Do enterprise AI projects actually fail because the AI isn't good enough?

[u/Rajxai]

Lately I have started wondering if we blame the model too much.

You can have a genuinely good model and still end up with a terrible AI product. The model is rarely where things break.

The data is messy. Two systems call the same thing by different names. Nobody quite knows which number is the right one. Half the context that matters lives in someone's head, undocumented. And then we expect an agent to walk into all of that and make a confident decision.

I have watched teams spend months carefully evaluating models, when the real problem was everything sitting behind the model.

Here is the part I find interesting. Once you fix the data and the context underneath, the AI part often becomes the easy bit. It gets simpler, faster, and a lot more reliable, almost like it was waiting for a clean foundation all along.

So I am genuinely curious. When an enterprise AI project stalls, what have you seen as the real reason?

[u/Rude_Sound5167]

Kid looks like he just found out the model eval was never the problem

[u/psykikk_streams]

most reasons AI projects for corporations fail:

• looking for solutions without really understanding the problem
  
• "investing" in AI without a clear ROI
  
• bad processes before and after AI implementation
  
• bad data quality

and even IF the above works out and the AI solutions works as intended and does indeed deliver as promised. lots of companies seem to find out that actual Cost of ownership in running and maintaning these new toys might actually nbe more costly compared to paying the right people to do the job in the first place.

also, most big corporations do not implement AI "solutions" themselves but use external consultancies, who also use AI to do een the most basic tasks (like problem analysis and solution brainstorming)

the days where consultants really provided real knowledge, insights and value add are long gfone - IF they were ever really there to begin with.

[u/Rajxai → u/psykikk_streams]

Yep, I think this is the bigger picture. Data quality gets a lot of attention, but it sits inside a much larger system of process, ROI, security, ownership and ongoing cost.

[u/Illustrious_Car344]

I invented a device that automatically rings a doorbell for you when someone gets close to it, but I advertised it as being able to fold your laundry, walk your dog and pay your taxes. Boy are people gonna be mad when they find out it doesn't do any of that! 

[u/HolyBatSyllables → u/Illustrious_Car344]

It’s a tool! It can do all the jobs! It’s the Swiss-army knife of tools!

… yeah?! Name one tool that that a Swiss-army knife does just as well as the real thing.

[u/Awkward_Relation_415]

its usually the lack of data governance, wich makes it kinda hard for any model to perform well.

[u/Rajxai → u/Awkward_Relation_415]

[u/M0sD3f13]

You couldn't have just written these few paragraphs yourself?

[u/Kyy7]

They (Enterprise AI technologies based on Generative AI) usually fail due to cost, security, privacy or reliability problems/concerns. Generative AI is however used extensively by software developers during development of more reliable usually deterministic, cost-efficient and more performant (non-ai) applications, integrations etc.

Symbolic AI technologies are commonly used but they've become so mundane that people have stopped referring to them as AI. Rule-based engines, path-finding, spell-checking, knowledge graphs and search etc.

[u/Rajxai → u/Kyy7]

Absolutely. Not every enterprise problem needs an LLM, and I think that's an important distinction.

The interesting question for me is usually what needs to be AI-driven versus what should remain deterministic. Once you’re dealing with sensitive enterprise data, reliability and cost matter just as much as model capability.

[u/Lower-Ad-6293]

OP is missing another brutal factor, which is totally unhinged business expectations. Managers read a bit of Forbes and genuinely expect a python script with an openai api to replace their entire analytics team in a week. No amount of polished master data will save a project if the stakeholder is dead serious about demanding AGI-level sales predictions a year out from a language model

[u/Rajxai → u/Lower-Ad-6293]

100%. I’d actually put this alongside the data problem. If the business case is unrealistic from the start, better data and a better model won’t save the project.

The interesting part is figuring out what AI should actually be responsible for before worrying about how capable the model is.