AI Fluency Workshop

Johannes Schleith
4 min readOct 18, 2021

So you want to be an AI Innovator? Do you know where to apply AI within your domain? How can you tell what is the right approach?

Seeing groups of participants grow their ideas organically, balance and rebalance their approach was the highlight for us at the workshop we held at the AI Summit London.

Lean AI Design Workshop at AI Summit 2021

If you want to stay tuned and learn more about AI Fluency, sign up, and tell us more here https://bit.ly/aifluency

Definition & Alignment

AI opportunity detection is poorly understood [1]. AI use case definition can be a struggle for innovation efforts. Ideation on AI challenges more often than not lead us to overly general statements, with buzzwords aplenty, and a mind locked into exciting, but narrow use of a specific technology.

Great products are built by interdisciplinary teams. Successful teams combine subject matter experts’ domain knowledge and technical expertise in engineering and data science. Each discipline brings a wealth of invaluable knowledge to the conversation, yet each come at it from a different place.

‘I want actionable insights to increase customer satisfaction!’ exclaim the first. ‘Using anomaly detection would be so powerful with this data.’ dream the second. The first statement is so general as to be meaningless — the second locks us into a specific solution way before we have discovered the right problem to solve.

Time and time again, we have seen this phenomenon play out in settings where the difficulty of getting to a pragmatic conversation about AI is misinterpreted as ‘mismatch in technical knowledge’. It’s far from that, it’s simply the wrong level of detail about the problem at hand.

Cognitive Work Analysis

We believe that great AI opportunities are best detected by decomposing a problem into specific jobs-to-be-done [2] and cognitive tasks [3], that lend themselves well for AI automation. By combining Design Thinking techniques with Data Science expertise we are creating a framework that facilitates a conversation between technologists and domain experts.

Cognitive Work Analysis, Source: Fidel et al. — A Multidimensional Approach to the Study of Human- Information Interaction: A Case Study of Collaborative Information Retrieval [3]

During the workshop Lean AI at the AI Summit 2021, we trialed an early version of our approach. Participants were guided through a series of exercises as a form of a framework that would provide help. First, we investigate the work environment and map stakeholders in our system …

Illustration Stakeholder Map

… we then decompose a scenario into process, tasks and actionable insights per stakeholder…

Illustration Process Map

Reframe Tasks as AI Problems

Zooming into specific stages, we investigate which input data a subject matter expert uses currently? Which tasks do they carry out? What artefacts do they work with? What’s the desired output of the activity? What’s that mysterious process about? How can we more clearly define cognitive tasks, and start reframing user needs as AI problems

Illustration Reframing Cognitive Tasks as AI Problems

‘It was so interesting to see, that one process or problem has so many smaller processes and components within it — and what’s important is to choose the right one’ AI Summit 2021 participant

‘It didn’t feel like two hours at all’ AI Summit 2021 participant

Daniella

About Us

We are two innovators, bringing in different perspectives to AI Innovation …
Daniella Tsar (@dani_tsar) with a background in Data Science, Leading Data Science innovaviton

Johannes

Johannes Schleith (@jshlth) with a focus on Design Thinking, User-centered Design

We are passionate about researching and experimenting with the facilitation and translation between AI Technologists and (non-technical) Subject Matter Experts.

Stay tuned

If you want to stay tuned and learn more about AI Fluency, sign up, and tell us more here https://bit.ly/aifluency

References

  1. 3 Barriers to AI Adoption, https://www.gartner.com/smarterwithgartner/3-barriers-to-ai-adoption
  2. Jobs to Be Done: Theory to Practice Book by Anthony W. Ulwick, https://jobs-to-be-done.com/
  3. Fidel et al. — A Multidimensional Approach to the Study of Human- Information Interaction: A Case Study of Collaborative Information Retrieval, http://faculty.washington.edu/fidelr/RayaPubs/MultiDimensionalApproach.pdf

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Johannes Schleith

Senior Product Manager at Thomson Reuters. Passionate about User-centered Innovation, User Experience and Design Thinking and Human Centred AI