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.
The data offers little consensus toward most important business problems until someone starts asking clear questions that provoke deeper discussions. Unknowingly, customers jump directly to a solution before they fully discuss and understand the business problems they wish to solve.
Business Intelligence (BI) dashboards provide general guidance and a sense of history. That’s why BI dashboards alone are rarely the right answer out-of-the-box. Dashboards don’t tell me all I need to know.
Artificial Intelligence (AI) offers specific next actions at the point of decision – only if they take into account real resource constraints such as marketing budgets and sales capacity. As a result, AI is fast becoming the new BI.
Dashboards made more sense when it was impossible to provide actionable insight to the end-user at the moment of making a decision.
Think about it: Would you rather know three things you can do right now to increase the probability of winning a deal, or would you rather see a graph of win rates across thousands of deals, of which, only a small percentage is relevant to your goals and objectives?
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