ML Engineers: Partners for Scaling AI in Enterprises

credits: author copywrite

Enterprises across many industries are adopting AI and ML at a rapid pace. There are many reasons for this accelerated adoption, including a need to realize value out of the massive amounts of data generated by multi-channel customer interactions and the increasing stores of data from all facets of an enterprise’s operations. This growth prompts a question, what knowledge and skill sets are needed to help organizations scale AI & ML in the enterprise? 

To answer this question, it’s important to understand what types of transformations enterprises are going through as they aim to make better use of their data.

Growing AI/ML Maturity 

Many large organizations have moved beyond pilot or sample AI/ML use cases within a single team, to figuring out how to solidify their data science projects and scale them to other areas of the business. As data changes or gets updated, organizations need ways to continually optimize the outcomes from their ML models. 

Mainstreaming Data Science 

Data Science has moved into the mainstream of many organizations. People working in various line-of-business teams like product, marketing and supply chain are eager to apply predictive analytics. With this growth, decentralized data science teams are popping up all over a single enterprise. For many people looking to apply predictive techniques, they have limited training in data science or knowledge of the infrastructure fundamentals for production-scale AI/ML. Additionally, enterprises are faced with a proliferation of ad-hoc technologies, tools and processes.  

Increasing Complexity of Data 

Having achieved some early wins, often with structured or tabular data use cases, organizations are eager to derive value out of the massive amounts of unstructured data, including language, vision, natural language and others. One role that organizations are increasingly turning to for help to meet these challenges is the Machine Learning Engineer.  

What is a Machine Learning Engineer?

I have observed that as organizations mature in their AI/ML practices, they move beyond Data Scientists toward hiring people with ML Engineering skills. A review of hundreds of Machine Learning Engineer job postings sheds light on why this role is one way to meet the transformative needs of the enterprise. For example, examining the frequency of certain terms in the free text of the job postings surfaces several themes; 


ML Engineers are closely affiliated with the software engineering function. Organizations hiring ML Engineers have achieved some wins in their initial AI/ML pilots and they are moving up the ML adoption curve from implementing ML use cases to scaling, operationalizing and optimizing ML in their organizations. Many job postings emphasize the software engineering aspects of ML over the pure data science skills. ML Engineers need to apply software engineering practices and write performant production-quality code. 


Enterprises are looking for people with the ability to create pipelines or reusable processes for various aspects of the ML workflow. This involves both collaborating with Data Engineers (another in-demand role) and creating the infrastructure for robust data practices throughout the end-to-end ML process. In other words, ML Engineers create processes and partnerships to help with cleaning, labeling and working with large scale data from across the enterprise. 


Many employers look for ML Engineers who have experience with the end-to-end ML process, especially taking ML models to production. ML Engineers work with Data Scientists to productionize their work; building pipelines for continuous training, automated validation and version control of the model.  


Many ML Engineers are hired to help organizations put the architecture, systems and best-practices in place to take AI/ML models to production. ML Engineers deploy ML models to production either on cloud environments, or on-premise infrastructure. The emphasis on systems and best practices helps to drive consistency as people with limited Data Science or infrastructure fundamentals learn to derive value from predictive analytics. This focus on systematizing AI/ML is also a critical prerequisite for developing an AI/ML governance strategy. 

This qualitative analysis of ML Engineering jobs is not based on an assessment of a specific job posting or even one specific to the enterprise I work in, it reflects a qualitative evaluation of general themes across the spectrum of publicly available job postings for ML Engineers – a critical role for enterprises to scale AI/ML. 

What do ML Engineers work on?

  • Big data 
  • Complex problems
  • Driving insights
  • Realizing business value
  • Large scale – projects impacting millions of users
  • Establishing best practices

credits: author copywrite
credits: author copywrite

How do ML Engineers work? 

  • Collaboration with many roles
  • Cross-business and cross-function
  • Software development practices
  • Agile
  • Leveraging best practices

In what teams do ML Engineers work?

