It’s everywhere. Workflows, data entry, personalisation, forecasts, lead generation, analytics, and customer journeys. It has the ability, says Infosys, to automate and orchestrate tasks while delivering ‘timely moments of truth’.
The AI Business Value Radar report found that sales (24%), marketing (26%) and CRM (24%) systems are currently high priorities for companies who want to invest in AI at scale, coming in not far below IT (38%) and security (30%).
And these are just some of the numbers that circle AI and its ability to change the shape of the business. There’s no shortage of conversation around this technology or how it can reimagine your CRM investments.
Eldon Bothma, sales executive, and Hayley Blane, Dynamics 365 CE product owner and solution architect at Braintree, unpack how AI can help companies find the value hidden within their CRM investments.
Thing is, for most South African companies, the question isn’t whether AI is built in, but rather whether or not AI is going to build anything useful. The answer is more complicated than simply unlocking another license tier with a credit card.
Across dozens of CRM implementations, the value proposition often remains hidden. Inaccurate fields, duplicated data, missed follow-ups, abandoned leads, and disjointed teams are often the result of CRM systems struggling to extract data and value.
Right now, companies are still trying to get the basics right and many are still server-based. Among those already in the cloud, there’s a noticeable gap in CRM maturity. Users are stuck in transactional workflows and there’s a tendency to treat CRM as a task list instead of a growth tool.
The data exists, the potential is there, but it isn’t being used to predict, personalise or plan. And this is where AI could finally start earning its keep by helping companies to use what they already have. It can help dig out the data already sitting in the system across client histories, lead patterns, sales rhythms, customer behaviours, and make these actionable. For example, simple AI models can flag deals that are likely to stall based on past pipeline activity. They can highlight which customers are most likely to churn or even suggest next-best actions that align with actual user behaviours.
The challenge now isn’t so much about finding the data, but in helping companies find the appetite for AI that will give them the value locked inside this data. They want the functionality but hesitate when it comes to investing in the additional licenses or data clean-ups needed to enable real use cases. Others struggle to understand what they’re actually buying.
Even when some features are technically available – like Microsoft Copilot built into its CRM solutions – users often ignore them.
Adoption is slow and curiosity is low, so the perceived value doesn’t justify the cost.
The problem is that AI is sold as a leap forward when you actually just want a way of improving what you already have. When AI is seen as a strategic tool, then it becomes more relevant. You don’t want to rewrite your business processes; you want them faster and more responsive.
Fortunately, it’s entirely possible to tap into AI’s potential incrementally. First, honestly assess where your business is. If you don’t have standardised fields or trained users, AI will only amplify the noise because you need clean data, relevant workflows and clear security roles.
Once this is done, you’ll not only feel the value in your CRM’s service delivery, but you can then phase AI in as needed. You can use it to automate lead scoring or create intelligent reminders or even for predictive service case routing. The steps can be slow and relevant.
Another concern many companies have is around where the data goes and what AI features do under the proverbial hood. However, these can be addressed with clear protocols around governance and data handling policies, and by implementing AI in a way that gives you a sense of control.
AI, like CRM, only works when it’s aligned with what your business actually needs so its implementation needs to be meticulous and relevant, supporting smarter decisions and better relationships by becoming a tool everyone actually uses.
Bring AI in. Bring it in a way that prompts sales teams to follow up at the right time, helps customer service reps predict issues before escalation, and improves forecasting. Let it streamline scheduling and predict resource needs for field service teams, or automate segmentation and personalise campaigns in marketing. And let it do all this at your speed within a system that’s mature enough to benefit with people who understand it.