Data Structure, Quantity & Quality:
AI built on the static & incomplete data structure may not derive the expected results. For this the CRM should help the sales team to frame the required data structures relevance to their business, so that needed data points on customer profile, customer communications and importantly the sales transactions, can be captured in details as required.
Once the well-structured data foundation is in place, sales team has to be continuously motivated to regularly feed the data into the CRM. Because the real-time data availability is the key for the predictive analytics to provide meaningful sales insights. Also right data has to be fed, otherwise bad data will lead to wrong AI predictions.
CRM can also pitch in improving the data quantity & quality by providing provisions to automatically capture emails, phone calls, meetings and sales activities of the team.
Configurability:
Until unless the mechanism that drives the machine learning to capture required data intelligence is not fine-tuned with relevance to the sales process of the business, no much real benefit of AI will be noticed. This can be solved only if the factors that derives the AI intelligence and automation are not static and mandatorily has to be dynamic, that can be altered time to time based on the changing business sales strategies. For this the CRM should enable provisions for the sales team in the form of business rules, to optimize the AI implementations to drive the desired results.
For an example, it might be sufficient to capture intelligence data on day-to-day time interval for some business, but whereas hour-to-hour is required for other. Also the patterns to triggers AI based automation will definitely differ from business to business. To put it simple as a use case, if AI is applied to auto track the progress of any sales activity/goal and to prompt the sales team on road blocks, then the time deadline/conditions to trigger prompts will automatically differ from business to business.