Within enterprises, ML Engineers reside in a variety of teams including Data Science, Software Engineering, Research & Development, Product Groups, Process/Operations and other business units.

What industries seek talent to help productionize ML?

While demand for ML Engineers is at an all time high, there are several industries that are at the forefront of hiring these roles. The industries with the highest demand for ML Engineers include; computers and software, finance and banking and professional services. 

As AI and Machine Learning continues to grow and mature as practice in enterprises, Machine Learning Engineers play a pivotal role in helping to scale AI/ML usage and outcomes. ML Engineers enable Data Scientists to focus on what they do best by establishing infrastructure, processes and best practices to realize business value from AI/ML models in production. This is especially the case as data volumes and complexity grows. 

Challenges and Opportunities for AI & ML in Scientific Discovery

credits: Adobe Stock

Looking around, there are many examples where scientific communities are again working to understand how to draw benefits from AI & ML. Today, AI drives cross-disciplinary scientific discovery across many domains like climate change, healthcare, space science and material science. There are increasing examples of AI applied to drug discovery.

Renewed global interest in understanding how AI and ML can improve the process of scientific discovery is taking shape. One of the more visible examples is DeepMind’s AlphaFold project where AI techniques used by a multidisciplinary team accelerated new scientific discoveries. DeepMind developed 3D models of proteins at such a rapid pace and scale that it had a profound impact in solving one of the hardest problems in biology.

What specific challenges are driving the scientific community to explore AI now? Several of these are; 

Running scientific experiments can be time consuming. It takes hundreds of hours to prepare, conduct and evaluate results from experiments especially in cases where there is a broad space of variables to explore.

Difficulties handling large quantities of data. The volume of data produced for scientific discovery is vast and many teams have not fully developed the capacity to analyze this amount of data. Some examples of this include astronomy where telescoping images can capture millions of stars and biology-based discovery where microscopes can capture molecular-scale processes and details.

Complex data. In addition to the volumes of data, scientific communities are often dealing with complex types of data. Research teams may need to capture many parameters like color, shape, size, relationships and other details from advanced sensing devices and instruments.

Lack of metadata. Once all this data is captured, it is rarely usable because of the inability to capture metadata. Missing or incomplete metadata about all the experimental conditions (e.g. temperature, pressure, sample composition and orientation) makes it hard to do neural network training.

Cost and time for data acquisition. Production of data with many laboratory or advanced instruments is costly and time consuming. Many data acquisition instruments are specialized and require significant training, setup and maintenance.

Compute power. Lack of computational power to do complex analysis.

Collaborative science. Another challenge for scientific communities is the need to support research collaborations.

For almost all of these challenges there are multiple tooling and technology developments both with the open source community and enterprise data science platform providers. For example, 

AI can enhance how powerful scientific tools work, regardless of the scale of the subject – solar system or molecule. AI could be used for automating data processing steps like ingestion, cleaning and joining of large data sets. AI can transform the process of experimentation. AI can help scientists improve measurement strategies, essentially pinpointing what samples to explore and what details to capture. Several research projects apply generative AI/ML techniques to help reduce the problem or solution space. Improvements in this area can help distributed teams of scientists to collaborate on data and experiments. 

Automation. One final opportunity where AI can help the scientific process is in automation using robotics. One university lab created a robotic “lab assistant” that automated many mundane laboratory tasks. This assistant even operated during the Covid-19 pandemic, when social distancing prevented in-person lab work.

In Conclusion 

These are just a few examples of how the scientific community can benefit from AI /ML. There is a lot that the data science community can do to help facilitate adoption in this relatively nascent but potentially impactful area. 

Transformation and Adaptation in Data Science: Emergence of the Citizen Data Scientist

Role Related Research

One trend that I had been cautiously curious about is the emerging Citizen Data Scientist role. Citizen Data Scientist refers to an advanced data analysis professional or data professional who wants to do more than basic data analysis, they want to create or generate models that leverage predictive or prescriptive analytics or machine learning. This individual typically has a primary job function that is outside of the field of statistics and analytics. I conducted a literature review about Citizen Data Scientists and learned that while Gartner created the term several years ago, there are conflicting perspectives about the definition, scope and truthfully about the value of this role (Wilson, 2020). After examining multiple definitions of what a Citizen Data Scientist is, I learned that this role is significant mostly for what it helped me to understand about organizational change and adaptation.

