Challenges and Opportunities for AI & ML in Scientific Discovery

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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 Design Practices

While people who work in design and design-related roles such as user research and human-computer interaction (HCI) may be aware of Artificial Intelligence(AI), the speed that this technology is being adopted calls for some consideration. About 20% of organizations are considered AI pioneers. Having achieved successes in initial AI experiments, these firms are now rapidly infusing all areas in their enterprise with AI. What does this mean for design practice?

You may have heard that AI automates repetitive, mundane and often time-consuming tasks but it may be harder to see what that means in the context of creative design roles. For people who know a bit more about AI and Machine Learning (a specific form of AI) they may believe that AI could commoditize the practice of design and lead to products that lack creative processes and outcomes. To understand the impacts of AI on design practice, first consider what AI is.  

What exactly is AI?

When I give talks about AI and User Experience(UX) I explain that most of AI boils down to one word, prediction. Prediction means using machines to process data and create new information. With this one concept, it’s possible to frame all other applications of AI. Various forms of a prediction can be; 

  • Classification – predict how to associate data with categories defined by a human
  • Clustering – predict how to group data into categories defined by the machine
  • Recommendations – predict a choice for a user based on assumed or actual preferences 
  • Generative design – using existing content to predict or generate variations of content 

You may have already interacted with a design system such as Wix, an example of artificial design intelligence (ADI) that automates generation of variant webpages. There are numerous examples of AI assistant tools applied to ideation and creation in design. 

What are the benefits of AI? 

AI equips design teams with tools to take the concept of user-centered design, to the most granular level, design for individuals versus designs for usually poorly-segmented groups of users. One example is how Netflix combines user behavior modeling with recommendations to offer each viewer a personalized viewing experience. 

What does this mean for Designers?

Designers need to learn about AI/ML, what it can do and how to strategically apply it to improve user experiences or to improve their design practice. While designers may not need to become data scientists (in the same ways that they don’t need to be coders), they can work to understand the applications, risks and nuances of AI as the first step to benefiting from its power. 

Process Automation: AI enables automation of problem solving tasks such as collecting and analyzing large amounts of data to discover invisible patterns without limitations of volume and speed. With this knowledge, designers can then pivot to focus on higher value tasks.  

Problem Identification: With AI taking on some repetitive tasks, this enables designers to focus more on “sensemaking” — understanding what problems need to be solved. 

Problem Framing: With AI, designers can shift to focus more on problem framing, clarifying the underlying questions and assumptions that the solution needs to respond to. They can also learn to architect frameworks to explore design alternatives and leverage machines to run through alternatives, freeing their time from some of the more mundane aspects of design. 

Human Element: AI is weak in areas that require knowledge of culture, emotion, context and identity, and ethics, to name a few. This is where designers’ impact would be well spent. Designers can have some time freed up from repetitive tasks that pave that way for more time to explore how the problem definition and solution variants could be optimized against these needs. 

Risk Mitigation: AI is a tool that opens the door for designers to create new experiences and to achieve process efficiencies. However, it also raises new risks around security, privacy, fairness and equity. A designer would lean heavily into the work of subject-matter experts in these areas to optimize solutions that consider the full impact of AI on a user’s experience. 

Is there anything to fear with the emergence of AI in design practice and allied roles? According to a report by Oxford University and Deloitte, 800,000 jobs were lost in the UK due to automation between 2001–2015. But according to the same report, 3.5 million new jobs were created. AI is already having an impact on the practice of design, and will likely lead to transformational shifts in how design work is done. 

More information: 

Basu, Ritupriya. (2019, Dec. 6) Algorithms Are a Designer’s New BFF – Here’s Proof. Adobe XD Ideas.

Irfan, Mirza. Artificial Intelligence and the Future of Web Design. Usability Geek.

Basu, Ritupriya. (2019, Dec. 6) Algorithms Are a Designer’s New BFF – Here’s Proof. Adobe XD Ideas.

Lant, Karla. (2018) Art by computers: how artificial intelligence will shape the future of design. 99 Designs.

Deep, Akash. (2019, May 15) Real-World Applications of AI in Design. Hackernoon.

Insall, J.. Borrtharkur, A. From Brawn to Brains: The Impact of Technology on Jobs in the UK. Deloitte Insight.

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

Białek, Bartek. (2019, March 26) Artificial Intelligence Disrupts UX and Product Design Like No Other Industry.

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

Koponen, Jarno M. (2019, February 9) UI + AI: Combine user experience design with machine learning to build smarter products. Venture Beat.

Clark, Josh. (2019, Nov 5) AI is Your New Design Material. Presentation from Amuse UX Conference.