Structured approach to discovering good AI use cases

Although our core offering is our Service Intelligence platform Untrite Core, often we lead our clients through their AI journey already a step earlier - by helping them discover the right AI use cases that can have a positive, long-lasting effect on their organisation and its people.

To do that, at?Untrite we run AI Discovery Workshops that are designed to be highly interactive, aiming to bring stakeholders from across the business together to collectively identify business problems. Those challenges should have both a high business value and for which data is readily available for fast execution.?Where possible, we help with the execution or suggest best approaches. We identify "low-hanging fruit" areas, ripe for automation, where company can run quick, self-contained PoCs (Proof of Concept) and see the value of?AI.

In this article, I will elaborate more on what goes on in our?AI Discovery?Workshops where AI use cases are selected and prioritised.

AI prototyping, unlike traditional software prototyping, requires preparing, unifying data and experimenting with models and algorithms to determine the feasibility of use cases. Both cost time and money. Organisations must select the right use cases to pursue to avoid wasted efforts and find internal champions for such technology to increase the probability of AI adoption.

During our work with larger private and public organisations, I have witnessed common mistakes that business leaders make when identifying AI use cases. These mistakes result in the development of unfeasible or unimpactful AI use cases that diminish future interest in AI. For the biggest chances of a successful AI selection and adoption the following mistakes should be avoided:

Common mistakes made when selecting good AI Use?Cases

1. Self-censorship within groupthink

Business leaders acting as an AI champion 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 on innovation goals (ideally, such goals should be already defined), employees presenting their ideas, or with an informal roundtable discussion.

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 while those who prefer to take a moment to formulate a good ideas may not get a chance to share them. All this will result in a list of low-risk and ‘common-sense’ ideas that fail to harness the full potential of AI.

It's essential to create a safe space environment,where original, challenging opinions are not considered risky or treated as “bad news” and the people who voice such opinions are not punished. Otherwise, self-censorship will occur. People will feel that criticism is not be welcome, so they will keep quiet.

2. Misaligned?teams

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 project deployment would require aligned teams.?

Business leaders, however, often overlook team alignments during the initial stages and do not include them in early conversations. This results in a lack of support and adoption of AI initiatives as teams with different priorities will not invest time and effort to adjust their existing processes. The AI solution will die a natural death, with its potential not being explored.

3. Lack of facilitator who has AI expertise

We have seen companies selecting use cases that 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 or another technology such as RPA or even a good old regex is more suitable. It is unproductive as the participants have to redo the exercise to identify better and relevant AI use cases.

What is AI Business Use Case canvas and how can it help?

We believe that the simplest, most frictionless approaches have the highest chances of success and chances for adoption. It's not difficult to design something complex. It's difficult to make complex simple.

During our AI Discovery Workshops we use simple AI Business Use Case canvas which help to cover and discuss each essential components for successful AI use case selection and prioritisation.

Often, the companies we work with will have prepared a list of innovation goals / use cases prior to the workshop. Those business challenges which present a high business value and for which data is readily available for fast execution are prioritised. A deep dive session can be conducted to identify the more specific challenges and opportunities that arise from implementing an AI solution to a selected business problem. The Deep Dive is designed to provide AI consultants with enough information to accurately determine the scope, schedule, and ROI estimates for each targeted AI-based solution.

The facilitator of a one day workshop 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.

For each workshop, a final report is prepared that includes recommendations for the business, a business case (if feasible), and suggestions for follow-up projects or programs that will prepare the business to maximise the benefits of future AI implementations.

What steps are involved in designing an AI roadmap and prioritising use cases?

There are seven steps for designing a good AI roadmap:

1. Articulating your vision for AI

2. Defining the business objectives that you want AI to help you attain

3. Identifying potential use cases for AI that contribute to attaining these business objectives

4. Articulating and quantifying the impact/business value of each potential use case

5. Articulating and quantifying the ease of implementation and costs of each potential use case

6. Prioritising use cases according to ROI horizons and investment strategy

7. Incorporating the appropriate governance mechanisms for managing AI risks

Business leaders are a key part of the internal AI strategy debate to identify the essential AI use case opportunities and to help calculate their potential return on investment (ROI).

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. Untrite team 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?[email protected]?for further information.

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Author: Kamila Hankiewicz, Managing Director at Untrite, [email protected]