One key reason why most AI fails for business is that it generates predictions, but doesn’t take it to the next step – recommending proscriptive actions that result in business impact. Traditional AI tells a salesperson what the probability of a conversion is, but doesn’t tell them whether they should pursue a deal. But AI needs to go beyond mere predictions in order to create true value for business.
At the beginning of a project, datasets are never as pristine as they are in the lab. Additionally, the business questions being asked are rarely well-formed enough to make sense from the mess found in real-world data.
What your business needs from AI is realistic predictions that identify hidden opportunities to maximize business impact. AI can help predict which of your sales opportunities are more likely to close, which marketing campaigns will generate more leads, or which online promotions will prevent abandoned shopping carts. Accuracy certainly seems like what we want – we want the predictions from our AI to be right. So, where does accuracy go wrong?
Does your AI have high accuracy but still not achieve the results that you want? Are you training it on the results that you care about?