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Is it possible to tell truth from marketing spin?

Artificial Intelligence (AI) has become an industry buzzword. It’s at the top of the hype cycle right now. As a result, sales and marketing people are using “AI” in sales pitches all the time. You see “AI” everywhere in the ad copy for all kinds of software solutions. If you’ve been to any trade show recently, then you’ve been blasted with “AI” messaging along every aisle of the show floor.

VC’s are smart enough to recognize the difference, but are you?

VC’s have seen the trend coming for at least two years. Last year, at an investor conference that I attended, one of the VC’s on stage said that AI has become so trendy that the running joke between the partners was that they would immediately fund any company who pitched them a solution without mentioning AI in the pitch.

In a typical solution implementation, these “artificial intelligence” companies are actually just doing basic data analysis using classic software programming techniques. Bottom line: making data contextually relevant is not AI. If a software company is not using AI as a core technology, then their solution will never get smarter over time nor will it reach any level of autonomy.

Stop with all the marketing hype

Many software companies today are incorrectly using AI as catchall phrase for anything that has to do with data or workflow or robotics. However, at the center of any “True AI” company there has to be a data product: some data offering which is the output of the autonomous processing of vast amounts of information. Automating workflow or automated processing of data by itself does not constitute artificial intelligence.

Get smart with your vendors

Here are seven things to consider about your software vendor to determine if they are really employing AI in their solution or if they are just jumping on the AI marketing bandwagon.

  • Check their job board. The job market for AI and machine learning engineers is extremely competitive right now. It’s so competitive in fact, that most software vendors who are leveraging AI within their solution need to be on the constant lookout for skilled engineers. Look for engineering job openings with titles like: “machine learning engineer”, “data scientist”, “artificial intelligence”, “data science” and “big data”.
  • Who are their founders? Do the founders have a deep technical understanding of machine learning models and do they understand the need to apply it to large data problems? This isn’t to imply that a large enterprise software vendor who’s embarking on a new generation of AI-based solutions, has to fire their CEO and replace them with an AI-literate individual. However, most AI startups today are the brainchild of deeply AI-literate founders and it will be easy to determine if AI is a core part of their solution stack.
  • Are they targeting enterprise applications or SMB? This is a generalization, but AI-based solutions may not work adequately for small companies, unless the problem is defined by a larger data set that can be used to train a model. That model can then be used to process the smaller organization’s data set. A good example here is Google or Facebook using the image data of everyone on the platform to be able to identify faces in your single image.
  • Can you use your own data in a demo or pilot? Any AI-based solution should be able to process your data during a demo or a pilot. It will be in your best interest during the demo to provide the largest data set possible as the vendor may need to use some of the data for training; some of the data for testing; and some of the data for demo’ing the output. Note: you should be clear about where any how and proprietary demo is consumed during a demo or pilot.
  • What data sources have they used to train the system? If they say that they don’t need training data, then they likely aren’t using AI or machine learning. If, however, they are able to explain the process by which they’ve trained their model and can point you to the source of their training data, then this is a good sign that they are actually using AI.
  • Can they provide detail on the algorithm used to process the data? Companies who use recurrent neural networks (RNN) to process their models don’t have any trade secrets. The use and implementation of RNN’s is an industry standard and the company should be able to explain how they’ve implemented an RNN into the workflow that you are interested in automating. The secret sauce, however, is in the combination of training data that the company has employed (or will employ) and how skillful they are at deriving a viable and repeatable model with a minimum of bias.
  • Do they have reference customers? If so, then talk to their reference customers about the reality of their AI claims.


The AI hype won’t end anytime soon. The best that you can do for now is to educate yourself and be prepared to ask the tough questions during any vendor evaluation process. Good Luck!

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