A Closer Look: What Is a Citizen Data Scientist? 

One reference described Citizen Data Scientists as software power users who can do moderate data analysis tasks. They don’t replace expert data scientists. Instead, they use software features like drag-and-drop tools, prebuilt models, and data pipelines to create models without code (GetApp, 2019).

Several references describe Citizen Data Scientists as a new type of business analyst with diverse business responsibilities.  They apply sophisticated analytical tools, and complex methods of analysis (mostly around big data) to improve business results – all without the training or assistance of Data Scientists or IT team members (Blais, 2020).

While there are differences in definitions of the role some commonalities include:

  • Citizen Data Scientists come from Lines of Business
  • Role resides somewhere between a Business Analyst and Data Scientist
  • Performs more complex analysis than a Business Analyst
  • Rely on specialized tools, software and building blocks from more advanced roles

Where do Citizen Data Scientists reside in an organization?

Diagram showing relationship of Citizen Data Scientists to Other Roles
Figure 1: Relationship of Citizen Data Scientists to Other Roles

What are the skills of a Citizen Data Scientist?

Citizen Data Scientist is not a role that organizations are looking to fill from external sources. Most of the references about the role touted that you can’t find job postings for this role. Spoiler Alert: I did find actually one job posting that accurately reflected some of the common definitions of Citizen Data Scientist. But that singular job posting aside, that statement is generally true. Very simply stated Citizen Data Scientists “do cool things with data” (Ghosh, 2021). I learned that there are basically 4 core skills of a Citizen Data Scientist, although various references are not aligned about how involved this role is in the area of model development and coding. Citizen Data Scientists primary and secondary skills include;


  • Work with large data sets (Big Data)
  • Data preparation, algorithms and queries
  • Document the data extraction process
  • Ability to visualize data


  • Knowledge of data models and statistics
  • Coding / proficiency in at least one programming language
  • Modify, create and deploy predictive models
  • Create data science models using advanced and predictive analytics


  • Strong analytical skills
  • Ability to perform fairly complex data analysis
  • Power users of business applications such as (familiarity with spreadsheets)


  • Document their findings and communicate that with business staff, business Analysts, Business Intelligence(BI) and IT leaders

Why all the hype around Citizen Data Scientists? 

Some people may be quick to dismiss this role and the hype around it mostly because they are a bit confused about the title. I initially affiliated Citizen Data Scientist with the term “Citizen Scientists” which meant more of a hobbyist. For these reasons, it did not seem relevant to the primarily large enterprise contexts that I historically work in. However, digging under the hood to learn what factors lead to its emergence, uncovered some interesting findings.

The primary driver for the emergence of this role is the high demand for, and shortage of trained Data Scientists (Maffeo, 2019). Gartner predicted that, ” ‘the number of Citizen Data Scientists will grow five times faster than the number of highly skilled data scientists'” (Patel, 2020). The growth projections and supply of people who may be called Citizen Data Scientists is larger than the pool of Data Scientists. Companies are exploring different adaptations in people, technology, process and other areas to meet those needs (Arora 2021, Tibco, VentureBeat) 

Digital transformation initiatives have impacted every aspect of how organizations do business today. These data-driven changes have led to more and more business leaders turning to Citizen Data Scientists to fill the gap between the demand for data and analytics and the limited supply of skilled data scientists in the market today (Tibco).

Big Data. Behind most of these transformation initiatives is the need to generate insights from large quantities of data. There are cost and time impacts associated with working with bigger and more complicated datasets. A new skill set is needed to meet these challenges. As the data gets more complex and large, it increases the length of time it takes to get data for reports and analysis. In many industries, there may also be changes in reporting requirements that add a layer of complexity to the task of working with Big Data (Chmiel, 2021).

