The original idea behind CRM was to give sales teams better visibility into customers and opportunities so they could spend more time selling. The tech would remove admin, improve forecasting, and help businesses build stronger customer relationships. But the reality never quite matched up to the theory.
While CRM platforms have become essential for recording sales, they’ve also created an unexpected operational burden. Every task triggers other tasks, with activities needing to be logged, tracked, updated. Reports generated, meeting notes documented. So, we’ve reached the stage where this admin-reducing technology has actually created more of it. It’s frustrating, time-consuming, and often causes bottlenecks because too much relies on people remembering that they need to do these things. AI agents can change that.
The role of AI agents
AI agents are being used in a whole range of scenarios now, but they have particular benefit with CRMs, taking over some of the duties that previously required employee input. They don’t just process information, but can automatically enter and maintain it within the CRM, even creating follow-up actions, identifying risks, and making recommendations. And this changes the CRM from an admin-heavy system to a system that can actively support the sales process, alleviating admin and supporting informed decision-making.
What AI agents can actually do
The value of AI agents is that they can actually do a lot. They can flag when a potential customer goes unexpectedly quiet. Assess leads based on criteria that you’ve defined. Rate sale opportunities according to how likely they are to actually convert. And create a whole range of follow-up tasks, from drafting emails to alerting account executives when a particular action is needed. In short, they turn previously labour-intensive software into an actively participating partner that provides real benefit. And because AI agents operate continuously, they can genuinely enhance productivity. Helping businesses to respond faster to both customers and markets.
The hidden risks
For all these benefits, there’s still a but. While AI agents can be an incredible efficiency solution, they also hold the potential to make things so much worse. Because rather than being outright problem solvers, AI agents amplify what’s there. They accelerate whatever situation they are deployed in. So, if a business has poor-quality data, inconsistent sales processes, fragmented customer records, or weak governance controls, AI agents will not solve those problems. They will automate them. And that can be… problematic.
If you have a lead qualification agent trained on flawed criteria, for example, all it can do is qualify the wrong leads faster. The same applies to a forecasting agent working from inaccurate data. It will generate predictions more efficiently than a person might. But they’ll still be unreliable. The tech isn’t failing. It’s doing exactly what you’ve asked it to do. It just so happens that you’ve deployed it inappropriately. And that can do no end of damage to a business. That’s why you have to put in the groundwork before adopting AI.
Getting the foundations right
Too many businesses are buying into AI without preparation. The belief is there that AI is somehow a silver bullet that can do all, cure all, optimise all. And it really can help. But you have to take some responsibility and get your foundations in order first. And that means several different things.
First, is data quality. Customer records must be accurate, complete, and consistently maintained. Duplicate records and disconnected systems have to be dealt with before they cause silos and errors.
Then there’s the matter of process definition. Businesses need clear processes for every stage: lead qualification, customer handoffs, escalation. AI agents perform best when they have well-established frameworks to work within.
Governance is also essential. Businesses must have clear boundaries around what AI agents can do independently, when human oversight is required, and how decisions are monitored and audited. Transparency and accountability have become the watchwords of modern business, and they are even more essential where AI is involved because they form the backbone of risk management.
What good AI agent use looks like
The businesses that realise the greatest value from AI agents will not necessarily be those with the most sophisticated or expensive tech. They will be the ones that combine AI with strong operational foundations. If you can use AI to manage those unaccountable reams of admin that crept in alongside your CRM, you’ll be helping your sales team to sell more effectively. And that’s really what the CRM was there to do in the first place. It just somehow lost its way in the deployment stage.
The end of manual CRM may finally be within reach. But it has the potential to bring both efficiency and chaos, depending on the foundations you put in place before you introduce AI.
Satish Thiagarajan is the founder of Brysa, a Salesforce and data consultancy based in the UK. His company advises media, industrial, and services clients on using Data Cloud and Agentforce to turn signals into action. His work focuses on closing the loop between insight and execution in sales, marketing, and service.
