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The AI Journey Needs Education

by JoAnn M Stadtmueller, SR Director, Marketing


At lucd we consume all we can to understand our landscape and create the best AI Enterprise solution for businesses.

So when our CTO, David Bauer Ph.D. came across the article How to Overcome the Inertia That Keeps Businesses From Deploying AI by Harry Kabadaian this morning it compelled him to write a note to the team to address each bullet point to have us better define where Lucd exists in the Enterprise AI space.

In order to embark on the AI journey we realize organizations need education. Education to identify where they can utilize AI and in an essence ‘become the data scientist’ in order to optimize their data. In short, you can simply focus on the task at hand: using AI to get to business outcomes.

We have addressed each bullet point, added our thoughts and believe AI can be adopted faster and easier with our solution. We encourage you to read the wonderful thought leadership piece Mr Kbadaian wrote then read our responses in order to turn your data into enterprise AI outcomes!


1. Only 4% of CIOs have deployed AI (Gartner)

  • Lucd AI-as-a-S means you don't have to deploy, it’s already in place, speeding AI adoption

2. Many organizations view AI's implementation as a task for the IT department."-

  • Again, AIaaS eliminates the need for the IT department.

  • Lucd's end-to-end capability enables you to focus on the business initiative, not the infrastructure / implementation

3. "a prediction-data pipeline. Creating that pipeline is a process unto itself,"

  • Lucd provides a complete prediction-data pipeline, eliminating the development of that process

4. "success with AI implementation increase when the whole team is on board to acquire data, analyze it, and develop complex systems to work with the information."

  • Lucd provides easy to use mechanism's for your team to acquire data (bring your data) and analyze it (bring your model)

  • Lucd has implemented the complex system to work with the information so that you don’t need to.


  1. This is one I struggle with because it doesn't seem like a technical problem, but then you read the proposed solution: "AI-driven enterprises often search out data scientists with deep knowledge of their business. A better approach would be teaching employees to identify problems that AI can solve and then guiding workers to create their own models."

  2. This sentence is contradictory, because once you teach employees to "identify problems AI can solve" and guide them to "create their own models" they are, in effect data scientists. So it seems hiring data scientists would be the solution.

  3. How does Lucd help with this? In reality, the problem is a large problem that requires multiple people to be involved. An organization needs people that can identify data; where is the data?, how do I get it? and how does it need to be transformed? A second person needs to understand the actual business processes and how AI could help. A third person needs to be capable of constructing these models. And then someone needs to be able to measure the impact over time. Where Lucd comes in is that we provide the platform that facilitates EACH of those tasks and makes them easy. We provide a highly featured ETL platform for getting data from where it is to where it needs to be for analysis. We fuse across datasets so you can leverage all of the data. You don't need to be a Dask or Spark programmer. You can use our 3D UI to transform the data for modeling. You don't have to be the world's best model developer, we can optimize your model for the best performance (accuracy) automatically. In short, you can simply focus on the task at hand: using AI to get to business outcomes.


1. Not sure this is realistic? Its like asking the sales professional to build a machine learning model. Just learn python, data management, learn all the math behind machine learning so you know what to create, etc etc. The two expertise are mutually exclusive.

2. "Respondents in the Gartner survey revealed their teams took an average of 52 days to build a predictive model and even longer to deploy it into production."

  • Lucd makes going from development to production seamless

  • Lucd greatly reduces model development times (especially once an organization already has several datasets loaded into Lucd, this effectively becomes free or zero-cost after the first model)

4. Management teams often have little means to determine the model's quality, even after months of development by data scientists."

  • We provide this OOTB (out of the box), and go further by automating the optimization of models!

5. "Equipping your staff with the right tools and skills"

  • We provide all of the right tools

  • We provide graphical approaches to the implementation of those tools to greatly reduce the skills gap

In essence with Lucd the difference is between creating the infrastructure in-house versus deploying our infrastructure in-house. Lucd is out of the box ready for your data!