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The vast majority of what companies brand as “AI” is nothing more than basic BI reporting with some fancy marketing. The disappointment comes more from the real

The vast majority of what companies brand as “AI” is nothing more than basic BI reporting with some fancy marketing. The disappointment comes more from the realization that the “AI” that was purchased or deployed is just this. Like other popular terms (“Big Data”, “Blockchain” ...) companies fear being left behind so they scrape up whatever they were already doing and get the marketing team to just say “now with AI” and carry on doing what they always did/sold.

ThalesX 6y ago
I worked as consultant for a company, they basically had a database filled with data but they had no way of visualizing it. I quickly threw in an open source MySQL data vizualizer and pitched it as a quick solution until they figure out their larger needs. They were sold by another team with adding ML and AI to their solution and I was tasked with helping with the implementation. After three months of discussions explanations and meetings and all the jazz, I asked them for a summary of the proposed solutions on their side and the costs as we were already making no progress and the cost estimates they were throwing around were massive. At the end, the cost estimate was huge, and the solution was basically "we're gonna try doing X and at the end it might work, or it might not work because what we do is magic". So when talking to the CEO of the company whether we should embark on this expensive journey and decided against it. But because they had no other solution, they went with the vizualization tool I added and suddenly they realized it fits all their needs. Since half a dozen years, they've been using just that tool and extracting insane value from it. I guess I lost my train of thought there, but to conclude, I believe until execs and managers learn to use the tools already available at their disposal, and generate awesome reports with almost 0 SQL knowledge - that can really cover pretty much every traditional business scenario I've encountered so far -, AI and ML are very much outside of their grasp and is more useful for technology businesses.
- chris_st 6y ago
  What a great story! And congratulations on getting a great tool into use. Way back, I was getting to know about stock market analysis. I concluded that the recommended (technical) analysis was, by any individual recommender, just outside their area of mathematical competence. Someone with very little math would be impressed with (say) moving averages, but someone who understood some statistics would denigrate moving averages, but be impressed by Bollinger bands, etc. Since no one understands neural networks :-) everyone was impressed by them. Seems like a similar thing with AI/ML -- "Hey, this is beyond my level of understanding, it must be magic! Buy buy buy!"
  - ansgri 6y ago
    This is a great insight. My company recently started working with agricultural analysts, and your example about moving averages is spot on. Initially, they also wanted the “magic” of NNs, and fortunately, after several prototypes, they understood that what they need is much simpler. As a result, after a couple of years, we’re starting to actually apply those NNs productively, having solved the simpler problems.
    - chris_st 6y ago
      Well done! How did you get them to work with simpler models?
      - ansgri 6y ago
        Basically by convincing them that to make "the real AI" work, they would need a lot of high-quality data that they wouldn't be able to produce. After several iterations with less data-hungry statistical methods they finally realized that they need a real product real soon (they were some kind of innovation department within a large corporation) and realized that the thing they need most is just a centralized GIS with basic computational capabilities. Having successfully built that, those capabilities could be applied at scale and then we started experiments with more advanced analyses, this time more successful since both we had much more data and the customer became familiar with the data-intensive development.
- worldsayshi 6y ago
  Sounds about right. AI is for when you have already tried everything else and you still can't solve a well understood problem. Not "when you have a bunch of data and want to extract value".
- bacondude3 6y ago
  I've used Google Data Studio + Heroku Postgres for a similar purpose. Works great, but I wish there were an OSS alternative to Data Studio.
  - acidbaseextract 6y ago
    Check out https://github.com/metabase/metabase https://github.com/metabase/metabase . I'm not clear on their open source vs paid model, but I'm pretty sure you can host it yourself and their core is open source.
    - bacondude3 6y ago
      Looks promising. Thanks!
- spicyramen 6y ago
  This resonates with me. I was tasked in my organization to apply ML to most of the processes and sub teams..just because. People hand me over spreadsheets with 10 or 100 records expecting black magic. Many companies need to create a stream line process before they embark into the ML path. And once they decide to take that trip from what I have seen is that hiring ML engineers and DS, may not make sense at the very beginning. What I believe is that path from Analytics, then API/black box products from Cloud vendors selling "MLaaS" where they use tons of data to offer solutions in specific areas, such as finance, manufacturing, etc. From there pivot and continue to invest more in ML if it makes sense.

