AI for the Enterprise: The Citizen Data Scientist

Rick Rider Rick Rider
February 13, 2018 AI & Machine Learning
Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab.
Enterprise AI is the new hot topic in technology, especially as the consumer space blossoms with sales and adoption. However, the assumption cannot be that the same approach for the consumer market can be taken directly to the enterprise. Consumers push the expectations of AI for the business to new heights – and if not carefully prepared, solutions will inevitably fail. In fact, we have seen this already, as software vendors – ranging from start-ups to large software conglomerates – watch users struggle to adopt, understand, and ultimately see the value in AI.
In the consumer space, customers are generally willing to embrace new technology, take more risks, or even understand the current limitations. However, in a business setting, too much is at stake. For instance, a user cannot trust AI solutions to fully execute or even assist with a potentially multi-million-dollar task related to production, sales, or distribution. Users cannot assume the same risk, with so much more on the line. Therefore, the skills and objectives must be clearly defined, with specific parameters, in addition to constant feedback cycles.
This is not an easy task to achieve, especially when it comes to the same issues that enterprise solutions are facing today, such as complex integrations, data aggregation, upgrades, and more. AI software providers will find success by capitalizing and placing an increased focus on the following:

Self-sustaining data repository

Stitching solutions together to keep them in tune is not easy, especially for cloud solutions where IT has limited access and instead relies upon support. As we still exist in an age of modifications, although decreasing, the upgrade process for newer technologies can be a nightmare, specifically for AI.
This can all be made easier if access to the data, and the tools themselves, are part of the solution – from the beginning. We all know that the value of AI is completely driven by the breadth and quality of the data. Layering services on top of a separate repository is not enough. The best solutions will allow for data ingestion and training off a data lake. This will help control the complex integration points to prepare, cleanse, or even update data points. Users should not be responsible for this maintenance.

Embedded security model

Security in the age of enterprise AI could arguably be the sleeping giant. Not every business model can take on additional security risks. Today, with voice-driven solutions, this opens an entirely new form of security threats. Some solutions may have controls in place, but how do you tie that back to your core business security?
Perhaps the answer is to authenticate a user based on voice, PIN, or even facial analysis – but, how do you then effectively acknowledge their role, association, groups, or any other security defined protocol with their business environment? Unless this is built into the core solution, the same stitching problem exists, only this time it pertains to security. Bet on the vendors that can account for this dilemma from the onset.

Constant feedback mechanisms

Feedback cycles are a required checkbox for any AI solution. Without proper audit trails or validation links, enterprise users will never fully rely upon the technology and achieve its true potential. An AI platform must account for visual proof of data and execution, access to validate the source of the information, and even data behind systematic decision-making.
Humans sometimes struggle to describe their own decision-making parameters, and taking this to the next level with deep learning adds even further complexity. Feedback mechanisms should be considered as creatively as possible in every modeling environment.

Citizen developer toolkits

Lastly, AI is still an acquired knowledge. We rely upon expert code and data scientists to define the experience. In the same way that citizen developer terminology became the keyword of software development, data science will follow the same trend. While the mathematics and development skill-set will continue to be a critical need, a new layer of experience must be delivered to capitalize on the opportunity that AI presents. Users should not feel scared to experiment with capabilities due to limited development knowledge.
As technology continues to innovate, consumers use what is currently available to explore their options. They do not necessarily need to know the how or why; they just need to see the results. This is what ultimately drives technological revolutions – building upon knowledge with new critical thinking is made possible by using previous innovation. Spielberg may not know the inner workings of a cinema camera, but he still directs masterful films through how it is used. The same notion should be considered with AI: focus on the new target experience and how users can innovate, not on the technology itself.
  • Experfy Insights

    Top articles, research, podcasts, webinars and more delivered to you monthly.

  • Rick Rider

    Tags
    Artificial Intelligence
    © 2021, Experfy Inc. All rights reserved.
    Leave a Comment
    Next Post
    Putting Data to Work Hospitals are Awash in Patient Data—Why is So Much of it Never Put to Use?

    Putting Data to Work Hospitals are Awash in Patient Data—Why is So Much of it Never Put to Use?

    Leave a Reply Cancel reply

    Your email address will not be published. Required fields are marked *

    More in AI & Machine Learning
    AI & Machine Learning,Future of Work
    AI’s Role in the Future of Work

    Artificial intelligence is shaping the future of work around the world in virtually every field. The role AI will play in employment in the years ahead is dynamic and collaborative. Rather than eliminating jobs altogether, AI will augment the capabilities and resources of employees and businesses, allowing them to do more with less. In more

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    How Can AI Help Improve Legal Services Delivery?

    Everybody is discussing Artificial Intelligence (AI) and machine learning, and some legal professionals are already leveraging these technological capabilities.  AI is not the future expectation; it is the present reality.  Aside from law, AI is widely used in various fields such as transportation and manufacturing, education, employment, defense, health care, business intelligence, robotics, and so

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    5 AI Applications Changing the Energy Industry

    The energy industry faces some significant challenges, but AI applications could help. Increasing demand, population expansion, and climate change necessitate creative solutions that could fundamentally alter how businesses generate and utilize electricity. Industry researchers looking for ways to solve these problems have turned to data and new data-processing technology. Artificial intelligence, in particular — and

    3 MINUTES READ Continue Reading »

    About Us

    Incubated in Harvard Innovation Lab, Experfy specializes in pipelining and deploying the world's best AI and engineering talent at breakneck speed, with exceptional focus on quality and compliance. Enterprises and governments also leverage our award-winning SaaS platform to build their own customized future of work solutions such as talent clouds.

    Join Us At

    Contact Us

    1700 West Park Drive, Suite 190
    Westborough, MA 01581

    Email: support@experfy.com

    Toll Free: (844) EXPERFY or
    (844) 397-3739

    © 2025, Experfy Inc. All rights reserved.