A Structured Approach for Ideating AI Use Cases With AI Discovery

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Earlier this month, I had the opportunity to share in a webinar about AI Clinic and AI Discovery, part of the comprehensive programme in AI Singapore (AISG®) to help organisations accelerate their AI adoption. In this article, I will elaborate more on what goes on in an AI Discovery workshop where AI use cases are ideated and prioritised.

AI prototyping, unlike software prototyping, requires preparing data and experimenting with algorithms to determine the feasibility of use cases. Both cost time and money. Organisations must ideate the right use cases to pursue to reduce wasted efforts and increase the probability of AI adoption.

As an Assistant Head at AI Singapore, I have witnessed common mistakes that business leaders made when identifying AI use cases. These mistakes resulted in the development of unfeasible or unimpactful AI use cases that continue to haunt the organisation and put a dent to future interest in AI. These could have been minimised.


Common Mistakes of Ideating AI Use Cases

1. Didn’t minimise groupthink

Business leaders will typically gather representatives from different departments to brainstorm on potential AI use cases. The brainstorm session usually starts by either business leaders sharing their thoughts, employees presenting their ideas, or with an informal roundtable discussion.

“The first project that the CEO suggested is not the right one to invest in.” 

– Andrew Ng, CEO and Founder of Landing AI, during Amazon re:MARS 2019

All of these approaches have serious flaws: groupthink usually happens after senior management have shared their thoughts; good ideas might have already been filtered out before presentation to management; extroverts will dominate discussions. All this will result in a list of low-risk and ‘common-sense’ ideas that fail to harness the full potential of AI.

2. Didn’t align teams

Deploying AI solutions is beyond simply installing new software. AI, given its capabilities, will often require organisations to adjust their business processes and employees to adjust their workflows to harness the full potential of it. In other words, successful AI model deployment would require aligned teams. 

Business leaders, however, often overlook team alignments during ideation. This results in a lack of support and adoption of AI model as teams with different priorities will not invest time and effort to adjust their existing processes. The AI solution will die a natural death.

3. Didn’t engage facilitator who has AI expertise

I have seen companies selecting use cases that are not AI-related after their internal ‘AI ideation sessions’. The root cause? No one in the session had expertise in AI. They were unable to decipher whether the selected use cases are possible for AI development. It is unproductive as the participants have to redo the ideation exercise to identify better and relevant AI use cases.

For instance, one of the companies I worked with chose ‘using AI to ensure customers are onsite to collect delivery’. This use case is more of a business process problem than an AI use case. It could have been archived earlier during the ideation workshop to focus everyone’s attention on AI-related use cases.


What is Design Sprint and How It Can Help?

Google Ventures developed Design Sprint to ideate and test use cases in a structured approach. It is designed specifically to addresses the common mistakes of brainstorming mentioned. The original process, however, is not optimised for ideating AI use cases. 

Based on my experience working with various companies, I have adapted it for ideating AI use cases by making one major modification: the facilitator will filter out non-AI use cases when participants are voting on challenges to focus on. In addition, the first part of the workshop will end with ‘search for inspiration’ instead of ‘target mapping’ as described in the original Design Sprint.


Case Study

The best way to understand how the modified ideation process works is through a case study. The case study below is from an actual session that I conducted; the company name and sensitive information have been masked for confidentiality purpose.

Client’s background

BETTER BRAIN is an institute of higher learning. Their CEO is interested in using AI in their organisation. However, the CEO is unsure where to start. He decided to engage AISG to facilitate an ideation workshop for his team.

Step 0: Preparation work

The facilitator will discuss with the project sponsor or the most senior person attending the workshop to identify a suitable theme for the workshop. This puts a soft boundary around exploration to keep the discussion within a defined space.

The facilitator will also advise who should be attending the workshop. The best is 4–7 participants from different departments/functions that are related to the theme of the workshop.

The CEO of BETTER BRAIN has decided to gather employees from marketing, IT, and programme to attend the workshop. He has also decided on the theme: improve learners’ experience and outcome.