Business Context. Data Scientists don’t always have the business context for their work to have maximum impact. Knowledge of the business context where a Citizen Data Scientist adds the greatest value.

Experimentation. With all this data, enterprise business users will want to experiment and try different hypotheses.

Career Growth. For some people, the idea of progressing towards a Citizen Data Scientist role seems like a more attainable path to becoming a Data Scientist.

What does this role need to succeed in an organization?

In order to meet these complex needs, there are a number of conditions that need to be in place. 

Accessible Tooling. Various sources describe Data Scientists as somewhere between a business user using self-service analytics and a data scientist who is well versed in advanced analytics. Given the skill limitations, Citizen Data Scientists need augmented or new self-service tools to do big data analytics or augmented analytics. Several resources refer to the need for self-service “point-and-click” or “drag-and-drop” tools. New tools need to be easy to use, taking into account the lack of skills such as coding, statistics and automation (pipelines). Additionally, these tools need to make accessing data easier. With these new tools, developers need to be more conscious of the human-computer interaction requirements. 

Collaboration. Because Citizen Data Scientists are primarily in the lines-of-business, they can benefit by working closely with more formal Data Scientists in the organizations. Data Scientists will continue to work on advanced analysis and statistics. Additionally, they can create processes, tools and infrastructure to support Citizen Data Science (Sakpal 2021, Tibco).

Invest in Training. Organizations need to invest in reskilling or upskilling people who take on Citizen Data Scientist roles. Based on the lack of agreement on the primary and secondary skills, organizations will need to do a thorough skills assessment to understand the full scope of the skills. Additionally,  the Data Scientist Role will need to include process development for Citizen Data Scientist and an approach to validate the quality (QA) of the Citizen Data Scientist’s outputs. and QA of the CDS outputs. Because of the organization-specific differences, Citizen Data Scientist training can not support a one-size fits all approach. In exploring several of the companies that surfaced during this literature review, I learned that a number were investing in training initiatives.  

Organizational Policies. Because of the changes in people, technology, process – organizations need to be deliberate and establish policies to make this new model of data science work (Blais). For example, there needs to be appropriate levels of transparency and sharing as the numbers of people doing aspects of data science increase (Tibco, Chmiel 2021).

Business Leadership. For business leadership, it’s important for them to be aware of these changes and to deliberately create the conditions to help Citizen Data Scientist work (SAS).

What are the impacts and benefits of cultivating Citizen Data Scientist Roles?

Benefits to Individuals

 At least one author believes that this is a viable career choice for someone that wants to get into Data Science without the “time and expense” of an advanced degree (Arora, 2021).

Benefits to Organizations

Gartner predicted that in 2019, this role would have a larger impact on businesses than traditional Data Scientists (Datarobot). Experienced or skilled data scientists will be able to focus on more complex problems. Smarten talks about data democratization initiatives which can “optimize the time and resources of Data Scientists and improve productivity, empowerment and accountability for business users” (Smarten Blog, 2021). The emergence of the CDS role enabled the functional areas to draw the most benefit from data given the limited DS resources and to do that in a way that enables IT to maintain the most appropriate level of process and security over the systems.

I mentioned earlier that company policies and relationships will need to change. This will also impact the IT organization who has to make the bulk of policies and procedures. Security and governance policies will be more critical.

Benefits to Industry Overall

As ML gets more simplified you will see more of the companies who have not started their AI/ML journey applying Data Science and machine learning. 

What does this all mean?

I explored some of the implications for individuals, organizations and even industries but what does this mean in the context of UX? Taking the time out to understand a user role that is outside of my normal scope was a valuable exercise. It helped me to bust some biases I had that prevented me from seeing past the title. It also highlighted the critical role that HCI and UX can play in the enterprise context. In this exploration of the Citizen Data Scientist role, I learned that perhaps the confusion about the title / role was a signal of important transformations and corresponding adaptations that organizations need to make.