ThalesX 6y ago

I worked as consultant for a company, they basically had a database filled with data but they had no way of visualizing it. I quickly threw in an open source MySQL data vizualizer and pitched it as a quick solution until they figure out their larger needs. They were sold by another team with adding ML and AI to their solution and I was tasked with helping with the implementation. After three months of discussions explanations and meetings and all the jazz, I asked them for a summary of the proposed solutions on their side and the costs as we were already making no progress and the cost estimates they were throwing around were massive. At the end, the cost estimate was huge, and the solution was basically "we're gonna try doing X and at the end it might work, or it might not work because what we do is magic". So when talking to the CEO of the company whether we should embark on this expensive journey and decided against it. But because they had no other solution, they went with the vizualization tool I added and suddenly they realized it fits all their needs. Since half a dozen years, they've been using just that tool and extracting insane value from it. I guess I lost my train of thought there, but to conclude, I believe until execs and managers learn to use the tools already available at their disposal, and generate awesome reports with almost 0 SQL knowledge - that can really cover pretty much every traditional business scenario I've encountered so far -, AI and ML are very much outside of their grasp and is more useful for technology businesses.

chris_st 6y ago
What a great story! And congratulations on getting a great tool into use. Way back, I was getting to know about stock market analysis. I concluded that the recommended (technical) analysis was, by any individual recommender, just outside their area of mathematical competence. Someone with very little math would be impressed with (say) moving averages, but someone who understood some statistics would denigrate moving averages, but be impressed by Bollinger bands, etc. Since no one understands neural networks :-) everyone was impressed by them. Seems like a similar thing with AI/ML -- "Hey, this is beyond my level of understanding, it must be magic! Buy buy buy!"
- ansgri 6y ago
  This is a great insight. My company recently started working with agricultural analysts, and your example about moving averages is spot on. Initially, they also wanted the “magic” of NNs, and fortunately, after several prototypes, they understood that what they need is much simpler. As a result, after a couple of years, we’re starting to actually apply those NNs productively, having solved the simpler problems.
  - chris_st 6y ago
    Well done! How did you get them to work with simpler models?
    - ansgri 6y ago
      Basically by convincing them that to make "the real AI" work, they would need a lot of high-quality data that they wouldn't be able to produce. After several iterations with less data-hungry statistical methods they finally realized that they need a real product real soon (they were some kind of innovation department within a large corporation) and realized that the thing they need most is just a centralized GIS with basic computational capabilities. Having successfully built that, those capabilities could be applied at scale and then we started experiments with more advanced analyses, this time more successful since both we had much more data and the customer became familiar with the data-intensive development.
worldsayshi 6y ago
Sounds about right. AI is for when you have already tried everything else and you still can't solve a well understood problem. Not "when you have a bunch of data and want to extract value".
bacondude3 6y ago
I've used Google Data Studio + Heroku Postgres for a similar purpose. Works great, but I wish there were an OSS alternative to Data Studio.
- acidbaseextract 6y ago
  Check out https://github.com/metabase/metabase https://github.com/metabase/metabase . I'm not clear on their open source vs paid model, but I'm pretty sure you can host it yourself and their core is open source.
  - bacondude3 6y ago
    Looks promising. Thanks!
spicyramen 6y ago
This resonates with me. I was tasked in my organization to apply ML to most of the processes and sub teams..just because. People hand me over spreadsheets with 10 or 100 records expecting black magic. Many companies need to create a stream line process before they embark into the ML path. And once they decide to take that trip from what I have seen is that hiring ML engineers and DS, may not make sense at the very beginning. What I believe is that path from Analytics, then API/black box products from Cloud vendors selling "MLaaS" where they use tons of data to offer solutions in specific areas, such as finance, manufacturing, etc. From there pivot and continue to invest more in ML if it makes sense.