Step 1: Participants sharing and converting challenges into ‘How Might We’ (HMW)

Each participant takes turn to share their business challenges. The rest of the participants, including the facilitator, will convert the challenges shared into opportunity areas. The questions will be written in ‘How might we…’ (HMW) format.

The purpose is to generate potential ideas to solve business challenges shared. As each participant has a different perspective, there will be different ideas generated for the same business challenges. There is no discussion among participants to minimise groupthink and prevent extroverts from dominating the discussion.

“We use the How Might We format because it suggests that a solution is possible and because they offer you the chance to answer them in a variety of ways. A properly framed How Might We doesn’t suggest a particular solution, but gives you the perfect frame for innovative thinking.” 

– IDEO

In the example below, after the Programme Manager from BETTER BRAIN shared her business challenge, the rest convert it into questions in HMW format:

Participants convert business challenges shared into HMW format.

Step 2: Categorise and vote on challenges

Facilitator and participants will identify common themes across the HMWs and categorise them accordingly. Each participant is then given two votes to decide which are the challenges worth pursuing. There is no discussion among the participants.

The categorising of challenges is to facilitate voting; the voting on challenges is to identify important and relevant challenges that the team wants to pursue.

The illustration below shows the result of voting for BETTER BRAIN:

HMWs are categorised based on similarities and voted to decide relevant AI use cases.

Step 3: Filter challenges

Challenges with less than two votes are removed. The facilitator will also remove challenges that are non-AI related and explain to participants why they are non-AI related; this is one of the modifications made to the original process.

After this step, all the remaining use cases are potential candidates for AI solutions development.

HMWs with less than two votes and non-AI related HMWs are removed.

Step 4: Define goal

Each participant writes a goal in “In two years’ time, we will…” format. Participants then vote to decide which is the goal worth pursuing; everyone has one vote.

The purpose of this exercise is for participants to envision from their perspectives, what the company or business unit will look like if all the challenges identified are solved.

For BETTER BRAIN, the participants have identified that their key goal in two years’ time is to make use of data to guide students on selecting suitable courses.

Participants identify potential 2-year goals and vote to decide the relevant goal.

Step 5: Identify blockers

Each participant will list down 2–3 potential blockers that could stop them from reaching their top voted goal. The blockers should be written in “Can we…” format. Each participant is then given 3 votes to select the most relevant blockers to the goal identified.

The purpose is to highlight potential pitfalls that could derail the AI project or prevent the team from achieving their goal. This allows management to preemptively address them to increase the likelihood of AI project success.

For BETTER BRAIN, the participants have identified the top three blockers that could stop them from achieving their goal:

List of blockers identified and voted. Participants list potential blockers and vote to decide relevant blockers.

Step 6: Search for inspirations

Participants are given 15 minutes to search for 1–2 inspirations to solve similar challenges identified and note down the key ideas of each inspiration. These inspirations need not be from the same industry.

By the time participants reached this section, they would have initial ideas on how potential solutions might look like. The challenge with sharing ideas is that they might be hard to explain and people have different interpretations of the same abstract explanations. 

Therefore, the purpose of this section is to let participants manifest their ideas in concrete form and share the key applications of their ideas.

Potential applications shared by participants to solve voted problems.

Overall results

At the end of the Mapping process, the participants will have the following:

  • List of identified opportunity areas and potential solutions.
  • The goal that the participants envisioned the AI use cases would help achieve and potential blockers that might derail the AI project.
  • Alignment among participants, through the series of voting sessions, on key issues to tackle.

The management could consider these inputs when deciding which AI use cases they should be developing.

Artefacts created at the end of the workshop.

Conclusion

Identifying feasible and impactful AI use cases is challenging. The common approaches used by organisations to identify AI use cases are unsuitable for the ambiguous and data-dependent nature of AI. AISG has adapted the Design Sprint into a structured and proven approach to ideate AI use cases. If you wish to find out more, you can reach us at ai-advisory@aisingapore.org for further information.

Author

  • Tern Poh is an Assistant Head at AI Singapore. He provides consulting services to enable customers to undertake the development and implementation of AI minimum viable models within their organisations.

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