How AI Transforms the User Experience – Personalization

I have worked at the intersection of UX and AI for several years and my focus has been on understanding the enterprise side of the equation. Even with this focus, there is a great need to understand and plan for the total experience of those ultimately impacted by this sweeping technology, not just the enterprise developers. Several designers who have written about the UX of AI spend their time focused on interaction patterns and the impacts of AI on the design profession, with less insight on broader aspects of the human element. This is what I would like to address more. But first some context.

What powers AI?

AI already impacts daily life in many ways, often unknown to most people. AI is a driving force for some of the largest companies across the globe. Whether you are receiving a price on an online purchase, viewing a list of results from a search engine, ordering a shared ride, getting approved for an online transaction or finding a new song through a recommendation — AI is there influencing or driving many parts of those daily interactions.

AI algorithms are fed by massive amounts of data to find patterns that enable your interactions with various companies. Through this complex network of data and algorithms AI removes limits in learning. People then apply this data and algorithms to interesting goals. For example, some data science researchers are exploring ways to use AI to automate user research. They experiment with techniques like understanding sentiment, classifying feedback  — with limited success deriving truly meaningful and actionable impacts.

What is AI-enabled personalization?

At the highest level, two of the leading drivers for AI adoption are delivering a better customer experience and helping employees to get better at their jobs. Some AI futurists believe that “consumers will demand even more customized experiences –  giving rise to hyper-personalization and greater customer experience within the e-commerce sector” (Weissgraeber, 2021). AI makes it possible to realize something that has eluded marketers and product owners for years. It makes it possible to have large scale personalization, true one-to-one experiences for all. Note that this is very different from the earlier concept of mass-customization of the early 2000’s which focused on the user agency to choose a design or product configuration that they desired.

What are some examples of AI-enabled personalization?

Here are some current and future examples of personalization and the reasons they need a stronger focus on the user experience:

Retail and eCommerce – AI will continue to empower “hyper-personalized” ecommerce experiences. Companies like Salesforce realize this level of personalization by applying personalized recommendations and by providing tools that enable web developers to customize the layout of content that users see. AI in design enables user-centered design, to “an extreme level of granularity” – concept of design for every single person (personalization at scale). 

Entertainment – One example in media and entertainment is how Netflix combines user behavior modeling with recommendations to offer each viewer a personalized viewing experience. AI provides for more efficient classification, tagging and recommendations of existing and newly generated content like online videos (Rao, 2017). 

Dialog interfaces are an interesting case of an AI enabled solution that are supposed to drive better user experiences in customer service. While this is a complex area dominated by data scientists, people recognize the need to focus on basic UX principles and people-centered design, such as giving the user a way to bypass an automated system that is not working out and reducing the user’s cognitive load. While this is a noble goal, most deployments of dialog Interfaces are centered on driving cost benefits to the service provider versus driving real beneficial user experiences.

Healthcare, Personalized Medicine and Precision Medicine. In the comparatively AI-nascent field of healthcare, the future prospects are for diseases are more quickly and accurately diagnosed, drug discovery is sped up and streamlined, virtual nursing assistants monitor patients and big data analysis helps to create a more personalized patient experience (Thomas, 2019).

These are just a few examples of how AI-enabled personalization is impacting life today and will in the future. While the proposed benefits are many, there are tremendous risks that make an emphasis on the human element of this powerful technology even more critical.

What are the risks of increased AI-enabled personalization?

Privacy sacrificed for convenience. The convenience of more personalized experiences requires even greater access to personal data.  This raises the tremendous risk of encroaching on personal privacy by the mixing of data and techniques.

Security. With increased access to personal data there is always the looming risk of security breaches related to the massive amounts of data that needs to be accessed and stored.

Loss of Personal Agency or Control. Some designers focus on hiding the AI from users as a way to minimize exposure to complexity. Lack of visibility makes it hard to understand or challenge the outcomes. This raises the question: Does too much personalization inhibit discovery? Will a loss of that skill for discovery inhibit people’s opportunity to experience serendipity?

