There’s a stark difference in success rates between companies that purchase AI tools from vendors and those that build them internally.

Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L.

The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.

  • hardcoreufo@lemmy.world
    9·
    2 days ago

    A few years ago we haf these stupid mandatory AI classes all about how AI could help you do your job better. It was supposed to be multiple parts but we never got passed the first one. I think they realized it wouldn’t help most of the company but did leave our bespoke chatbot up for our customers/sales people. It is pretty good at helping with our products but I assume a lot of tuning has been done. I assume if we fed a local AI our data we could make it helpful but none of them have more than a basic knowledge of anything I do on a day to day basis.

    • jj4211@lemmy.world
      1·
      2 days ago

      Usually fit those chatbots you take a trained model and use RAG, essentially turning the question into a traditional search and asking the LLM to summarize the contents from the result. So it’s frequently a convenient front end to a search engine, which is how it avoid s having to train to produce relevant responses. Is generally just prohibitively difficult in various ways to fine tune LLM through training and manage to get the desired behavior. So it can act like it “knows” about the stuff you do despite zero training if other methods are stuffing the prompts with the right answers.