Why Is User Centered Design Literacy Key To AI Competency Development?

This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO’s to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results.

In this blog series, I have identified forty skill domains in an AI Leadership Brain Trust Framework to guide board directors and CEO’s to ensure they can develop and accelerate their investments in successful AI initiatives. You can see the full roster of the forty leadership Brain Trust skills in my first blog. 

Each of the blogs in this series explores either a group of skills or does a deep dive into one of the skill areas. I have come to the conclusion that to unlock the last mile of AI value realization that board directors and CEOs must accelerate building a unified brain trust (a unified set of leadership skills that are hardwired in relevant digital and AI skills) to modernize their organizations more rapidly.

Knowledge is key and if you locked up a room of board directors and CEOs in a board room and asked them (1) What steps are required to build a successful AI Strategic Plan and Journey Roadmap – what do you think would be the outcome? or (2) Where are your AI Investments and have you inventoried them or audited them? or (3) What is the difference between a computing scientist, a data scientist, and an AI scientist – would their digital literacy skills be sufficient enough to lead and guide their organizations forward? (4) What has been your Return on Investment (ROI) and value realization in your AI programs or AI products/solutions?

Sadly, I think we would find some very serious operational execution gaps in realizing the last mile in AI. A great deal of R&D exploration and AI modelling exploration is underway but moving to sustaining operating practices and ensuring the ongoing knowledge of AI modelling outcomes, and value realization practices remain a major gap in the strategic deployments of AI programs.

Last month, I discussed the importance of Agile Literacy and in this blog, I will discuss the importance of User Centered Design Literacy, one of the key technical literacy skills in building AI capabilities that are robust and operational focused.

Technical Skills:

1.    Research Methods Literacy

2.   Agile Methods Literacy

3.  User Centered Design Literacy

4.   Data Analytics Literacy

5.   Digital Literacy (Cloud, SaaS, Computers, etc.)

6.   Mathematics Literacy

7.   Statistics Literacy

8.  Sciences (Computing Science, Complexity Science, Physics) Literacy

9.   Artificial Intelligence (AI) and Machine Learning (ML) Literacy

10.Sustainability Literacy

User Centered Design Literacy

Last month, we discussed that skilled AI data scientists must be agile, as AI projects require investigative methods that are agile, and resilient to ensure executives have confidence in the exploration process. At the same time, agility is required to continually provide value outcomes to build executive sustainability confidence.

Hence, a balance between demonstrating short term value is equally as important as longer term value. Having robust communication practices is also critical in AI programs, as in the fast paced and often attention deficit economy, board directors, CEOs and many executives need reassured confidence that their AI investments are generating positive returns.

This blog defines user centered design literacy, and frames the implications and risks of data bias and fairness AI principles if they are not front and center in AI design practices.

It is imperative that AI project teams have strong user centric experts guiding diversity, inclusivity and fairness approaches in framing all aspects of an AI product or solution development…