Bias and Discrimination. We already know that companies leverage third-party cookies and complex algorithms to track users’ online activities and that information is used to serve different prices to different customers. With little commercial guidance on fairness or transparency in pricing and other automated decisioning processes, how  can we ensure people are not penalized based on personal characteristics like gender, race or economic status?

Less Human Interaction more Automation. While AI will enable retail and entertainment experiences that are more personalized, this increased personalization raises the risk of decreasing interpersonal interactions.

What are some future research directions?

I have not yet found a lot of research or good examples of how end users are engaged in identifying the benefits of this new technology. Most user research focuses on improving the interaction with technology, not finding the real benefit to the end user who generally does not have agency in whether or not to use the technology. What is the prospect for personalization driven by AI,  if the benefit to humans is not clear?


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CBC Marketplace.  (2017, November 24) Exposing price discrimination in online shopping. CBC News.

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Guszcza, Jim. (2018, January 22) AI Needs Human-Centered Design. Wired Magazine.

Havasi, C. (2019, May 10) Beyond ‘citizen data science’: the need for user-centric AI design. Information Age.

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Salesforce (2020, October 20) The future of user experience design starts with AI. FastCompany.

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Thomas, M. (2019, June 8). The Future of AI: How AI will Change the World. Built In.

Verganti, R., Vendraminelli, L., Iansiti, M. Working Paper: Design in the Age of AI.

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Zaghdoudi, S. Glomann, L. (Jan 2021) AI-enabled user research. Advances in Artificial Intelligence, Software and Systems Engineering (pp.187-193). DOI:10.1007/978-3-030-51328-3_27

Impacts of the Covid-19 Pandemic on Design

The ways we interact, the ways we work, the ways we think, and the ways we innovate have been indelibly impacted by the pandemic. It has altered many patterns we may have taken for granted. To give some examples, look at the implications this global pandemic has had on design —  specifically the interior built-environment. 

Color Trends

The CEO of Henrybuilt brought out that although product development may slow down, these spaces of isolation that many individuals are faced with will spur a surplus in creativity for designers. One aspect of user-centered design involves the portrayal of consumer attitudes reflected in the color scheme of a design. The pandemic has created a heightened sense of unrest, grief, and anxiety at a scale and scope many have never experienced. This translates to the desire for reassurance, comfort, and peace that corresponds with certain colors. Senior coloring marketer Dee Schlotter points to a return to nature-like color schemes because “these colors promote internal peace in an age where mental and physical well-being are critical.”

Programming in a Home Space 

Society can not fail to overlook the risks “essential workers” face, carrying out tasks that benefit so many. But for a moment, consider those that are able to work from home. The pandemic has forced many to adopt office-like conditions in home settings which poses a question: what kind of space are we willing to live and work in now? The importance of acoustic barriers to accommodate everyone working from home and the philosophy of having less so you feel as though you have more space has been emphasized as a result.  The pandemic has changed our perspective of looking at a space, as we question whether or not it would be feasible or possible to remain there for an extended period of time. 

Defensive Design

In 1933, the Paimio Sanatorium in Finland was a facility designed by Hugo Alvar Henrik to function as a sort of medical instrument given the tuberculosis outbreak of that time. Thought was given to the geometry of the walls, the lighting of the rooms, the surfaces of the ceilings all set to work in tandem and eradicate the spread of the disease. That outbreak gave rise to modern architecture, and it is not far off to think that Covid-19 pandemic can have the same effect. Many express that the space necessary for quarantine is defensive with “taped lines and plexiglass walls segmenting the outside world into zones of socially distanced safety” according to the New Yorker. Grocery stores have altered the flow of people and the distances between aisles as a result. 

As we experience this shift in how society experiences design in the built-environment, it will be interesting to see what patterns remain.

Remembering the Prolific Industrial Designer, Charles “Chuck” Harrison (1931-2018)

“I don’t think designers can change the world, rather they can take what’s here and make the most of it.”

