gen AI.

Next, these enhancements can be ranked and prioritized using any of the common methodologies, such as Impact – Confidence – Effort (ICE), Cost of Delay Divided by Duration (CD3), or a combination of them.

Mistakes to Avoid

A very common mistake is the delegation of the ideation phase to a group of senior executives or appointed AI experts, internal or external.

Here is a typical scenario:

  1. A group of AI experts comes up with a list of Gen AI use cases
  2. Executives approve the use cases and allocate a budget
  3. Use cases are implemented by consultants or an in-house skunkworks team
  4. Use cases are pushed to knowledge workers
  5. Knowledge workers view them as gimmicks at best and threats at worst
  6. Adoption peters out and use cases fail to take off
  7. Gen AI is declared as “not good or too early for us”

All the while, knowledge workers across the company kept using ChatGPT, whether sanctioned or not, to enhance their personal productivity. According to Microsoft and OpenAI, ChatGPT adoption in the enterprise is approaching 100%.

Is there a better way?

Gen AI Innovation Tournaments and Use Case Crowdsourcing

To increase the chances of successful Gen AI transformation, companies must expand the pool of Gen AI use cases that are considered during the ideation and prioritization phases.

This can be achieved by augmenting top-down strategic planning with spontaneous bottom-up crowdsourcing.

In the simplest case, an organization can set up a dropbox for collecting ideas for using Gen AI to enhance productivity. A cross-functional team of AI and subject matter experts can evaluate and prioritize use cases submitted by the knowledge workers.

Noémie Ellezam’s team at Societe Generale used this approach to collect and evaluate 100's of qualified Gen AI use cases.

In a more elaborate scenario, a company can host an innovation tournament to not only collect ideas for Gen AI use cases, but also prototype and test them in cross-functional teams of knowledge workers. This simultaneously improves the quality of ideas and increases the chance of adoption.

These Gen AI innovation tournaments must be open to all kinds of professionals from sales, marketing, legal, human resources, and other areas. They cannot turn, as is often the case, into Python and LLM hackathons.

There are plenty of tools, such as ChatGPT and AnyQuest, that enable non-technical users to quickly prototype and evaluate ideas for transformative Gen AI use cases.

At AnyQuest, we love hosting and facilitating Gen AI innovation tournaments. Reach out, and we will help.

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