Charles “Chuck” Harrison (1931-2018). What comes to mind when you hear the name? He was one of the first Black industrial designers, and a gracious creative who excelled in a profession that deliberately excluded people of color. In his lifetime, Harrison designed over 700 products.  His designs influence everyday life around the globe, even today.  Picture this:You are a young child who is curious to know what a fictional world looks like. Chuck Harrison allowed millions to experience many real and fictional scenes by his transformation of the original View Master to a more modern design that enabled 3D images. Harrison’s contributions do not stop there.

I first learned of Chuck Harrison while working on a research assignment for a design class. A biography of the designer had just been completed so I reached out to the publisher to see what else I could learn or where I could find examples of his work. They went even further than I expected. A few days later, I got a call from Chuck Harrison and an opportunity to know first hand about his life, many achievements, design principles and challenges. In spite of the racism that he experienced that could bring some down, Chuck Harrison spent 30+ years as a product designer at Sears, Roebuck and Company.  Eventually, he became chief designer, where he oversaw the creation and improvement of everyday consumer items such as the see through measuring cup, the cordless shaver, the riding lawn mower, the groundbreaking polypropylene trash can on wheels and much more than I can even name.

Plastic trash can
Harrison’s iconic redesign of a common household trash can (credits: A Life’s Design)

Harrison wanted to make sure EVERYONE would be able to use his projects. That was his trademark. Harrison had a learning disorder, called dyslexia, which made tasks such as reading, writing and math difficult for him. Because of that, he designed with the idea that anything he made would not need instructions to use. Harrison also believed design should be basic, not embellished or ornamental. One of his childhood friends Ernest Norris said: “Charles was a pretty smart kid, pretty innovative…I remember especially when we were building model airplanes and I learned a lot from him. He used just the right amount of glue. Things weren’t smeared over.”

Chuck Harrison lived by the principles of aesthetics, ingenuity, honesty and sincerity.  In 2008, he received the Cooper Hewitt Smithsonian Design Museum’s Lifetime Achievement Award, becoming the first African American to do so. Chuck Harrison’s achievements extended beyond the awards, as he became a successful author and educator covering his expertise in art, language and history. 

(credits: A Life’s Design)

In the book “A Life’s Design” we learn more about Harrison’s influences, qualities and countless design ideas that drove his success.  The vice president of Sears and Roebuck stated: “The first word that comes to mind when describing Chuck is integrity. He’s completely honest, not only in his personal relationships but in approaching a job.”

Harrison noted that “I don’t think a designer can change the world. Rather we can take what we have and make the best of it.” To sum up Charles “Chuck” Harrison’s life work in a couple of paragraphs does not do the under-appreciated yet significant impact he had on everyday life justice. But, to shed light on a man who saw the potential for greatness in the art of simplicity is a step in the right direction. Learn more at; A Life’s Design.

Remembering Pioneering Industrial Designer Sara Little Turnbull

Sara Little Turnbull consulting with clients credits: Foreseer

“If you don’t Stretch, you don’t know Where the edge Is” was a quote on a wall hanging that Sara Little Turnbull gave to Jim Collins, professor at the Stanford Graduate School of Business. Born in 1917, Sara Little Turnbull (nee: Finkelstein) epitomized those words. Throughout her long history in the industrial design profession, she was known for moving people to see beyond the obvious, to ask why, and to always remember the customer. In fact, in the 1940’s and 1950’s she wrote a few high-impact articles that raised the consciousness of corporations to regain a focus on the user. She left an indelible impact on corporate giants such as Corning, Coca Cola, 3M and even on entities such as NASA and public administrations around the globe. In her 70’s Sara was the founder and director of the Process of Change Laboratory at the Stanford Graduate School of Business, which served as an idea catalyzing space for business, engineering and art students. I had the privilege of studying under Sara as she also co-taught a graduate course that enabled students from the engineering and business schools to learn about integrated product development by collaborating on a project from user need-finding, to concept development and then launch. What I remember most about Sara was her curiosity and intensity. I believe these qualities were at the heart of her longevity and impact as a designer and educator.
Learn more about Sara